Edge computing is an optimized and distributed approach (read: Fog Computing) to cloud computing systems. Offering several advantages by removing recurrent data processing from the cloud using resources at the network edge, much nearer to the source of data. Edge resources affords technical advantages over cloud-only processing and greatly improves scalability of systems while optimizing network efficiency reducing the amount of round-trips to the datacenter.
The physical advantages from the proximity of edge devices improves real-time data analytics and lowers barriers-of-entry for on-premise hardware used in real-time applications like machine learning and AR/VR.
The ability to capture insights from real-time data, without impacts from latency and network-related bottlenecks is changing the way many industries can operate— bringing tangible benefits to consumers and companies alike. In this article, we’ll be looking at 5 major use case examples of edge computing in modern solutions used today.
So, let’s jump straight in!
1-Grid Edge Control and Analytics
Smart Grids, as we now know them, essentially work by establishing two-way communication channels between power distribution infrastructure, the recipient consumers (residential households, commercial buildings, etc.) and the utility head-end. This is done by using the tried and proven wide-area network (WAN) internet protocols.
The incredible growth-rate the internet of things is experiencing has steadily poured over into the industrial side (IIoT), bringing with it numerous technologies that can seamlessly monitor, manage and control the various functions within the electric grid’s distribution infrastructure.
With the accelerating movement away from fossil fuels onto distributed renewable energies (especially solar), modern power grids are strained, now tasked with embracing proven smart technologies that are capable of integrating and managing all of these distributed energy resources into existing grids, creating a harmonized and viable distribution network– a smart grid.
Edge grid computing technologies are enabling utilities with advanced real-time monitoring and analytics capabilities, generating actionable and valuable insights on distributed energy generating resources like renewables. This is something SCADA-based systems could never do, as they were designed well before the renewable and technological boom.
From residential rooftop solar to commercial solar farms, electric vehicles, wind farms and hydroelectric dams– smart meters are generating massive amount of useful data that can assist utilities with analytics on energy production, availability, requirements and peak usage predictions. This allows utilities to more intuitively avoid outages and overcompensation reducing overall costs and energy waste.
For practicality, verbose meter data needs to be preprocessed at the source within Grid Edge Controllers, compressing and filtering the data into important, actionable packets before being transmitted through the utility’s network (usually a low-power wireless WAN).
Grid Edge Controllers are intelligent servers deployed as an interface between the edge nodes and the utility’s core network.
2-Oil and Gas Remote Monitoring
Real-time Safety monitoring is of the utmost importance for critical infrastructure and utilities like oil and gas. With this safety and reliability in mind, many cutting edge IoT monitoring devices are still being developed in order to safeguard critical machinery and systems against disaster.
Modern advanced machinery uses Internet of Things sensory devices for temperature, humidity, pressure, sound, moisture and radiation. Together with the broad vision capabilities of internet protocol enabled cameras (IP cameras) and other technologies, this produces an enormous and continuous amount of data that is then combined and analyzed to provide key insights that can reliably evaluate the health of any running system.
Computing resources at the edge allow data to be analyzed, processed and delivered to end-users in real-time. Enabling control centers with access to the data as it occurs, foreseeing and preventing malfunctions in the most optimized timely manner.
This is the most practical solution, as time is of the essence in these critical systems. This rings most true when dealing with critical infrastructure such as oil, gas and other energy services, any failures within certain tend to be catastrophic in nature and should always be maintained with utmost precaution and safety procedures.
3-Edge Video Orchestration
Edge video orchestration uses edge computing resources to implement a highly optimized delivery method for the widely used yet bandwidth-heavy resource– video. Instead of delivering video from a centralized core network through all the network hops, it intelligently orchestrates, caches and distributes video files as close to the the device as possible. Think o fit as a highly efficient and specialized instance of a content download network (CDN) just for video, right at the edge for end-users.
MEC-powered video orchestration is most useful for large public venues. Sports stadiums, Concerts and other localized events rely heavily on live video streaming and analytics to create and increase revenue streams.
Freshly created video clips and live streams can quickly be served to paying customers in venues through rich media processing applications running on mobile edge servers and hotspots. This lowers the service costs and avoids many quality issues arising from bottleneck situations with terabytes of heavy video traffic hitting the mobile networks.
This is something 5G edge computing is designed to solve in the coming years. Currently, network operator EE is investigating the potential for these types of services in collaboration with Wembley Stadium, the national soccer stadium of the UK.
Due to the computationally expensive complexities of traffic management efficiency (Read: Traveling salesman problem), one of the best ways to optimize traffic management systems is by improving real-time data. Intelligent transportation systems make extensive use of edge computing technologies, especially for traffic management processes
The influx of IoT devices and massive amounts of live data necessitate preprocessing and filtering closer to the devices, before these thousands of data streams can hit the core/cloud networks.
Using edge computing the gigabytes of sensory and special data is analyzed, filtered and compressed before being transmitted on IoT edge Gateways to several systems for further use. This edge processing saves on network expenses, storage and operating costs for traffic management solutions.
While autonomous vehicles are not yet ready for the mainstream, without edge computing techniques their viability would be many more years in the future. With the slowdown of moore’s law and overall advance computational power the onboard computers will now form a sizeable expense of autonomous vehicles.
The myriad of complex sensory technologies involved in autonomous vehicles require massive bandwidth and real-time parallel computing capabilities. Edge and distributed computing techniques increase safety, spatial awareness and interoperability with current-generation hardware.
With mobile edge computing, vehicles can exchange real-time sensory data, corroborate and improve decisions with less onboard-resources lowering the growing expense of autonomous AI systems.