As the world embraces connected devices, it’s imperative that we begin to discuss the future of connectivity, especially with the mass adoption of IoT technology. There are a handful of rivals to the ‘traditional’ cloud model of data storage and analysis, but two - fog computing and edge computing - may be the most realistic challengers to the cloud at this time. Both are aimed to collect, analyze, and process data from physical devices in a more efficient manner than cloud architecture.
The main problem with relying on cloud computing for IoT is performance, amongst other issues such as security and storage. While simple devices like thermostats or fitness trackers may work easily on a one-off basis, multiplying their use puts a greater strain on cloud servers to process and retransmit the data they receive. If you factor in smart cars or jet engines that send terabytes worth of data to the cloud for analysis, it begins to make more sense to analyze as quickly and close to the source as possible.
Fog computing is a model where data is collected and processed through devices at the edge of the network, such as through smart devices, instead of the cloud. When the smart device, such as a sensor, collects data, it transmits information down to a fog node or IoT gateway for processing. It then returns necessary data back to the device while storing other data to the cloud or server.
While fog computing requires the use of an external node or gateway, edge computing processes data directly on the devices themselves. Instead of using a gateway, devices as small as Raspberry Pis can handle data processing for a variety of smart devices.
More and more machines are recording, storing, and sending data - roughly 5.6 billion IoT devices owned by enterprises and governments will utilize edge computing by 2020. But why? As our world continues to become more connected, the amounts of devices and the data they are responsible for will continue to rise - data that we’ll need to handle in a way that’s practical, cost-efficient, and secure.
By containing data to a local environment, security and operational risks are severely reduced by keeping data out of the cloud. With data processing occurring where data is collected, latency is significantly lowered with near real-time analysis and processing. Remember when Amazon S3 shut down and reliant business operations were put on standstill? On an edge computing system, this won’t happen; data is exactly where you need, when you need it.
Interested in utilizing edge computing for your projects? The Forest Giant platform offers two open-source tools, Stela and Iris, to kickstart your edge computing goals. Stela is a distributed discovery service that enables other services and apps you’ve registered to be discoverable on your edge network. Iris is a streaming key-value store service that allows information to be distributed across edge devices rather than stored at a single source or fragmented across many sources. Learn more about Stela and Iris through our introductory post and explore the platform at platform.forestgiant.com.