## 2 Related Work
The high computing cost of this technology limits it to single-task applications. In [8], a dynamic mobility-aware offloading algorithm (DMPO) is proposed. After the mobile user moves, the offloading path is minimized in DMPO, and a short-term prediction of the user mobility pattern is obtained. Since DMPO considers one-dimensional user mobility, it performs better when the base stations have even distribution. For bigger mobile edge networks, the authors came up with an energy-aware mobility management model that could help them run better [23]. It performs the task offloading by considering the user task size, location, and the number of base stations in the network. The heuristic mobility aware offloading algorithm, a heuristics-based technique that enables vehicular mobile devices to perform task offloading, is proposed in [24]. The system predicts user position by anticipating edge server capacity and radio resource health. While it reduces latency and energy consumption, it only has a short-term influence on mobility. Due to this limitation, the best services cannot be guaranteed for offloaded or cached jobs. Furthermore, a technique that considers all real-time mobility characteristics such as location, velocity, and user orientation is difficult to come across. In this direction, some efforts have been performed to examine the machine learning methods. However, few notable works exist in mobility-aware task offloading using ML techniques in the MEC domain. The user moving direction is critically important to be considered while predicting the next location and finding an optimized edge server. Mobility-aware deep reinforcement learning [25] is a model in MEC to provide optimal service provisioning. It uses a glimpse mobility model to figure out what the next place a person is likely to visit ahead. The mobility-aware edge service placement methodology is similar to that used in [24]. This model achieves numerous goals and is supported by machine learning techniques that use user mobility trajectory data as input to estimate the likely future position of users. For predicting the amount of power needed for offloading jobs, an energy-cum-mobility aware offloading framework is presented in which [PERSON]'s algorithm and machine learning are combined to anticipate the user's next location and power consumption requirements to offload or migrate a task. [PERSON] et al. introduced a distributed technique based on deep learning that leverages the power of numerous concurrent neural networks to find optimal solutions without the need for manual data labelling [26]. [PERSON] et al. defined a task offloading issue with the goal of minimizing long-term resource consumption and suggested a distributed reinforcement learning algorithm in which each device can make task decisions independently [27]. [PERSON] et al. suggested a method for performing knowledge transfer and policy model compression in a single training session on edge devices while taking into account their resource constraints [28]. The time required to train an edge policy using this method is much less than the time required to train an edge policy from scratch. [PERSON] et al. considered terminal device communication in an ultra-dense LAN, where users can select multiple base stations for task offloading [29]. The performance is enhanced by reducing the energy consumption of tasks in the computational queue and the channel queue between end users and base stations. Similarly, [PERSON] et al. aim to improve resource use, energy consumption,and network delay by forecasting user behavior and solving the problem with a variation of the DQN algorithm [30]. We emphasized the significance of user mobility in MEC and reviewed recent studies on user mobility. Some models consider the geographic location or previously observed mobility patterns of mobile users when using SDN-based solutions to solve mobility problems. Then, some models take into consideration the real-time network throughput and the utilization of edge server.