Edge Computing

Every day, more and more devices are connected to the internet through the Internet of Things (IoT). While some argue as to whether or not we’re headed towards a new industrial revolution as a result of this development, one thing’s for sure: our data transfers will never be the same.

This is because IoT networks rely on centralized cloud computing models which means that all data needs to first reach these remote centers before it can be acted upon and communicated back. It also means that these devices need to connect wirelessly over large distances – which is challenging in itself, but particularly problematic when you consider how expensive running such communications can be.

So what options do we have? Well, one alternative might just come in the form of ‘edge computing’.

What is edge computing?

First of all, it’s important to note that ‘edge computing’ has not always been defined as a single, unified concept. As with cloud computing, definitions have changed over time and continue to change as new technologies become part of the mainstream vernacular. That said, most people would define edge computing today as any type of model which moves data processing closer to the source – i.e., closer to where data is collected rather than further away in remote servers.

This could be anything from additional storage capacity installed right at the site (such as security systems that record footage locally), or an analytics platform like Splunk or Cisco WSA which can ‘listen’ to data as it’s being collected, but just as likely it might be an actual physical server located right at the source.

Perhaps one of the major reasons that edge computing has become a more viable alternative for IoT networks is that mobile technology has reached a level of maturity where users are now willing to sacrifice speed and quality in order to achieve greater coverage. For example, mobile devices on 4G networks will regularly switch between 5GHz and 2.4GHz frequencies depending on which offers the best coverage at any given time – which means they’ll be slower, but able to connect from further away as well as offer better battery life as your device doesn’t have to constantly battle against radio waves from neighboring cells.

A final point to note is that edge computing is often considered as part of something more general: fog computing (see below).

What are the advantages and disadvantages of edge computing and why does it make more sense for IoT?

There are a number of obvious benefits. By moving data processing closer to the source, we can reduce bandwidth use by removing the need to send your data over long distances – which also means you’re using less energy and spending less money where possible. For businesses, this cost saving could be significant – particularly if you have a high volume of transaction-based traffic such as in banking or micropayments.

The privacy concerns around internet connected devices has also seen edge computing grow in popularity since it helps to keep data secure. Rather than sending your data wirelessly and making it vulnerable to interception at any point between you and the server, processing is moved closer to the source such that it can’t be accessed without physically breaking into one of your devices (think of how safe your smartphone is when its tucked away in your pocket or bag).

The Security Edge: Why IoT Needs Location-Aware Edge Computing

There are also a number of security benefits as well – particularly where edge computing involves putting servers and other equipment right at the source and connecting these directly with minimal wireless interference.

The main advantage for businesses here is that because we’re no longer dealing with large datasets which need to be transferred, we can reduce the scope for cyber-criminals and hackers in their attempts to intercept and alter this data. As such, edge computing is seen as a key part of getting businesses closer to their customers without sacrificing security or safety.

One example that has been used is that of self-driving cars: when you’re traveling at high speed on a busy road, it’s not safe looking up your bank balance in case you crash – but if the car analyses it automatically and keeps only the relevant details for display while maintaining security by not sending your full banking information wirelessly, then this will work much better.

How does edge computing affect mobile networks?

Of course, mobile networks are also part of the equation here, but our desire to reduce bandwidth use isn’t just about saving money – it’s also about being more cost-effective. For example, cutting down on time spent streaming data or uploading photos means that users are able to enjoy their devices for longer without draining the battery.

But how will this affect existing mobile networks? Well, one option is for them to simply increase their capacity so there’ll always be enough room in their airwaves for everyone’s data. However if companies can find a way to stop sending your data over long distances whenever possible then they’ll have all the scope necessary to improve coverage and priorities important traffic over less important requests.

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