Task offloading, load balancing, and resource allocation in MEC networks

Enhancing trust and security in IoT computing offloading through game theory and blockchain‐based control strategy

“…From Figure 9, it can be seen that the proposed scheme consistently achieves the highest total offloading gain. For a small number of devices (10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20), the total gain of the proposed scheme and the full offloading scheme is similar. This is because there is sufficient computational resources, and the competition between devices is low, allowing both methods to effectively improve the effectiveness of the decision-making process.…”

“…7,8 Furthermore, within the broader context of edge computing, some scholarly contributions have been made towards addressing the crucial issue of load balancing within the network. Works by Shu et al 9 and Li et al 10 exemplify the endeavors to optimize resource allocation and distribution in order to enhance the overall performance of edge computing systems. Cai et al 11 in their study proposed a multi-objective intelligent algorithm to efficiently perform task scheduling, which improves the efficiency of task offloading in IoT.…”

Review on Offloading of Vehicle Edge Computing

“…Li et al proposed a heuristic algorithm with low complexity to obtain the offloading strategy and ensure load balance among multiple MECs simultaneously. Then the Lagrange dual decomposition method was used to solve the computational resource allocation sub-problem [37]. Ding et al proposed a cache-based NOMA downlink heterogeneous network architecture, formulated a messaging user coordination strategy, and allocated users' power reasonably.…”

“…Optimization objectives [37] Multidimensional optimization of offloading strategy formulation, load balancing and computing resource allocation A low complexity heuristic algorithm is proposed obtain the offloading and ensure the load balance among multiple MECs. Lagrange dual decomposition method is used to solve the computational resource allocation sub-problem Minimize the weighted sum of total delay and energy consumption of all devices in MEC network [38] Base station user matching and power allocation A cache-based NOMA downlink heterogeneous network architecture is proposed, and the messaging user coordination strategy is formulated to reasonably allocates Improve network cache hit rate and minimize energy consumption [39] Calculate privacy disclosure during uninstall A wireless communication and computing model of partial computing offload and resource allocation considering time-varying channel state, bandwidth constraints, random arrival of workload and privacy protection is established, and a scheme of local computing offload and resource allocation based on distributed reinforcement learning is proposed Minimize the calculation and offloading delay and energy consumption, while protecting users' privacy [40] Task calculation offloading problem A new MEC edge computing offload system based on D2D communication is proposed, and the problem of computing offload is solved by game theory…”

Wireless body area networks task offloading method combined with multiple communication and computing resources supported by MEC

“…Optimizing the task offloading decision to balance the MECSs load is also important to work. In reference [20], the authors optimize the load balancing of each MECS while considering the minimum weighted sum of user delay and energy consumption and obtain the optimal solution to the problem through a multi-objective optimization algorithm. To avoid the overload phenomenon of MECSs, the authors of reference [21] proposed a dynamic game load distribution strategy using idle resources, improving the system performance.…”