Jump to content

Tim Kannegieter

Administrator
  • Content count

    279
  • Joined

  • Last visited

  • Days Won

    87

Everything posted by Tim Kannegieter

  1. Telstra's NB IoT network launched

    According to a CRN News report, Telstra has turned on its national IoT network. See https://www.crn.com.au/news/telstra-quietly-switches-on-internet-of-things-network-473757 I cant see any announcements of Telstra's website though. If you know any more please link in the comments. Meanwhile, Telstra has announced the first four IoT startups to to be supported by its Muru-D incubator. https://www.telstra.com.au/aboutus/media/media-releases/Telstra-announces-first-IoT-focused-cohort-with-muru-D-MEL1
  2. until
    This webinar is an activity of EA’s Applied IoT Engineering Community. See http://iot.engineersaustralia.org.au/ for more information. Title: Smart metering for water with the Internet of Things Presenters: Rian Sullings, Manager Smart Metering and IoT, WaterGroup What you will learn: How IoT is revolutionising the water industry How to fast-track IoT implementations Key challenges in adopting IoT and how to overcome them Description: The application internet of things technologies to high water users is delivering significant results, as evidenced by WaterGroup receiving awards for the highest impact of IoT technologies to date. The company has developed low cost, high volume remote sensing devices using new low power wide area 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. About the presenter: Rian Sullings helps people understand their utility resource use to improve efficiency and reduce costs with the latest IoT tools and business models. With a key focus on the adoption of new technologies, Rian has been instrumental in the successful adoption of smart metering and remote sensing by some of Australia’s largest utilities and water users. Some of his achievements include the successful delivery of millions of dollars of water saving IoT projects for organisations such as QANTAS, Coles, Sydney Water, Honeywell, and the Department of Education, as well as the development of the first Sigfox enabled smart water metering device outside Europe and North America. When: 12 midday in Sydney. If you are in a state with a different time zone from NSW, please determine your local time. The date is above. The presentation will last 30 minutes followed by question time. Where: The presentation is by webinar. After registering you will be sent details of how to logon. Cost: This presentation is free to members of Engineers Australia (EA), the Australian Computer Society (ACS), the Institution of Engineering and Technology (IET) and IEEE. Just provide your membership number during registration for the event. The cost for non-members is $30. How to register: Please register on the Engineers Australia event system, link above. Note, to register you need to have a free EA ID which you can get on the first screen of the registration page. Take note of your ID number for future events.
  3. Transforming businesses in a digital world

    The University of Technology Sydney is offering a 5-day industry short course “Transforming businesses in a digital world” - more information in the attached pdf below. The course helps put IoT in the context of business strategy. It starts on 13 October and is spread over 4 weeks to 12 November. The course is designed for business leaders and managers who are required to identify and drive business opportunities and disruption in the new digital world, enabled by IoT technologies. The course as targeted at high achievers and executives who would gain from apply practical methods for transforming their organisation’s business operation, as well as developing an alumni of fellow students and industry presenters. Frank Zeichner, CEO of the IoT Alliance Australian and Director of the Knowledge Economy Institute will be co-delivering the course together with 7 other industry leaders. Courtesy of the Applied IoT Engineering Community leader Geoff Sizer being a member of the course advisory board, Engineers Australia members are being offered a 10% discount on the advertised course price of $4,600 by entering the following coupon code during registration: DIGITALEA Prospective students can register for the course through the link below: https://www.eventbrite.com.au/e/transforming-businesses-in-a-digital-world-tickets-36848395543 20909 UTS - Transforming businesses in a digital world - Short Course.pdf
  4. IoT and STEM Outreach

    Well done Chi, Hats off to you. We need more things like this for school holiday programs. I have just enrolled my daughter in a coding camp for two days but they just create games. Something like this is much more real world. Tim
  5. Sensors and Embedded Systems

    Types of sensors Sensors can measure virtually anything. Examples include GPS, moisture, water levels, tank levels, carbon dioxide, volatile organic hydrocarbons, particulates, radiant temperature, temperature, wind speed sensors and more. In addition to measuring specific attributes, there are other kinds of inputs to IoT systems such as machine vision. Interfacing sensors to an IoT system Sensors, at a very basic level, are inputs to an IoT system. Sensors typically physically interface with IoT system using a communication bus such as I²C, serial and USB, 0-10 V or 4-20 mA using. These systems use sensors and electrical contacts that have been around a long time so all the normal considerations with conventional sensors apply for IoT. For example, the digital signals from contact closures need to have debounce protection. Similarly, outputs from an IoT system may be digital or analogue and will interface to actuators that make changes to things, such as opening or closing of gates, opening or closing valves, switching pumps etc, often using electrical or solid state relays. Again there are well known things that needs to be addressed, such as the characteristics of the load including the voltage, the current, whether it is an inductive load. Sensors typically interface with RF modules, which have analogue and digital I/O pins. Many RF modules also have optional integrated microprocessor. RF modules also require an antenna connection. One challenge of IoT systems is discovering where the IoT devices are on a network. A key technology for addressing this is the W3C's Semantic Sensor Networks. Cost and power limitations of sensor communications for IoT The cost and power requirements of communications technologies can limit the amount of sensors deployed in IoT solutions. Many communications technologies used for IoT, such as wifi, are power hungry. Others, like satellite, are expensive. Low power solutions are emerging, including the Sigfox low power wide area (LPWA) network. Aggregating sensors in an array around a user terminal for satellite communications can reduce the power and cost of satellite communications for IoT applications, by eliminating the need for a dedicated uplink and downlink for each sensor. Visualisation of IoT sensor data Technologies such as augmented reality can be used to provide a visual display of IoT sensor data overlaid on the physical device which is updated live in the cloud for 'in context' visualisation of device data. Hardware The following diagram gives a representation of the architecture of a typical deployed Thing. In many cases you typically have a single sensor, a single actuator and battery storage, but when you generalise a Thing to a slightly higher level the following elements may all be represented. The sensors and actuators shown above are just a few examples. They will interface to an intelligence in a micro-controller via, typically via an interface of some sort. The microcontroller would typically be a system on chip with thousands of options. Ultimately the microcontroller is responsible for communicating via an interface which could be low power Wide Area Networks among other communication options. In addition to designing an IoT device from scratch, it is also possible to buy a single board computer such as Raspberry Pi and configure this for use in many IoT contexts. Firmware Firmware is the software on the microcontroller embedded into the Thing. The following diagram presumes a typical configuration of one or more sensors and one or more actuators with input and output drivers that communicate with a network. All this is managed by an operating system. At the simplest level there is a master polling loop microcontroller architecture but typically the more advanced microcontrollers available are running RTOS which give you a high level of sophistication. Linux is also a possibility and Contiki is often tied to 6LoWPAN communications. The structure of the firmware includes input and output drivers, middleware that takes the information and converts it via an applications programme, interfaced to some form that the business logic of the device can decide what to do with that information. That can include communications up via the network or control commands from the network. It can also include local logic operations that relay input drivers or input devices and sensors to output drivers that drive actuators so you can have local control functions standalone from the network. The firmware includes a communications driver to interface with the communications device be it a radio or a UART etc. Behind that is a communications protocol stack. For example, for a Bluetooth low energy or for 6LoWPAN the communications must be managed in terms of the packet payload encapsulation, and the various layers in the communications protocol. An important aspect that's sometimes overlooked is the connection manager. The purpose of the connection manager is to establish the network communications and to then monitor and manage that. If the communication drops out it must re-establish communications. It typically to include some form of health heartbeat, so even when the Thing is not reporting data, the device is telling the server that it is alive and happy. Conversely you could have a ping from the network down to the Thing so that the Thing knows it has the necessary connectivity to fulfil it's part in the IoT system. Overlaid on top of all of these software layers is energy management, that applies top to bottom in terms of how much energy we use for communicating with our sensors and actuators, how much energy is used for communications traffic and how much is consumed by the logical processing functions of the device. Another overlay top to bottom is having the appropriate security at the network level and then appropriate integrity in all of the processing layers. Design Considerations In terms of your typical Thing, we're really talking about standalone battery powered devices, so we need energy storage and desirably some form of external source into that, or it may be a self-contained primary cell. It's paramount that we carefully manage the energy. You'll hear power management tools often mentioned in IoT but it's not actually power we're trying to manage, it's energy. How many transmissions or sensing operations can we get out of the Thing, per day, per week, per month, and how many years will that battery last while performing that function. Getting that equation right is absolutely critical to having a practical thing. So an early starting point in considering the design of a Thing is to look at the energy budget over the life-cycle of the device and of its internal energy storage. A design decision must be made on whether to select a RF and microprocessor combination module or a separate module for each function. A particular application might require microprocessor specifications that are not met by an integrated microprocessor. Or, it might be cheaper to implement intelligence on a separate microprocessor rather than paying the difference in cost between the RF module, and the combination RF module with an integrated the CPU. It is hard to hard to separate sensor selection and the design of embedded electronics from consideration of the communication technologies available. The regulatory maximum power level for all "things" is at at the usual 920 MHz is one watt, which is 30 dBm. A key influencing factor is the receiver’s sensitivity. The various communication technologies vary in their sensitivity (e.g. Bluetooth is 90 dBm. Zigbee is typically -100 dBm).LoRa can be up to 138 dBm which is why they are suited to the applications requiring long range. They can get distances of up to 15 kilometers. The reason for that is they've got three bandwidths. There's seven spread factors, giving normal bit rates from 290 bits per second up to 37 1/2 kilobits per second. Other design considerations include the choice of antenna and the range of radio frequency (RF) considerations that must be taken into account, to ensure any IoT device is compliant with Australian regulations and the system will work as intended in the deployment environment. Another consideration is to determine if the data needs to be encrypted, typically using the Advanced Encryption Standard (AES) and the associated security considerations. Power budgets must also be taken into account, especially where battery operation is required. What data rate is required and how much power will that use? Is there an option for recharging. What battery options are available for the device package and budget. These questions can affect the design or choice of sensing devices and embedded electrics dramatically. Another design consideration is the level of uncertainty which may be introduced by the context, or environment, in which the sensor is used, and whether its performance will vary over time. This is discussed further in the section on design thinking for IoT. Sources: Material on this page has primarily been sourced from the following: Presentation by Phillip Lark, Engineering Manager, Braetec titled Front End Integration: Connecting sensors to the cloud 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
  6. Recording: This webinar has now passed. Members of Engineers Australia can access the recording for free on MyPortal. Navigate to IoT Technologies / Communication Technologies. Non-members can purchase the recording for $30 on the EABooks website. Title: Satellites and the new industrial frontier – how new space technology is intersecting with the Internet of Things Presenter: Flavia Tata Nardina, Co-founder and CEO, Fleet Space Technologies What you will learn: How miniaturisation is driving a new generation of satellite technologies Practical applications of nanosatellites Key elements required to create industrial solutions leveraging space technology Description: Outer space and terrestrial industries may seem light years apart, but new space technology is about to change that. Nanosatellite technology is rapidly approaching practical application as a disruptive new option for ubiquitous internet connectivity and efficiency, powering the new wave of industrial applications powered by the Internet of Things. From farms to factories, and shipping to mining, satellites have unique advantages for connecting sensors in remote locations or for tracking applications across wide geographical distributions. In a world of globalised supply chains, this technology is being seen as a game changer. However, end to end solutions are still evolving and required to enable large-scale deployment of low-cost solutions. Fleet Space Technologies is launching the first two of a 100 satellites constellation at the beginning of 2018. It will provide a global backhaul service for the Internet of Things. This presentation will cover Fleets activities to date and discusses the practical applications of the technology for engineers. About the presenter: Flavia Tata Nardini began her career at the European Space Agency as Propulsion Test Engineer. She then joined TNO – the Netherlands Organisation for applied scientific research – to work on advanced space propulsion projects. In 2015, Flavia co-founded Fleet, a connectivity company set to maximise the resource efficiency of human civilisation. When: 12 midday Sydney time on 12 September 2016. The presentation will last 30 minutes followed by question time. Where: The presentation is by webinar. After registering you will be sent details of how to logon. Cost: This presentation is free to members of Engineers Australia (EA), the Australian Computer Society (ACS), the Institution of Engineering and Technology (IET) and IEEE. Just provide your membership number during registration for the event. The cost for non-members is $30. How to register: Please register on the Engineers Australia event system. Note, to register you need to have a free EA ID which you can get on the first screen of the registration page. Take note of your ID number for future events.
  7. Cisco acquires Springpath

    On the same day as Cisco announced major upgrades to its Spark product, it also announced its intent to purchase Springpath to bolster its data centre business. This is all part of a general trend for the company to move toward more software services, bridging the gap into IoT. For an analysis by media read: https://channellife.com.au/story/cisco-acquire-hyperconverged-software-vendor-springpath-us320m-deal/
  8. Platforms

    Introduction We need a page that describes the huge range of "IoT platforms". What is a platform? What are the different classes of platforms? What is the general purpose of each? Perhaps links to a directory of providers in each class. e.g. Reekoh is a platform focused on integrating large business system like Salesforce, Microsoft, Oracle, etc. The aim is to provide enterprises with a way to create IoT services. They have a plug and play marketplace idea where as you log into their system, you literally buy the plug-ins that you need. Then, you start to connect services together so you could connect your assets within your building to your Salesforce platform etc
  9. IoT startups

    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
  10. OpenIOT

    Introduction OpenIoT is a collective effort of eight partners, including CSIRO, in the European Union FP7 project. OpenIoT is an open source mature middleware platform that brings sensor networks, analytics and cloud computing together. It allows data sources such as smart devices, sensors and actuators to be discovered and aggregated in the cloud without having to hard-code the names and locations of the sensors. It also offers utility-based (pay-as-you-go) IoT services. A conceptual diagram of Open IoT is shown below. Diagram courtesy of Prem Prakash Jayaraman, Swinburne University of Technology Description OpenIoT is the result of a collaborative research effort using semantic technologies by a range of research organisations including CSIRO. The software is open source, using LGPL license 3.0, so any customer can build on top of that platform. OpenIoT provides a cloud-based middleware infrastructure in order to deliver on-demand access to IoT services over multiple infrastructure providers. This is what is called horizontal integration of IoT silos in order to benefit a multitude of applicational services that were not originally designed for such use. OpenIoT allows internet connected objects to be deployed and registered. They can be dynamically discovered by location or by a sensor type. Then there is an ID integrated development environment which could offer users service mashups and composition of services. The architecture of OpenIoT consists of three layers. The lower layer of OpenIoT is physical, where the sensor networks are deployed, which can use deploy any protocol. The upper layer is the utility application layer, where users can specify the workflow, and then this workflow goes into the scheduler and scheduler discovers the data that the workflow needs. Once the data is discovered, the activity metrics are generated to present to the user what is the cost of that service, how much such a service could cost, and then once the user okays it, the scheduler runs the workflow and then comes up with a visualisation of the workflow in the request presentation. In the middle are service tools like configuration management and monitoring tools. The application level is built on top of the in-built sensor and semantic data management, and provides simple tools where users can design their own interface to discover sensors, see the data that the sensor produces, compose queries on the data in the database (for example, on the last hour of data collected), and then visualise that data in a graph using the request presentation tools. The user interface does not require coding, but uses a number of buttons to allow users to design their interface. The application tools in the utility-app plane are supported by OpenIoT’s semantic data management, where all the data is linked. This layer defines the relationship between the sensors and the data: who created and owns the data. It also includes controls for levels at which the data can be shared with other users and services. There are also supporting services including the scheduler, which takes care of executing any sort of real-time queries and real-time requirements from the users. The underlying system in the physical plane is called the sensor management system, which uses a component called X-GSN. X-GSN is similar to the Apache Storm or Spark systems, but was developed earlier. X-GSN is sensor agnostic, and is used to write ‘wrappers’ that allow integration of any sensor into OpenIoT. The data from all inputs (mobile devices, sensors, enterprise systems, etc) comes into X-GSN. X-GSN annotates the data with the required metadata. Before data can come into the system, each sensor needs to be registered with the IoT database system here, using the SSN ontology description language. Once registered this system can push data into the centralised Cloud or the data can also be distributed. X-GSN takes care of all the streaming data and allows it to be queried and aggregated before it is transferred into OpenIoT. A diagram of the registration process for sensors is shown below. Diagram courtesy of Prem Prakash Jayaraman, Swinburne University of Technology OpenIoT also provides a tool called Schematic which allows users to register sensors using the SSN ontology, so that they can then be registered in OpenIoT. Capabilities The capabilities of OpenIoT are summarised in the diagram below. Diagram courtesy of Prem Prakash Jayaraman, Swinburne University of Technology OpenIoT and other open source technologies OpenIoT is used in IoT design to integrate the heterogenous inputs from discovered sensors and store them in the cloud. To do this, it makes use of the semantic sensor networks (SSN) ontology. X-GSN, which is used to provide the wrappers for the sensors and filter the data, is also open source, as is the SPARQL query browser which allows sophisticated queries to be executed on the stored data. Security OpenIoT uses a single token-based centralised authentication system based on Apache Shiro. Every component must be authorised and authenticated before it can push data into the system or get data out of the system. Challenges of developing an open IoT architecture For an open architecture approach to IoT, a key challenge was overcoming the problem that many existing and developing IoT systems are silos. For example, a smart home solution from a particular provider might only work with devices from the same provider, which locks consumers into outfitting their home with proprietary devices from one source. Integrating the large number of devices, vendors, gateways, wireless networks, standards, and backend servers with multiple systems and enterprise applications, is a complex task, as shown in the diagram below. Diagram courtesy of Prem Prakash Jayaraman, Swinburne University of Technology Other challenges faced were the necessity for international collaboration and partnership, finding the value-add in business models, the cost of sensors and actuators and computer-generated context and situation awareness. Specific challenges to be overcome were to: · integrate all sensors regardless of make and model · discover sensors and data · automatic data integration · preserve privacy · provide timely access to data · resolve data ownership issues (Who owns the data? Who decides who uses it?) · provide utility-based data access (returns to data owners for profits made from their data). Why is openness important? The importance of openness can be demonstrated by exploring the automation of a simple scenario. A parent (John) is picking up a child (Hanna) from school. If the parent is driving and running late, and the process of organising another appropriately known, authorised and available parent (Alice) to pick up the child is to be automated, the systems of the parents’ cars, all parties’ phones and potentially the school’s information system need to be able to communicate. These could potentially all come from different manufacturers, which means sharing data across many silos. This is illustrated in the diagram below. Diagram courtesy of Prem Prakash Jayaraman, Swinburne University of Technology Sources The information on this page has been sourced primarily from the following: CSIRO site participating in the open source project A presentation by Arkady Zaslasky, Data 61, CSIRO, titled Harnessing the IoT Data Flood A webinar titled IoT application development with open data-driven computing platforms by Prof Dimitrios Georgakopoulos, Swinburne University of Technology A webinar titled An Open Source approach to the Internet of Things by Prem Prakash Jayaraman, Research Fellow, Key Lab for IOT, Swinburne University of Technology Further reading More information can be found on the OpenIoT website.
  11. Thinxtra has obtained a $10 million in funding from the Australian government See http://www.cefc.com.au/media/files/energy-efficiency-benefits-as-cefc-helps-thinxtra-scale-up-its-network-for-the-internet-of-things.aspx and http://www.environment.gov.au/minister/frydenberg/media-releases/mr20170817a.html and https://www.thinxtra.com/2017/08/cornerstone_investor_cefc/ This has been reported as the government taking a 15% equity stake in the business.
  12. Blockchain

    Introduction Blockchain is a relatively new technology that underpins transactional applications such as those associated with cyrpto currencies like Bitcoin. In essence, all transactions in a blockchain are added as blocks in a linear, chronological order by a node or computer connected to the blockchain, providing a complete and accurate recording. Transactions are enabled using a private and public key. The technology protects against the tampering and revision of data records, helping create trust, accountability and transparency as well as streamlining business processes. The adoption of blockchain has primarily been in the financial sectors. The application in IoT has been hyped by a number of vendors because it is seen as a potential solution to the perennial concerns about IoT security, particularly in controlling botnet attacks because it can potentially prevent hijacked devices from being used in denial of service attacks or otherwise disrupting its environment. Blockchain technology is built for decentralised control meaning there is no master computer controlling the entire chain. Rather, each node in the network have a copy of the chain. So is seen as less vulnerable and more scaleable than traditional security approaches. The distributed nature of the technology helps remove single points of failure. It also lends itself to the IoT potential for massive numbers of things being interconnected across different networks, without the need for centralised cloud servers. Potentially, blockchain could also enable the monetisation of data, where owners of IoT sensors could sell data for digital currency (e.g. see tileplay) Potential industrial application Blockchain is a way of creating digital assets, or tokenising a thing, that can then be transferred or traded. Virtually anything of value can be tokenised, e.g. eco-credits, work-hours, rights to buy products/services, commodities, electricity etc. For example the energy produced by rooftop solar or any other energy source, could generate income in the form of cryptocurrency that is registered on the blockchain. Having established a large blockchain, it would then be possible to form secondary markets for trading of these digital assets as you can assign owners of these assets. It is also being seen as a way of ensuring trusted readings from sensors in areas such as drug safety, food quality and other certification processes, anywhere where the end-user or regulator needs to be assured of a immutable record of the conditions monitored. Blockchain is also "public", which means everyone participating in the chain can see the transactions stored in them, while the cryptographic algorithms underpinning it also provides greater data security against hackers. One of the biggest areas of potential industrial application to streamline supply chain processes in many sectors. Global supply chains obviously have a very large number of transactions and have massively complicated, and arguably bloated, computational systems to handle and secure them. Blockchain would help provenance, by tracking objects throughout the supply chain while enabling line-of-credit contracts and incremental payments. Every physical thing in a supply chain could have a digital passport, that proves authenticity - things like existence, origin, condition, location. It also enables "smart contracts" However, while there has been much excitement over blockchain, its application is still embryonic. The technology Blockchains are a distributed ledger technology, which is a peer-to-peer, insert only datastore that uses consensus to synchronise cyrptographically secured data. The Peer-to-peer (P2P) component partitions tasks or work loads between peers or nodes. Peers are equally privileged in the application. Insert only datastores can only create and read data, not update or delete data. A key challenge in internet enabled systems is to build a consensus on what is to be trusted. The consensus problem involves determining ways of facilitating isolated computing processes to agree on something, when some of them may be faulty. Faults can be benign, such as when a node goes down and is just unresponsive. However, faults can also be hostile where actors are trying to fool the system and this needs to be protected against. There are a large number of mechanisms to deliver consensus including proof of stake, proof of work, federated consensus, round robin, proprietary distributed ledger, etc. Application considerations and limitations While blockchain offers the potential for application in IoT, it is by no means certain it will be taken up. Its application in financial sectors is relatively simple compared to the requirements of device authentication, security and control layers. In particular, if 51% of processing power in an blockchain network were subverted, and this is possible in many small IoT networks, an attacker could change the supposedly secure data records. A key limitation is that blockchain is computationally intensive and many IoT devices lack the processing power to participate in a blockchain without compromising the required speed. Also, because every record is stored and never deleted, the ledger in any blockchain will grow continuously and this needs to be stored in every node. While the public nature of blockchains is one of it's key advantages, it also generates a limitation in that data is not likely to be private. So commercially sensitive data should not be shared, although researchers are working on methods to get around this. Researchers and commercial vendors around the world are working on feasible models to apply in the IoT space, e.g: UNSW: Blockchain for IoT Security and Privacy: The Case Study of a Smart Home Researchers are working on simplified computational methods to make it feasible for IoT. However, commercial knowledge of blockchain is limited and combined with the lack of broadbased IoT engineering skills, widespread adoption seems to be someway off. Links: Hyperledger - A Linux Foundation Project Vendors Modum - data integrity for supply chain operations powered by blockchain Sources: Information on this page was primarily sourced from the following. A webinar titled Blockchain Technology by Nick Addison, Chief Technology Officer, Finhaus Labs
  13. Manufacturing

    Introduction: Manufacturing is a key area of application of IoT world wide. McKinsey Global Institute said it is the industry with the highest potential for economic impact of IoT. IoT systems can collect data to be processed and then act on decisions through robotic devices, completing the sensor to actuator loop. In addition, every single physical item in the manufacturing process can in theory be internet enabled, including raw materials, sub-components, machinery, transportation element etc. Each of these things will have information associated with it and the ability to communicate that with the wider IoT systems, both internal and external to the manufacturer. There will, potentially, be millions of things talking to each other. Interoperability across all organisations in the manufacturing value chain is a critical component in realising the full potential of IoT and this is enabled by reference architectures (see Standards below). A CSIRO study found that manufacturing companies generally do not have digital strategy, have only rudimentary eCommerce systems in place and are not looking to implement new business models enabled by digital technology, including the IoT. The major shift in business models is "servitisation", turning products into service opportunities. Many products will have maintenance or consumables replenishment, for example. A typical change of business model is to offer the product for free in return for a service contract. So there is a large opportunity in manufacturing to realise significant economic potential from the adoption of IoT technologies. However, the ICT landscape in Manufacturing is complicated as shown by the following diagram: Source: CSIRO In this landscape there are a number of ways in which IoT is relevant to manufacturing. For example, in Enterprise Resource Planning, IoT enables more and easier data collection/processing throughout the entire life cycle of products from design, manufacture and use in the field. Germany has a national plan to digitize 80% of its value chains by 2020 and IoT plays a key part of that through the Industrie 4.0 initiative (see below). A key constraint of driving uptake of IoT in Manufacturing is that 90% of all manufacturing companies have 30 employees or less. This inherently constrains their ability to understand the impact of IoT on their business and to invest in it. Relevant standards and regulations: Taking advantage of the IoT in manufacturing is a challenge due to the complicated landscape set out above. In order to address this, a number of international initiatives have emerged to enable organisations to collaborate in the development of reference architectures and approaches to realising the potential, as follows: Industrie 4.0: This is an initiative led by the government of Germany to build on that country's strength in embedded systems. It's mainly a German initiative but due to the nature of global supply chains, it is gaining traction around the world through a number of government-to-goverment initiatives and the activity of leading German manufacturers. Industrial Internet: This was initiated by GE in partnership with AT&T, CISCO, IBM and Intel and is now led by the US based Industrial Internet Consortium with hundreds of members. The Industrial internet spans all industries, not just manufacturing. Apart from a reference architecture, this group also facilitates the establishment of test beds. Test beds are collaborations of a variety of private companies wanting to test the viability of IoT products and applications in their industry. In Australia, the CSIRO is leading initiatives including i3 Hub and iManufacturing. Reportedly, Industrie 4.0 and IIC are working to make their reference architectures compatible. Sources: Information on this page was primarily sourced from: A webinar on IOT in manufacturing by Nico Adams of the CSIRO.
  14. Enterprise Resource Planning

    Introduction The internet of things can be a key enabler of improvements in enterprise resource planning (ERP). Enterprises that produce products of any sorts aspire to shorter product runs, more agility in implementing design changes, faster deliver to market, flexibility in packaging and distribution, better forecasting / supply chain management, improved product traceability and feedback from end users. However, despite decades of experience with ERP systems many organisations still take orders by emailed pdf which is entered manually into an enterprise resource planning system. The IoT and ERP IoT data of use in ERP systems is as varied as the context of application, but may relate to: Quantity Quality Machine status, faults and their causes.
  15. Intellectual Property

    Introduction A particular challenge that may affect the development and implementation of IoT systems in commercial settings is consideration of intellectual property rights. This is primarily the case in the case of new IoT enabled products being developed for market, particularly if exporting, but also for innovations in business or engineering processes enabled by the IoT that organisation are investing large amounts of money in. Intellectual property rights are typically protected by obtaining patents and often this is considered a prerequisite to obtain funding from venture capitalists and the like. Having a patent provides leverage in business negotiations and disputes, prevents others from copying your innovation, and can provide licencing revenues. A key reason to consider intellectual property rights is to have freedom to operate. Because IoT is new, many people and organisations world-wide are applying for patents. Organisations developing or implementing IoT system may inadvertently infringe the rights of others and huge upfront investments may be forfeited when challenged. This can be avoided by careful patent infringement searches prior to the investment phase. When patents of concern are discovered they can be carefully analysed by a patent attorney to determine if they do actually constrain your activity. If the patent does restrict your activity, it may be possible to work around the issue by obtaining a licence or coming to some other commercial arrangement. Obtaining patents in the computer technology space is difficult and many patents, when challenged, are found to be invalid. So if you receive a notice of patent infringement, a patent lawyer should be consulted to determine if the patent is valid before caving in to any unreasonable demands. Basics of IP A patentable an invention must be: New; and Not be obvious to someone with knowledge and experience in the subject Not disclosed by publication or otherwise Is useful No prior secret commercial use Just connecting a thing that has never been internet-enabled before is unlikely to be patentable. Rather, inventions need to be something that makes the IoT device work and is truly a new improvement in technology. Good IP Practices A key focus in any innovative work is to ensure you own the patent. Ownership of the invention originates with the inventor but under common law flows to the employer when the invention was made in the normal course of their duties to the employer. It's important to have this clarified when employing contractors to support the innovative work, particularly if this includes the provision of software and systems that involve the integration of systems from multiple suppliers. Non-disclosure agreements should be standard in any discussions with third parties. In practice IoT product developers need to make a decision whether to protect their work through secrecy or to apply for a patent. Secrecy is paramount in any case, during the period leading up to a patent application, to ensure it remains valid. However, it may be commercially sensible to simply maintain the secrecy of the invention, particularly given that the costs of obtaining patents are not inconsiderable, particularly if applying world-wide. Sources: Information on this page was primarily obtained from the following sources: A webinar titled Dodging Dragons and Catching Unicorns by Justin Blows, Phoenix Intellectual Property
  16. Designing for IOT

    Introduction Designing Internet of Things systems goes beyond selecting the correct architecture or business planning. On this page we discuss some design principles that may inform the creation of effective IoT systems. Design Thinking Design thinking put humans at the centre of problem solving to ensure that the solution is desirable, useful and meaningful to the people who use it. It involves identifying a sweet spot in between requirements of the people using a product or service, the technology and the business case. Source: Deloittes Australia Design thinking focuses not on the business requirements of a product, but on the thinking and the process that produces these products. By starting with people, produces and services are more likely to be human-centric, holistic, and simple, delivering a desireable solution. Typically, design thinking involves a research phase, followed by ideation and prototyping. Research involves contextual inquiry, where the end users are observed in the field. The prototyping phase involves taking the prototype back to the end users in their context to gain feedback. This process may iterate a number of times before the final design is settled on. Design thinking involves a fostering creative mindset among participants fostering curiousity, adventure, experimentalism and optimism, reframing problems as opportunities. It often draws in related disciplines such as graphic design, UX design, industrial design and a range of artistic disciplines such as graphic design. Uncertainty and Tacit Knowledge A more technical design concept is the level of uncertainty that systems can tolerate while still making reliable decisions. ‘Tacit knowledge’ can be defined as knowledge that is difficult to express logically and pass on to others. This can make it difficult to define system requirements fully before building a system. In simple systems, it can be useful to use information to drive and improve system requirements through an iterative process, as shown in the diagram below. Diagram courtesy of Ryan Messina, Messina Vision Systems Ideally, information systems should be ‘contextless’ and ‘timeless’. Contextless means that the behaviour of the system is consistent for every environment. For example, a temperature sensor should give a reading in degrees, with an acceptable percentage of accuracy, which is unaffected by humidity, pressure, and other factors which may vary with physical position. Timeless systems perform consistently over time. Their performance today should be identical to their performance tomorrow and in 100 years’ time. Although purely contextless and timeless systems are impossible, engineers need to analyse and define the context and time span for which the system will operate consistently. This is done using validity, accuracy and quality control. Validity is about using the right technology for the task. If you were to measure temperature with a ruler, you should expect a very poor result, and you will get a very poor accuracy. Accuracy is defined by past performance, and cannot be used to predict future states. Quality control measures are put in place to meet expectations of future performance. Information’s validity and accuracy is affected by the technology and sensors used, and the environment in which it is captured. Information that will be used to support decisions made by people and processes can be conceptualised as the intersection of people, process and technology as illustrated below. Diagram courtesy of Ryan Messina, Messina Vision Systems Key engineering challenges A challenge is to design smart services and products that can make use of the information from potentially very large numbers of IoT connected devices. Three challenges in designing IoT data-driven services and products are: discovery integration analysis. Discovery involves finding which machines and sensors are available to use. These may be legacy sensors or the property of other people or organisations. Integration involves finding methods to connect and use the data from the discovered sensors, which may be made by different vendors and produce data in heterogeneous formats. The final step is analysing the data to produce high-value information for the target product or service. This is a recurrent cycle, as the sensors on the Things are not owned by the service or product provider, and can fail, be destroyed by environmental factors or abandoned by the sensor owner. If the existing sensors are lost, discovery, integration and analysis must be repeated. The frequency of repetition depends on the volatility of the application environment. There is also an infrastructure challenge, which is how to do all of this securely. Discovery, integration and analysis may need to be performed on the move: when either or both of the sensors and the platform that is conducting the discovery, integration and analysis are moving; and they must be able to happen in the cloud. There are readily available open source technologies that can help in solving these fundamental challenges. These include: OpenIoT for sensor integration Semantic Sensor Networks (SSN) ontology for sensor discovery. Sources: The information on this page has been sourced primarily from the following: A webinar titled 'How Machine Vision Helps Realise the Smart City Concept' by Ryan Messina, Director and System Engineer, Messina Vision Systems delivered to this community on 4 July 2017 A webinar titled IoT application development with open data-driven computing platforms by Prof Dimitrios Georgakopoulos, Swinburne University of Technology A webinar titled Design Thinking for the Internet of Things by Dr Lauren Tan, Director-Design for Business and Betrand Marcau from Deloitte Touche Tohmatsu
  17. Challenges

    There are a number of key challenges remaining before the opportunity presented by the Internet of Things can be fully realised. Batteries There is a perception that the cost of a "Thing" can be in the order of a dollar or so, due to the tremendous reduction in the cost of embedded electronics. While it is true that you can buy a system on chip communications device for approximately a dollar, the battery might cost $15 or so for a 10 year life. While the cost of batteries remains an order of magnitude above the electronics, mass deployment of IoT devices may be cost prohibitive. IPV6 Addressing One aspiration in the Internet of Things is that everything has an IPV6 address. However, at this stage its not possible for a IoT system developer to simply order thousands of IPV6 addresses in a batch. This is mainly because IPV6 addresses are managed by the Internet Assigned Numbers Authority and are granted to Internet Service Providers who are generally not yet organised to support IoT system developers. At present developers are often creating devices that are capable of being addressed with an IPV6 formatted address, in the anticipation internet service providers do provide such services in the future. At this point in time (early 2017) IoT architectures must use proxies which give virtual IPV6 addresses to Things and connect to a gateway that actually communicates to the network. One way of identifying devices is to embed an EUI-64 chip as per RFC2373. This gives effectively a unique MAC address to every device and the EUI-64 becomes part of the overall IPV6 address. Industry collaboration There is need for a coherent national strategy to develop our IoT industry to foster innovation through the uptake of IoT, as well as startups based on IoT technologies. There could be greater adoption of crowd based innovation. There needs to be a sectorial approach and liaison with existing industry growth centres. For example in food and agribusiness, CASA regulations restricting use of drones to "line of sight" is inhibiting the uptake of many potential IoT solutions. In utilities, smart metering will enable many applications but could be encouraged. For smart cities, local governments need to be encouraged to experiment and liaise with other city-wide authorities. There needs to be a better approach to open data, interoperability, and encouraging IoT-led growth based on shared data across supply chains and industries. This requires development of national or sector data sharing principles, guidelines for contracting and allocation of liability around shared data. The current frameworks for spectrum and licencing need to be revewed to take into account the special needs of IoT. This includes the need for mass sensor connectivity, and better ways of sharing the licenced and unlicensed spectrum, as well as real time monitoring to enable spectrum farming. There also needs to be better guidelines around security, including data protection. There is a need for consumer trusted models and updates to the Telecommunications act to cover IoT security. These issues are being addressed by the IoT Alliance Australia.
  18. Test: Introduction to IoT

    This is a recording of a webinar delivered by Geoff Sizer.
  19. Data Analytics

    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 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. 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 Downtime minimisation Network performance management Device performance effectiveness Load balancing optimisation Loss prevention Capacity planning Asset management Demand forecasting Inventory tracking 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
  20. Big Data

    Introduction One definition of big data is that the amount of data collected is sufficiently large to allow the development of insights that would be impossible with smaller data collections. Another definition is that big data cannot be dealt with by traditional data analytics techologies. If the questions being asked of the volume of data cannot be easily answered by traditional technologies, then it is big data. The primary purpose of big data is to create data based products, whereas traditional analytics' primary purpose is for internal decision support. One way of looking at difference between big data and traditional analytics is shown in the table below. In summary, big data is very large, unstructured and fast moving compared to traditional analytics, which calls for a different approach. In order to be able to analyse information, present it in a meaningful way and visualise it, an enterprise needs to collect and store all data in their legacy systems, CRM systems or ERP systems and data from third party solutions and applications in a data warehouse. A simplified diagram of a typical data warehouse is shown below (excluding ETL software, business intelligence, dashboards and advanced analytic tools). Diagram courtesy of Arthur Baoustanos, aib Consulting Services Big data requires a different storage and aggregation approach. Information from emails, documents, weblogs, social media sources, images and videos is collected in one storage system, or platform. One commonly used open source platform is Hadoop, which stores data in a Hadoop distributor file system (HDFS). Big data in a Hadoop environment is extremely useful for storing and retrieving very large amounts of data. If it is necessary to join databases or different datasets, other tools, such as in-memory computing tools, will be needed to provide the necessary computing power. Other technologies used for storing and processing big data are shown in the diagram below. Diagram courtesy of Arthur Baoustanos, aib Consulting Services These technologies are used to create a big data environment as shown in the following diagram. Diagram courtesy of Arthur Baoustanos, aib Consulting Services Big data is stored by combining the traditional data warehouse with the big data environment as shown below. Diagram courtesy of Arthur Baoustanos, aib Consulting Services Aggregation vs correlation Much of the focus when analysing Big Data is aggregation of data, which is how to reduce the size of data. Data correlation, or relating seemingly unrelated data through other data, is challenging with Big Data in an unaggregated form, as in a multi-dimensional data space with a lot of attributes, and a lot of data, the wrong hypothesis will result in the wrong conclusion. User interaction with Big Data is through summaries or aggregations. For example, the data from a group of sensors, can be characterised in terms of one-minute, daily, weekly or yearly summaries. In many IoT applications, users do not need to see the source data. There are other ways of aggregating Big Data. For example, anomaly detection is an aggregation approach because it takes a lot of data to produce very few results. Some machine learning algorithms can also be thought of as aggregations, as they follow a similar approach. Solutions have been developed that analyse source data on insertion and instantaneously stream aggregations of Big Data for users as micro- and macro-summaries which are useful for real-time monitoring and decision support systems. 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 A webinar titled IoT application development with open data-driven computing platforms by Professor Dimitrios Georgakopoulos, Swinburne University of Technology Case Study titled Studying movement behaviour in a building: A case study of obtaining analytics from IoT data
  21. Apparently this new platform called Hyperledger Fabric 1.0should be useful for industrial IoT applications. I dont fully understand how this works or applies in IoT, but would welcome comment and examples. https://www.hyperledger.org/announcements/2017/07/11/hyperledger-announces-production-ready-hyperledger-fabric-1-0
  22. Recording: The webinar has now passed. Members of Engineers Australia can access the recording for free on MyPortal. Navigate to IoT Technologies / Interoperability. Non-members can purchase the recording for $30 on the EABooks website. This webinar is an activity of EA’s Applied IoT Engineering Community. See http://iot.engineersaustralia.org.au/ for more information. Title: An Open Source approach to the Internet of Things Presenter: Prem Prakash Jayaraman, Research Fellow, Department of Computer Science and Software Engineering, Swinburne University of Technology What you will learn: • Challenges in the design and development of an open architecture for developing IoT solutions • Introduction to OpenIoT • How to use this platform to integrate, discover, query and visualise IoT sensors and data Description: This presentation provides an overview of the challenges in developing IoT architectures with a focus on an open source IoT platform called OpenIoT which is a middleware infrastructure supporting flexible configuration and deployment of algorithms for collection, and filtering information streams stemming from internet-connected objects, while at the same time generating and processing business/applications events. The presentation will demonstrate some of the capabilities of this platform including the ability to integrate any sensors platform, provide a do-it-yourself interface to discover IoT sensors, compose queries and visualise the IoT sensor data. About the presenter: Prem was previously a Post Doctoral Research Scientist in the Digital Productivity and Services Flagship of Commonwealth Scientific and Industrial Research Organization (CSIRO – Australian Government’s Premier Research Agency). He is broadly interested in the emerging areas of Distributed Systems in particular Internet of Things (IoT), Mobile computing and Cloud Computing. He was a key contributor and one of the architects of the Open Source Internet of Things project (OpenIoT) that has won the prestigious Black Duck Rookie of the Year Award in 2013. In the past 5 years, Prem has worked on several industry-funded IoT projects in multiple sectors including agriculture, future manufacturing and smart cities. When: 12 midday AEST (Sydney) on 15 August 2017. The presentation will last 30 minutes followed by question time. Where: The presentation is by webinar. After registering you will be sent details of how to logon. Cost: This presentation is free to members of Engineers Australia (EA), the Australian Computer Society (ACS), the Institution of Engineering and Technology (IET) and IEEE. Just provide your membership number during registration for the event. The cost for non-members is $30. How to register: Please register on the Engineers Australia event system. Note, to register you need to have a free EA ID which you can get on the first screen of the registration page. Take note of your ID number for future events.
  23. Legal considerations for IoT

    Introduction Internet of Things (IoT) projects are a complex multiparty undertaking, requiring the cooperation of asset owners, technology providers, consultants, communication service providers, and a range of other stakeholders. IoT projects have a range of technologies that have legal implications such as copyright ownership of circuit board designs and firmware. Adding to this, the securing of legal rights for the use and maintenance of the ICT systems is critical to the ongoing operation of these projects. Successful delivery and operation of these assets requires effective communication, a sound understanding of the legal landscape, and practical systems and procedures to secure the strength of your legal position if things escalate. A key thing that leads to legal disputes is that often the rights people think they have were never put down in writing or that rights arise or are broken by the common practice arising from conduct of the parties in dispute. Many of the legal considerations on this page are the same for other ICT and engineering projects. This page summarises these and provides some IoT specific context. Impacts arising from legal issues There are a range of considerations in any IoT project as follows: Continuity: A project owner or contractor supporting an IoT project must be able to ensure continuity of parts and services required to keep the system running. Practical approaches to mitigating legal risks: Consider legal rights as components of: the IoT system (IP licenses) and the business model (exclusivity & duration). Vulnerability of software element of system considerations mitigated through: Ongoing control & access; consideration of IP issues – developers, libraries, employee/contractor (e.g. IPC Global v Pavetest [2017] FCA 82). Avoid relying on verbal assurances: Through keeping written logs of discussions with other parties and confirming meetings in writing. Effective (alternate) dispute resolution Balance of leverage and vulnerabilities Strength of legal position and cost of options Game Theory – BATNA EQ – long term strategy, reputation capital Heads of agreement Statutory requirements: ACL – addressing: unfair terms; unconscionable, misleading & deceptive conduct Security of payment – deadlines, legal assistance with payment schedules (e.g. Ampcontrol SWG Pty Limited -v- Gujarat NRE Wonga [2013] NSWSC 707) Fields of law to be considered Following are the major fields of law that need to be considered in IoT projects. Privacy Considerations: Privacy policy; Mandatory reporting of data breaches. Telecommunications Including the Telecommunications Act 1997 and the Radiocommunications (Low Interference Potential Devices) Class Licence 2015. Australian Consumer Law (ACL) Covering (Unfair terms: consumers and small business); Safety defect; Warranties (extended by representations); Industry standards; Misleading & deceptive conduct. Common law Incorporating: Negligence; Contract Breaches – liquidated damages, indemnities including: payment, warranties, unfair terms, misleading consumers about their ACL rights; Limiting liability through understanding the scope for claims including time for bringing claims Supporting legal documentation Use of form contracts This may include AS4000 (General conditions of contract), however, be sure to use only relevant terms adapted for an IOTproject; Use correct reference documentation revisions; Clearly define intellectual ownership to avoid later detrimental outcomes. Legally acceptable documentation Approaches include: Email minutes of the discussion and what has been agreed to the other party; Encourage the other party to write back confirming receipt and agreement; Lawyers regularly merge files to keep a running log on each file or project; Alternately, encourage the other party to initial printed or otherwise hard copy documentation. Liability clauses Be scared and in turn cautious of the potential cost of lack of focus on liability (e.g. a project in TMA case ran overtime resulting in a horrific penalty of about $30,000 a day). Potential cost to client of time overrun includes: deprivation of use and profitability. Potential cost to IOT Developer Mismanagement Dependencies for consideration Construction Security of payment Home Building Act Intellectual Property Patenting Confidential information Copyright consider the legal rights that you're going to need to ensure that you continue to operate that system and retain control over it reusing third-party code without permission (e.g. IPC Global v Pavetest) What if a software supplier ceases to be able operate due to a failure for them to correctly protect intellectual property. So, in turn you should consider what indemnities and guarantees are you going to need? Include legal rights in as deliverables in your project. IOT projects are going to have software and firmware involved. An example given for which Jeff Sizer was a professional witness involved damages being sought on the basis of an intellectual property breach through reuse of a relatively small amount of code resulting in breached copyright. Designs Circuit boards Independent contractors considerations (Written assignment of IP; Protecting trade secrets; Confidential information; Non-disclosure agreements) Restraint of trade for key employees Managing Legal Risk through Clear Contracts Common Practise is to have standard terms and conditions, and to use this approach effectively, show the client and have them initial and date at the bottom of each page. The intention is to encourage the parties to turn their mind to things that might go wrong. It is certainly best to consider and fix such issues as early as possible in the engagement. Estoppel This term is used to refer to how you are able to backup a claim that varies from the initial agreement. To say, "I recall somebody told this to me a number of years ago" and no one else was around to witness what was being said can be hard to prove if it contradicts a previously signed agreement. This may be mitigated by standard terms implied at law, such as warranties. There can be terms implied but not written down. Such terms need to be consistent with the parent intentions. Terms need to capable of clear expression. The more convoluted and complicated the term is, the less likely it will be entertained by a court. Verbal assurances tend not to get a lot of weight from the courts. If you consider you may suffer detriment if the other party doesn't stick to the terms, then this needs to be documented in writing if you can. Australian Consumer Law is broader than general law. There may be consideration around whether you were impaired in your ability to make decisions to protect your own interests, and the other person exploited that. Consideration may be given as to whether you were given the opportunity to negotiate terms.Did you have any notice before this person took action. Good faith Alternate Dispute Resolution Easy to use record keeping systems Technically literate legal support Sources: The information on this page was primarily from the following: Presentation by Ashley Kelso, Senior Associate, AustraLaw titled Managing the legal risk of IoT projects
  24. until
    Recording: This event has now passed. If you are a member of Engineers Australia, you will be able to view the recording on MyPortal. Logon and navigate to the section on IoT Technologies > Data Analytics. Others can purchase the recording at EABooks for AU$30. This webinar was an activity of EA’s Applied IoT Engineering Community. See http://iot.engineersaustralia.org.au/ for more information. Title: Augmented Reality for ‘in context’ visualisation of IoT data Presenters: Allan Thompson, PTC Technical Manager, LEAP Australia What you will learn: What is Augmented Reality (AR) and how does it work? How does AR differ from VR? How will AR change the way companies & engineers design, service and operate their smart, connected products? What value does AR bring to Industry 4.0 / IoT strategies for the industrial enterprise? Description: Developments in Augmented Reality (AR) have transformed the way engineers and developers in industry create, service, and operate their products in the new smart, connected world. Engineers within the industrial enterprise can now quickly and easily create immersive AR experiences, without requiring any programming or expertise in AR. Recent developments in hands-free AR headwear, combined with the wealth of IoT data available from smart connected products and systems, are now providing industry with the ability to provide technicians and customers with ‘in context’ visualisation of the digital attributes of their physical assets, in turn triggering huge changes in how they deliver training, servicing and preventive maintenance. The presentation will include examples that show how the use of AR delivers value to the enterprise across sales/marketing, R&D, training and operations teams. About the presenter: For 19 years, Allan Thompson has worked with companies across Australia & New Zealand, helping them transform the way they develop, design and service their products through Digital Engineering. During this time, he has worked with thousands of companies in multiple verticals to successfully implement CAD\CAM\CAE, PDM & PLM and more recently, IoT and AR solutions. When: 12 midday AEST (Sydney) on 29 August 2017. The presentation will last 30 minutes followed by question time. Where: The presentation is by webinar. After registering you will be sent details of how to logon. Cost: This presentation is free to members of Engineers Australia (EA), the Australian Computer Society (ACS), the Institution of Engineering and Technology (IET) and IEEE. Just provide your membership number during registration for the event. The cost for non-members is $30. How to register: Please register on the Engineers Australia event system. Note, to register you need to have a free EA ID which you can get on the first screen of the registration page. Take note of your ID number for future events.
  25. core members meeting

    until
    GoToMeeting Information New Meeting Thu, 6 Apr 2017 12:00 PM - 12:30 PM AEST Please join my meeting from your computer, tablet or smartphone. https://global.gotomeeting.com/join/598419997 You can also dial in using your phone. Australia: +61 2 8355 1038 Access Code: 598-419-997 First GoToMeeting? Try a test session: http://help.citrix.com/getready
×