Tim Kannegieter

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/

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

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

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.

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.

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

This is a recording of a webinar delivered by Geoff Sizer.

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

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

Interesting developments in NZ which will see the country with no less than four competing LPWAN networks to enable the IoT. See this report from stuff.co.nz https://www.stuff.co.nz/business/industries/94336747/spark-and-vodafone-announce-competing-investments-in-internet-of-things

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

Introduction Data management is a generic field of Information Technology that supports the Internet of Things (IoT) and underpins data analytics. The purpose of this page is to introduce the principles of data management and show how it intersects with the IoT. Key data management processes Data management is the development and execution of architectures, policies, practices, and procedures in order to manage the information life cycle needs of an organisation in an effective manner. The development and execution of the architectures, policies, practices, and procedures needed to manage information will fall into one of the phases or stages of the information value chain shown in the diagram below. Diagram courtesy of Arthur Baoustanos, aib Consulting Services The stages are to acquire the data, store, then analyse it to present the data to the user in a meaningful way that adds value. The data can be acquired in a number of ways, including: a simple sensor (temperature, load cell or part of a scatter system); creating a bar code; reading an RFID tag; or through vision systems. The data is stored for aggregation and processing in a data warehouse, enterprise resource planning system, or the cloud. Data is then analysed, using methods from simple spreadsheet analysis, to OLAP, to sophisticated methods including data mining and machine learning. The data needs to be presented in a form useful to users, whether that be a static report, or interactive reporting, in the case of OLAP. Informed decision-making requires data. Data is good if it provides insight into a process in a timely manner. In this respect, the relevant data is what counts. The role of the IoT in data management is shown in the IoT technology stack diagram below. Diagram courtesy of Arthur Baoustanos, aib Consulting Services The stack starts with machines, or physical assets, which are equipped with sensors, actuators or a CPU. The next part of the stack is the communications networks and technologies that connect the machines to the internet. Once the data from the machines is stored on the internet, it provides a platform to enable rapid and efficient data analytics including data management, algorithm creation and data moulds. The final component of the stack consists of applications that run on real time data. The lower cost of communication networks, such as low power, wide area networks (LP LANs, eg. Lora, Sigfox and Zigbee) and advanced data storage is helping the IoT play a pivotal role in data management. The relationship between Operational Technology (OT) and Information Technology (IT) is also shown in the diagram below. Diagram curtesy of Arthur Baoustanos, aib Consulting Services OT includes SCADA systems, distributor control systems, PLCs and factory floor and plant environment sensors. IT includes the items on the right hand side of the diagram above. Value is derived by ensuring that data flows from the factory floor or plant all the way to the enterprise and business systems. Sources: The information on this page has been sourced primarily from the following: A webinar titled The data management perspective on IoT by Arthur Baoustanos, Managing Director, aib Consulting Services