Today IoT edge computing is defining the future of IoT (Internet of things). It is imparting stability to the IoT devices and addressing latency issues by providing data processing closer to the source. Leveraging the power of cloud, IoT has grown exponentially and has been installing several billion smart devices in the IoT network every year.
Such a vast network is now demanding for high capacity data processing techniques. Data scientists and data analysts are facing major challenges in data analysis, especially where data processing needs to be done in real time. This problem is now being handled by IoT Edge Computing Technology.
What is IoT Edge Computing
International Data Corporation (IDC) has defined IoT Edge Computing as a network of small data centers where critical data is stored and processed locally. These data centers are connected in the form of a mesh and they push the data received to a centrally located storage repository. This is typically within a span of 100 sq ft or less.
Simply put, IoT Edge Computing is used for data analysis and processing closer to the data source. Smart devices used in IoT Edge Computing are capable of processing critical data fragments and provide a quick real time response. These devices prevent the delay caused by sending the data through internet to cloud and linger for cloud response.
These devices are designed to act as tiny data centers that provide nearly zero latency. With this enhanced capability, data processing is decentralized and network traffic is greatly reduced. This data can later be collected by the cloud for further evaluation and processing.
Fig. 1 – IoT Edge Computing at a Glance
Types of IoT Edge Computing
In general, we can define three kinds of IoT Edge Computing:
- Local devices for accommodating a specific and well-defined purpose. These can be easily deployed and maintained.
- Local data centers for providing significant processing and storage capacities. These are generally pre- engineered and customized. They are assembled onsite and provide for good capital expenditure savings.
- Regionally located data centers that have a distinct advantage of being closer to the source of data. While they have greater processing and storage power than local data centers, they are expensive and require more maintenance. Such edge devices are designed either with prefabricated or made-to-order variants.
Architecture of IoT Edge Computing
Due to the advantages of power, cost and space, conventional analytical clusters do not support edge computing. The power, cooling, space and such other functional costs make these clusters expensive. They also do not offer the simplicity or speed that is necessary for the feasibility of edge computing.
Businesses have now gone beyond the x86 clustered architectures that have hindered real time analytical innovation. They are seeking accelerated systems that provide the size, performance and required speed.
These new systems are using hybrid technologies that integrate various computing technologies like x86, FPGA or GPU. They are compact, require little power and yet provide high performance that surpass today’s conventional systems.
In scenarios where there is a scarcity of resources, these versatile systems complement incumbent infrastructure and improve the performance of the existing clusters.
Fig. 2 – IOT Edge Computing Architecture
Advantages of IoT Edge Computing
As IoT Edge Computing is being adopted and taken mainstream, large number of industries are reaping its potential advantages. Edge computing, particularly, brings in seven major advantages in smart manufacturing.
1. Quicker Response Time
Power of computation and data storage is local and distributed. Avoiding a cloud round trip is key for reducing latency and facilitates faster responses. This will assist in preventing the breaking down of vital machine operations or the occurrence of hazardous incidents.
2. Consistent Operations with Sporadic Connectivity
For many remote assets, supervising unpredictable internet connectivity areas like farm pumps, oil wells, windmills or solar farms can be challenging. Edge devices’ capability for local storage and data processing ensures that no data is lost and prevents operational failures in case of limited internet connection.
IoT Edge Computing has made the transfer of data between the cloud and devices redundant. It is now possible to filter sensitive data locally and transmit only the data model over to the cloud.
This allows the user to build a satisfactory security framework that is necessary for enterprise audits and security.
Fig. 4 – Advantages of IOT Edge Computing
4. Cost Effective Solution
A major practical concern while adopting IoT was the cost incurred due to data storage, computational power and network bandwidth.
IoT Edge computing enables local data computations that allow business establishments to distinguish between services that have to be run locally and those that must be sent to cloud. This helps in reducing the closing costs of establishing a complete IoT solution.
5. Interoperability between Modern and Legacy Devices
Edge devices function as a communication link between contemporary and legacy machines. This provides the legacy machines to interact with modern devices for IoT solutions.
6. Increased Resiliency
Decentralized architecture of edge computing enables the other network devices to become resilient to a greater extent. This is a great virtue since a single machine failing on the cloud would mean thousands of IoT devices getting affected.
Failure of one device on edge will not affect other network devices.
7. Decreased Data Exposure
As we know, edge computing minimizes data transfer through the network. This in turn helps reduce data exposure during transit. In few cases, sensitive information like Personally Identifiable Information (PII) and Payment Card Industry (PCI) may not be sent at all.
Such instances will help in avoiding several legal, security and privacy complications. Further, data encryption and access control can make it highly secure against familiar threats.
IoT Edge Computing Applications
IoT Edge Computing is being used in various applications. We will discuss two of it most popular use cases here.
Data Analysis and Monitoring using IoT Edge Computing Sensors
IoT sensors are gathering large data that is set to grow exponentially every year. Using IoT Edge Computing allows businesses to simplify and speed up the analytics and get the right insights at the right time.
Fig. 3 – Data Analysis & Monitoring using IoT Edge Computing Technology
Thinning Mobile Data using IoT Edge Computing
Like IoT data, mobile data is also being created rapidly. However, the drawback of such massive data is that not all but only a selective part of it is required for queries related to data analysis.
For instance, how a business establishment can decide on the most profitable Ad? How can it eliminate the unwanted noise? IoT Edge Computing enables better understanding of data and help process only information that is essential to the query.