Wiring the Internet of Things with Node-RED, @IoTConf talk, September '14

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Wiring the Internet of Things with Node-RED, @IoTConf talk, September '14

Workflows are a key component of server side of IoT solution along with Analytics, Rule Engine and IoT device management. IoT focused Workflow tools draw their inspiration of classical workflow tools that exist in market, but focus more on IoT use cases. For example they are able to connect with IoT devices using IoT specific protocols like CoAP or MQTT. Node-RED is a visual tool for wiring together hardware devices, APIs and online services in new and interesting ways. It’s build by IBM Emerging Technology team from group for IoT, though it’s not limited only to IoT.

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Das Gesetz der großen Zahlen gilt immer: Die statistische Sicherheit nimmt mit der Anzahl der Datenpunkte immer zu, sofern die Datennahme fair erfolgt. Leider kostet das Sammeln der Daten oftmals Geld, und so ist man vor allem im Bereich der Sensorik (Stichwort: Internet der Dinge) gezwungen, sinnvolle Kompromisse einzugehen. In diesem Vortrag fasse ich die Erkenntnisse eines Projekts zusammen, in dem die Datenanalytik zeigte, dass man zukünftig nur 60% der ausgebrachten Sensoren wirklich braucht. Auch muss es nicht immer Echtzeit-Analyse sein: Mit einer auf den Business-Case abgestimmten Datenstrategie lassen sich unnötige Ausgaben vermeiden.

A significant proportion of developments in the Internet of Things (IoT) is driven by non-technical innovators and ambitious hobbyists. Node-RED targets this audience and offers a widely used rapid prototyping platform for IoT data plumbing on the basis of JavaScript. Data platforms for the IoT provide storage facilities and value in the form of visualisation & analytics to business and end users alike. This report details how Node-RED connects to 11 different platforms and what additional services these provide.

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Thingmonk 2017: End-to-end IoT solutions are often highly integrated. Even small changes to the UX of a product can have profound impact on hardware requirements, while physical constraints such as battery capacity can dictate software architecture. A holistic understanding of IoT is key to efficient implementation, the “T-shaped engineer” the star in every development team. Contrast this to intellectual silos and matrix organisation, and you may see why especially large companies fail to move quickly into IoT. Similar issues strike the application of IoT. Deploying a solution in the enterprise is just a cost factor if processes are not adjusted to leverage the connected device and its data. However, changes in process often affect companies across their entire organisational structure. This can require a change of mindsets, making the success of an IoT solution depending on the human factor.

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Big data! Fast data! Real-time analytics! These are buzzwords commonly associated with platform offerings around IoT. Although the Law of large numbers always applies, just because you can deploy more sensors doesn't automatically mean that you should. After all, they cost money, bandwidth, and can be a pain to maintain. On the example of the Westminster Parking Trial, I'd like to show how analytics on preliminary survey data could have reduced the number of deployed sensors significantly. A similar logic goes for fast and real-time analytics. While being advertised as killer features, many people new to IoT and analytics are not even aware that they might get away with batch processing. On the example of flying a drone, I'd like to discuss for which use cases I'd apply edge processing (on the drone), stream or micro-batch analytics (when data arrives at the platform) or work on batched data (stored in a database).

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Software is increasingly playing a big part in scientific research, but in most cases the growth is organic. The life time of research software is often as short as the duration of a postdoctoral contract: Once the researcher moves on, custom-written niche code is frequently not well documented, components are not reusable, and the overall development effort is likely lost. This is a case study in looking at the evolution of software for research in the field of genomics within my research group at the Department of Genetics at Cambridge University. While our research questions changed over the past decade, we moved from Perl code and regular expressions to R and statistical analysis, and from there to agent-based simulations in Java. Not only will I discuss the languages and tools used as well as the processes and how they have evolved over the years. It also covers the factors that influence the nature of the growth, such as funding, but also how 'open source' as a default has changed our development work. We also take a look into the future to see how we predict the software usage will grow. Also, in presenting the problems and discussing possible solution, this talk will look at the role institutions play in helping address these issues. In particular the Software Sustainability Institute (SSI, http://software.ac.uk/) works in the UK to promote the development, maintenance and (re)use of research software. The Eclipse Foundation, with the Science Working Group, works to facilitate software sharing and reuse. How can organisations like the SSI and Eclipse align their strategies and activities for maximum effect?

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