Gartner estimates that there will be around 8.4 billion connected devices installed worldwide by the end of 2017, up 31% on 2016, with roughly 37% of these devices set to be used by businesses and the rest by consumers. By 2020, it’s estimated that there will be more than 20 billion connected devices. Given this scale of IoT adoption, edge computing capabilities will have a significant impact on businesses, and their ability to compete in a dynamic market.
What is Edge Computing?
Edge computing refers to the processing of data of the Internet of Things (IoT) closer to where it is created, unlike cloud computing where it is sent over longer routes to data centers. In this way, data is processed at the edge of the network, by performing analytics and knowledge generation at or near the source of data.
Examples of edge computing include a wide range of technologies, from wireless sensor networks, mobile data acquisition, mobile signature analysis to cooperated distributed peer-to-peer analysis ad-hoc networking.
What drives the need for it?
Cloud computing provides many benefits that today’s agile businesses can’t ignore, typically by transmitting data to a centralized computing location in the cloud. Cloud computing has made great strides in real-time data access, but latency is still an issue, which suggests universal centralization isn’t always the best idea for an organization.
IoT networks produce a huge amount of data. And even if it is possible to process this data, doing it on the cloud becomes impractical in terms of:
Computational capacity requirements
Network latency for critical actions
How will Edge computing power the future of IoT?
Irrespective of the industry you are in—whether it’s manufacturing, energy, transportation or any other—IoT will have a big impact on your business. Edge computing helps you manage and analyze all of the generated data at an increased speed with reduced load on the internet networks transmitting huge amounts of data.
Edge computing use cases
Cameras, sensors, production line machines, cars and industrial equipment are a few examples of industries where edge computing is expected to play a larger role. Effective applications, enabling computation of data output at the edge would not only help in real-time decision making, mitigating any latency, but also save costs and improve RoI. A few use cases in various industry verticals are listed below:
Oil & Gas: Edge computing is being deployed in a top-notch oil and gas company to detect faults at the machinery level, before they are found using predictive analysis.
Chemical Industries: It is being used to build smart petroleum refineries where the process is well analyzed to increase productivity and workplace safety.
Energy Sector: It is being used smartly in various energy producing industries to reduce power loss and make energy equipment reliable and efficient.
Commuting Sector: Various transportation companies are making use of edge powered IoT devices and computing services to help find the right parking area and reduce parking downtime. Various AI algorithms work with these edge devices to optimize parking spaces and to collect real-time traffic and navigation data. They use analytics to make well-informed decisions.
Industrial Uses: Leveraging information at the edge, operations like shutting down systems can be carried out without having to query servers.
How it works
Edge computing works by pushing data, applications and computing power away from the centralized network to its extremes, enabling fragments of information to lie scattered across distributed networks of the server. Its target users remain any internet client using commercial internet application services. Earlier available to large-scale organizations, it’s now available to small and medium organizations because of the cost reductions in large-scale implementations.
Technologies used to enable computing at the edge of the networks
Mobile Edge Computing: Mobile edge computing or multi-access edge computing is a network architecture that enables the placement of computational and storage resources within the radio access network (RAN) to improve network efficiency and the delivery of content to end users.
Fog Computing: This is a term used to describe a decentralized computing infrastructure which both extends cloud computing to the edge of a network while also placing data, compute, storage and applications in the most logical and efficient place between the cloud and the origin of the data. This is sometimes known as being placed “out in the fog.”
Cloudlets: These are mobility-enhanced, small-scale cloud data centers located at the edge of a network and represent the second tier in a three-tier hierarchy: Mobile or smart device, Cloudlet and Cloud. The purpose of cloudlets is to improve resource intensive and interactive mobile applications by providing more capable computing resources with lower latency to mobile devices within a close geographical proximity.
Micro Data Centers: These are smaller, reach-level systems that provide all the essential components of a traditional data center. It is estimated that micro data centers will be most beneficial to SMEs that don’t have their own data centers as larger corporations will tend to have more resources and thus not need such solutions.
For large enterprises with global operations, IoT, and consequently edge computing, are more a matter of 'when', rather than 'if'. The earlier businesses stat evaluating their requirements, the faster they can zero in on the necessary technology and implementation strategies.
Srijan is already helping enterprises access and analyze their IoT data on interactive visualization dashboards. So how about we get that conversation started, and see how Srijan's expert teams can help with implementing IoT ecosystems and edge computing for your enterprise.