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  1. 3 points
    Introduction: Data Analytics has traditionally been associated with the processes involved in using data to inform decision making. It builds on the underpinning principles of data management that are required to build any kind of IT system, including the integration of IoT operational and back-end business systems. In the context of IoT, Data analytics encompasses many approaches including big data, in-memory computing, cloud computing, NoSQL databases, data integration, and interactive analytics, as shown in the diagram below. Diagram courtesy of Jorge Lizama. GHD Historically, data analytics took the form of Decision / Executive Support Systems starting in the 1970s, then evolving into Online Analytical Processing (OLAP), Business Intelligence (BI) in the 1990s. It is common to think of data analytics in terms of the volume, velocity, and variety of the data. Volume refers to the quantity of data, velocity to the speed at which the data is generated, and variety to the different types of data. Over the past few years, two new Vs, value and veracity have been introduced. Veracity refers to the quality of the data, and value refers to the benefit that the organisations can gain from the volume and variety of data that is being delivered with great velocity, if they are able to depend on its veracity. Diagram courtesy of Arthur Baoustanos, aib Consulting Services The current approach to managing data collected from IoT devices is to sense/observe the data, move it into the cloud, process and analyse it there, visualise it for decision making purposes (using technologies including augmented reality), then either store or discard it partially/completely. In recent times the exponential growth of data has created situations where "traditional" analytical methods are not viable and the term big data analytics is being used to describe new analytical techniques developed to cope with these situations. Big data analytics is often associated with IoT because many IoT applications involve large numbers of sensors generating large volumes of data. Also, many IoT applications involve the integration of a large variety of data formats such as weather data, machine vision and the like. A key challenge of IoT systems that generate or integrate a lot of data is how to make sense of it and how best to make use of it. This is driving the uptake of cognitive computing systems that assist analysts in determining insights and drive outcomes not possible with traditional analysis. Planning for data analytics The critical questions that organisations will need to answer when embarking on the journey to advanced data analytics are: Where does the organisation want to go (goals)? How will we get there? What do we need to get there? Will our current structure allows us to get there? What changes do I need to make to get us there? It is important to start with the business objective: define critical business issues and decide where value will be derived. Then evaluate which data is required to assess the identified issues and determine any gaps in relevant data. Be as specific as possible about what decisions the company will make based on that information. Departments and divisions within the organisation should collaborate to understand exactly what information is required to address common business goals. Data could also be purchased from outside sources to complement internal data collection. Once an organisation has decided to optimise their efficiency using data analytics, they should look at long as well as short term goals, and set specific efficiency or process change targets in order to get the most out of their investment as shown in the diagram below. Diagram courtesy of Umesh Bhutoria, EnergyTech Ventures A gap analysis of people skills (users, engineers, managers), data (points and frequency) and investment needed to reach goals should also be carried out, to ensure that all stakeholders are willing to see the process out through trials to implementation. When approaching vendors, care should be taken not to over- or under- specify requirements. For more information, visit the Project Management for IoT page. It may also be beneficial to invite shortlisted vendors to site to conduct data discovery tasks or solve smaller problems that will help determine if they will be a good fit to help the organisation implement a large-scale data analytic solution. Challenges Industry uses a small fraction of available data due to siloed data in legacy systems and leaders’ scepticism about the impact of technologies such as IoT. For example, added value for a commercial building could be derived from integration of available data into building management systems and building intelligence systems to perform energy analytics and management to improve efficiency, or condition monitoring and predictive maintenance. Three factors which contribute to the slow uptake of IoT data analytics in industry are: multiple data points (including electrical, thermal and mechanical energy, as well as process and production data) which may be housed in separate servers proprietary or inflexible data collection and storage solutions which are difficult to integrate skills gaps in staff and management in understanding the benefits of data analytics Types of solutions There are several different models of IoT data analytics solutions as shown in the diagram below. Diagram courtesy of Umesh Bhutoria, EnergyTech Ventures A stand-alone system could involve purchasing metres or sensors and asking a vendor to integrate them. This model has the potential to be influenced by the vendor rather than the user organisation’s requirement and does not provide integration with existing data. The second model, data as a service, provides monitoring and automated reports, but will not necessarily include integration with legacy data. Insights as a service is a model that is gaining in popularity, and is applicable to organisations with mature data infrastructure, who know what data is available and how the organisation aims to use it. It is typically a cloud-based service that uses company, user and third-party data to provide insights, as well as offering support in using these insights to meet the goals of the organisation. Existing data is also connected and centralised, as shown in the diagram below. Diagram courtesy of Umesh Bhutoria, EnergyTech Ventures The choice of solution should be based on the benefits it will bring to the organisation, weighed against the pre- and post- purchase effort, cost and ease and flexibility of use. The role of data analytics in IoT A non-exhaustive list of advanced data analytic applications within IOT applications is listed below. The majority of the applications listed revolve around the broad categories of asset management, planning, and performance management. The IOT has helped businesses to address these applications in a more holistic manner than was previously possible. Predictive maintenance Energy usage optimisation and energy analytics Downtime minimisation Network performance management Device performance effectiveness Load balancing optimisation Loss prevention Capacity planning Asset management and inventory tracking Demand forecasting Pricing optimisation Disaster planning and recovery Yield management Sources: The information on this page has been sourced primarily from the following: Webinar titled The data management perspective on IoT by Arthur Baoustanos, Managing Director, aib Consulting Services Case Study titled Studying movement behaviour in a building: A case study of obtaining analytics from IoT Data Webinar titled “The Data Indigestion Crisis: New approaches to Energy Analytics” by Umesh Bhtoria, Founder and CEO, EnergyTech Ventures
  2. 1 point
    Introduction IoT engineers need the ability to think at a systems level, and acquire cross-domain skills across engineering, business and application sectors. The main technical skill domains for IoT can be simplified to sensors and embedded systems, data analytics and integration, security, and IoT platform interoperability. To commercialise IoT solutions through startups or other commercial structures, business planning and innovation skills are also important. Having skills that span business engineering and multiple application areas can be very useful for IoT engineers, business people and other professionals. For example, being able to conceptualise how IoT systems can be applied to the needs of specific industries, or used to address cross-industry issues such as climate change or mobility. As the IoT uses similar technologies to address the needs of multiple industries and cross-industry applications, it is likely that as IoT uptake grows, the demand for IoT professionals will fall at the intersection of engineering, business and domain skill sets as shown in the diagram below. Diagram courtesy of Frank Zeichner, University of Technology Sydney. Another reason to expand skills beyond the technical domain is the need to understand the market context of IoT solutions in particular application industries. For example when designing the physical nature of Things and their required data analytics, IoT professionals need to understand why the Things have been deployed, where they are deployed and their value to the owner. The IoT Alliance Australia has done some work in mapping skills to IoT roles. A summary of the skills mix for some key roles for IoT engineers and business professionals is shown in the diagram below. Diagram courtesy of Frank Zeichner, University of Technology Sydney. Many of these skill levels are yet to be attained. For example, board members’ knowledge of IoT data security and how it relates to risk management is typically quite low. It is also a challenge to up-skill staff while maintaining business as usual. IoT education Formal education and skills development for IoT is in its very early days. The IoT is a complex ecosystem of technologies, sensing, actuating, communications applications and services, visualisation, security and operating systems. While academic courses for individual technical elements are plentiful, not many cover all or most of these areas, as well as incorporating business skills and domain specific learning. A non-exhaustive list of Australian courses that cover some IoT skills areas are shown in the table below. Diagram courtesy of Frank Zeichner, University of Technology Sydney. Accrediting bodies within Australia for the development of IoT education courses are shown in the following table. Diagram courtesy of Frank Zeichner, University of Technology Sydney. The emerging model for tertiary education in IoT is shorter, more focused units, and there is also a key role to be played by skill-based learning in the VET sector. It is likely that these two sectors will overlap for IoT education. There is also considerable industry involvement in developing courses to give IoT professionals the skills that they will need in the workforce. Because the IoT is changing the models of how we collect, share, analyse and interpret data, much IoT skill development will be gained through trialling and piloting solutions in real world contexts, which can then be recorded and eventually passed on through formal education. Educational institutions are also starting to embed industry and application specific practical exercises in their courses, to allow graduates to be more ready to conceptualise and solve problems in practical and business contexts. Engineering IoT skill sets Engineers wishing to enter the field of IOT have a number learning curves they need to go up in order to become fully competent. The diagram lists general areas where learning curves must be tackled, with just a few examples are relevant to those particular areas in the right hand column. Table by Geoff Sizer, CEO, Genesys Electronics Design At the systems level, IOT involves quite complex systems, so we need to conceptualize, visualize and specify what those systems must be. A business case must be developed that make sense and convinces those controlling the purse strings that there's a benefit or money to be made. If accepted, we then need to implement it. Because of the cross-disciplinary skills required, for most parties, this will mean finding experts who can assist them in the process. User interfaces present their challenges in that you have to decide what platform, what the user interface will look like. What are the design approaches to reducing the complexity experienced by the user? Then of course we have the design skills required to implement user interfaces on platforms such as iOS and Android and mobile devices, and web browsers. A thick client is a client application running on the end platform that providers a higher level of capability than a simple web browser. In terms of cloud services, we need to select a platform, establish and then operate the services on that platform. The virtual servers running in the cloud typically are hosting the database and big data analytics are applied over this, feeding back into the user interface. In terms of connectivity, we have wide area networks and low-powered wide area networks emerging, and then local area networks and personal area networks. Collectively, these provide connectivity between the cloud services and user interfaces, and the databases, down to the deployed things. This is hierarchy is typical. LTE stands for long-term evolution of cellular data and cellular telephony. 3G, 4G, 5G, and emerging Narrowband IoT. There are also standalone systems separate from the telecommunications network, providing wide area connectivity for extremely low-power devices at low cost. e.g. Sigfox, LORA, Ingenu and Taggle. Local area networks and personal area networks, there are ones that people will be familiar with. Typically Bluetooth and perhaps USB and WiFi and Ethernet. Cable-based connection systems still play their part, so Ethernet, RS-485 and CAN bus, and wireless mesh networks. Zigbee, people may be familiar with, but the 6LoWPAN, which is an open source equivalent of that, providing meshed wireless connectivity down at the premises or deploy site level. Things are the devices themselves. Challenges exist in this area because a thing consists not only of the electronics and firmware to undertake the core function of the device, but also the elements required to provide connectivity. A developer in this space may be familiar with what he needs to do to control the device or interface with the system down at the deployed equipment level, but achieving connectivity back up the chain to the rest of the Internet of Things may be an additional challenge outside of a core skillset that then requires additional expertise. For student engineers and recent graduates, the Internet of Things will provide a rewarding career paths and there are plenty of opportunities for more experienced professionals. The slide below outlines career paths from the perspective of ICT, electronics and software professionals, and considers emerging and evolving technologies where new skill sets will have to proliferate through the engineering community. Table by Geoff Sizer, CEO, Genesys Electronics Design There are parallel skill sets to be learned, particularly how to apply the technologies at the system level in the systems that the engineers are developing and deploying. Also, how to interface with the service providers in the space, which is going to be a significant challenge and calls for a lot of collaboration. Sources: Webinar titled “Flattening the IoT Learning Curve” by Frank Zeichner, Industry Associate Professor, Schools of Systems, Management and Leadership, University of Technology Sydney Presentation by Geoff Sizer, Chair of Engineers Australia’s ITEE College and CEO, Genesys Electronics Design titled How the Internet of Things will affect every engineer
  3. 1 point
    Introduction Energy harvesting, also known as power scavenging, is the term used to describe methods for powering IoT devices from its local environment, rather than by mains power or primary batteries. The main sources of environmental power are photovoltaic, thermoelectric, kinetic, and radio frequency. These are complement by energy harvesting and power storage systems. A key misconception is that people equate power scavenging with perpetual life, that device will run forever. However, all systems have limitations. For example, a rechargeable cell powered by a solar panel will die after a period of time or a set number of cycles. So the intelligent design of energy harvesting systems is important, and this may or may not include a battery. Kinetic Kinetic energy harvesting systems are powered by physical motion. Available wherever thing are moving. Examples range from sources of micro-power, such as switches/buttons and watches/wearables through to larger sources such as wind and water. The micro-sources produce a small spike of energy that is just enough to send a small piece of information. The larger sources do not have to be traditional wind power or hydroelectric systems. From an IOT perspective, it is possible to create miniature devices that fit inside pipes to power a single device. It is possible to fit energy harvesting devices inside pipes with moving water to power an IOT device measuring the flow in remote locations. Thermoelectric Thermoelectric energy harvesting systems are powered by differences in temperature, usually between a source at a higher or lower temperature and the ambient environment. Thermoelectric sources are often available in industrial settings which often have, for example, cold or hot pipes. There are even products that can generate power from the difference between skin temperature and the surrounding air, to power a wearable device. Solar Solar, also known as optical energy, has been used for a long time has been used in many different applications because the power density that can be generated from a solar cell is reasonable significant for its size. The main challenge with optical energy is to model how big a solar panel, and associated power storage system, needs to be to make sure that an IoT system will function through natural variations in light levels and in the worst case scenario. Radio Frequencies RF energy harvesting system, and the closely related induction charging, can extract energy from radio waves, in the same way that old crystal set radios extracted enough energy from AM broadcasts to listen to them without a batter. However, this approach has the lowest efficiency of all the harvesting techniques because the amount of power that must be broadcast in order to get a tiny little bit of power exchange over even a small distance is huge. The most useful example of this technique is the use of passive RFID tags, which normally consist of a tiny chip and very thin antenna. As the RFID tag passes through a gate or scanner, there is a wireless power exchange that's very short range. The main reason RFID tags can be manufactured for few cents and last such a long time is because have no battery. Engineering challenges The main engineering challenge is knowing when it is appropriate to use energy harvesting. There are a small number of applications where energy harvesting just makes sense, such as switches and some solar cells on devices that are visited regularly. However, many people fall into the trap of including energy harvesting in their IoT design because they can, when it fact it might not make sense to use it. For example, a kinetically charged dog tracking collar is possible but a battery may much more cost effective. Possible applications where energy harvesting does make sense are: Unusual form factors –e,g, where you've got to get something really thin, woven into clothing etc. Massive deployment applications – e.g. where it's not commercially feasible to replace or recharge batteries. Inconvenient locations – e.g. places that are really difficult to get to. Power storage Power storage option range from batteries through super-capacitors to solid-state options. The main factors to consider are cycle life, before the component needs to be replaced, the rate at which it goes flat, the overall storage capacity and the length of time the charge is available to execute the IoT device’s function. A comparison of common power storage options. Diagram curtesy of Simon Blyth, LX Group. High density rechargeable battery technologies generally have a self-discharge problem and can be hard to charge up using the small sources of power available via some sources of energy harvesting. Super capacities obviously only hold their charge for a very short time but provide an alternative in the right contexts, particularly where the device is being charged/discharged frequently. Examples may be on rotating equipment etc. Energy harvesting chips Many manufacturers are now making chip-based solutions that make it easier to design an energy harvesting system into an IoT device. Comparison of a range of chip-based energy harvesting systems. Diagram curtesy of Simon Blyth, LX Group. Selection of the right energy harvesting chip would relate to the overall architecture and design of the IoT device. Technology companies Key suppliers of energy harvesting technologies include: Micropelt Laird PowerFilm IXYS Kinetron Volture WiTricity IDT Cota Powercast muRata Panasonic Maxwell Cymbet Infinite Power Solutions Sources: Information on this page was primarily sourced from the following: A webinar titled Power Scavenging in IoT Design by Simon Blyth, CEO, LX Group
  4. 1 point
    Introduction An IoT startup is a technically-lead small business that typically has yet to define its business model. Startups usually try several different routes to market prior to settling on an approach that has a good market fit. A key early goal for IoT startups is to identify the problem that is being solved by the use of IoT technology. The problem also has to be big enough for organisations to justify investing in a solution. Once a problem has been identified, the startup describes their hypothesis and identifies assumptions and risks. The next phase is to plan and test, building something simple to test the assumptions. Results are analysed and the hypothesis re-evaluated, and so on in a spiral fashion until a final business model is proven. The above process can be an emotional roller coaster, with many peaks and troughs. Peaks can be associated with initial excitement around an idea, seeing prototypes working, interest from a potential customer, obtaining funding etc. Troughs are associated with the realisation that its not as easy as first thought, mistakes, lost customer opportunities, cashflow crunches, realisation of a lack of skills, etc. Other challenges include decisions around quitting a day job etc. Individuals who launch or lead the establishment of new businesses are often described as entrepreneurs. Entrepreneurs need to have a certain amount of resilience to cope with the above challenges. They also need a lot of energy and self-motivation. There is a huge amount of literature around innovation generally and the Lean Startup methodology has found favour in recent times. This includes concepts such as minimum viable product to test ideas before committing further. A whole industry has grown up around support for technology led start ups. This include business accelerators/incubators and a range of investment companies ranging from seed/early stage angel groups, equity crowdfunding and late stage venture capital. These organisations often host several startups that share technical and business system resources. IoT specific challenges Startups in the IoT space is more challenging that other fields because it requires a combination of hardware, software and business models. Technical challenges that need to be addressed during business planning include consideration of the full range of technologies and practices outlined in this wiki. In addition, there are a number of national inhibitors/enablers of the entire IoT industry in Australia which really need to be addressed in order to foster more IoT Startups, illustrated below: Source: A report commissioned by the Communications Alliance Australia on Enabling the Internet of Things for Australia For example, it is currently difficult to deliver IoT led innovation in the healthcare sector due to the very high number of regulatory barriers that must be cleared. Similarly, the smart city concept is difficult to address due to the highly fragmented nature of efforts around this area. Links The following organisations are encouraging IoT Startups in Australia: The IoT Alliance Australia has a workstream on Startups and Innovation. The Australian government supports the IoT Ecosystem, e.g. Thinxtra obtained funding to roll out its Sigfox LPWAN network Sources: The information on this page was primarily sourced from the following: A webinar titled Your brilliant idea! Technology start-ups dissected by Stuart Waite, CEO, Timpani
  5. 1 point
    The spectrum of technologies that enable IOT include advanced electronics and sensor/actuator technologies, next generation communication networks, cloud services to store the massive proliferation of data, big data analytics to make sense of it, mobile app development to interface with it and a whole range of protocols to enable it all to work together.