In order to improve resource utilization and network capacity, we propose the Device-to-Device (D2D) enabled Mobile Edge Computing (MEC) system, where multiple Smart Devices (SDs) transmit the offloading data to the MEC server with the help of wireless access point (WAP) selected from multiple WAPs. The SD uses the chosen WAP as the communication relay between the MEC server and itself. Aimed to minimize the total energy consumption of the system and satisfy the SDs‘ demand on delay, we jointly optimize relay selection and resource allocation in D2D-enabled MEC system. The problem is formulated as an integer-mixed non-convex optimization problem which is a NP-hard problem. We thus propose a two-phase optimization algorithm that jointly optimizes relay selection policy and resource allocation strategy. In first phase, the original problem is converted into a convex optimization problem by using convex optimization techniques, and the optimal relay selection policy can be achieved by solving the relay selection problem. After obtaining the relay selection policy, the original problem is transformed into a resource allocation problem solved by leveraging the Lagrange Method in the second phase. Furthermore, the proposed algorithm is a low-complexity algorithm which is associated with the root finding method. The optimal relay selection policy and resource allocation strategy can be found in polynomial time. The extensive simulation results are provided to indicate that the D2D-enabled MEC system achieves remarkable results in energy saving. Compared with other baseline methods, our proposed algorithm can not only achieve the optimal solution with less time cost, but also improve the energy efficiency and network capacity.

The advantages of mobile edge computing (MEC) in lower energy consumption, improved bandwidth and reduced delay have attracted extensive studies. We consider a multiple smart wearable devices (SWDs) single smart mobile device (SMD) MEC system where the computation-intensive tasks can be offloaded to SMD and MEC server to improve resources utilization in this paper. To further minimize the energy consumption and satisfy the delay constraints of SWDs, SMD cooperation is introduced and we jointly optimize two-tier computation and communication resources in this system to achieve our objective. The formulated problem is a non-convex optimization problem where we design an efficient iterative algorithm by integrating the block coordinate descent and convex optimization techniques together. The proposed algorithm ensures convergence to a suboptimal solution based on the properties of convex optimization problem. In particular, the original non-convex optimization problem is decomposed into two convex optimization subproblems. Furthermore, we simplify the first subproblem using Lagrange method which is transformed into solving a set of non-linear equations. The extensive simulation results indicate that the proposed algorithm by jointly optimizing two-tier computation and communication resources can reduce energy consumption compared with other baseline methods. The solution of proposed algorithm is close to the optimal solution and can be generated in a shorter time.

With the rapid popularity of mobile devices (MDs), mobile edge computing (MEC) networks and wireless power transmission (WPT) will receive more attention. Naturally, by integrating these two technologies, the inherent energy consumption during task execution can be effectively reduced, and the collected energy can be provided to charge the MD. In this article, our research focuses on extending the battery time of MDs by maximizing the harvested energy and minimizing the consumed energy in the MEC–WPT system, which is formulated as a residual energy maximization problem and also a non-convex optimization problem. On the basis of study on maximizing the residual energy under multi-users and multi-time blocks, we propose an effective jointly optimization method (i.e., jointly optimize the energy harvesting time, task-offloading time, task-offloading size and the MDs’ CPU frequency), which combines the convex optimization method and the augmented Lagrangian to solve the residual energy maximum problem. We leverage Time Division Multiple Access (TMDA) mode to coordinate computation offloading. Simulation results show that our scheme has better performance than the benchmark schemes on maximizing residual energy. In particular, our proposed scheme is outstanding in the failure rate of multiple MDs and can adapt to the task size to minimize the failure rate.

The combination of mobile edge computing (MEC) and wireless power transfer (WPT) is recognized as a promising technology to solve the problem of limited battery capacities and insufficient computation capabilities of mobile devices. This technology can transfer energy to users by radio frequency (RF) in wireless powered mobile edge computing. The user converts the harvested energy, stores it in the battery, and utilizes the harvested energy to execute corresponding local computing and offloading tasks. This paper adopts the Frequency Division Multiple Access (FDMA) technique to achieve task offloading from multiple mobile devices to the MEC server simultaneously. Our objective is to study multiuser dynamic joint optimization of computation and wireless resource allocation under multiple time blocks to solve the problem of maximizing residual energy. To this end, we formalize it as a nonconvex problem that jointly optimizes the number of offloaded bits, energy harvesting time, and transmission bandwidth. We adopt convex optimization technology, combine with Karush–Kuhn–Tucker (KKT) conditions, and finally transform the problem into a univariate constrained convex optimization problem. Furthermore, to solve the problem, we propose a combined method of Bisection method and sequential unconstrained minimization based on Reformulation-Linearization Technique (RLT). Numerical results demonstrate that the performance of our joint optimization method outperforms other benchmark schemes for the residual energy maximization problem. Besides, the algorithm can maximize the residual energy, reduce the computation complexity, and improve computation efficiency.

Container technology enables rapid deployment of computing services, while edge computing reduces the latency of task computing and improves performance. However, there are limits to the types, number and performance of containers that can be supported by different edge servers, and a sensible task deployment strategy and rapid response to the policy is a must. Therefore, by jointly optimizing the strategies of task deployment, offloading decisions, edge cache and resource allocation, this paper aims to minimize the overall energy consumption of a mobile edge computing (MEC) system composed of multiple mobile devices (MD) and multiple edge servers integrated with different containers. The problem is formalized as a combinatorial optimization problem containing multiple discrete variables when constraints of container type, transmission power, latency, task offloading and deployment strategies are satisfied. To solve the NP-hard problem and achieve fast response for sub-optimal policy, this paper proposes an energy-efficient edge caching and task deployment policy based on Deep Q-Learning (DQCD). Firstly, the pruning and optimization of the exponential action space consisting of offloading decisions, task deployment and caching policy is completed to accelerate the training of the model. Then, the iterative optimization of the training model is completed using a deep neural network. Finally, the sub-optimal task deployment, offloading and caching policies are obtained based on the training model. Simulation results demonstrate that the proposed algorithm is able to converge the model in very few iterations and results in a great improvement in terms of reducing system energy consumption and policy response delay compared to other algorithms.