IOT in the City

This post was created automatically via an RSS feed and was originally published at http://blog.opensensors.io/blog/2014/03/12/iot-in-the-city/

Architecting Internet of Things Software for Cities

We are often asked to advise on how best to build scalable products for the Internet of Things, specifically to provide City wide services. City scale projects, also marketed as Smart or Future Cities, within an IOT context are projects that combine aspects of the physical and digital worlds to provide infrastructure and services. Inherently these projects have to tackle the challenge of scalable software management as well as distributed data management i.e. Big Data.

Among the projects we are involved in are parking management applications using parking sensors, real time analysis of pollution data from pollution monitors and using telemetry data to map routes for people with disabilities.

The problem space that connected devices are used in is diverse but technologically speaking the architectural model is fairly common. There are usually a number of different types of sensors each measuring distinct factors which in turn are listened to by either other devices or software services. In the parking situation, a connected car can listen to sensors in a particular area in order to ascertain the nearest parking availability. Any serious IOT project will quickly evolve to having thousands, if not millions, of devices connected to it. It will also need to be able to cope with potentially millions of listeners such as the individual cars in the parking scenario.

Architectural Model

We strongly favour the Publish/Subscribe (aka PUB/SUB) model of building software in IOT. In pub/sub, “Publishers” are usually the devices but can also be data from smart phones and the “Subscribers” are all the services that care about the data that the device is emitting. In the PUB/SUB model devices can have one or many subscribers and subscribers can listen to one or many publishers. This is essential when building systems for Cities. Let’s take the example of pollution monitoring, there are many potential groups interested in this data from environmental groups to those concerned by the impact on health. Each group should be free to build applications that are relevant to them without being impacted by the needs of the other subscribers. You do not want a situation where each pollution monitor can only talk to one monolytic, and often proprietary, system thereby needing to set up another pollution monitor on the same street for each group which is frankly impractical and wasteful. The PUB/SUB model eliminates this situation.

Scaling this model has been our obsession since the start of opensensors.IO. We have open sourced our engine to enable others to be able to also create scalable services. Azondi is our MQTT based engine to enable processing device data at scale.

All of this has been possible by standing on the shoulders of giants using battle test components. Our MQTT broker relies on Netty in order to provide an extensible broker. Netty is used by a host of tech companies to build various real time systems such as Twitter, Facebook and Avast. We also rely on Project Reactor to get a non-blocking dispatcher for event driven programming based on the Reactor Pattern. This dispatcher acts as a kind of sorting office between devices and their listeners. It receives all messages and ‘delivers’ messages to interested listeners.

The most important motivation behind the technological choices we made to build Azondi is the need to avoid polling at all costs. When you have potentially 100,000s of services listening to each device message you never want a situation where you are being simultaneously hit by requests.

Model of a City

Putting the above theory into a real world model, below is a diagram on how Azondi would be implemented in reality. Let us pretend that we are processing device data from disparate sources for the London Borough of Camden.

In the example, there are environmental monitors that measure pollution and noise as well as a weather station monitoring temperature and wind speeds. In addition, cars send information about traffic in their vicinity. On the other hand, Mary’s car is listening for local parking information and Sophie’s phone listens to information on noise, pollution, temperature and energy readings. Both Mary’s car and Sophie would have the option to filter the information they receive i.e. local information only or when pollution hits dangerous levels. Camden Council cares about all of the data sets and would probably have a dashboard or a decision support system.

Illustration of Azondi in action

Click on each device to get it to publish (random) data and watch the subscribers receive their information.

Posted in Benefits of open data, Commercial opportunities, Greater efficiency, Informing Decision-making, Innovation, Internet of Things, Posts from feeds, Saving money, Smart communities Tagged with: , , , ,