Choosing the right technologies to build an urban-scale IoT system can be challenging. There is often a focus on low-level architectural details such as the scalability of message handling. In our experience building an IoT information system requires a high-level holistic approach that mixes traditional data collection from vendor-specific cloud backends, together with data collected directly from embedded hardware and mobile devices. Supporting this heterogeneous environment can prove challenging and lead to complex systems that are difficult to develop and deploy in a timely fashion. In this paper we describe how we address these challenges by proposing a three-tiered DevOps model which we used to build an information system that is capable of providing real-time analytics of Electric Vehicle (EV) mobility usage and management within a smart city project.

The energy drivers use to charge their Electric Vehicles (EVs) comes from various sources. Of those, some are renewable green energy sources such as solar photovoltaic systems (SolarPV) and home storage battery whilst some are called brown sources such as gas turbine, coal and oil. To analyse the behaviour of EV drivers as to how they use household green and brown energy, separating the mix is a necessity. This paper argues that the Internet of Things (IoT) can be helpful in achieving that goal and demonstrates a pilot study showing the process of separating the green and brown energy from the EV charging.