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Nadine Cranenburgh

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  1. Introduction The IoT era has been marked by an exponential increase in hardware and software capabilities. This has meant that industrial automation control systems have moved from stand-alone, discrete, relay-based automation systems towards multi-processor systems, edge- or cloud-connected systems as shown below. Diagram courtesy of Chris Vains, Siemens Australia NZ This transformation in automation systems has been mirrored in the data formats, which have moved from paper charts and manual reports to big data applications which can collect insights from multiple sites around the globe and enable predictive analysis and decision making. Diagram courtesy of Chris Vains, Siemens Australia NZ A major shift in focus has been the increasing trend to use industrial data to improve processes in the future as well as find out why things went wrong in the past. One example is the use of ‘prescription’ which is the process of changing the use of assets to extend their life or improve processes when data analytics have predicted a failure is imminent. Industrial IoT (IIoT) requirements and data model IoT solutions for industry have specific requirements. As well as needing to be reliable and robust enough to survive in an industrial environment, they need to be scalable and meet industrial cybersecurity standards. Interoperability with legacy systems, which may be 10 to 20 years old, is also key, and industrial clients require the ability to customise solutions to meet their specific requirements. Solutions need to be commercially available in the long term to provide continuous business models to clients. A general example of an IoT data model for a manufacturing application is shown below. Diagram courtesy of Chris Vains, Siemens Australia NZ In industrial applications, the cost of monitoring reliability of equipment to minimise production downtime needs to be balanced against the cost of the parts, as it might be cheaper to run to failure rather than monitor depending on the cost of downtime. The diagram below shows the kinds of input data and analysis that might be implemented for a packaging production line application. Diagram courtesy of Chris Vains, Siemens Australia NZ Platform as a service One cloud-computing tool for the IIoT is ‘platform as a service’ (PaaS). This is an IoT operating system that connects things and collects data. API interfaces allow other service providers to connect and offer additional functionality such as data analytics. It can also be used for application development, testing and deployment. Examples include Siemen’s MindSphere, Honeywell Sentience and GE Predix. Staged approach Rolling out IoT-based automation in industry generally follows a staged approach. For example, operations might start by implementing a data-driven approach to improve transparency and asset management and use data to automate a standalone process. As the IoT solution merges with the company’s systems and data collection and integration becomes more mature, it will start to drive proactive automation of processes. Industry can then progress to introducing digital ‘twins’ for products, production and performance to feed data on potential changes into offline models before implementing them in the production plant. The final stage is using tools such as augmented reality and AI to gain high-level insights from data that can drive innovation and system optimisation. This concept is illustrated in the diagram below. Diagram courtesy of Chris Vains, Siemens Australia NZ Future of IIoT Automation As technology continues to advance, future applications of IoT automation could include: distributed intelligence edge control eg. analytics and model-based control local optimisation eg. line monitoring and control devices to optimise performance of a production line connectivity to data outside of the reach of today’s control systems eg. environmental or weather data real-time simulation eg. data feedback for process optimisation or testing measures to improve performance. The diagram below shows a possible future data model for IIoT solutions, where edge control is the gateway for information flowing to and from the cloud to drive decision making. Diagram courtesy of Chris Vains, Siemens Australia NZ Sources: The content on this page was primarily sourced from the following: Webinar titled ‘Automation in the IoT Era’ by Chris Vains, Head of Digital Enterprise, Siemens Australia and NZ
  2. Description: Four years ago ATF Services made a strategic decision to move into high-tech video surveillance solutions. This case study outlines the product development process for their intelligent multi-function IoT security alarm. Source: Based on a webinar titled ‘Case Study on IoT Product Development’ delivered on 14 August 2018 by Robin Mysell, CEO, ATF Services. Biography: Robin Mysell has been CEO of ATF Services for nearly six years. He has used his strategic and leadership skills to successfully transform underperforming companies faced with tough economic and competitive environments in Australia, New Zealand, and the United Kingdom. Robin is a firm believer in using technology and innovation to help improve business efficiency. Implementing continuous improvement and lean principles is one of his key transformation strategies. Introduction ATF Services needed to diversify from its core business of temporary fencing and edge protection due to market saturation with hire equipment following the end of the mining boom. They decided to move into camera-based security surveillance systems, starting with the development of a single solar panel, 4G security camera system with infrared light for night operation. The system was intended for operation at medium to large sites. Challenges to be overcome were false alerts from sources such as animals and objects flapping in the wind. Systems also needed to operate continuously, and have power backup in the case of power failure. Early stages ATF had a steep learning curve in solar power generation for their first generation cameras. Issues faced were a need to clearly define power requirements, and reliance on an expensive lithium ion phosphate battery. Climatic variations over the target markets across Australia and New Zealand, meaning that products had to be able to operate reliably in a wide range of temperatures and weather conditions. The first hurdle was to decrease power consumption to allow the move to less expensive batteries. Perfecting power management allowed the company to save around $2000 per camera by using cheaper VRSLA batteries. An image of the mature camera system is shown below. Diagram courtesy of Robin Mysell, ATF Services The team developed a stable camera system, but it was unable to see through buildings or fences, meaning that sites were vulnerable to break-ins from areas not covered by camera surveillance. To cover black-spots on site, an IP66-rated, vandal-proof wireless sensor was needed. The sensor also needed to be able to communicate with the security cameras and have a one-year battery life. After testing third party devices, ATF decided to develop an in-house solution in partnership with Genesys Electronics Design. IoT security alarm design process Most of the motion alarms on the market are built for indoor or household settings, where environmental variables are highly controlled. ATF needed industrial-grade sensors which could cope with variations in temperature and weather, as well as distinguish false alarms from animals and flapping objects. This meant that third party sensors were either inadequate or too expensive. Once the decision had been made to develop an in-house device in partnership with Genesys, AFR incorporated additional sensors into the design. These included: a tamper switch to detect interference an accelerometer to detect if the device was hit or improperly installed a microphone to detect unexpected noises such as breaking glass Limitations faced were size and power consumption. An image of the current alarm device is shown below. Diagram courtesy of Robin Mysell, ATF Services The scope of the product continually changed during the development process, based on client feedback and a growing understanding of what could be achieved. AFR is still growing its understanding of potential applications for the device. For example, using recently rolled out LPWAN technologies, the company has discovered that the security alarm device can be installed in a standalone configuration without a co-located camera. Installation in shipping containers (shown in the image below) proved to be a challenge, as sealed containers create a shielding effect and block communications. The solution implemented was to install the alarm on the container door so that communications were restored as soon as the container door was opened and a message could be sent back to base. Diagram courtesy of Robin Mysell, ATF Services ATF Services also developed a custom app for the alarm in parallel with the hardware and firmware development process. Lessons learned One lesson learned during the design process was that being involved in the product design process can mean a loss of objectivity. This means that customer feedback can be useful to gain a fresh perspective on features that would be useful to end users. Another learning was that keeping the app interface smooth and simple for clients meant a lot of work at the backend, and scoping the end user experience was crucial early in the design process as redesigning an app’s features and the device framework that communicates with the app later in the process is a huge undertaking. While writing the device framework at the same time as developing cloud services and the software backend was a challenge, AFR now has the benefit of full ownership and control of all system assets in order to make changes and undertake future development. Another lesson was in ensuring thorough up-front project scoping. Scope-creep can be a risk when building technology which is the first of its kind, and the many scope changes and time delays came at a heavy cost – doubling the original development budget. While this was a major issue in the short term, in the medium term it has provided a much more capable system, with applications that are still emerging. Since the security alarm project, AFR has started putting together a ‘wish list’ for added functionality and features at the start of the product design process and prioritising those features to understand the cost-benefit of each, with a view to how it may increase the product value over the medium to long term. Upgrade paths should be designed into a system, to take into account the rapid pace of technological development in the IoT sector. The system should also be scalable, to cope with user growth over time. The solid partnership with Genesys was another crucial factor in the success of the project.
  3. Introduction IoT, with its ability to capture and analyse data to generate insights, is emerging as an enabling technology for defence in Australia, which is currently undergoing the most significant re-equipment since World War II. While defence was a driver of technological developments such as GPS up until the 1960s and 1970s, private commerce is driving the development of technologies such as IoT, and defence is positioning itself to leverage these technologies for its operations. This raises concerns about reliability and security of commercially sourced devices and communications networks. A major application is in national security, where defence will increasingly rely on IoT, and advanced and autonomous systems to protect society from threats such as terrorism. Data and digital warfare, intelligent bases, and maintaining the health and wellbeing of personnel are also areas where IoT solutions could be beneficial. Defence is taking an integrated approach to developing new technologies in collaboration with universities and industry, supported by the Next Generation Technologies Fund. As well as custom solutions, defence makes use of solutions and equipment developed for other industries, including satellites and nanosatellites, drones, warehousing, autonomous robots, smart energy, microgrids, and machine vision. In order for defence to take full advantage of IoT, it will need to update approaches to management and acquisition frameworks as well as considering the technical aspects of solutions. Complexity The architectural complexity of defence solutions is dependent on the usage requirements, and whether users are self-contained (as for a single mission) or distributed over a geographical area (for multiple missions or network capability). IoT solutions for defence typically fall into the ‘systems of systems’ area, including communities of interest and collaboration nets, as illustrated in the diagram below. Diagram courtesy of Kerry Lunney, Thales For commercial off the shelf (COTS) solutions, a principle that applies is intentional obsolescence. This is the idea that the solution is not designed to last forever, but with the assumption that it will be replaced after a certain number of years. Areas of IoT application and benefits The applications of IoT in defence can be divided into four areas (as described in the paper "A Review on Internet of Things for Defense and Public Safety", see Further Reading on this page): surveillance: situational awareness, troop monitoring, intrusion detection equipment tracking: forces, tanks, fighter jets, radios, technical devices emergency services: peacekeeping, homeland and broader security, firefighting, search and rescue, disaster relief, battlefield communications, energy harvesting public infrastructure: environmental monitoring, law enforcement, public protection IoT applications within defence can also be divided into the categories of logistics and supply chain, digital/data warfare, health and wellbeing and intelligent bases. Health and wellbeing of military personnel can be related to those who have been injured in conflict or monitoring how people are dealing with the stress and pressure of operations. Intelligent bases encompass remote deployed bases rather than mainland bases. These remote bases are more intelligent, and interconnected with wifi, bluetooth, and other communication protocols, but can have additional security concerns. The major benefits provided by IoT solutions within the second set of categories are summarised in the diagram below. Diagram courtesy of Kerry Lunney, Thales Land applications The Fight Recorder is based on the concept of a ‘black box’ recorder for soldiers, this device uses sensors to capture information about soldiers, their actions and motions, the operating environment, and can also be used as an emergency distress beacon. Data is transmitted via micro-satellite communications, and there is also an independently powered recording device on board. Once integrated, the onboard global navigation satellite system (GNSS) chip is expected to operate once every few seconds or event and intervals of a minute unless it is actively transmitting as a beacon. One of the challenges of integrating the inertial measurement unit (IMU) and the GNSS chip is that the chip is power hungry and will drain batteries quickly. A potential problem with using microsatellite connected IoT devices as emergency beacons for soldiers is that the latency of low earth orbit satellites might cause a delay in response. The data captured by the fight recorder can be used to inform, design and performance soldier equipment and protective wear. A diagram of the Fight Recorder’s components is below. Diagram courtesy of Dr Alex Zelinski, Department of Defence This device also has the potential to be used to collect, log and transmit life signs such as heart rate and respiration. An operational concept diagram is shown below. Diagram courtesy of Dr Alex Zelinski, Department of Defence Recent developments in IoT low power electronic components, as well as long life battery technology, has made the deployment of these devices in real life defence environments more practical, as the battery could last for years without needing to be changed. Motion data can be downloaded at the end of the mission, but also progressively uploaded through micro-satellites currently in development. The analysis of motion data will be used in conjunction with computer models to develop accurate models of soldiers’ body movement. In turn, this will be used to develop event reconstruction algorithms for the Fight Recorder. An example of data analysis using other motion-detection devices is shown below. Diagram courtesy of Dr Alex Zelinski, Department of Defence Underwater applications Defence has used sonar array networks (using sonar buoys) for underwater IoT applications. They also make use of seabed sensor arrays deployed by organisations including the Australian Institute of Maritime Science and <spell out> NOAA in the US. In Australia, which has limited seabed arrays, there is also potential to use networked sensors mounted on unmanned underwater vehicles or vehicles on the surface of the water (unmanned boats) which could communicate among themselves or via microsatellite networks. The Internet of Military Things The Internet of Military Things concept uses sensors in remote, austere or degraded environments, internetworked through Low Earth Orbit (LEO) micro or nanosatellites. The aim is to provide persistent situational awareness of operational environments, for example, chemical threats; as well as the awareness of forces, for example, monitoring the health status of soldiers through Fight Recorders. This is illustrated in the diagram below. Diagram courtesy of Dr Alex Zelinski, Department of Defence The idea is to enable defence personnel to be able to rapidly deploy a network of sensors, including mobile sensors such as UAVs, which is uploaded to a cloud and shared with operators. A multinational defence trial was recently held in Adelaide to investigate the benefits of integrating wide-area aerial intelligence, surveillance and reconnaissance (ISR), ground sensors and target data to tactical operations in urban environments. Using an open systems architecture <link to open systems> for unattended sensors designed by the US military, the five nations participating in the trial (US, UK, Canada, NZ and Australia) were able to integrate technologies developed in each of their nations within a week for field testing. A second trial is planned in Canada and will integrate additional technologies including GPS-denied 3D mapping and an Internet of Military Things concept demonstrator. Collaboration with academia and industry Australian Defence is funding partnerships with IoT small to medium enterprises and universities through the Small Business Innovation Research for Defence (SBIRD) program which offers grants and collaborative research opportunities. This approach has been successful in stimulating innovation in Defence IoT and other technologies in the US. Challenges The diagram below highlights some of the potential challenges for IoT in defence. The left column highlights the main area of interest in using IoT solutions for systems of systems, the middle column the challenges, and the right-hand column the characteristics, capabilities, and features that defence needs to control. Diagram courtesy of Kerry Lunney, Thales For example, IoT can introduce additional vulnerability through factors including cybersecurity, security, resilience, privacy, and control. Defence employs solutions outside of the IoT sphere, and shares solutions with allies, so commonality and integration are important, as is data ownership. Environmental conditions can be harsh, including heat, dust, and water. Another challenge is adjusting to an expendable mentality with intentional obsolescence. Things move fast in IoT and defence is not used to continuous change. Solutions to these problems will require not only technical input but changes in non-technical areas such as strategic, contract and project management; standards; supply chain and acquisition; risk management and collaboration boundaries. Communications Communications present specific challenges for IoT in defence. Military operations in remote areas or at sea may not have access to terrestrial communications networks. They are addressing this problem by tapping into low power, low-cost microsatellite communications. Other concerns are that in a conflict, satellite communications might not be available and that communication can reveal the location of the defence operator, which could put them in danger. One solution to these challenges which has been deployed in Defence is using UAVs as ‘data mules’ that move between various locations to collect and deliver data as illustrated below. Diagram courtesy of Dr Alex Zelinski, Department of Defence A fleet of UAVs intelligently can arbitrate among themselves to determine which vehicle goes to each particular location to collect or deliver data. This approach has challenges, including controlling multiple UAVs from the ground, the limited time of UAV continuous operation (typically three to six hours). Another challenge is keeping fixed sensors in the air at around 20,000 km above the Earth’s surface. Balloons have been trialled but were blown out of position by the wind. Heavy sensors and equipment can also be a challenge in the air. To decrease their reliance on satellite communications, Defence is also looking, in the long term, at directed point-to-point communication systems which combine electronic warfare radar and communications through a single device. Security Another challenge is ensuring that defence IoT devices and communications networks are not accessed by hostile parties. IoT technologies in Defence are currently in the experimental phase. When operational, significant investment will be made in anti-tamper devices such as auto-erase when a device is attacked. Satellites, particularly large, expensive satellites, are also vulnerable to attack. To address this issue, the US military is considering a private Defence Low Earth Orbit (LEO) satellite constellation with persistent global coverage. Overcoming challenges Some areas to take into account when addressing challenges to IoT implementation in defence are: equivalency acquisition/development cycles problem definitions technology advancements digital twins risk profiles Equivalency could involve considering adoption solutions which have been built in accordance with international rather than Australian or military standards, as well as planning for short product life. If an IoT solution is trusted and proven in other settings, validation in a defence setting using a pilot program might be a better use of resources than revalidating the solution completely. Operational libraries can be built using a digital twin or ‘sister’ (similar but not quite identical system). Digital twins are worthwhile in the defence environment for setting up bases, logistics, and training. They can also replace the need for maintaining excessive system documentation. Importantly, changing a system to comply with military or Australian standards, or adding capability, means that it is no longer an off-the-shelf IoT solution. An alternative flexible solution is designing an additional transforming application that enhances the functionality of the existing solution. Recommendations for acquisition and development lifecycle considerations include looking into incremental, agile, lean and evolutionary lifecycles, as well as further use of digital twins. Virtual engineering approaches – which use virtual or augmented reality could also be an option; as could model-based engineering and a minimum viable product approach. It is a good idea to avoid defining contract milestones prior to agreeing on the lifecycle to be followed, and excessive documentation. Because risk profile will vary over time with a commercial IoT solution, it should be made adjustable and shared between stakeholders as much as possible. When defining the problem to be solved some of the approaches above could also be used in design as well as designing to specification, and designing to market. Designing to flexible capability or dedicated capability could also be used. The best approach is to take advantage of the best engineering approach to address challenges and avoid over-specifying, as especially with AI, robotics and autonomous systems they will not be deterministic, and predictions and set test programs will be difficult at the start of a project. There is also potential for automating some decision making in environments which it is difficult for humans to access or control, and evolving technologies such as blockchain can assist in addressing vulnerabilities in cybersecurity. Big data analytics can help users gain insights from the large amount of data available in defence applications. Because environments are becoming more unpredictable, a shift to less rigid technical governance, which avoids defining details for the unknown is a good approach. Instead, project managers and engineers should plan to contain disruptions if they occur, and implement learnings in the future. Similarly, risk profiles need to shift from assigning responsibilities at the start of programs and taking a penalty-based approach to becoming more flexible with shared responsibility in increasingly uncertain conditions. Sources The information on this page was primarily sourced from the following sources: Webinar titled ‘Defence Next Generation Technologies: Driving Innovation in Defence’ by Dr Alex Zelinsky AO, Chief Defence Scientist, Department of Defence. Webinar titled ‘IoT in Defence Part 2: Case Studies from Thales’ by Kerry Lunney, Country Engineering Director & Chief Engineer, Thales Australia Further reading "A Review on Internet of Things for Defense and Public Safety", 2016
  4. Description: Taggle currently manages over three million water meter readings per day, making it one of the largest remote sensing operators in Australia and arguably the most successful IoT implementation to date. While the company is still heavily focused on the water industry, it is now beginning to look at adjacent markets including agriculture and environmental monitoring. This case study describes the development of Taggle’s one-way water metering and remote sensing solution using LPWAN. Source: Based on a webinar delivered on 1 May 2018 to the Applied IOT Engineering Community of Engineers Australia by Mark Halliwell, Business Development Manager, Taggle Systems Biography: Mark Halliwell has a background in Electrical Engineering, and 20 years’ experience in business development roles with systems associated with SCADA, industrial automation, communications, environmental, AMR and other remote monitoring systems. He has previously worked for companies such as Advantech, Halytech and Schneider Electric. Introduction This case study describes the development of Taggle’s one-way water metering and remote sensing solution using LPWAN. While IoT developers and designers may get excited about what they can build into their latest creation, this can increase project costs by increasing the functionality beyond the user or client’s requirements. For example, if water meters are to have built-in leaks alerts, they require electronics which can monitor the flow of water, parameters to determine what constitutes a leak, power and electronics to allow parameter changes, and the processing power to detect leaks and communicate the alarm back to base. Because water utilities deploy millions of water meters, every dollar of functionality in each meter adds millions to the cost of rolling it out across their assets. An alternative is to take raw consumption data, feed it to the cloud, and process the data to answer the questions that the user needs answered. This is a much more economical approach, and changing the parameters is simpler as only the analytical algorithm needs to change, while the meters remain unchanged. The decision on whether one-way or two-way communications is used should be based on the requirements of the application and the minimum level of communications required to meet the users’ needs. The same applies to the choice of communications technology. A commonly used technology is a low power wide area network (LPWAN). One-way communications is very well suited to the collection of high volumes of data, and applications such as water metering which do not require remote control or communication back to devices. Two-way communications is better suited to low volume remote control applications where users collect data from the field and respond with a control output using the same communications network. Company focus Taggle’s business focus is to provide a cost-effective IoT solution for monitoring of distribution networks. Their goal is to enable customers to use data, rather than collect data. To this end the company provides “network as a service” to collect data for users, and allow them to analyse and use it as they need. The company first decided to concentrate on the water industry in 2012 or 2013. The diagram below shows a rough snapshot of the water supply system from the point of capture, through water treatment and delivery to reservoirs at that time. Diagram courtesy of Mark Halliwell, Taggle In the top part of the diagram above, the network asset value and the potential for saving water are relatively low, due to the high level of monitoring. But the distribution pipe networks between the reservoirs and consumers’ homes have a high network asset value (in the order of 60 to 70%). The level of monitoring is also low, and limited to a small number of pressure reducing valves. This leads to a high potential for further water savings with improved monitoring. Taggle realised that by adding smart water meters for every domestic customer, the utility providers could very quickly get a sense of what was happening in the distribution pipe network, and a view of what is now called “dark assets”. Communication Systems Taggle’s proprietary network is a one-way LPWAN solution, however they are equipped to provide two-way LPWAN and have recently deployed a combined Taggle/LoRa Base station in Townsville. They are also prepared to feed narrowband IoT through their network as a service, and can cater to 3G and satellite communications when required. System overview An overview of the Taggle remote sensing system is shown in the diagram below. Diagram courtesy of Mark Halliwell, Taggle Sensors are used to send data to the Taggle receiver network, which is then sent to a cloud server using 3G or 4G data communications where it can be accessed by users using analytical tools. Receiver Because the technology is based on pushing data from sensors to receiver, the receiver is the core of the system. Taggle uses a star topology with the receiver at the centre, which they describe as a very sophisticated software-defined radio (SDR). A high gain antenna allows the receiver to detect extremely weak signals, similarly to the way that mobile phones are able to detect the weak signals from GPS transmitters from satellites tens of kilometres away. A diagram of the receiver is shown below: Diagram courtesy of Mark Halliwell, Taggle The software allows the receiver to be easily reconfigured in the field. Each of the company’s 300 receivers conducts a spectrum analysis three times a day, and these spectrum snapshots are analysed to find interference or degraded signal strength. The company uses this information to make changes via their SDR to optimise reception. The receivers also feature Active Interference Cancellation to block out any interfering radio signals. This mechanism can be compared to noise cancelling headphones for radio, although it operates a bit differently. As a SDR which uses Direct Sequence Spread Spectrum technology, the receiver has a very high capacity when compared to other LPWAN receivers. Direct Sequence Spread Spectrum is similar to the technology used for CDMA and is the basis for some military communications. It is a frequency hopping technology invented in 1941 to provide secure communications for guiding torpedoes. Transmitters When Taggle built its network technology with receivers at its core, the company recognised that there was no point in building a network and hoping that people would join. Taggle saw it as necessary to also develop transmitters which could be used on their network. Because the company had discovered a niche market in collecting data for water meters, or automatic meter reading, it started to design transmitters specifically for water meters as shown in the diagram below. Diagram courtesy of Mark Halliwell, Taggle The transmitter in the middle of the diagram above was designed specifically for the Elster B100 meter, which is the most widely used water meter in Australia (about 75-80% of domestic water meters). The top transmitter is an Elster (now Honeywell) meter with a built-in Taggle radio that is totally integrated. The difference shows the transmitters’ evolution over the past seven or eight years. Transmitters can also be used for pressure and level sensors, and Taggle has used them in Adelaide with SA Water for water cooling. They are also starting to be used widely for rain guages, both for water utilities and also agriculture. They are also used in weather stations. The company also makes more sophisticated transmitters for multi-parameter devices. Some examples are shown below. Diagram courtesy of Mark Halliwell, Taggle Example of system deployment The diagram below shows an example of the application of Taggle’s water monitoring system in the New South Wales town of Narrandera. Diagram courtesy of Mark Halliwell, Taggle That network comprises about 2200 water metres, with data collected every hour by a single receiver (shown as a blue dot in the middle of the diagram). The range from transmitter to receiver is in the order of two kilometres. The following diagram shows the wider area around Narrandera, Goldenfields Water. The diagram shows that there is a much bigger network in that area, with Narrandera in the bottom left hand corner. Diagram courtesy of Mark Halliwell, Taggle The Goldenfields Water network includes approximately 12,500 water meters, spread over an area of around 22,500 square kilometres. The whole network is serviced by 30 receivers. This is a good example of how LPWANs can add value when collecting data from wide areas. Customer results Taggle’s first system deployment was with Mackay Regional Council in Queensland. The Mackay network comprises about 40,000 thousand meters. The data from these meters has told the Council that about 2.5% of properties have a leak (1500 properties per year). Armed with this data, the Council now sends out leak notices to ratepayers, which is good for public relations because the customer saves money and can address maintenance issues early to avoid structural damage. Mackay Council have also been able to use the data, to reduce the time to repair leaks from 150 to 60 days, and the overall consumption in the Mackay region has dropped by about 12%. This has meant that the Council has been able to defer a $100 million investment in a new water treatment plant by at least 12 years. Ratepayers have also benefited, with water charges on hold and expected to be reduced in the future. Taggle now has more than 25 water utilities as customers and has networks (including transmitters and receivers) collecting data from an area of about 300,000 square kilometres. This involves monitoring around 120,000 endpoints and delivering 3.5 million readings to customers daily. The diagram below shows the deployment of Taggle systems around Australia. Diagram courtesy of Mark Halliwell, Taggle Sources: The information on this page was primarily sourced from: Webinar titled ‘Water metering and remote sensing: When one-way is the better way’ by Mark Halliwell, Business Development Manager, Taggle Systems.
  5. Introduction Energy analytics is a specific application of IoT data analytics which can be used for several purposes including energy management, predictive maintenance, usage forecasts, anomaly detection and automated reporting. To perform effective data analytics using IoT, a good first step is integrating all sources of energy data to a central location for analysis. It is worth noting that the difference between energy monitoring and analytics is that monitoring only allows users to view data in a particular format, while analytics provides insights to the user through alerts (for example when there is a system failure or unusually high usage). These insights can also be pushed to business intelligence (BI) tools, building management systems <link>, and data visualisation platforms. A diagram depicting a typical energy analytics system is shown below. Diagram courtesy of Umesh Bhutoria, EnergyTech Ventures Energy Management and anomaly detection Like the insights derived from smart metering of water and analysis of the resulting data, the insights from energy analytics can be used to form an understanding of the energy consumption of machinery and plant, and use this to optimise energy consumption and identify problems early. The demand for energy management systems is growing fast, with investment in the tens of billions of dollars. An example of an energy management application is shown in the diagram below, the dark green line shows the expected behaviour for a chiller network in a commercial building based on the present week’s consumption. The yellow line shows the expected behaviour based on an earlier consumption period. The difference in the two lines shows that the chiller’s energy consumption has increased over time. This could be linked to maintenance or increased activity. Having this analysis available allows engineers and maintainers to identify and find the source of problems. Diagram courtesy of Umesh Bhutoria, EnergyTech Ventures Similarly, anomaly detection using energy analytics looks for variations in observed patterns of energy usage to determine if there is a problem to be rectified. Bringing all the available data together, automating analysis and setting alerts, makes anomaly detection easier than trying to extract information from raw data points in a traditional building management system (BMS). Predictive maintenance To move towards predictive analytics and predictive maintenance using IoT energy analytics, it is essential to be able to review how systems have behaved in the past, in order to forecast how they will behave in the future. While tools such as artificial intelligence and machine learning have potential to predict maintenance requirements, it is necessary to have several years’ worth of maintenance and operation data to train them to predict maintenance requirements effectively. A good starting point is putting systems and infrastructure in place and conducting a gap analysis similarly to any other data analytics <link> system implementation and clearly defining your scope and specifications (see the Project Management page for more information) Sector-specific energy analytics While energy analytic algorithms exist for many generic purposes such as time series and general forecasting, there is a gap in the availability of algorithms specific to particular industry sectors or machinery types. For instance, if the objective was to optimise a chiller system, it would be unlikely that an off the shelf algorithmic solution would be available. For industry or organisation specific analysis tasks, it might be necessary to seek the expertise of vendors who are able to build the organisation’s understanding of the data to be analysed and to develop tools to use the data to meet business goals. Sources: The content on this page was primarily sourced from: Webinar titled “The Data Indigestion Crisis: New approaches to Energy Analytics” by Umesh Bhtoria, Founder and CEO, EnergyTech Ventures
  6. Introduction The concept of IoT Building Management Systems (BMS) as a service is poised to change the building industry. As the price of internet connected sensors comes down, a large number of sensors can be placed in a building to provide multiple data points connected to advanced cloud based analytical systems. This delivers superior BMS performance to traditional engineering approaches. Building owners own their data, while allowing service providers to help them optimise the efficiency and sustainability of their facilities. This approach also facilitates auditing of the actual performance of building management systems during the critical Defects Liability Period. Traditional vs IoT BMS The purpose of BMS is to achieve sustainable buildings and cities. They should increase efficiency, resilience, security and productivity, as well as reducing environmental impact. They may also incorporate intelligence (as in smart cities) and have the capacity to detect and fix damage. Traditional BMS were pioneered several decades ago. They started the process of automated control and data collection. A diagram of a traditional BMS is shown below. Diagram courtesy of Bob Sharon, Blue IoT Traditional BMS were often proprietary systems. They were expensive to purchase, and modifications to data extraction rules or reporting functions (“steering wheel options”) were also costly. This meant that many BMS owners did not use their systems to their full potential. Other challenges included the long lead times to make changes to the system, the high cost of the cabling to connect extra sensors, specialist programming services required (also costly), and limited alarm complexity without blowing out the budget. Data extraction and report customisation was typically complex and expensive, as was integrating additional data sets from additional systems or devices. And in many cases, the BMS vendor owned the data. A diagram of an IoT BMS is shown below. Diagram courtesy of Bob Sharon, Blue IoT IoT BMS solved many of the issues of traditional BMS systems through open system architectures, wireless technology instead of cabling, increased agility and integration, and reduced operation, modification and maintenance costs. A general comparison between traditional and IoT BMS is shown in the table below. One comment is that some tradition BMS are also starting to become more open. Diagram courtesy of Bob Sharon, Blue IoT Democratisation of data is another advantage IoT BMS have over traditional systems. Open source platforms where the client owns the data allow system modifications to be easily made, and the client to change vendors to meet their service requirements. This trend is set to increase in BMS and other IoT applications. The transition of BMS from traditional to IoT systems is still progressing. So for mission critical applications it may be advisable to use an open source traditional BMS with two-way communications and control form the cloud, with the option to shift to complete cloud operation as the technology matures. Architecture considerations for IoT BMS Considerations when choosing the data aggregation and IoT architecture for an IoT BMS include: Which protocols should connect the sensors and IoT platform? What form of communications technology best suits the application (eg Zigbee, wifi 802.x, Sigfox, Bluetooth low energy (BLE) and LoRaWAN? How will your application engage with the cloud? Who will own the application data (vendor, building owner, users of devices)? Is an open or closed architecture most suitable? It is also recommended that a highly resilient (tier 3 or tier 4) data centre is used for BMS to ensure that data management meets requirements. Sensors and Predictive maintenance One of the problems with traditional BMS is the cost of adding additional sensors, which means that the minimum number is used. With IoT BMS, this cost is greatly reduced, which opens up opportunities for a wide range of data collection to be integrated. It is important to pay attention to data calibration and validation, to ensure that high quality, accurate data is collected. A diagram of some of the sensors which could be used in an IoT BMS is shown below. Diagram courtesy of Bob Sharon, Blue IoT Self-healing and predictive maintenance In particular accelerometers, vibration transmitters and switches can be used to monitor critical rotating machines, and perform predictive maintenance. For example, accelerometers can be used to measure vibration and measure the harmonics of motors. Through monitoring, faults can be fixed before they fail. Advanced machine learning tools will be invaluable for implementing self-healing machines that can dramatically reduce maintenance costs and risks of outages and out of hours maintenance. These cost reductions can offset the cost of installing an IoT BMS. Data analytics There are various data analytics platforms that can take data from thousands of sensors in disparate building management systems (over thousands of buildings if necessary) and create effective interactive analytics and visualisations for end users. This data can be interpreted by engineers and other experts to solve issues that are detected. Security Security of IoT BMS is crucial to ensure that hackers do not take control of the BMS or use it as a pathway to corporate networks, both of which can cause significant damage. A robust, holistic security architecture should be chosen, which implements security at every level including choice and security measures and levels for all of the following components of the BMS: sensor hardware communications protocol cloud IoT platform gateways cloud data centre Other considerations are whether encryption is used, if AI is used to check for unwanted signatures, whether a mesh network being used for sensor communication (can introduce additional risks), which geographic locations the data is going to before it reaches the data centre (and associated risks vs timely transmission of data). While risks cannot be entirely eliminated, they can be greatly reduced with careful security planning and design. An example of how BMS security can be implemented using LoRaWAN is shown below. Diagram courtesy of Bob Sharon, Blue IoT LoRaWAN has the advantage of being able to be encrypted, and the sensors are isolated. The data goes from the sensor directly to the gateway. From the gateway, it goes out over either 3G or 4G, or to another LoRaWAN base station, depending on the system design. This lowers the risk of hacking and additional AI layers can be added for further security. Two-way communication may also be available depending on the class of LoRaWAN used. This example is suitable for low bandwidth data. Sources: The content on this page has been primarily sourced from: Webinar titled “The death of Building Management Systems as we know them” by Bob Sharon, Chief Innovation Officer, Blue IoT See also the article of the same title in our discussion forum with some comments.
  7. Introduction Replacing prisons with high tech systems capable of detaining prisoners in their own homes and the use of artificial intelligence to predict and prevent imminent offenses may sound the stuff of science fiction, but rapid advances in technology surrounding IoT makes such a vision a possibility worth discussing. A system that effectively turns prisoners into internet nodes using IoT wearables with the ability to deliver electric shocks would have significant social impact. These ramifications need to be taken into account along with engineering design and legal considerations. This application of IoT falls at the intersection of engineering, technology and law, and as such, needs an interdisciplinary approach. The case for technological incarceration Big data, IoT, and AI can be useful in reforming and improving certain aspects of the legal system. One candidate is the prison system, which has remained largely unchanged for hundreds of years. In Australia and many other Western countries, the rate of incarceration is increasing as governments respond to voter pressure to be tough on crime. This comes at a high social and financial cost. In the US, the cost of running prisons is tens of billions of dollars per year. In Australia the annual cost is in the billions. It would actually be cheaper (although not practical) to assign an individual police officer to each prisoner. For prisoners, incarceration causes effects following release including diminished life expectancy, prolonged unemployment and reduced income. This leads to further costs to the public purse. In addition, a disproportionate number of underprivileged and minority groups are imprisoned, including Indigenous Australians and African Americans. One of the main arguments for incarceration is to deter people from committing crimes. Research has shown that a more effective deterrent than fear of prison is the belief by a potential criminal that their crime is likely to be detected, and that prisoners with a harsh sentence reoffend at a marginally higher rate than those dealt with leniently. Protection of the community through incarceration of violent criminals is also limited to the length of sentences. How could technological incarceration work? Technological incarceration has the potential to punish criminals and keep the community safe while reducing the financial and social costs of traditional incarceration. One proposal is to implement a variant of home detention which uses electronic bracelets or anklets along with an IoT system to achieve: real-time tracking of offenders’ locations constant surveillance of offenders’ actions immediate immobilisation of offenders who are committing a crime or escaping Challenges One challenge of technological incarceration is that GPS tracking with wearables is not an adequate substitute for prison because it cannot prevent offenders from harming others in their location or if they escape. To solve this issues, the wearables need to be able to report to a central location in real-time. For constant surveillance, and prevention of harm to the public, the cost of corrections officers viewing CCTV for every offender is too expensive. Therefore a computer-monitoring solution needs to be found. The final challenge is how to immobilise offenders who are reoffending or escaping. This could be achieved by incorporating a device such as a taser into the offender’s anklet, which could be remotely activated if incapacitation was required. Technological incarceration could be perceived as “soft” by the community, and education might be needed to convince the public that deprivation of liberty is a harsh punishment in itself. Conversely, some may see it as too harsh, due to complete loss of privacy and the risks of tasering. It could be argued that these concerns are not as great as the current ramifications of traditional incarceration. Technological incarceration would also place a burden on families, be vulnerable to technological failure, and present privacy concerns to family members and engineers and technicians involved in maintenance of the incarceration equipment. An important question is the number of technology triggered taser-related deaths, or failures of tasers leading to public danger that society is willing to tolerate, similar to the issues of driverless vehicle-caused fatalities and casualties. This needs to be put in context with current issues including deaths and violent attacks in prisons, and crimes committed by offenders on bail. Another question is whether technological incarceration would be made available to every offender, or only those who are not violent or dangerous. As the offenders would be imprisoned in their own homes, provisions would also have to be made for accommodation for homeless offenders. Technology The electronic anklet is existing technology. There are two forms: one uses RF tracking capability and the other GPS. The GPS version has the capability to accurately track offenders to within around 10 centimeters. They are fitted with an alarm for tampering, and cost around a sixth to a tenth of traditional imprisonment. In existing devices, fibre optic technology is used to provide tamper-proofing: a beam is interrupted when offenders try to remove their device. However, this technology is only used currently for offenders on parole or with a non-custodial sentence. To solve the more complex problem of monitoring and incapacitating offenders in real time if they are posing a danger to others, proponents of technological incarceration have proposed the use of sensor vests in conjunction with computer-based monitoring with technologies such as machine vision. Rather than installing fixed sensors (infrared temperature sensors (IRT), audio sensors and cameras) in offenders’ homes, these sensors could be installed in modified police vests. This has already been trialled with cameras in vests to provide police accountability. Machine vision has the potential to detect suspicious movements such as fast hand and leg movement, or picking up implements.There is also a lot of promise in using sensors and machine vision interpretation with convolutional neural networks (ConvNets or CNNs) which have proven effective in image recognition and classification in driverless cars and robot vision. One issue is the transmission of sensor data (particularly high definition video) in real time for analysis. This could be resolved by analysing the data locally on the vest, and transmitting interpretations, however, it is yet to be determined if available interpretation technology is small enough to be mobile. Another area for further investigation is how integrated audio, visual and other sensor data can be used to gain a picture of the offender's activities than high definition video alone. Biosensors (which are used in the monitoring of athlete’s condition) could also be used to monitor offenders’ emotional state. Stable communications are also necessary for the transmission of real time data and triggering of tasers. This would require a reliable 4G, or preferably signal in the offender’s home. If the data connection is lost, police officers would need to be called in. This is another argument for only using technological incarceration for lower risk offenders. Low battery charge levels on the tasering device would also trigger a police visit. Facial recognition technology also has the potential to allow monitoring of the gradual reintegration of offenders into society after their sentence has been served. Progress Technological incarceration using IoT systems is feasible, but its implementation is limited by social and legal concerns and challenges. Once these challenges and concerns have been addressed, it might be possible to trial technological incarceration on less dangerous offenders (elderly, female and white collar) in controlled conditions. If society does go down the path of technological incarceration, it is unlikely that people would be completely removed from offender management. In the case of a suspicious movement, an alarm could alert corrections officers and provide them with a visual feed to make a decision on the appropriate response. Once the technology has been proven, it might be possible to hand over more control of the response to the AI system, in a similar way that we are now allowing driverless cars to make judgement calls on the road. The manufacturing and supply of devices that could be used in technological incarceration is primarily based in the US at the moment, but there is potential for it to expand to Australia and other nations if society accepts its implementation. Sources: The content on this page was primarily derived from the following: Webinar titled “The Internet of Incarceration” by Professor Dan Hunter, Dean, Swinburne Law School
  8. Introduction Management of an IoT project is likely to pose challenges for those used to stand-alone product development, as it combines detailed product and technology development and application with systems engineering. While recognising and overcoming technical risks is necessary for all projects, it can be particularly critical for project management of IoT applications because of the complexity of the technology used, which may include a broad range of technologies involving several layers of the IoT architecture. This can lead to project teams needing to seek assistance from suppliers, systems engineers, and expert consultants to understand unfamiliar technologies, their risks, how to specify technical requirements, and the best technologies for the solution being developed. Other important considerations are: ensuring stakeholders share an understanding of key terms and language identifying legal and regulatory requirements and how to comply with them determining what team skills and competencies are required reducing complexity by implementing a staged approach to development. The importance of a common language A key issue troubling systems integration in IoT projects is the different language and expectations used by various project stakeholders. The main stakeholders in IoT projects can be described as belonging to three groups, each with a different understanding of key terms used in IoT projects, as shown in the diagram below. Diagram courtesy of Heath Raftery, NewieVentures Operational Technology (OT) professionals are skilled in industrial automation and traditional SCADA systems (such as Modbus). They also have particular definitions of key terms such as ‘protocol converter’ and ‘edge computing’, which can differ from the definitions used by IT professionals or IoT vendors, leading to misunderstandings that mean that the solutions purchased may not meet user expectations unless expectations and specifications are spelled out in plain language. For example, OT professionals used distributed control systems (DCS) to give the processing power to the devices being controlled in SCADA systems before the similar concept of edge computing was adopted by the IT and IoT world. Information Technology (IT) professionals are skilled in communications technology and networking, but may have different expectations for success than those possible with IoT technology. For example, an IT professional may expect a carrier grade radio link to have a 99.9 percent uptime, whereas the message failure rate of a LoRaWAN system can be more than one in three when the receiver is at a significant distance from the gate, and a success rate of 95 percent or more close to the gate. So in this case, a specific message success rate would be more meaningful than the phrase ‘carrier grade’. Consumers without an IT or OT background are likely to be familiar with some enabling technologies for IoT solutions, and the value they can provide. However, they may use terminology in an imprecise way that hinders communication with IT and OT professionals working to provide them with a solution. This can be overcome by asking deeper questions about what the customer wants and why, to come to a common understanding of expectations and success criteria. Reducing complexity IoT projects can be complex compared with other engineering projects due to the range of technologies and architectural levels they encompass. Dividing the development project into discrete stages reduces the complexity of each of those stages, and allows for review points between the stages. The following development stages can help to reduce the complexity of IoT projects: Conceptualisation: What are we trying to do? What does the user want? Specification, planning, estimating: What is the scope, duration, cost? Design and development hardware: produce schematics and circuit boards, or integrate third-party modules firmware, software: connectivity, functionality Verification and validation: verification determines if the integrated hardware and software performs to specification, and validation tests whether the specification meets user requirements. In some cases, the specification may need to be changed to more accurately reflect requirements and the design, development, verification and validation repeated Regulatory compliance testing, certification: electromagnetic compatibility, electrical safety and product safety requirements as for other engineering projects and products, and specifically radio communications compliance Production engineering: determine if product is feasible to manufacture and test Manufacturing, deployment and maintenance. Technical specification, planning and estimating Specifying and documenting the user’s technical requirements in a technical specification needs to be completed before embarking on the the design stage of IoT projects, as if a product or system is developed without a well-documented and agreed technical specification, project cost and time can increase, and the relationship with the user can be adversely affected. A technical specification defines what the system needs to do in terms of high-level user requirements and functional requirements. There is often a difference between what the user asks for and what they actually need. IoT engineers need to interpret the user's non-technical requests and convert them into a meaningful technical specification that can be implemented in the project. The specification should include the following system characteristics: Function Performance Environmental: eg. is a waterproof enclosure required for an outdoor installation? Reliability: how long does the system need to operate, what maintenance is required, what downtime is allowable? Power and energy requirements: battery-life required to meet reliability and performance requirements for battery-powered systems. Also budget constraints, how power will be supplied, and the duty cycle. Once the required system characteristics are defined, the constraints directly affecting development, and their technical solutions should be specified. Key areas to be addressed include physical constraints such as size and weight of systems and devices, the complexity for the end user, and whether the system will be plug and play, or have other deployment requirements. Budget, time, quality restraints and regulatory and legal compliance also need to be addressed and documented. Useful tools for documenting timelines are work breakdown structures (WBS) and Gantt charts, as shown below. Diagram courtesy of Geoff Sizer, Genesys Electronics Design The WBS needs to be taken to a level of granularity that allows reasonable estimating to be done. In some cases, that might be down to a person week's worth of work. Sometimes it might be to a person day or even finer granularity to give clarity on what needs to be done and the interactions of the task. Budget estimates should include costs of standards, safety and regulatory compliance and safety testing, which can be of comparable cost to the technological development. For startups without a good understanding of the costs and timelines in the IoT space, it is a good idea to seek help from more experienced practitioners, or specialist consultants to ensure that estimates are reasonable and don't underestimate project complexity. Design, development and documentation The first step in the design and development stage is to identify the key technology elements required. For example, sensors, actuation, micro-controllers and energy source (eg. rechargeable/alkaline batteries, solar cells or mains power). The intelligence of the platform needs to be determined: for example, a complex or simple micro-controller. The next step is to determine what communication technologies will be used to connect to the IoT, perform data collection and data analytics, and select user interfaces, and data presentation for the user, which could include interactive analytics tools. Finally, deployment, commissioning, fault detection and maintenance need to be designed. All design decisions need to be documented in detail, to ensure that all aspects of the project are considered and nothing is missed. For example, at the conceptual phase, a project plan and a functional requirements specification might be produced, and during development, manuals might be developed for both hardware and software/firmware to document how the functional specification will be implemented. Through the life of the project, those documents evolve and have detail added to them, and ultimately become a design description for what's being developed or deployed. Other documentation could include: communications protocol specification, electromagnetic compatibility compliance plans, certification plans, test plans, and also deployment plans, and manufacturing documentation suites. It is good practice to recognise the documentation requirements upfront, and build them into the project plan. Then ensure that project team participants produce the documentation as they go along, to avoid issues with incomplete definition and a rush to produce documentation at project completion. Staying in control To maintain control during the course of the development project and the transition into manufacture and deployment into the field, it's important to establish configuration control. Software repository tools can be used to keep control of software releases and make sure that you have a firm definition of the software that you are testing and deploying. Document release and engineering change notification (ECN) procedures also need to be established for the initial documentation release and updates to maintain version control. This avoids problems such as providing out of date documentation to manufacturers. Issue tracking is also essential during the course of development, release and pilot trials, and through the manufacturing process. Several issue tracking systems are available, to track problems such as software bugs and suggested product improvements. Issue tracking systems should not be used as a substitute for properly updating the system specifications when changes are made. Effective and regular communication between team members is essential, and online, cloud-based collaboration tools are available to facilitate this, although they are only as good as the team who use them. Finally, a well-defined team leader is needed for the project, to make judgements and decisions about how the project proceeds. For the engineering team, this may be the lead engineer, who both project manages and is a technical participant in the project. Technical risks and standards In an IoT project, there are risks arising from the broad range of complex technologies used. Electrical safety, in particular shipping lithium ion batteries by air is an issue, as they can be barred from aeroplanes. Imported products may not meet local regulatory requirements, particularly in the case of electromagnetic and radio communications compliance. For instance they may not cover local frequency bands. As there are severe legal penalties for deploying non-compliant systems, this is an area which needs to be covered in the risk assessment. Resource capability is another risk. Does the project team have the depth and breadth of skills to do the job? Or are there some of the technology elements that are stretching team capability? Intellectual copyright and design security are particular concerns, and regulatory compliance requirements and licensing needs need to be identified. These areas are discussed in detail on the legal considerations for IoT page. One recommendation is to make sure that there is a copyright claim at the top of every file of your source code, along with any necessary the comments and descriptions. During the risk assessment, engineers are responsible for identifying and complying with the relevant standards for the specific project to ensure product safety. If the engineers need assistance with identifying relevant standards, specialist organisations (eg. OzTest and EMC Technologies) can provide consulting services to assist with this process. Quality management standards, including the general commercial standard ISO 9001, and specific quality management standards for product areas with more stringent quality requirements (such as ISO 13485 for medical devices) can be used as a tool to put in place consistent and effective project management methodology as described on this page. Team skills and competencies, when to seek expert advice IoT projects, like all others, require sound project management and team leadership is a given. A well-defined leader and project manager needs to be appointed and recognised by the team as being in control. Systems engineering expertise is also important. Because of the complexity of IoT projects, it requires skills beyond electronics and firmware. Electronics hardware design requirements can range from simple to quite complex, as can the software and software engineering skills required for individual projects. Other specialist knowledge required includes, but is not limited to: communications protocols, antenna design, wireless communications, embedded firmware, cloud-based server software and data analysis and processing, and App development for user interfaces. Security is also a major concern. Compliance testing, laboratory services, manufacturing services, deployment and logistics may also be outsourced if the skills and capacity are not available in-house. Most teams will be stretched in mustering all the skills in-house to be able to deal with everything they need to do. It is important to recognise these limitations upfront, and then supplement the project team with the required specialist consultants to do tasks outside of team expertise quickly and efficiently. If a company is involved in IoT technology development on an ongoing basis, the required skills should be brought into the team over time. There should also be team members with the responsibility of keeping abreast of developments in the rapidly evolving IoT technology space. The relationship between expert consultants and in-house project teams will vary depending on the knowledge and experience that the team has in the IoT area. If knowledge and experience is low, an expert consultant would guide and manage the staged project as described in the previous sections. Where knowledge levels are higher, the in-house team might do the high-level project management and control. Areas where there are skill gaps would be delegated to consultants. This is best done by delegating work packages (eg. for electronics hardware) and communicating regularly to make sure that consultants are keeping on track with overall system development. Ideally, an integrated and collaborative approach should be taken to avoid an “us-and-them” situation. Being an informed purchaser Given the complexity of IoT technology, it is difficult for clients adopting IoT to acquire the requisite knowledge to keep control of the project, and not just delegate the full ownership of the project to a sub-contractor. Seeking knowledge from online communities such as this one, or engaging an independent consultant or consulting engineer can be helpful in becoming an informed purchaser. The IoT Alliance Australia is another group which brings together companies in the IoT space and seeks to help them gain understanding of technologies. Project management and collaboration tools While tools should not be used as “crutches”, they can make project management and associated processes more efficient when used in conjunction with good practice. Gantt charts can be prepared in Microsoft Project, or a project management tool suite that has the capabilities needed to manage the project. Some available products are cloud-based. Software repositories for configuration control include Version and Github. Collaboration tools include Confluence and OurTeam for PC. JIRA is a cloud-based issue tracking and development tool which has been used in some IoT projects to record and deal with bugs and product improvements. Sources: Content on this page was primarily sourced from: Webinar titled ‘Project Management for the Internet of Things’ by Geoff Sizer, CEO Genesys Electronics Design Webinar titled ‘Ground Truths in IoT’ by Heath Raftery, NewieVentures
  9. Introduction Smart metering using IoT has the potential to increase efficient use of utilities and identify and resolve issues in supply infrastructure in the utility industry. One definition of smart metering is the collection of metering data on utility (electricity, water, gas) use, and the systems and processes that derive value from the data. It also enables two-way communications from the meter to utility providers and users, and involves intelligence and processing within the meter that differentiates it from simple automated meter-reading systems that send a reading at specified intervals. Smart metering is widely used in the electrical power industry, due to ready availability of power for IoT devices, however has proved more challenging in metering other utilities such as water. Smart meters enable users and suppliers to gain insights into the utility use of a particular site, piece of equipment, house: anything with a meter. It also gives electricity distribution or water network operators insights into the operation of the network. On the supply end of the meter, utility providers can start to understand what demand is on the network, and when and where there is demand. Smart metering solutions have the following key focus areas: sustainability: reducing the amount of resources that we consume and the energy required to treat or distribute the resource increasing labour efficiency (eg. installing a sensor rather than having a person check manually) increasing economic efficiency As with any IoT project, the cost of smart metering solutions needs to be offset by efficiency or cost savings driven by value extracted from the data collected. One key to increasing efficiency with smart metering is to provide customer friendly data visualisation, interactive analytics and data sharing to allow users to monitor and modify their utility usage. This may not be necessary for corporate users, who will require integration of smart metering data with their own business systems to drive economic and operational benefits. Enrichment of data with additional sources (such as weather and home automation data) also adds value, as does automating processes and work flows by feeding smart metering data or summaries into scheduling, reporting and service systems. Monitoring water use can also businesses predict future utility use for more informed financial planning. Smart metering of water The water industry has historically lacked economically IoT solutions for smart metering. Only around one percent of residential water meters (95% of the water market) are smart enabled. However the application of IoT technologies to high water users is now delivering significant results. Low cost, high volume remote sensing devices are using new low power wide area network (LPWAN) communication technologies and advanced data analytics to develop new business models for the management of water use. Users are more easily able to identify water leaks and consumption trends, to generate insights and facilitate smarter action. The key components of IoT in the water industry are similar to other vertical IoT solutions: · physical layer: low cost, low power remote sensors and devices · network layer: LPWAN, other connectivity · Cloud and edge computing · Data storage, analytics, machine learning · Integration with operational and business systems These components and their relation to each other are shown in the diagram below. Diagram courtesy of Rian Sullings, WaterGroup Pty Ltd Machine learning is used to improve the efficiency data analysis, especially for large data sets for cities rather than individual buildings. Integration of smart water metering systems with operational and business systems is a developing area, as historically they have been stand alone systems rolled out by water utilities with links to billing and some data analytics. Future developments are likely to include data connections to systems that schedule maintenance work, and automatic alerts to operators to resolve detected water leaks. Smart water meters distinguish baseflow (constant, steady flow) from leaks (steady or slightly fluctuating use of water which varies from the norm). Data analysis can inform other efficiencies including timing of air conditioning operation to maximise efficient use of cooling towers. Leak detection provides the greatest economic benefit of smart water metering. A recent project delivered water savings that covered the cost of smart meter installation in less than a year. The diagram below shows the increase in water usage (dark blue line) caused by a leak in the roof sprinklers during the new year’s break at a facility. An automatic alert sent by the smart metering system allowed the leak to be detected and fixed in a matter of days. Diagram courtesy of Rian Sullings, WaterGroup Pty Ltd Challenges Challenges to IoT smart metering solutions can be industry specific. For example, the water industry has infrastructure, such as underground pipes and meters, that are very difficult to access and successfully establish data communications with. This has historically made implementation of IoT and other electronic solutions (end-to-end telematics, SCADA) solutions challenging, as they require deployment of devices with access to power and communications channels. Prototypes are being developed for Sigfox smart metering devices outside North America and Europe, as the frequencies vary between regions, and smart metering devices have not been widely used outside these areas. There is also a limited amount of knowledge of developing smart metering applications for IoT, particularly in the water industry, so collaboration with professional organisations and between developers is important. Another challenge is educating utility users that installing a smart metering system will not increase sustainable resource use unless there are clearly defined goals and methods to store, analyse and feed data into decision making to change behaviour to maximise efficiency. To do this, smart metering systems need to be integrated with business or operational systems, which can be challenging as some utilities (eg. water), currently have limited standards to aid integration of IoT data. Security of smart metering systems is also a concern, particularly for government run systems. This can result in private servers being set up rather than hosting smart metering data in the cloud. Data ownership and privacy are also challenging, particularly for sharing of data collected from private homes. Suppliers and Innovators Australian smart metering company WaterGroup has formed a partnership with IoT communications provider Thinxstra to use the Sigfox LPWAN to allow high water users to detect leaks, and has received an award from the Australian communications industry for positive application of IoT technologies. Over the past few years, South East Water (SEW) in Victoria have been trialling a range of different Internet of Things (IOT) technologies with the goal of creating the most advanced water and waste water network in Australia. The trials are aimed at identifying an IOT platform that will allow the connection of around one million monitoring and controlling devices across SEW’s water and wastewater network using a low power wide area network. More information is contained in the case study page for this project. Standards The Australian national data standard standard for energy smart metering is NEM-12, administered by the Australian Energy Market Operator (AEMO). Standards for IoT-based smart metering of water are still being developed. There is no standard format data storage and transfer, so there are many different file types and formats, which are difficult to integrate. Middleware software can be used to convert data into a common format for integration. Other areas for development in IoT smart water metering data standards include reliability, communications protocols and security. IoT smart metering applications in Australia also use the Hypercat standard for cataloguing and storing IoT data. Sources The content in this page has been primarily sourced from: Webinar titled ‘Smart Metering for Water with the Internet of Things’ by Rian Sullings, Manager Smart Metering & IoT, WaterGroup Pty Ltd Further information Discussion of audience questions from Webinar titled ‘Smart Metering for Water with the Internet of Things’ by Rian Sullings, Manager Smart Metering & IoT, WaterGroup Pty Ltd
  10. Introduction Traditional satellite solutions for providing a communications backhaul are obviously applicable for applications involving remote sensors, Defence, tracking across wide geographic areas including oceans, as well as developing solutions for global supply chains. In remote areas, terrestrial communication technology connectivity for IoT devices can be largely absent or very expensive. Existing data communication satellites (eg. Iridium and Globalstar) are a solution, but can be expensive, limiting the number of sensors that can be deployed to relay data, and how often data can be sent through IoT. The biggest cost of space communication technology is infrastructure: of building and launching a traditional satellite. This expense is passed on to the consumer through the high price of satellite data. A more task appropriate option in the process of being deployed is the use of nanosatellites. Nanosatellites Nanosatellite constellations have the potential to provide a lower cost satellite communications option for low-power, small-data IoT systems, particularly in terrestrial communications blackspots. The end-to-end solutions required for large-scale deployment of low-cost nanosatellite IOT communications are still in the evolutionary stage, but a number of companies are scheduled to launch the first nanosatellite systems in early 2018, including South Australia based companies Myriota and Fleet Space Technologies. Nanosatellites represent the current trend in space technology towards cheaper, smaller and faster to build systems. The CubeSat standard for nanosatellites was developed in 1999 by Stanford University and Cal Poly. In the past fifteen years, CubeSats have been used in universities for numerous projects which engage people to use satellites. This big change in the satellite community has been driven by new technologies, like the miniaturisation of electrical components, PCBs, manufacturing and 3D printing. These technologies have meant that it is possible to now build a very small spacecraft with the same capabilities as a big one. The CubeSat is 10x10cm and can be constructed into modules up to 30x40cm. They generally have a mass of less than 50kg. Cubesat nanosatellites are still expensive to build and operate (over 1 million $AUS), but much cheaper than their larger counterparts. Advances in space technology Advances in commercial space technology such as micro and nanosatellites as well as low-cost commercial rockets with payloads capable of deploying multiple micro or nanosatellites in a single launch are being developed by companies such as New Zealand’s RocketLab and have the potential to bring the cost of the technology down even further. The recently established Australian Space Agency is also likely to open increased opportunities for Australian industry and Defence to become more involved in establishing micro and nanosatellite networks for IoT communications in applications such as Defence. Nanosatellites are usually launched into space via rocket as part of a bigger spacecraft launch. However, some companies are now building dedicated launchers for nanosatellites, which also has the potential to make nanosatellite communications for IoT a more economical option. Nanosatellites vs geostationary satellites Nanosatellites are launched into low earth orbit (LEO), rather than geostationary orbit. One example of a large geostationary satellite is the NBN Sky Muster satellite which has constant data coverage over the Australia continent. Nanosatellites cover a much larger area of the earth than geostationary satellites, but there is a latency of between 30 seconds to 30 minutes, depending on how close the LEO nanosatellite is to the ground station. This makes nanosatellite communications unsuitable for GPS tracking applications, although the development of triangulation geolocation techniques to provide this data are underway. Constellations of multiple nanosatellites also reduce latency. Geostationary satellites are suitable for GPS applications and big data IoT solutions. Commercial nanosatellite applications Commercial applications of nanosatellites include satellite imagery services, weather prediction, ship tracking as well as IoT data. South Australian company Myriota produces low-cost IoT modem technology for use in remote areas. This technology communicates via nanosatellites, although Myriota do not deploy their own satellites. Fleet Space Technologies is launching the first two of a 100 satellite constellation at the beginning of 2018, with the aim of being online by 2022. These constellations will provide a free data via a global backhaul service for industrial users of IoT. Once the constellation is online, users who buy sensors, gateways and terminals from vendors providing products containing the Fleet communications chip will be able to operate them without ongoing satellite data costs. Satellite vs terrestrial communication While launching a communications satellite is much more expensive than building a terrestrial base station, satellite communications provide much wider coverage. This means that the overall cost of coverage is greatly reduced, although the initial infrastructure outlay is much higher, as shown in the diagram below. Diagram courtesy of Flavia Tata Nardina, Fleet Space Technologies Satellite communications are also a more effective solution for coverage within oceans and remote areas. Latency Initially, nanosatellite solutions will have a slow latency, with only a few passes per day providing the opportunity to upload data. This means that it will not initially be feasible to develop real-time solutions using this technology. However, as more satellites join any given constellation, the latency will drop to minutes or conceivably seconds. Sources The information on this page was primarily sourced from: Webinar titled Satellites and the new industrial frontier – how new space technology is intersecting with the Internet of Things by Flavia Tata Nardina, Co-founder and CEO, Fleet Space Technologies Webinar titled ‘Defence Next Generation Technologies: Driving Innovation in Defence’ by Dr Alex Zelinsky AO, Chief Defence Scientist, Department of Defence.
  11. Introduction IoT collects the information from physical assets (Things) in the real world, while augmented reality (AR) takes digital information and displays it in the real world. By combining IoT with AR, it is possible to create an immersive ‘in-context’ visualisation that aids understanding of products and Things, as shown in the diagram below. Diagram courtesy of Allan Thompson, LEAP Australia The earliest examples of AR included heads-up displays in military aircraft in the early 1960s, and later civilian aircraft. A recent popular example of location-based AR is the online game Pokemon Go, which has greatly raised the public profile of the technology. Mobile handheld devices, including smartphones and tablets, have inbuilt cameras and can be used to generate 3D graphics in real time, which has made AR an increasingly accessible technology. Examples of AR technology are the re-emerging Google Glass, and the advanced Microsoft Hololens digital eyewear. AR can also be used in applications such as industrial automation. Augmented reality (AR) vs virtual reality (VR) The primary difference between virtual reality (VR) and AR is that VR uses computers to create a completely virtual environment, whereas AR allows users to maintain a view of the real world as well as the superimposed computer-generated visualisation. In industrial applications, AR is a safer option, as it allows users to avoid hazards such as forklifts and tripping hazards. VR also requires much greater computing power than AR, which is a major limitation of VR technology. Applications of AR in industry AR is used in industry for three main applications: information visualisation: Enhance the user’s view of the physical world with the overlay of actual or hypothetical digital information eg. a CAD model of a drink machine superimposed over the area where it will be installed instruction: eg. overlaying a step-by-step sequence over an object to provide graphical instructions or real-time expert guidance on technical procedures interaction with Things: View or manipulate digital information with natural user interfaces or control a product through an augmented digital user interface. Information visualisation is the most common application of AR in industry. Using AR for instruction is becoming more popular as more companies are starting to work with 3D data. This has the advantage of removing the need for paper-based manuals and translating instructions for multiple areas of an operation. Some companies also use AR animations as sales and marketing tools for their products. The last application is the most relevant to IoT. For example, a Raspberry Pi can be used to stream data to an app which creates a visualisation of the data generated by the device in the field, which is updated live in the AR. It is also possible to configure the app to push data back out to IoT devices for two-way connectivity, and build in security to AR applets to ensure that only appropriately authorised users can log in to access IoT devices. Features, development and limitations of AR In the past, it has taken a large and multi-disciplinary team to create custom AR applications. Team members have included 3D specialists, programmers, cloud experts, and app developers. This means that custom AR apps were usually created in-house by big companies such as Lego and Ikea, or by external contractors at a sizable cost. Pixel counts and sizes of models have also caused limitations for AR applications, due to the computing power required to run them, as AR is typically designed to run on mobile, less powerful devices than VR. This can be addressed by building appropriate compression software into the software. The development of software which takes 3D data and builds AR display applets without the need for custom coding has the potential to make AR a more accessible and affordable solution for 3D visualisation of IoT data in the field. There is also the facility to implement two-way voice interaction into AR applications. High quality digital visualisation headsets, like the Microsoft Hololens, are also expensive, which limits their widespread uptake in industry, however other companies, including Google and NEC, are designing lower-cost eyewear. AR Software The PTC Thingworx Studio and Vuforia AR software are two examples of software in many existing AR applications. Currently the Thingworx app is only designed to work with the AR markers for Microsoft Hololens, however PTC Vuforia works with a greater range of glasses. PTC software does not require custom coding, but automatically generates AR applets from a click-button user-interface. Apple also offers an AR developer’s kit, which is provided free to users who sign up for developer’s camp. This kit requires programming skills. Sources: The information on this page was primarily sourced from: Webinar titled Augmented reality for ‘in context' visualisation of IOT data by Allan Thompson, PTC Technical Manager, LEAP Australia.
  12. Description: The Internet of Things is creating a whole new digital agenda for oil and gas. This case study details how DiUS helped Environmental Monitoring Solutions use the cloud and IoT to tackle the global petroleum industry problem of petrol station inefficiencies and make a positive environmental impact. Source: Based on a webinar delivered on 1 August 2017 to the Applied IOT Engineering Community of Engineers Australia by Zoran Angelovski, DiUS Principal Consultant and Russell Dupuy, Managing Director of Environmental Monitoring Solutions Biographies: Zoran Angelovski has ridden the wild technology wave for over 20 years. He has a background in hardware development, broadband telecommunications and more recently electric vehicle chargers, smart energy devices and IoT products. Russell Dupuy has over 25 years’ experience in fuel system automation. He is an industry leader, disruptor and innovator. Forged from a formal engineering background, he has developed leak detection systems and wetstock management solutions for major oils in Australia, Europe, Japan and the USA. With a passion for the environment, Russell leads a number of environmental and industry workgroups to drive innovation and sustainability for what is often referred to as a mature and dirty industry. Title: Disrupting Retail Petroleum Introduction This case study describes the development of the Fuelsuite remote monitoring and 24/7 support service for retail petroleum outlets. This system connects onsite data from service stations to the Cloud in a scalable and cost effective manner to provide insights to clients in order to anticipate issues before they occur. A conceptual diagram of the Fuelsuite solution is shown in the diagram below. Diagram courtesy of Zoran Angelovski, DiUS and Russell Dupuy, EMS The petroleum industry can be divided into two segments: upstream petroleum: exploration of crude oil; shipping of crude and refined oil; and cracking to the finished consumer product downstream petroleum: bulk storage of oil at major storage facilities around the world; distribution and transportation of the oil; and retail marketing for consumer consumption. There are several examples of uptake of IoT systems in upstream petroleum: in seaboard terminals, refining or cracking plants, and ships. However most are usually embedded in some form of MESH or SCADA system. Fuelsuite is an IoT solution for downstream petroleum, which at this point has not shown good uptake of IoT technology. The retail petroleum market incorporates approximately 540,000 developed retail service stations around the globe, as shown in green on the diagram below. For the markets coloured dark grey in the diagram, verifiable information on the number of service stations is not available, however, research conducted by DiUS indicates that in excess of one million retail service stations exist. Diagram courtesy of Russell Dupuy, EMS and Zoran Angelovski, DiUS Developed retail petroleum markets typically have a point of sale and self-serve multi-hose fuelling for consumers. They also feature cash as well as other payment systems, and a high level of equipment automation. In contrast, attended sites, for example in Africa, generally operate on a docket or a cash system. Over the last 15 years, the number of manufacturers supplying petrol stations with equipment globally has decreased from 250 to 50. There are five dominant manufacturers, which are predominantly US- or European-based. These five companies are heavily invested in acquiring companies through consolidation, and with continuing proprietary systems for commercial reasons. Client profile and IoT solution goals Target clients are retail petroleum marketers, with the following profile: own and operate up 800 service stations sell up to 3 billion litres of fuel per year spend up to $15 million per year cleaning up spills and leaks spend up to $45 million per year in maintenance. Typical client technologies include the following: POS-BOS automatic tank gauge for measuring fuel automated dispensers to deliver fuel to the hose intelligent pumps to push the fuel from the tank leak detection systems water management and monitoring systems fridges and air compressors pie, coffee, slurpee, bain-marie sensors for a range of things The goals of the IoT solution are to reduce: environmental spend by greater than 50% maintenance spend by more than 15% per year fuel variance more than 0.2%. Challenges In retail petroleum, the various systems at a service station are not integrated, and clients are resistant to open architecture solutions as proprietary enterprise systems available from the small pool of global equipment manufacturers offer commercial benefits. Often, service stations run on old hardware and protocols such as a current loops connecting petrol pumps to point of sale terminals, which are mostly standards compliant, but may produce signals which are out of specification. Many retail service stations have automated technologies but revert to manual processes such as metering the fuel being delivered into tanks. This leads to safety and environmental issues including: employees including being struck by moving vehicles or assaulted by customers above ground spills leading to serious fires. Another challenge is maintaining underground tanks to meet environmental compliance standards for preventing fuel leaks. In the US, over a ten year period to 1998, 1.5-million underground tanks were closed due to non-compliance There were 380,000 sites to be cleaned up, at a run rate of 19,000 a year. Inventory management are often quite basic, resulting in high fuel variances, which means that clients are unable to accurately account for fuel underground. Solution The solution developed by Environmental Monitoring Solutions (EMS) incorporated the following steps: develop hardware to connect devices on site connect it to the cloud build a leading cloud platform migrate our smarts into the cloud choose a build partner choose the right platform. These steps are described further in the subsections below. Develop hardware One fundamental challenge was to develop hardware to connect all the devices on site. As there were commercial benefits in clients maintaining their existing enterprise equipment, it was decided to create a custom device for use with existing equipment on site at retail service stations, rather than swap out existing equipment to use a range of third-party devices. Firstly, the solution needed to connect to the gauge on the fuel tank, in order to collect fuel levels, water detection, fuel leakage, temperature and other readings. Secondly, a custom piece of hardware (a pump communications module) needed to be designed to connect to the pumps to detect how much fuel was being dispensed. This module had to be non-intrusive to the rest of the current loop that physically connected equipment on site. This allowed the data loop to be closed in terms of how much petrol was underground in the tanks, and how much was being dispensed through the pumps. The third task of the initial phase of the project was connecting to the price board. This was especially critical for remote sites with no attendant to change the price on site, so this is a task that ideally needs to be done remotely. The aim of this connectivity was to build the capability to collect data that is available in the Cloud that can be analysed in real time using advanced data analytics techniques. The availability of this data will help shift clients from reacting to problems that have already occurred to anticipating problems before they occur as shown in the diagram below. Diagram courtesy of Russell Dupuy, EMS and Zoran Angelovski, DiUS Connect to cloud and migrate system It was then necessary to build a leading cloud platform, as the existing legacy system was outdated: it was an enterprise website, not a true cloud application. All algorithms and intelligence needed to be migrated into that cloud. A physical diagram of the Fuelsuite solution is shown below. Diagram courtesy of Russell Dupuy, EMS and Zoran Angelovski, DiUS As shown in the diagram above, the Things (tank gauge, pump communications module and price board) at the petrol station were connected via a single board computer module and LTE modem to the cellular data network. Data was transmitted via the data network to the IoT Cloud infrastructure to consumers: the Fuelsuite management tools and the users that used the analysed data to take action to prevent problems before they occur. For example, turn off a pump when water contamination is detected. Build partner and IoT platform EMS chose DiUS as their build partner. The platform chosen was Amazon Web Services (AWS) for both Cloud and IoT. An architectural diagram of Fuelsuite is shown below. Diagram courtesy of Russell Dupuy, EMS and Zoran Angelovski, DiUS At one end are the Things, (devices and hardware). As it is intended to deploy thousand of Things over a multitude of petrol stations, the IoT infrastructure provides an effective way to communicate over a network across to the Cloud so that the Fuelsuite management tools can process the data and deliver information to users. The solution leverages an IoT connection from AWS, which incorporates an SDK software development chip, but essentially it operates on a simple single board computer module that gives connectivity from the remote end into the hub or the IoT gateway that is in the Cloud. This provides a secure end-to-end connection across the mobile data network. It also provides mechanisms to authenticate the devices using AWS generated certificates. Additionally, the solution needed a protocol that runs across that mobile connection. The protocol chosen was MQTT, because it will operate in a bandwidth-restricted environment. This was important because the developers anticipated the future deployment of narrow band IoT technologies and wanted to be able to leverage it. MQTT is also light protocol, so when thousands of devices are deployed. This will reduce the cost of the data plan necessary to communicate with thousands of deployed devices in many service stations compared to HTTP and other internet-based protocols. Once the data is in the Cloud, it is routed via the AWS Rules Engine. This is a very simple way to route data to other services, so that it can be manipulated, managed and delivered to the consumer. It also provides a very clear demarcation between the Cloud and the remote devices. If the platform provider brings out a new feature, or if new capabilities are required for the Fuelsuite solution, the routing and be easily adjusted on the Cloud side only without relying on a firmware upgrade, which is very convenient because with many devices out in the field it is desirable to avoid upgrading firmware remotely. The equipment supporting the service is managed through the Device Shadow, which provides a simple way of determining whether a device is online or offline and a clear view of the requested versus reported configurations. If there are any differences, the Cloud configuration can be reconciled with the actual hardware configuration and the equipment can go away and do its job. The Device Shadow also works with intermittent connectivity, which is really critical when the data is connected over wireless networks such as 3G or 4G. Lastly, the Device Shadow enables pre-configuration of devices before the devices being physically available. As devices are installed, their configuration is reconciled if there are any differences to the pre-configuration settings. This allows field staff to operate without needing to make changes in the Cloud. Other services used in the Cloud are: Kinesis Stream: which provides a scalable way to capture and manage the large volumes of data that are funnelled into this concentrated point Firehose: which provides the ability to stream data to other upstream services, such as Elastic Search and the notification queues Elastic Search: enables the use of indexed searching. Next steps The custom hardware produced for this solution will be rolled out to about 1,000 sites in the second half of 2017. This will make tangible gains in the data collected by the industry, as currently only around 20% of tanks are connected to the internet, and they provide data about once a day. There is no pump data being connected at present. Further investigations will also be conducted into how to use the data to better target environmental monitoring and use limited resources to get better outcomes.
  13. Introduction IoT applications discover and store huge volumes of data from multiple sources, and process it using various forms of data analytics. The results of this analysis need to be presented in a way that is useful to end users and aid in their decision making. Presenting data in visual forms, such as charts and graphs, enables users to understand what is happening at a glance and conceptualise what further investigations are required to understand a complex phenomenon (such as building vibrations). Interactive analytics are a set of tools that allow data analysts to investigate the data and create visualisations that present easy to read results and figures that relate directly to users' requirements. They also provide the ability to share these results with others. The main vendors of interactive analytics tools include: Power BI from Microsoft and Tableau, IBM, SAP Oracle, SAS, MicroStrategy and QlikTech. Technologies such as augmented reality can also be used to project IoT data visualisations onto devices or objects in the field. Sources: The information on this page has been sourced primarily from the following: Case Study titled Studying movement behaviour in a building: A case study of obtaining analytics from IoT Data
  14. Introduction Data for IoT applications often comes from many heterogenous sources, which may not be easily brought together for analysis. Data integration is one approach that is used in IoT solutions to allow disparate data to be used to provide useful decision-making support. It is a tool that is often used in data warehousing. Data integration makes use of Extract, Transform, Load (ETL) tools. There are also other new tools including Enterprise Feedback Management (EFM). These are the tools that are utilised to bring the data from one point to the other. ETL vendors are listed in the diagram below. Diagram courtesy of Jorge Lizama, GHD Sources: The content in this page was primarily sourced from Case Study titled Studying movement behaviour in a building: A case study of obtaining analytics from IoT Data Further reading: Data integration info website
  15. Introduction A major challenge In processing the volume of data required for IoT solutions is sourcing sufficient computing power to perform before-aggregation computations. When this needs to be carried out record by record, many traditional data environments, which use disk storage to store data, are not sufficiently powerful to complete the task, or may take days to deliver results. An in-memory database (IMDB) is a database management system that primarily relies on main memory (RAM) for computer data storage. It can be thousands of times faster than a disk storage database and is useful for real-time analytics that need to happen very quickly. In-memory computing also has very good compression algorithms, which makes it possible to better utilise the storage space. Diagram courtesy of Jorge Lizama, GHD Some key vendors of in-memory computer systems are shown in the diagram below. Diagram courtesy of Jorge Lizama, GHD Sources: The information on this page has been sourced primarily from the following: Case Study titled Studying movement behaviour in a building: A case study of obtaining analytics from IoT data
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