*2.2. Edge-Related Aspects*

As a new generation of computational offloading, Edge arrived to allocate the resources at the network edge, i.e., closer to various office and home appliances such as mobile devices, Internet of Things (IoT) devices, clients, and client's sensors. In recent years, there has been fast growth in industrial and research investment in Edge computing. The pivot for Edge computing is the physical availability and closeness, of which end-toend latency is influenced by this essential point of Cloudlets, with bandwidth achievable economically, trust creation, and ability to survive [18].

Communication overheads between a customer and a server site are reduced due to a decrease in actual transmission distances (in terms of geography and number of hops) brought about by the Edge computing in the network. As one of the definitions, "Edge computing is a networking philosophy focused on bringing computing as close to the source of data as possible to reduce latency and bandwidth use. In simpler terms, Edge computing means running fewer processes in the Cloud and moving those processes to local places, such as on a user's computer, an IoT device, or an Edge server" [19]. Some other

definitions of Edge computing are "a physical compute infrastructure positioned on the spectrum between the device and the hyper-scale Cloud, supporting various applications. Edge computing brings processing capabilities closer to the end-user/device/source of data which eliminates the journey to the Cloud data center and reduces latency" [20]. There are several cases in which architectural designs are specifically intended, considering their work plan and setting up the infrastructure is based on its need.

Considered a state-of-the-art paradigm, Edge computing takes services and applications from the Cloud known to be centralized to the nearest sites to the main source and offers computational power to process data. It also provides added links for connecting the Cloud and the end-user devices. One of the best ways to solve or reduce Cloud computing issues is to make sure there is an increase in Edge nodes in a particular location, which will also help in decreasing the number of devices attributed to a sole Cloud [21].

Overall, the main Edge service consumers are resource-constrained devices, e.g., wearables, tracker bands for fitness and medical uses, or smartphones [22]. Fog devices, in turn, subdues the shortcomings of Cloud by transferring some of the core functions of Cloud towards the network Edge while keeping the Cloud-like operation possible [23], e.g., Edge and Fog nodes may act as interfaces attaching these devices to the Cloud [24].

A typical Edge computing architecture comprises three important nodes (see Figure 1): the Cloud, local Edge, and the Edge Device. Notably, Local Edge involves a well-defined structure with several sublayers of different Edge servers with a bottom-up power flow in computation. Both Access Points (APs) and Base Stations (BSs) are Edge servers situated at the sublayer considered to be the lowest together with proximity-based communications [25]. These are particularly installed to obtain data during communication from various Edge devices, returning a control flow using several wireless interfaces.

Cellular BSs transmit the data to the Edge servers found in the (upper) sublayer after receiving data from Edge devices. Here, the upper sublayer is particularly concerned with operating computation work. Very fundamental analysis and computation are done after data are forwarded from BSs. At a recent Edge server, the computational restriction is placed such that if the difficulty in a given work surpasses it, the work is offloaded and sent to the upper sublayers with adequate computation abilities. A chain of flow control is then concluded by these servers with passing back to the access points, and finally, in the end, send them to Edge devices [26].

The Edge architecture allowed to switch more delay intolerant applications closer to the computation demanders, e.g., Augmented/Virtual/Mixed Reality (AR/VR/MR) gaming, cellular offloading, etc., all together following the proximity-driven nature of the paradigm [27]. Generally, there are two approaches to the proximity between the Edge and user's equipment: physical and logical proximity.

Physical proximity refers to the exact distance between the top segmen<sup>t</sup> of data computation and user equipment. Logical proximity refers to the count of hops between the Edge computing segmen<sup>t</sup> and the users' equipment. There are potential occurrences of congestion because of the lengthy route caused by multiple hops, leading to increased latency issues. To avoid queuing that can result in delays, logical proximity needs to limit such events at the back-haul of the computing network systems.

Despite the shortcomings of the normal Cloud paradigm innovations to match up with grea<sup>t</sup> demands, given lower energy level, real-time, and in particular security and privacy aspects, the Edge paradigm is not considered a substitute for the Cloud paradigm. Edge and Cloud paradigms are known to assist each other in a cordial manner in several situations. The Cloud and Edge paradigms cooperate in some network areas, including autonomous cars, industrial Internet, as well as smart cities, offices and homes. Importantly, Edge and Cloud paradigm collaboration offers many chances for reduced latency in robust software such as autonomous cars, network assets of companies, and information analysis on the IoT [28].

Nevertheless, Edge operation is executed through supported capabilities from several actors. Cellular LTE, short-range Bluetooth Low Energy (BLE), Zigbee, and Wi-Fi are various technologies that create connectivity by linking endpoint equipment and nodes of the Edge computing layer. There is grea<sup>t</sup> importance for access modalities as it establishes the endpoint equipment bandwidth availability, the connection scope, and the various device type assistance rendered [29].
