Edge computing and IoT product design

One of the hottest topics these days, along with the creation of IoT solutions, is the use of edge computing devices. As with many of the latest technology trends, it is important to dig past the hype to understand what is genuinely new and determine the value it provides. Simply implementing the latest buzzword technology only makes sense when it creates business value, which is defined by utility and appeal to the end user.

Edge computing is the implementation of information processing performed at the IoT device level. This is an alternative to cloud computing, thought they both can coexist within the same system. In cloud computing, data is transmitted from an IoT device and the information is then processed in cloud-based servers.

This is similar to the older practice of users connecting to large, centralized computers via less intuitive connected terminals. We are currently transitioning to a world of connected smart devices, but decentralized devices and some processing power has shifted toward edge devices while still connecting to centralized servers in the cloud. At the end of the day, the IoT world will look a lot like the general-purpose computer world where edge and cloud-based computing will coexist.

When getting to know edge computing, it’s important to evaluate the reasons for — and potential benefits of — shifting computation from the cloud to the edge. Here are a few reasons why you might want to consider adopting edge computing:

Managing use of bandwidth

Depending on the application, the amount of data sent from the IoT edge device can be substantial. The need to transmit massive data amounts in real time can drive the selection of radio technology, which has a further impact on product cost, size and power. Depending on the selected radio technology, there can also be a substantial impact on the cost associated with data transmission and fees from carriers. In an edge-optimized application, data processed on the edge device can be decreased or preprocessed for compression, which reduces the bandwidth requirements.

Managing latency

There is no question that transmitting vast amounts of data can flood a network or affect the real time availability of data. By preprocessing or compressing data, you can remove certain amounts from the network, thereby reducing latency.

Off-grid reliability

Depending on IoT implementation of edge computing, the individual or mesh of edge nodes can reduce dependency on wide area connectivity. If the WAN goes down, the edge devices can provide useful data to mobile devices or machine-to-machine without the need for wider connectivity. Of course, edge computing that is dependent on cloud-based data for import would be hampered by the loss of wide area connectivity, but edge computing enables remote IoT devices to provide a potentially reduced subset of information in the absence of cloud access.

The reality is that in the world of IoT solutions, there will not be a one-dimensional shift to edge computing. Applications that make the most sense for edge technology will be those leveraging benefits from the technology, but those also taking advantage of cloud-based infrastructures. In doing so, the entire IoT solution will utilize the inherent advantages of cloud-based systems, such as the large and vast data structures, high computational power, data redundancy and reliability, while also managing the issues that can be mitigated with edge computing.

It is up to the system’s solution architect to assess the right model for any IoT solution. If the data to be transmitted is small or infrequent, the WAN connectivity is robust and reliable, and the application requires connectivity to cloud to provide any useful value, then the need for edge computing is greatly reduced. Given the drawbacks of edge computing in hardware cost, size and battery capacity, one should carefully assess the requirements of the full solution needed to implement an IoT solution.

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