## 2 Literature Review and Motivation of This Work  
### Literature Review  
Recently, various studies have been published in the MEC domain, and this section provides a comprehensive review of these publications. The mobility management that has been widely used in various applications such as IoT generates realistic mobility patterns [11; 12]. In [13], a mobility-aware hybrid flow rule cache scheme is presented for tackling the problem of forwarding node. From another point of view, edge computing can provide the solutions for the cloud limitations in the current communication systems [14]. Latency factor is an important specification in a vehicular network that results in delays, generated from the high mobility of vehicles [15; 16]. Data traffic is expanding with the increase in massive connectivity. The favourable characteristics of MEC technology are suitable for emerging 5G networks as they include ultra-low latency response and real-time applications [17; 18]. It is expected that mobile broadband will increase to 8 billion by 2025. In 2017, the European Telecommunications Standards Institute (ETSI) changed the name'mobile edge computing' to'multi-access edge computing' for addressing non-cellular operators. MEC servers can now be deployed with a radio access network (RAN), BSs, Wi-Fi access points, and fixed connections [19]. The 3 GPP includes MEC technology in the 5G network with the technical specification of 3 GPP TS 23.051. Figure 1 presents a comprehensive summary of MEC servers with RAN, BSs (e.g., 4G, 5G, and 6G), Wi-Fi access points, and fixed connections. The MEC technology can be employed in various applications such as video analytics, location services, and data caching, which allows for flexible and rapid innovative applications. It can also be used in an autonomous vehicular network, sharing information with roadside units (RSU) and pedestrians without involving any cloud servers [20; 21; 22]. The development of MEC at the network edge is critical. The devices and sensors of the IoT require significant storage, computation resources, and suitable bandwidth, as the ever-increasing data have caused limited computational resources [23]. The MEC technology has two main challenges [4; 9]. The first challenge is appropriately allocating MEC resources, as multiple users are within the coverage region of the MEC server. Once the computational resource is allocated to a user, it will then decide whether to offload the task or execute it locally. Multiple BSs are available for a single user. Choosing the optimum BS for offloading is the current research topic in various MEC-related studies [24; 25; 26; 27; 28]. The most common solution is to develop a utility cost function that determines the optimum solution based on the optimisation methods. Because resource allocation, communication, computation, and user mobility must all be considered, joint optimisation problems and analysis should be accomplished. Most joint problems are non-convex and NP-hard [29; 30; 31]. The second main challenge in MEC-enabled 5G networks is that MEC servers are distributed within ultra-dense networks [3; 32]. Managing user mobility within the small-scale coverage of the MEC server is a significant issue [33; 34; 35; 36]. In the recently published literature, MEC often neglects user mobility and assumes they are constant due to complexity [37; 38; 39]. Tables 1 and 2 present the summary of mobility management and the related studies in this field in a comprehensive view, respectively. Table 2 demonstrates that our proposed method, in comparison with other reported methods including machine learning approach, is advanced as energy consumption of UEs, handover delay, and task offloading are concurrently considered. In simple words, what is lacking in the previous studies is concurrently considering these parameters. Our method leads the way of designers in mobility-aware offloading decisions. Figure 1: From cloud to network edges. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Ref.