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. |
1.3.2.3 IoT in Fog Computing (FC)
FC reduces the amount of information to be carried into the cloud and improves efficiency by extending the cloud nearer to the data generating device known as the fog nodes. These fog nodes are in the form of controllers, switches, servers, routers, cameras, etc. which provide the desired storage, network connection, and computing features is known as fog node. In general, CC is incapable of handling large data bandwidth as it is subjected to volume and latency that’s leads to the advent of FC. It is found useful in compliance and data security and is a non-trivial extension of CC. It bridges several sensors appearing at the network edge to the core computing structure of the cloud [1, 23]. The system requires the processing to be carried out either on the network edge of a gateway device or router or in the data hub of the mobile device.
Although, the FC is similar to EC appear similar the key difference lies in the location or the placement of computing power and intelligence. The FC places the intelligence at the local area network to transfer the data from end-users to a gateway for further transmission. On the contrary, EC intends to place the processing power and intelligence in devices such as embedded automation controllers. The concept of cloud, Edge, and FC is shown in Figure 1.7.
Similarly, EC appears directly on the devices where the sensors or controllers are attached or it may appear at the gateway device physically nearer to the sensors. On the other hand, FC carries the edge activities to several processors connected to the LAN physically away from the sensors, controllers, and actuators.
Advantages of FC
Since in EC the storage and processing of data to take place in LAN, the technique allows the organizations for the aggregation of information from multi-devices to regional stores.
The data processing capability of Fog architecture is larger than that of Edge as the former allows organizations to gather data from different various devices.
It improves and helps the real-time processing capabilities of the Edge architecture.
It is most suitable for structures that have to handle millions of devices sharing information between them.