## 5 Using MEC to Augment the Computing Capabilities of Vehicles
### Task Offloading
Tasks with high computation requirements must migrate to the MEC. The work in [102] formulates a cost minimization problem and provides a network model for Vehicle Edge Computing (VEC) relying on Non-orthogonal Multiple Access (NOMA) [103]. NOMA enables multiple Vehicular User Equipment (VUE) to access the same wireless resources, improving spectrum usage and system volume. The entire system cost is reduced by jointly optimizing offloading decision making, VUE platooning, sub-channel and processing resource assignment, and transmission power regulation under the assumption that all VUEs will be able to tolerate delays [102]. In ref. [104], authors examine a situation where devices can either send their tasks to a BS server or share them with stationary cars. The service time is estimated by considering car position and speed and the interval for which they are under the coverage of a BS. They consider parameters such as data volume, computation needs, and task priority for each task. The idea is to work together, using the BS server, cars, and the wireless channels of the BS, to minimize the time tasks take to complete. They formulate a mixed-integer non-linear problem to find the best candidate cars. Figure 7 shows the architecture of this work. Figure 7: The network topology of MEC-assisted moving vehicles [104]. In ref. [105], authors propose a solution named Mobility-aware Multihop Task Offloading (MMTO) to better utilize resources and enhance the QoE in VNs, which leverages idle cars' resources. The innovative trait of the offloading policy is that it considers multihop communication among cars in order to reduce task completion times. One-hop relaying is enough if a neighbor can complete the task and return the result before leaving the client vehicle's transmission range. However, multihop neighbors can provide service too, taking part in task processing. A MILP problem is proposed to realize this task-offloading strategy. A semidefinite relaxation method and an adaptive control procedure are proposed to solve it while taking into account the connection restrictions on both one-hop and multihop service provider cars. The architecture of this work is depicted in Figure 8.  
In ref. [106] researchers presented a model for task offloading that utilizes game theory for offloading decisions. They employed offloading methods without considering vehicles' information about network bandwidth and status. A hybrid DQN approach for task offloading strategy in a multi-MEC assisted architecture illustrated in [107]. With this method, they ensure that each user makes offloading decision with the help of collected data and experiences from network by themselves. Ref. [108] demonstrates the using of idle resources of vehicles for computing tasks. They use a pricing method in order to minimize the total cost of the system. Works [109; 110] propose a three-level architecture which consists of a vehicle level, an edge level, and a cloud level for VEC. This architecture allows for the local execution of some computation-intensive tasks by vehicles, whereas some others can be moved to a MEC or a cloud host. Researchers, in [109], proposed a method to reduce the average delay by incorporating edge-cloud processing. They tackled challenges related to the dynamic computing environment and the necessity for swift decisions by employing an effective task-offloading technique relying on DRL [111] for VEC. They use a Deep Q-network (DQN) with minimal computational burden and best convergence in the task-offloading scheme to adaptively discover the best offloading tactics between edge and cloud servers. They utilize the inspection phase to make the DQN training converge faster. In ref. [110], authors use the standard three-level VEC architecture. Cars communicate wirelessly with RSUs, which are connected to edge servers. RSUs are linked via optical cables to a cloud data center. Tasks can be offloaded from cars to the edge or cloud server using V2I wireless communication over RSUs. A cellular link allows task transfer to the cloud server.