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CC is an efficient mechanism to process the data that reduces at the network edge. New software domains have been developed that are more energy-efficient than CC are the Fog or EC [20, 21]. In EC the computation of the enabling technologies is carried out at the edge of the network. At downstream data, this function is performed using cloud services whereas, at upstream data, the IoT services are carried out.

In EC, the network resources are managed and controlled between the path of cloud data found useful in the case of a smartphone which is considered to be an edge between the body things and the cloud. Similarly, a gateway is an edge between campus things and cloud in green computing, or the cloudlet and the microdata center act as an edge between the cloud and the mobile device. While FC is oriented more towards the infrastructure side, EC is focused on the things-side. Hence, the latter remains an emerging technology at par with CC in the current scenario. The EC requires minimum use of refrigeration and maintenance as it needs a small data center for functioning. Ultimately, the technique remains energy efficient with a reduction in e-waste. The use of EC has reduced the response time to 169 ms as compared to 900 ms required by CC [22]. The hierarchy of EC is shown in Figure 1.5.

EC aims to save bandwidth and to reduce the response time by bringing the data storage and the computation closer to the desired location. It has recently been found to be applied in dealer locators, real-time data aggregators, shopping carts, and insertion engines. It is a computing technology that can deliver nearer to a request zone with low latency. As compared to CC that suits big data analysis, EC performs better in real-time processing of data generated by the users or sensors such as ‘instant data’. EC moves the computation away from data centers towards the edge of the network, thus helps smart things or objects such as network gateways or mobile phones to accomplish the desired services or tasks on behalf of the cloud. On account of this shift, it becomes reliable to facilitate service delivery, content catching, IoT management, and storage with reduced response time and efficient transmission and reception of data.

The advantages of EC are as follows and are shown in Figure 1.6.

 A reduction in data volume, as the task is performed at the edge.

 It reduces the consequent traffic and traveling distance of data.

 Low latency and transmission costs.

 It provides computing offloading in real-time applications, such as facial recognition with the low response time.Figure 1.5 The hierarchy of EC.Figure 1.6 The benefits of EC.

 Instead of using the resource-rich machines such as cloudlets in the vicinity of the mobile users in the case of CC, it is better to offload a few of the tasks at the edge node to reduce the execution time.

 EC optimizes data-driven and management capabilities by efficient facilitation of data gathering, processing, reporting, etc. very nearer to the end-user as possible.

Privacy and security

The distributed paradigm of EC allows data encryption using different mechanisms, hence provide added security. This is because the data can be transferred among several distributed nodes via the internet before arriving at the cloud. This introduces flexibility in security methods to be adopted, thus Edge nodes may suit resource-constrained devices. An eventual shift from centralized top-down cloud infrastructure to a suitable decentralized Edge model helps better the functioning of the network. Further, the edge data allows the shifting ownership of gathered data from service providers to end-users. On the contrary, CC is more vulnerable since all the data is fed to the cloud analyzer and a single attack can disrupt the entire system. As against it, the EC transfers fewer amounts of data that can be accessible to hackers or interceptors. So it helps industries to tackle local compliance, privacy regulations, and data sovereignty issues.

Scalability

In CC, it is essential to forward the data to a centrally located data center. Thus, the modification or expansion of the dedicated data centers remains expensive. On the other hand, the IoT devices can be deployed at the edge with their data management and processing tools in single implantation without waiting on the coordination efforts of personnel placed at multiple sites.

Reliability

In CC, if a crucial system or component fails, it is difficult to make the service alive. As compared to this, in EC using distributed nodes, if a single node fails or unreachable, it is still workable without interruption. Further, there is every possibility of redundant data in CC that may not be useful or have the same value. Hence spending money on these data is not advisable which leads to EC. It allows the categorization of data for the perspective of management. Focusing only on relevant data reduces the bandwidth requirement, hence cost. It optimizes the data flow to maximize the operating cost. It stores the data temporarily which is sent to the cloud for storage at a later stage that causes redundant overload in clouds.

Speed

EC facilitates the availability of analytical computational resources in the vicinity of the end-users, hence can improve the speed of response. The presence of a small amount of data and its management to remote locations reduces the overall loads in the traffic. The reduction in latency can enhance the speed of communication at the user-end.

Efficiency

In EC the end-user remains at the proximity of the computing which allows the application of sophisticated Artificial Intelligence and analytical tools at the edge of a system. Such schemes improve the overall operational efficiency of the system.

Cost Saving

EC has to deal with less data that is relevant to the end-user. Removal of redundant data reduces the cost of data handling, transportation, storage, and management. Further, the bandwidth cost is also reduced as it needs to deal with large data management.

Versatility

EC remains versatile since industries can target the coveted markets with their local cen-ters with fewer infrastructure investments. In this way, the enterprises may allow expert assistance with latency. The efficiency of EC can further increase with the involvement of advanced IoT gadgets without changing the current IT structure.

The Limitations of EC

Table 1.1 shows the limitations of EC.

Table 1.1 The limitations of EC.

Security	It is often difficult to maintain the desired security in a distributed network such as EC. The security further at risk due to the transfer of data outside the network edge. Further, the infiltration or pilferage of data when a new IoT device is introduced.
Incomplete Data	EC can analyze and process limited information and discard the rest of the data. Thus, there is a possibility of valuable information loss which compels the end-user to decide the type and amount of information before opting for such a scheme.
Investment Cost	The implementation of an EC infrastructure remains complex and costly and complex since it requires additional resources and equipment. It requires more local hardware for functioning.
Maintenance	The EC uses a distributed framework which is decentralized, thus, there are several combinations and variation in network architectures or modes. Hence the maintenance cost increases as compared to that of a centralized network such as CC.