## 1 Introduction
### Related Work  
A detailed survey is presented on the evolving multimedia internet-of-things (M-IoT) in [10]. The authors promote several innovative applications, aiming to improve the quality of life by connecting numerous smart devices through emerging enabling technologies. The main focus of the authors is to highlight the overview of M-IoT and the importance of M-IoT applications. Major problems while designing M-IoT networks architecture, protocols, and computing schemes are explored to provide stable IoT architecture. Similarly, a comprehensive survey is presented on the secure deployment of MESs for MEC networks in [11]. The concepts and applications of total offloading and partial offloading techniques are presented in [12; 13; 14; 15], respectively. The Markov decision process (MDP) is used to solve the offloading time optimization problem in [16]. The authors present computational offloading in MEC as an optimization problem and investigate the optimal offloading MES selection strategy. The authors also consider in the MDP framework, user mobility and heterogeneity of MESs jointly. The value iteration algorithm is used to solve the MDP and obtain the optimal offloading time. The problems of reliability-aware optimal computing offloading and resources allocation are considered in [17]. The authors consider a multi-user, multi-server scenario with limited resources of UEs. They present the computational offloading and allocation problem as a combinatorial optimization problem while considering offloading valuable basic (OVB) constraints. The paper also proposes a task merging strategy to reduce the complexity of the algorithm. The authors in [18; 19] consider the traditional optimization technique and game theoretic approach, respectively, for partial offloading in MEC to find the optimal offloading policy. However, the main issue of these techniques is the high complexity of algorithms, which makes their deployment impractical in MEC environment. The wireless energy transport concept is investigated in [20; 21; 22] to minimize the energy consumption of UEs through proper energy harvesting techniques. In their work, in a single time slot the protocol of first-harvest-then-offload is utilized. Based on the computation rate, a maximization problem is studied in [23] for the decrease in propagation loss that severely affects the harvested energy and computation performance of UE. The enhancement in the computational performance of active UEs by the user cooperation technique is investigated in [24], where the inactive UEs use their harvested energy for the help of active ones. For the computational offloading policy, the authors in [24] consider frequency division multiple access (FDMA) to improve the computation rate. A summery of different techniques in related work is given in Table 1.  
The maximum-minimum energy efficiency optimization problem (MMEP) with the joint optimization of energy consumption, time slots for computational offloading and energy transfer, and transmit power at a HAP in WP-MEC system are focused on in [25], and by the application of block coordinate descent (BCD) and fractional programming theory, the authors present algorithms with lower complexity. In [26], the computational energy efficiency of an entire system is improved by the joint consideration of the optimal allocation for UE's transmit power, CPU frequency, and time for transmission in a WP-MEC system. In [27], the maximization problem of system energy efficiency by the joint consideration of optimal time allocation, local computing capacity, energy consumption, and application offloading is discussed. In [28], the authors consider a stochastic method for battery management and resource allocation decisions in a time slot and propose an algorithm derived from the Lyapunov optimization technique. In [29], the authors utilize Lyapunov optimization for the evaluation of tradeoff between the delay and energy efficiency of a multi-user WP-MEC system.