JULY 2019CIOAPPLICATIONSEUROPE.COM8nternet of Things (IoT) applications are a true disruptive force; Gartner calculates that around 8.4 billion IoT devices were in use in 2017, up 31 percent from 2016, and this will likely reach 20.4 billion by 2020. Total spending on IoT endpoints and services will reach almost $2 trillion in 2017. According to Gartner, two-thirds of those devices are found in China, North America, and Western Europe.From a technical perspective, we attribute this growth to higher adoption of cloud technologies, which have removed limitations on computing power and storage, and ubiquitous connectivity enabled by Wi-Fi/Mobile that--with increasingly higher speed and bandwidth--enables sending larger data volumes to the cloud for storage and analysis. As the maturity of IoT solutions advances, new business problems are identified that require faster actions and feedback from the devices being monitored. What is IoT Edge Computing?Edge computing is the practice of bringing the processing and analysis of data closer to the devices being monitored. Instead of sending the data collected from the device to the cloud directly for storage, aggregation, and analysis, the required analysis/action is completed on the edge node. Edge computing is a form of decentralised processing that is done on a server close to the device. For example, in manufacturing, an edge node would be deployed to analyse the temperature and vibration data collected from sensors attached to a robot or a machine. The edge node monitors the values and calculates if a particular threshold is passed or reveals patterns in the data.Needs for Edge ComputingTraditionally, data analysis can be achieved by sending the data to a central data centre or the cloud. However, for some scenarios analysis closer to the device has advantages, for example:1. Low latency: The required action must be fast with a very short delay from when the event is being observed. In many preventive maintenance situations, for example, we cannot afford the latency of sending the data to the cloud for analysis.2. Bandwidth constraints: Some devices could generate large amounts of data, for example, video or photography, and there are constraints as to the available bandwidth. Analysis can be done on the edge node and a lower fidelity version of the raw data could be transferred upstream--to the cloud or data centre--within the constraints of the available bandwidth.MARWAN TAREK, DIRECTOR--IOT AND DIGITAL TRANSFORMATION (EMEA), HITACHI CONSULTINGIIoT Edge Computing--Opportunities and Challenges IN MYOPINION
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