Wireless Technologies

Smart cities and the internet of (connected) things

This is a contributed piece by Emil Eifrem co-founder and CEO of Neo Technology

Smart cities offer the promise of elegant, real-time operational control of a modern city, something emerging as a clear practical goal for many global politicians. Analysts put the global smart city market at over $300bn last year, set to grow to three quarters of a trillion dollars by 2020.

But how are we going to manage the foundation of the smart city?

After all, today’s cities are so much larger and more complex than their Industrial Revolution era forebears, which were hard enough even for engineering geniuses like Bazalgette to handle.

A smart city is a complex synergy of multiple sensors, networks, devices, CCTV cameras, power grids, utility frameworks, traffic lights and smart water and power meters. If you consider all the elements there, it’s clear that this is a connected internet (network) of many things (devices) – an IoT structure, by definition.

And what’s more, connected things. Because abstract that out a little and what you’re working with is complex data structures of many nodes, as it’s the only conceivable way of capturing all that density and inter-connectedness. And not just things – data, data constantly in renewal, flux and transmission. After all, what is behind all of these sensors in tomorrow’s smart parking meters, smart traffic lights, or the cameras in the hospital driveways? Multiple municipal or outsourced databases, capturing data continuously.

Meanwhile, the connections between devices and other entities can change faster than the data describing each thing. With telecoms data, for instance, each time you call a new person or authorise a new device, you make a new connection. The same is true in a smart city setting, when a new piece of equipment or sensor comes online. It may look around for the relevant controllers or other devices that it needs to listen to or send data to. The powering up or down of a device may also in turn make or break dozens of connections.


Connections between entities – at scale

Most IoT smart city applications require leveraging one or more data sets that are each highly connected in their own right, and often linked to one another.

Connections are more than lines between entities, by the way. They each include a richness of information, such as direction, type, quality, weight, and more, all of which can be represented, in a graph database, as an integral part of each relationship object. In this schema, relationship attributes describe each connection, while attributes may indicate when the connection was created, the type of connection, the data related to the connection, and more. Just like the data describing a thing, the attributes of connections may change rapidly.


Getting the smart city back on track

While it is theoretically possible to represent a graph with attributes for both nodes and connections in any number of database management systems, graph databases are the ideal option for big IoT systems because they process complex, multidimensional networks of connections very fast. While it’s true that simple graph problems could be handled by a relational database, it’s not a good fit, as they represent data as tables, not networks – and such queries strain a data structure that was not designed to map connections: as one user who troubleshoots networks described, “[I] don’t have to do a relational join between every machine with every other machine... 15 machines die and you don’t know which one caused the problem—now imagine that’s in a collection of 100 machines, or 1,000 machines”.

The analysts agree, with Ovum recently noting that, “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 accumulate over even the shortest period of time in any IoT scenario, traditional databases will struggle to get any coherent, helicopter view on what’s going on, which is precisely what the smart city administrator and her teams will demand.

By contrast, graph query can handle relationships at scale, no matter how frequently they change. Your downtown Metro light rail network’s just gone down, we think there’s a problem with the sensors in Sector A27, but we need to track down the gap really fast. Answer: traverse your big sensor node graph database to see what’s broken. Or, what is the best route for that ambulance with that critical injury given that Main St is closed? Answer: use a graph database to plot the shortest distance to travel to fix the issue.

Graph-based approaches to IoT management are the shortest way to get us to the smart city future, it seems.


Also read:
The next wave of disruption: Graph-based machine learning
Nokia helps advance Bristol’s smart city test bed


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