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Casino hacked via a thermometer in a lobby aquarium – what a nice story. Sad, there is no technical details in the article. http://www.businessinsider.de/hackers-stole-a-casinos-database-through-a-thermometer-in-the-lobby-fish-tank-2018-4?r=UK&IR=T

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.

One of my colleagues, Hugh McCann, recently presented our company with an update on his data analytics work. He kicked off with a summary of his home energy usage. I knew he'd been tracking this, and I believe we talked on the day he accidentally deleted 3 months of data! Yep, he's a nerd's nerd and hadn't thought to protect the data because it was just a home project, so you can imagine how he felt. He's just published a more detailed description of how and why he did it here. What immediately strikes me is that what he's done isn't particularly complex and it appears to me that it could be commoditised / built into new constructions. This ties in with some other discussions on smart metering but gives some insight into what can be done across a broader range of domestic power usage, and a way to engage the home owner. It seems an obvious extension to add a degree of machine learning and mass energy usage collection for a whole variety of purposes.

One of my colleagues, Hugh McCann, recently presented our company with an update on his data analytics work. He kicked off with a summary of his home energy usage. I knew he'd been tracking this, and I believe we talked on the day he accidentally deleted 3 months of data! Yep, he's a nerd's nerd and hadn't thought to protect the data because it was just a home project, so you can imagine how he felt. He's just published a more detailed description of how and why he did it here. What immediately strikes me is that what he's done isn't particularly complex and it appears to me that it could be commoditised / built into new constructions. This ties in with some other discussions on smart metering but gives some insight into what can be done across a broader range of domestic power usage, and a way to engage the home owner. It seems an obvious extension to add a degree of machine learning and mass energy usage collection for a whole variety of purposes.

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

March 31, 2017 - Taipei, Taiwan - Tibbo, a leading manufacturer of IoT devices and intelligent device management software, announced release 5.4 of AggreGate IoT Integration Platform. We've achieved great results in optimizing AggreGate server performance, especially event and value update storage performance. From now on, a single server can process and persistently store up to a hundred thousand events/updates per second, which is almost equal to 10 billion events per day. Such performance figures don't even require any high-end server hardware. A new chapter has been opened by this release, presenting AggreGate's graphical and textual programming languages inspired by IEC 61131-3 standard, also known as "SoftPLC". Millions of engineers are now able to use AggreGate as a process control logic development environment. One innovative feature of AggreGate's automation languages is tight integration of runtime with the Tibbo Project System hardware. Your programmed logic can access and control all Tibbit modules of a Linux-based TPS board/box. Currently available languages are: Function Block Diagram (graphical), Structured Text (graphical), Sequential Function Chart (textual). Widget capabilities are no longer limited by the standard set of components. Now it can be easily extended. New Widget Component SDK allows to implement custom visual components in Java and use them in AggreGate widgets. Extend AggreGate's wide component palette with UI controls best suited to your needs! We continue making our UI interface clearer and more user-friendly. The next step is lightweight icons. We redesigned them to be up-to-date with modern flat paradigm. New color coding assists users to navigate over various available toolbar actions. Other major improvements include: · Built-in timestamps and quality for data tables. · Component connectors that allow to visually link UI components with each other. · Secure and reliable Agent communications. Agent-Server communications now can be SSL-protected. When transferred data amount is critical, data compression can be enabled in parallel to encryption. · Granulation, a brand-new highly customizable data aggregation and consolidation tool. The granulation engine allows to combine datasets into compact representation that contains all important aspects of original information in virtually any form suitable for later processing. This allows to reduce memory and storage consumption along with boosting data processing performance. · Server remote upgrading. To reduce company's expenses, a remote AggreGate server upgrade operation is now supported. You can use our Unified Console application to connect to a remote server, upload a server upgrade bundle file and wait while the upgrade process is finished. That's it! All operations, including database backup, stopping server, upgrading and restarting will be performed at the server side automatically. We are bringing our IT & Network Management solution (AggreGate Network Manager) to a new level by turning it into a full-fledged IT Service Management System. In this release, we introduce several essential instruments for that: Configuration Management Database (CMDB), metrics engine and topology-based root-cause analysis tools. Another ITSM functionality - IP address management module - is now available and you can use it out-of-the-box. AggreGate 5.4 includes new device drivers: CoAP, MQTT, IEC 104, DLMS/COSEM, SMI-S. You can get detailed information on the new 5.4 release, download and try the updated AggreGate IoT Platform on our website: http://aggregate.tibbo.com/news/release-54.html.