# Jointly Optimize the Residual Energy of Multiple Mobile Devices in the MEC-WPT System
## 2 Related Work  
In recent years, with the development of mobile edge computing and mobile cloud computing, computation offloading attracts more attention. The development of computational offloading can effectively improve the energy efficiency and task processing rate of MDs. Reducing the power consumption of MDs and increasing the working hours of the battery is a great concern to users. In previous studies, it was proved that offloading to the cloud can save energy for MDs and extend battery standby time [7], and refs. [8; 9; 10] proposed different computation offloading frameworks. Therefore, there are two basic computing task offloading models in MEC, namely binary offloading and partial offloading [3]. The combination of MEC networks promotes the execution of a large number of computing tasks on low-power devices, arousing the great attention of the resource management of computing offloading in the academic community [11]. Specifically, the multi-user mobile-edge computation offloading (MECO) system is studied in [12; 13], and in [12], the mobile device delay minimization resource allocation of the MECO system under multiple access is mainly considered. In [14], the resource allocation in the asynchronous MECO system with heterogeneous input data arrival time and calculation deadline is studied. Of course, there are also related researches on resource scheduling strategies on edge servers. In [15], a two-level cooperative resource allocation mechanism is proposed, which provides formal guarantees regarding resource allocation and QoS specifications at the lower level, and a way to solve the problem of edge server workload balancing at the upper level. Computation offloading can effectively improve the task processing capabilities of MDs, which largely depends on the quality of communication because data transmission is essential. Combining previous research, we conclude that through the joint optimization of communication and computing resources [16], the obtainable is significantly better than the single optimization method, which prompts us to focus on joint optimization. Leveraging Augmented Reality (AR) property in [17], different user applications share part of the computational tasks and of the input and output data, to reduce communication and computing overhead via the joint optimization of communication resources and computing resources. In [18], a joint optimization of the transmission power, the number of bits per symbol and the CPU cycles assigned to each application was proposed to minimize the power consumption of the mobile terminal. [PERSON] et al. considered the joint optimization of communication, computational offloading, resource allocation and content caching, and transformed the original problem to the convex problem [19]. The distributed convex optimization method and the algorithm based on the Alternating Direction Multiplier Method (ADMM) are used to solve the optimization problem. In [20], the (TDMA) communication protocol is adopted, which jointly optimizes task allocation, time and power allocation, combined with the performance limitations of local and MEC edge servers to minimize the computation delay of local users. This is a mixed-integer nonlinear program (MINLP), which is relaxed to a convex problem and is solved by a sub-optimal solution based on the relaxed convex problem. The authors in [21] proposed a multi-layer data stream processing system, where the cloud server is in the top layer, the mobile edge computing server is in the middle layer and the edge devices are at the bottom layer, and the system offloads tasks and computing resources through multi-layer processing. However, the above methods are only expected to reduce energy consumption to achieve the goal of energy saving. Therefore, some studies propose the idea of combining WPT technology to charge MDs [22]. They apply energy harvesting to computation offloading and local computing to reduce energy consumption while effectively helping MDs to charge.