for applications with sensitive time functions as well as activities that strengthen counting [10]. Critical time operations are managed on the margins, and computer consolidation tasks are performed in the cloud. The three-dimensional structure focuses primarily on the Interface between the cloud and end, as well as the assigned function. Edge computing is part of the cloud-based IoT system. Edge computing improves the performance of the IoT system. To understand the need for edge computing, we need first to understand cloud-based IoT functioning.
Then computing has been backed by the centralized network with cloud computing, which allows the users to consume large numbers at any time in different places based on the pay concept. In the concept of cloud computing, there is a frequency of communication between the centralized server and user models such as smartphones, smartwatches, etc. [5].
The physical distance between the end device and the cloud server is typically vast. It leads to an increase in response time because of the considerable distance. In cloud computing, there are many challenges to provide uninterrupted service by having a good communication link to the end user, especially when the distance between the cloud server and end device is large and the device is on mobility. For example, a person with a mobile who moves from one place to a different place requires a large number of cloud servers with short response time and depends on the stability of cloud nodes. It triggers a lot of research – based on beyond the clouds towards the edge of the network, as shown in Figure 2.1 [8]. This Edge Computing system overcomes the challenges noted above of a cloud-based IoT system. As shown in Figure 2.1, edge network nodes are formulated in such a way they will be close to the end device and doing part or full computation of cloud server by offloading the load from it. Since the computation is done locally, the system’s performance can be improved with significantly less response time.
An extensive literature survey on edge computing with different applications has been done to improve performance in a cloud-based IoT system.
2.1.3 Edge Computing Motivation, Challenges and Opportunities
As discussed in the preceding section, the aim of having edge computation is to decrease the data strain from the cloud towards the edge of the network. Hence there is the possibility of deploying and running a number of applications at the edge of a network. The implementation of edge computing has a lot of potential and advancement in many industries.
i) Motivation for Edge Computing:
The motivation for edge computing is drawn from its speed of communication. Facial recognition is one of the applications from where it can be inferred that edge computing excels in applications that require short response time [1]. As computing is done closer to the source, visual guiding applications like Google maps create better user experiences. The cloud system becomes less loaded due to offloading offered by edge computing, thus becoming energy efficient. Communication between the initial layer, i.e., smart homes and cloud drain the battery, therefore, an alternative as the edge can overcome the problem [12]. Network traffic and data explosion is prosperity that tempts to adopt edge computing. There are yet many edge computing powers that are clearly mentioned in Figure 2.2. To achieve cloud decentralization and low latency in computing. Resource allocation of front-end devices can be overcome by sustainable energy consumption, which drives edge computing motivation. Edge computing also enhances and supports smart computing techniques.
ii) Edge computing challenges:
In order to implement edge computing, it is vital to take challenges into account.
Privacy and Security - As edge computing works with various nodes, traveling to and from different networks requires special encryption mechanisms for security (Figure 2.2). Resource containment being one of the properties of edge computing, security methods are required [1].
Optimization metrics - Edge computing is a distributed paradigm. The workload has to be deployed in various data centres effectively depending on bandwidth, latency, energy, and cost to reduce the response delay [12]. Experiencing uncompromising QoS is another challenge of edge computing (Figure 2.2).
Figure 2.2 Edge computing motivation, challenges and opportunities.
Programmability - Unlike cloud programming, the edge being the heterogeneous platform, the runtime of edge devices differs. The computing stream that determines the computing flow, efficiency must be addressed at the synchronization of the devices [12]. Discovering the edge nodes and general-purpose computing is also another challenge in edge node mentioned in Figure 2.2.
Naming - As the edge network work with various networking devices, the naming scheme has to be followed to avoid confusion with identifying and programming the different edge networks. Naming is also significant for providing security and protection, and mobility of the devices [12].
Data Abstraction - Edge network collects data from various data generators; a lot of unessential data and noise also get collected. The data is sent to the next layer (abstraction layer), where data abstraction takes place. Unwanted data gets filtered at this layer and is then sent to the upper layer for further process. It is important to effectively filter data as an application may not work if data is filtered when it is a mistake for noise [12]. Partitioning a task and applying offloading mechanism is another challenge when considering edge computing (Figure 2.2).
With many challenges described in Figure 2.2, virtual and physical security also has to be taken into account before considering edge computing.
iii) Edge opportunities:
In light of motivation and challenges, edge computing finds itself promising for both consumers and businesses by creating a seamless experience. Many opportunities are listed in Figure 2.2, where standards, benchmarking, and marketplace are edge opportunities. The possibility of creating a lightweight model framework and algorithms in the edge is another opportunity. Micro-operating system with virtualization is another opportunity of edge.
Field and Industrial IoT - Power, Transportation and manufacturing are the leading contenders of edge. Industrial devices that include HVAC systems, motors, oil turbines, RFID’s in the supply chain, etc., that use the information to analyze for security management, predictive maintenance, performance and usage tracking, demand forecasting, etc., can witness advancement using edge computing [13].
Smart Cities Architecture - Municipalities providing faster urban services, traffic management, green energy and public safety, intelligent bus stop are few smart cities applications that may benefit from edge computing [11, 13].
Retail and Hospitality - Customer care can be refined using edge and by analyzing customer sentiments using kiosks or point of scale terminal in retail and hospitality using edge computing. Customer experience enhances [13].
Connected
