Edge Computing: Expanding Possibilities

02 Dec, 2020 | Articles

Edge computing of late has found profound applications across various industrial sectors, owing to the rapid growth of IoT-connected devices while real-time computing capabilities have further driven rapid progress in this space. Faster and better networking technologies such as 5G wireless are further necessitating the demand for edge computing systems for several real-time applications such as automated driving, robotics, drug discovery and bioinformatics, video processing, and analytics. A major advantage that enterprises and businesses looking to invest in edge computing will observe is the capability to store and process data faster, enabling efficient real-time applications to operate that are critical to companies.

Amongst consumer electronic devices, smartphones have harnessed the maximum potential out of edge computing capabilities. Prior to its inception, a smartphone would have had to scan a person’s face and then run the facial recognition algorithm through a cloud, requiring significant time to process. However, with edge computing in play, the algorithm can be locally operated on an edge server/gateway or the smartphone. This augments the device response time and increases the output by a considerable margin.

Now let’s take a magnified look into some of the Use Cases of Edge Computing:

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Smart Grids:

Rapid proliferation of Industrial IoT (IIoT) has brought technologies that can seamlessly scrutinize, manage, and regulate the various functions within an electric grid’s distribution infrastructure. Edge grid computing is enabling the utility sector with advanced real-time monitoring and analysis, generating actionable insights on resources like renewables offering an edge over SCADA-based systems. Smart meters deployed across residential rooftop solar to commercial solar farms, wind, and hydroelectric power plants, generate a vast amount of data that could be harnessed to optimize production, sense peak usage predictions, and avoid outages and overloads. In order to accomplish this, the meter data needs to be pre-processed at the source within Grid Edge Controllers, converting the data into actionable insights. Here the Grid Edge Controllers act as the smart servers (or intelligent servers) forming an interface between edge nodes and the core utility network.


Autonomous Vehicles:

An automotive vehicle holds a myriad of complex sensory information necessitating higher bandwidth and real-time parallel computing. Without the availability of advanced computational power, an autonomous vehicle fails to operate and/or make decisions on the road. Edge computing techniques help in incorporating safety, spatial awareness, and interoperability within the current-generation hardware. Mobile edge computing can go a long way in enabling connected vehicles to exchange real-time sensory data and improve decisions with fewer resources in quick time.


Video Orchestration:  

Video Orchestration using edge computing results in implementing a highly optimized delivery of a bandwidth-heavy resource – video. Sports live-action, concerts, and other events banking on live video streaming and analytics require faster computation and analytical capabilities that mobile edge servers can offer. This lowers the cost and addresses quality issues associated with heavy real-time video traffic on mobile networks. 5G edge computing in this regard will have an important role to play in the future. Using edge computing, real-time video clips and live streaming content can be quickly offered to paying customers in other venues through rich media processing applications.

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Remote Healthcare Services:

IoT based medical devices such as wearable sensors, blood glucose monitors, have become largely common over this decade, collecting massive amounts of health data making it possible for healthcare professionals to better diagnose problems and monitor patient health over long periods of time. With the advancement in the field of medical technology, portable IoT healthcare devices are being developed with edge computing capabilities that can store, generate, and analyze critical data without the need of a network infrastructure. This results in quick diagnosis and enables remote healthcare service delivery in areas with poor connectivity. Besides retaining data, edge computing can also leverage real-time analytics to predict and respond to medical emergencies.


Sushmit Chakraborty

Team Lead – ICT

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