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Multiaccess edge computing (MEC) enables autonomous vehicles to handle time-critical and data-intensive computational tasks for emerging Internet-of-Vehicles (IoV) applications via computation offloading. However, a massive amount of data generated by colocated vehicles is typically redundant, introducing a critical issue due to limited network bandwidth. Moreover, on the edge server side, these computation-intensive tasks further impose severe pressure on the resource-finite MEC server, resulting in low-performance efficiency of applications. To solve these challenges, we model the data redundancy and collaborative task computing scheme to efficiently reduce the redundant data and utilize the idle resources in nearby MEC servers. First, the data redundancy problem is formulated as a set-covering problem according to the spatiotemporal coverage of captured images. Next, we exploit the submodular optimization technique to design an efficient algorithm to minimize the number of images transferred to the MEC servers without degrading the quality of IoV applications. To facilitate the task execution in the MEC server, we then propose a collaborative task computing scheme, where an MEC server intentionally encourages nearby resource-rich MEC servers to participate in a collaborative computing group. Accordingly, a cost model is formulated as an optimization problem, the objective of which is to prompt the MEC server to judiciously allocate computing tasks to nearby MEC servers with the goal of achieving the minimal cost while the latency of tasks is guaranteed. Experimental results show that the proposed scheme can efficiently mitigate data redundancy, conserve network bandwidth consumption, and achieve the lowest cost for processing tasks.

Mobile Edge Computing (MEC) enhances mobile cloud computing capabilities by fetching services close to the edge of the network, which adds 4C (Computing, Communication, Control and Caching) to the edges. MEC-enabled small cell network is regarded as the key technology in future 5G networks where for rapid task execution, a user offloads their tasks to the nearest small BS (SBS). The research work regarding MEC-enabled small cell network is still in its infancy. Recently, some researchers are trying to integrate MEC with small cell networks (SCNs) while ignoring the unlimited computation resource in a remote cloud and the computational capability of a single SBS-MEC server, which has the limited capacity for handling huge number of user request. To effectively handle latency-sensitive tasks and resources-hungry mobile applications in small-cell networks, two collaborative task offloading schemes of our proposed model is introduced in this paper. Our proposed collaborative model can make decision dynamically where the SBS-MEC server collaborate with mobile devices or remote cloud for executing the computation tasks. The simulation results confirm that collaborative task offloading between mobile with SBS-MEC scheme will reduce the average number of task failure more efficiently than other schemes and the collaborative task offloading between SBS-MEC with cloud scheme will provide lower task execution latency than others in small-cell networks.

In recent years, computation offloading in multi-access edge computing (MEC) has received a lot of attention. For example, the joint optimization of computing resource allocation and offloading decision was studied to reduce the overall latency and energy consumption of MEC offloading. Such problem of optimizing the computing resource allocation and offloading decision is often formulated as a mixed-integer nonlinear programming (MINLP) problem, which is also NP-hard in general. Various solution approaches have been proposed such as heuristics, dynamic programming, branch-and-bound, and machine learning. Motivated by applications to a variety of complex optimization problems, in this paper, we provide an alternative meta-heuristic method using whale optimization algorithm (WOA). We have proposed a joint computing resource allocation and offloading decision system using the WOA to minimize the total computing overhead of mobile users (MUs), including completion time and energy consumption. Through numerical simulations, our proposed algorithm is more efficient than several baseline schemes for reducing the total computing overhead of MUs.

In recent years, multi-access edge computing (MEC) has become a promising technology used in 5G networks based on its ability to offload computational tasks from mobile devices (MDs) to edge servers in order to address MD-specific limitations. Despite considerable research on computation offloading in 5G networks, this activity in multi-tier multi-MEC server systems continues to attract attention. Here, we investigated a two-tier computation-offloading strategy for multi-user multi-MEC servers in heterogeneous networks. For this scenario, we formulated a joint resource-allocation and computation-offloading decision strategy to minimize the total computing overhead of MDs, including completion time and energy consumption. The optimization problem was formulated as a mixed-integer nonlinear program problem of NP-hard complexity. Under complex optimization and various application constraints, we divided the original problem into two subproblems: decisions of resource allocation and computation offloading. We developed an efficient, low-complexity algorithm using particle swarm optimization capable of high-quality solutions and guaranteed convergence, with a high-level heuristic (i.e., meta-heuristic) that performed well at solving a challenging optimization problem. Simulation results indicated that the proposed algorithm significantly reduced the total computing overhead of MDs relative to several baseline methods while guaranteeing to converge to stable solutions.

Recently, Multi-Access Edge Computing (MEC), which has been emerged as a key technology in 5G networks, enhances computation capabilities and power limitations of mobile devices (MDs) by offloading computation task to the nearby MEC servers. However, offloading the computation tasks can increase network traffics and incur extra delays. Most existing approaches focus on the computation offloading with multi-user single-MEC scenarios to decrease energy consumption and latency of the MDs. Towards this goal, we investigate a computation offloading strategy for two-tier 5G heterogeneous networks integrated with multi-MEC. In addition, we propose a random offloading search algorithm, called ROSA, that rapidly achieve the minimized energy consumption of the system considering the computation offloading decision strategies. Simulation results show that our proposed algorithm based on offloading scheme outperforms other two schemes in terms of energy consumption.