Edge computing allows for faster response times that aren’t hampered by network latency, as well as decreased bandwidth by selectively transferring the right data to the cloud.
Edge computing architecture is directly applicable to IoT-linked devices.
A large amount of data is generated by remote sensors placed on a machine, component, or device. If the data is transported back across a long network link to be evaluated, logged, and monitored, it takes considerably longer than if it is handled at the edge, near to the data source.
Edge computing was formed as a result of the rapid growth of the Internet of Things (IoT) devices that connect to the internet to receive information from the cloud or to send data back to the cloud.
Advantages of Edge Computing
- Speed: Edge computing has the capacity to reduce latency and enhance network speed. By processing data closer to the source of information, substantially minimizes the distance it must travel.
- Scalability: You can utilize the edge to scale your own IoT network without worrying about storage requirements. Also, IoT devices may be installed here with just one implantation.
- Reliability: Edge computing excels at maintaining reliability. Edge computing provides an uninterruptible service since it does not rely on internet connections or servers most of the time. There is no need for users to be concerned about service disruptions or poor internet connections. It can also use microdata centers to store and possess data locally. As a result, IoT devices can be assured of a stable connection. As a result, edge computing is advised for usage in remote places where a stable network connection is unavailable.
- Cost: IoT services require additional network bandwidth, data storage, and processing power, so they can be expensive to implement. Edge computing for IoT allows users to minimize bandwidth and data storage requirements, and data centers may be replaced with device solutions. As a result, the cost of installing IoT devices and applications is significantly reduced. Also, not all of the information is transferred to the cloud. Only the most relevant data will be transmitted to the cloud, saving network bandwidth. This can lower overall infrastructure expenditures.
Disadvantages of Edge Computing
- Incomplete Data: Only partial sets of data may be processed and analysed using edge computing. The rest of the information is just discarded. Companies may lose a lot of important information as a result of this. As a result, companies must decide what sort of data they are prepared to lose before using edge computing.
- Investment Cost: Building an edge infrastructure may be time-consuming and costly. This is because of their complexity, which requires the use of more resources and equipment. Also, the IoT device with edge computing requires the use of more local hardware in order to function. Overall, this might lead to greater efficiency, but it will need considerable investment.
- More Storage Space: Edge computing requires a substantial increase in storage capacity on your device. This will not be a concern because storage devices are growing increasingly compact. It is, though, something to keep in mind while creating an IoT device.
- Maintenance: Edge computing, unlike a centralized cloud architecture, is a distributed system. This means that there are more network configurations with many compute nodes to choose from. This requires a greater level of maintenance than a centralized system.