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September 21, 2018
Telecoms.com periodically invites expert third parties to share their views on the industry’s most pressing issues. In this piece freelance Journalist Charlie Osborne explains the differences between edge and fog computing.
The Internet of Things (IoT) and connected devices, which are able to facilitate data collection and storage at the edge of networks, have the potential to transform enterprise systems and the way we manage data irrevocably. As the number of IoT devices in circulation is set to rise to 20.4 billion by 2020 and a 10-fold rise in worldwide data generation by 2025 forecast by IDC, in order to properly harness and manage data, new solutions at the edge are going to become critical.
The next generation of mobile LPWA and 5G technologies, including the rollout of NB-IoT and LTE-M networks, will become key to managing the IoT deployments of the future. Computing at the network perimeter, including edge computing and fog computing, will also play a crucial role in content delivery and workload management. The massive volume of data collected by enterprise players has given rise to edge computing, which is defined as a means to bring data collection and analysis close to the source of collection.
This bypasses the need to transfer data across centralized relays and to central cloud systems, which can be a costly endeavour demanding high bandwidth which can also negatively impact latency. Fog computing builds upon this concept. While edge computing is responsible for connecting edge gateways and connected devices, fog computing provides the intelligence and necessary protocols for such decisions to take place. The architecture brings cloud computing to the edge of networks and provides the support required for systems to decide what information needs to be transferred to core systems, and what data can be managed at the edge of networks. In turn, this can result in faster processing times and lower latency.
Edge and fog computing have found a place in systems ranging from corporate networks to industrial settings. Whenever an organization has the need to manage IoT device deployments — whether this is the factory floor for equipment monitoring, enterprise platforms working with data analytics in real time, or smart city connected devices, connected cars, or home mesh networks, these technologies can lessen the strain on cloud environments and bandwidth requirements.
When it comes to telecoms and 5G network deployment, some experts believe that fog and edge computing will provide the missing link between cloud architectures and end-users. 5G networks, due to roll out in 2020, aim to reach high mobile speeds of up to 1Gbps and reduce latency to the sub-milliseconds.
In order to achieve this, 5G will need to be able to utilize edge and fog computing to support dense networks and eradicate the risk of bottlenecks, caused by high bandwidth demands caused by transferring data to centralized cloud environments. While 5G will be driven by high-frequency radio waves, fog computing is a necessity due to the increase in latency which occurs due to cloud-based application requests from core networks to the cloud.
Without a means to bring processing power and compute close to the edge, 5G networks will still suffer from high latency problems. Edge and fog computing platforms are likely to be adopted at speed in the coming years as enterprises, telecoms, and industrial players understand the opportunities for growth and efficiency improvements created by utilizing not just core cloud environments, but peripheral networks.
IoT devices, next-generation communications protocols, and networks, coupled with our desire to make everything from the factory floor to our smartphones more intelligent, will all require a stable backbone to perform. Computing at the edge can supply the support we need.
Charlie Osborne is a professional journalist based in London, UK. She is a freelance editor, educational material creator and contributes to IoT World News as a feature writer with a focus on consumer technology, innovation, smart technology, mobility, edtech, and security.
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