The Internet of Things (IoT) ecosystem is getting smarter by the day. With the improvements in technology, the price of hardware is steadily falling and becoming widely accessible to all socioeconomic classes. By 2020, it is estimated that the world will own 5.6 billion IoT devices, and it is expected that these devices will be used across governments, enterprises and individuals. All these devices will use some form of edge technology to transfer data. It is for this reason that major companies in device manufacturing, network operations and cloud computing are proactively investing largely in Edge Computing.
What is Edge Computing?
Good internet connectivity is important for IoT devices to function effectively. Since IoT devices are cloud-only technology, they push data to the cloud via a sensor. The data then travels to a data center that is miles away followed which causes subsequent delay to the end user. Simple sensors and smart cloud solutions are generally deployed in tandem and work effectively as long as there is good connectivity with data centers. In some IoT devices, images and videos are sent for real-time processing, and any loss of connectivity will result in a poor experience for the user due to downtime or loss of data.
A large section of the IoT community agrees that this bottleneck can be taken care of if devices are just made smarter. If they are able to collect, process, store data and have resilient networking capabilities, IoT devices could be absolutely autonomous. This in turn would mean that less bandwidth is required for IoT devices, thereby completely avoiding the need for a “fat pipe” for transmission. This would be useful for remote areas where connectivity is a problem. By reducing the dependency on the Cloud for critical computing, these autonomous devices will experience minimum down time. With the ever-decreasing cost of hardware, these autonomous devices are not a distant reality. The study and development of applications and solutions for such autonomous devices to enhance the capability of the IoT spectrum via the cloud is called Edge Computing.
How can Edge Computing help us?
Edge computing has ushered in the age of real-time computing, smart devices and remote computing. Edge computing has allowed the possibility of computing capability near the devices. This has helped increase versatility and almost real-time decision making for critical scenarios. Here’s a quick breakdown of how edge computing has revolutionized business:
- On-boarding old and new remote assets for the digital journey.
- Reusing of existing devices for deriving insights from raw data.
- Critical decision-making in real time.
- Remote control of data.
- Ensuring security and compliance (for on-premise assets automatically).
- Enabling low footprint on cloud services leading to cost saving.
Similarly, edge computing has made a lot of difference in technology as well:
- Adhering to industry protocols such as CAN bus, Modbus or OPC for connecting assets inside the edge location.
- Utilizing standardized technologies such as Docker to adapt to different platform architectures such as ARM, x64, and x86. It also helps in code reusability and easy deployment.
- Providing built-in resilience and fault tolerance mechanism for processing.
- Enabling intelligent data aggregation for difficult to reach locations such as oilrigs, and hydroelectric dams.
- Utilizing local computation for providing machine learning and analytics capabilities, if available.
- Enabling large-scale IoT deployment at a global level as well as providing Over The Air (OTA) updates for runtime and computing upgrades.
The leaders in edge computing are organizations such as AWS Greengrass, Azure IoT Edge, and IBM Edge Analytics. A lot of emerging open source microkernel runtimes like Apache Edgent, EdgeX Foundry and Liota are supporting different forms of edge computing. Clearly there is a lot of innovation in the edge-computing field, and it’s only a matter of time when these autonomous IoT devices will be omnipresent in our personal lives and workspaces.