Policy makers around the world see the smart city as offering the best way to deliver fine-grained, real-time operational control of the modern city. According to many metrics, the impact of the internet of things (IoT) and smart cities is already being felt in multiple areas, from waste management, the power grid and utilities and public safety. As a means of addressing issues of ageing infrastructure, pollution, traffic congestion and crime, as well as climate change, smart cities are the hero of the hour.
That heroism doesn’t have to be glamorous, by the way. To take a recent example, Scotland’s Smart Cities programme intends to adopt intelligent street lighting and sensor-equipped rubbish bins in four urban areas to save energy and improve quality of life. Across the pond, New York State is using embedded sensors and smart meters in combination with big data to optimise energy usage in commercial properties. In Texas, the city of Houston loses 15% of its water supply from leakage, but with sensors and intelligent pump control systems, it is finally getting on top of the issue.
There are clearly technology and management issues in terms of realising these benefits. That’s because smart cities are big and difficult to manage. A smart city requires a huge network of sensors, networks, devices, CCTV cameras, power grids, utility frameworks, traffic lights and smart water and power meters. And they all need to function as a connected network of many things – an IoT structure and connected things, since this is a network of many relationships, to reflect all the interconnectedness, from bin to light to utility grid.
Another related challenge is that all this is always in renewal, flux and transition. That’s challenge enough when you just need to dig up the roads to fix water damage, say - the smart city version of this will be a thousand times more complex. In a smart city setting, when a new item of equipment or sensor comes online, it will seek local controllers or other devices to which it needs to listen or transmit data. The powering up or down of a sensor may create or end dozens of connections - and everything needs to happen in real-time.
All of which brings us to the topic of how to not just model all this complexity, but manage it. This is data we are discussing and we need the right database tool to manage it. Graph databases are, I would submit, the ideal option here as they process complex, multi-dimensional networks of connections with speed.
While it’s true that simple smart city IoT problems could be handled by a relational database, they’re not an especially satisfactory fit as they represent data as tables, not networks, and such queries strain a data structure not designed to map a huge mass of connections.
What’s more, IoT-based smart city applications require leveraging one or more datasets that are each highly connected in their own right and linked to each other. Additionally, connections are more than lines between entities - they include a lot of other information, such as direction, type, quality, weight, all of which needs to be an integral part of each smart city individual node.
For many informed observers, an IoT-powered graph is the way to get the kind of smart city management framework required. An analyst consensus has emerged around the issue, with Datanami noting that, “[a] key enabling technologies [of IoT] is graph databases,” while, for Ovum, “graph technology will allow the internet of things to be represented transparently, without the need to force fit into arbitrary relational models.”
Given the overwhelming amount of data and connections that need to be processed in real time in any smart city scenario, traditional databases will flag. As a result, any city manager and local government body should look carefully at their database infrastructure to make sure it’s fit for purpose if they are serious about making the smart city an urban reality, not an urban myth.