Wireless Powered Mobile Edge Computing System Research Articles

Intelligent Reflecting Surface Assisted Secure Computation of Wireless Powered MEC System

The integration of mobile edge computing (MEC) and wireless power transfer (WPT) can effectively improve the computing ability and energy sustainability of energy-constrained wireless devices in the Internet of Things (IoT) networks. Intelligent reflecting surface (IRS) has recently emerged as an effective technique to improve the performance of wireless systems by intelligently reconfiguring wireless environments. This paper studies the exploitation of IRS to improve the secure computation performance of WPT-MEC systems with a passive eavesdropper. A wireless access point (AP) first charge multiple users with the emitted energy signals, and then the users perform local computing and partial offloading to complete their computation tasks with the harvested energy in the presence of an eavesdropper, where the local computing can be executed during the whole process of WPT and offloading. Meanwhile, deploying IRS can improve the energy capture and secure offloading performance of the users. We maximize the secure computation task bits of users by jointly optimizing the AP energy transmit beamforming, the IRS phase shifts, the transmit power, users' offloading time, and the local computation frequency of users, which are tangled with each other. An iterative optimal algorithm is developed to solve this non-convex problem by combining Taylor expansion method, semidefinite relaxation (SDR) algorithm, the Lagrange duality theory and Karush-Kuhn-Tucker (KKT) conditions. The numerical results show that the proposed scheme can effectively increase the secure computation task bits compared with other benchmark schemes, especially for the maximum transmit power of AP, the improvement is above 45 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> .

Energy Minimization in Reconfigurable Intelligent Surface-Assisted Unmanned Aerial Vehicle-Enabled Wireless Powered Mobile Edge Computing Systems with Rate-Splitting Multiple Access

In this study, a reconfigurable intelligent surface (RIS)-assisted wireless-powered mobile edge computing (WP-MEC) system is proposed, where a single-antenna unmanned aerial vehicle (UAV)-mounted cloudlet provides offloading opportunities to K user equipments (UEs) with a single antenna, and the K UEs can harvest the energy from the broadcast radio-frequency signals of the UAV. In addition, rate-splitting multiple access is used to provide offloading opportunities to multiple UEs for effective power control and high spectral efficiency. The aim of this paper is to minimize the total energy consumption by jointly optimizing the resource allocation in terms of time, power, computing frequency, and task load, along with the UAV trajectory and RIS phase-shift matrix. Since coupling issues between optimization variable designs are caused, however, an alternating optimization-based algorithm is developed. The performance of the proposed algorithm is verified via simulations and compared with the benchmark schemes of partial optimizations of resource allocation, path planning, and RIS phase design. The proposed algorithm exhibits high performance in WP-MEC systems with insufficient resources, e.g., achieving up to 40% energy reduction for a UAV with eight elements of RIS.

Deep Reinforcement Learning-Based Joint Optimization of Delay and Privacy in Multiple-User MEC Systems

Mobile Edge Computing (MEC) enables mobile users to run various delay-sensitive applications via offloading computation tasks to MEC servers. However, the location privacy and the usage pattern privacy are disclosed to the untrusted MEC servers. In this paper, we propose a deep reinforcement learning based joint optimization of delay and privacy preservation during offloading for multiple-user wireless powered MEC systems, preserving users both location privacy and usage pattern privacy. The main idea is that, to protect both the two kinds of privacy, we propose to disguise users offloading decisions and deliberately offloading redundant tasks along with the actual tasks to the MEC servers. On this basis, we further formalize the task offloading as an optimization problem of computation rate and privacy preservation. Then, we design a deep reinforcement learning based offloading algorithm to solve such an non-convex problem, aiming to obtain the better tradeoff between the computation rate and the privacy preservation. Finally, extensive simulation results demonstrate that our algorithm can maintain a high level of computation rate while protecting users usage pattern privacy and location privacy, compared with two learning-based methods and two Baselines.

IRS-Aided Wireless Powered MEC Systems: TDMA or NOMA for Computation Offloading?

Anintelligent reflecting surface (IRS)-aided wireless-powered mobile edge computing (WP-MEC) system is conceived, where each device’s computational task can be divided into two parts for local computing and offloading to mobile edge computing (MEC) servers, respectively. Both time division multiple access (TDMA) and non-orthogonal multiple access (NOMA) schemes are considered for uplink (UL) offloading. To fully unleash the potential benefits of the IRS, employing multiple IRS beamforming (BF) patterns/vectors in the considered operating frame to create time-selectivity channels, i.e., dynamic IRS BF (DIBF), is in principle possible at the cost of additional signaling overhead. To strike a balance between the system performance and associated signalling overhead, we propose three cases of DIBF configurations based on the maximum number of IRS reconfiguration times. The degree-of-freedom provided by the IRS may introduce different impacts on the TDMA and NOMA-based UL offloading schemes. Thus, it is still fundamentally unknown which multiple access scheme is superior for MEC UL offloading by considering the impact of the IRS. To answer this question, we provide a comprehensively theoretical performance comparison for the TDMA and NOMA-based offloading schemes under the three cases of DIBF configurations by characterizing their achievable computation rate. Analytical results demonstrate that offloading adopting TDMA can achieve the same computation rate as that of NOMA, when all the devices share the same IRS BF vector during the UL offloading. By contrast, computation offloading exploiting TDMA outperforms NOMA, when the IRS BF vector can be flexibly adapted for UL offloading. Then, we propose computationally efficient algorithms by invoking alternating optimization for solving their associated computation rate maximization problems. Our numerical results demonstrate the significant performance gains achieved by the proposed designs over various benchmark schemes and also unveil that the optimal time allocated to downlink wireless power transfer can be effectively reduced with the aid of IRSs, which is beneficial for both the system’s spectral efficiency and its energy efficiency.

Time allocation improvement method for UAV-based wireless energy transfer cooperative mobile edge

Wireless power transfer (WPT) and mobile edge computing (MEC) are two advanced technologies that could improve the computing power and range of mobile devices. However, by integrating the unmanned aerial vehicle (UAV) into wireless powered MEC systems, wireless energy transfer will be susceptible to the “double near–far” effect. Therefore, in order to further overcome the influence of the “double near–far” effect, this paper considers the optimization of time slot allocation for UAV-assisted wireless powered cooperative MEC system, which includes an access point (UAV) and two mobile devices. The purpose of the study is to minimize the total transmission energy of the UAV while satisfying the delay and size of the computational tasks, so this paper proposed a 2:1:1 time-slot optimization allocation method. The method exploits the synergy of users so that the mobile device which is closer to the UAV acts as an offloading relay, by combining power and time slot optimization to minimize the total energy consumption of the UAV. Compared with the equal time slot scheme before the improvement, this method can not only utilize the wireless transmission energy to charge the mobile device for more time in the first period, but also can save the time of data transmission of the closer device in the third period, and it can enhance the rate of data transmission of the mobile devices at the same time. The results show that the task capacity of the system computed will be increased compared to the original scheme; the total transmission rate of the whole system is also improved by the same order of magnitude. The simulation results verify the effectiveness and reliability of the algorithm of the paper, and the comprehensive performance of the system can be maximized by the flexible offloading algorithm.

Average Age of Information in Wireless Powered Mobile Edge Computing System

Mobile edge computing (MEC) has been recognized as a promising technique to provide enhanced computation services for low-power wireless devices at the network edge. How to evaluate the timeliness of the task and data delivery is critical for the development of MEC applications. Considering a wireless powered MEC system, in this letter we study the average age of information (AoI), which is a crucial performance metric to measure the freshness of information. Specifically, in the considered system, a sensor node harvests energy from an energy transmitter and transmits computation tasks to the MEC server. The charging time of the sensor node’s capacitor, the waiting time and service time at the MEC server are taken into account when calculating the average AoI. The closed-form expression of the average AoI is obtained accordingly and evaluated through numerical simulations.

Online computation offloading and trajectory scheduling for UAV-enabled wireless powered mobile edge computing

The unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) architecture is expected to be a powerful technique to facilitate 5G and beyond ubiquitous wireless connectivity and diverse vertical applications and services, anytime and anywhere. Wireless power transfer (WPT) is another promising technology to prolong the operation time of low-power wireless devices in the era of Internet of Things (IoT). However, the integration of WPT and UAV-enabled MEC systems is far from being well studied, especially in dynamic environments. In order to tackle this issue, this paper aims to investigate the stochastic computation offloading and trajectory scheduling for the UAV-enabled wireless powered MEC system. A UAV offers both RF wireless power transmission and computation services for IoT devices. Considering the stochastic task arrivals and random channel conditions, a long-term average energy-efficiency (EE) minimization problem is formulated. Due to non-convexity and the time domain coupling of the variables in the formulated problem, a low-complexity online computation offloading and trajectory scheduling algorithm (OCOTSA) is proposed by exploiting Lyapunov optimization. Simulation results verify that there exists a balance between EE and the service delay, and demonstrate that the system EE performance obtained by the proposed scheme outperforms other benchmark schemes.

Resource Allocation for Intelligent Reflecting Surface Aided Wireless Powered Mobile Edge Computing in OFDM Systems

Wireless powered mobile edge computing (WP-MEC) has been recognized as a promising technique to provide both enhanced computational capability and sustainable energy supply to massive low-power wireless devices. However, its energy consumption becomes substantial, when the transmission link used for wireless energy transfer (WET) and for computation offloading is hostile. To mitigate this hindrance, we propose to employ the emerging technique of intelligent reflecting surface (IRS) in WP-MEC systems, which is capable of providing an additional link both for WET and for computation offloading. Specifically, we consider a multi-user scenario where both the WET and the computation offloading are based on orthogonal frequency-division multiplexing (OFDM) systems. Built on this model, an innovative framework is developed to minimize the energy consumption of the IRS-aided WP-MEC network, by optimizing the power allocation of the WET signals, the local computing frequencies of wireless devices, both the sub-band-device association and the power allocation used for computation offloading, as well as the IRS reflection coefficients. The major challenges of this optimization lie in the strong coupling between the settings of WET and of computing as well as the unit-modules constraint on IRS reflection coefficients. To tackle these issues, the technique of alternating optimization is invoked for decoupling the WET and computing designs, while two sets of locally optimal IRS reflection coefficients are provided for WET and for computation offloading separately relying on the successive convex approximation method. The numerical results demonstrate that our proposed scheme is capable of monumentally outperforming the conventional WP-MEC network without IRSs. Quantitatively, about 80% energy consumption reduction is attained over the conventional MEC system in a single cell, where 3 wireless devices are served via 16 sub-bands, with the aid of an IRS comprising of 50 elements.

Wireless Powered Mobile Edge Computing Systems: Simultaneous Time Allocation and Offloading Policies

To improve the computational power and limited battery capacity of mobile devices (MDs), wireless powered mobile edge computing (MEC) systems are gaining much importance. In this paper, we consider a wireless powered MEC system composed of one MD and a hybrid access point (HAP) attached to MEC. Our objective is to achieve a joint time allocation and offloading policy simultaneously. We propose a cost function that considers both the energy consumption and the time delay of an MD. The proposed algorithm, joint time allocation and offload policy (JTAOP), is used to train a neural network for reducing the complexity of our algorithm that depends on the resolution of time and the number of components in a task. The numerical results are compared with three benchmark schemes, namely, total local computation, total offloading and partial offloading. Simulations show that the proposed algorithm performs better in producing the minimum cost and energy consumption as compared to the considered benchmark schemes.

Resource Management for Computation Offloading in D2D-Aided Wireless Powered Mobile-Edge Computing Networks

The integration of mobile-edge computing (MEC) and energy harvesting (EH) can potentially improve the network performances and prolong the battery life of the device. In this article, we study the resource management problem in the device-to-device (D2D)-aided wireless powered MEC networks where one device can forward or execute computation data for other devices with its resources. Our problem seeks to optimize the computation offloading strategy, transmission power, energy transmit power, as well as CPU speed to maximize the long-term utility energy efficiency (UEE). UEE is defined as the achieved computation data per unit energy. Since the formulated problem is in fractional form and hard to solve, we employ the Dinkelbach algorithm to transform the problem into a parametric subtractive form. Furthermore, considering that the formulated problem is time varying and stochastic due to the dynamic task arrival rate and battery level, we transform the long-term problem into deterministic drift-plus-penalty subproblems for each time slot by introducing virtual queues and adopting the Lyapunov optimization theory. The proposed scheme can balance the optimal UEE and stable data queue by introducing the control parameter <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$V$ </tex-math></inline-formula> . Theoretically, we reveal the tradeoff between the UEE and stable queue length for wireless powered MEC systems as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$[O(1/V), O(V)]$ </tex-math></inline-formula> . Finally, the simulations illustrate the efficiency of the proposed scheme compared with the existed work in terms of the UEE, stable queue length, and battery level.

Power optimisation in UAV‐assisted wireless powered cooperative mobile edge computing systems

Wireless power transfer (WPT) and mobile edge computing (MEC) are two prospective technologies to enhance the computing power and endurance of mobile devices. Integrating unmanned aerial vehicle (UAV) into wireless powered MEC system, the energy collection efficiency can be effectively improved with the short-distance line-of-sight path power transfer. However, WPT is susceptible to the ‘double near-far’ effect. Therefore, in this study, the authors study power optimisation in UAV-assisted wireless powered cooperative MEC system, which utilises the user cooperation to make the mobile device which is closer to the UAV acting as a relay for offloading. They aim to minimise the total transmission energy of the UAV through the joint power optimisation while satisfying the delay and size of the computational task. Simulation results demonstrate the performance of the proposed scheme.

Resource optimization in anti-interference UAV powered cooperative mobile edge computing network

Mobile edge computing (MEC) is a prospective technology to enhance the computing power of mobile devices by offloading the tasks to MEC server. Due to the limited size of mobile devices, the insufficient of battery becomes a serious problem. Wireless power transfer (WPT) and mobile devices cooperation can extend the endurance of mobile devices. Integrating unmanned aerial vehicle (UAV) into wireless powered MEC system, the energy collection efficiency can be improved with the short direct path power transfer. However, mobile devices will interfere with each other by utilizing the same bandwidth during the devices cooperation. In this paper, we proposed an anti-interference UAV powered cooperative mobile edge computing scheme, in which mobile devices utilizes different subcarriers to transmit the information. Moreover, a resource optimization problem is formulated to minimize the transmit energy of the UAV through joint subcarriers and power allocation of mobile devices with the constraints of delay and computing tasks size. Simulation results demonstrate the performance of the proposed scheme.

Energy Efficiency and Delay Tradeoff for Wireless Powered Mobile-Edge Computing Systems With Multi-Access Schemes

The integration of Mobile-edge Computing (MEC) and Wireless Energy Transfer (WET) has been recognized as a promising technique to enhance computation capability and to prolong battery lifetime of resource-constrained wireless devices in the Internet of Things (IoT) era. However, it is challenging to jointly schedule energy, radio, and computational resources for coordinating heterogeneous performance requirements in wireless powered MEC systems. To fill this gap, this paper investigates the fundamental tradeoff between Energy Efficiency (EE) and delay in a multi-user wireless powered MEC system. Considering the random channel conditions and task arrivals, we formulate a stochastic optimization problem to study the EE-delay tradeoff, which optimizes network EE subject to network stability, maximum central processing unit frequency, peak transmission power, available communication resource, and energy causality constraints. Further, we propose the online computation offloading and resource allocation algorithm by transforming the original problem into a series of deterministic optimization problems in each time block based on Lyapunov optimization theory. In addition, theoretical analysis shows that the algorithm achieves the EE-delay tradeoff as [ ${O}(1/{V}), {O}({V})$ ] and introduces a control parameter ${V}$ to balance the EE-delay performance. Numerical results verify the theoretical analysis and reveal the impact of various parameters to the system performance.

Offloading and system resource allocation optimization in TDMA based wireless powered mobile edge computing

• A WP-MEC system with N WDs and one HAP is considered, where WDs and HAP are single antenna devices. • The goal is to maximize the weighted sum computation rate by joint optimization of system resources management and task computing time allocation. • An alternating direction multiplier method (ADMM) based distributed optimization method is proposed. • Experimental results show that the proposed method greatly increases the weighted sum computation rate while keeping the energy consumption at a low level. In this paper, a wireless powered mobile edge computing (WP-MEC) system is considered, in which a hybrid access point integrated with MEC servers can charge N wireless devices (WDs) by broadcasting radio-frequency signals, and the time division multiple access (TDMA) protocol is used for task offloading of WDs. The goal of this paper is to maximize the weighted sum computation rate by joint optimization of system resources management and task computing time allocation. To solve this optimization problem, an alternating direction multiplier method (ADMM) based distributed optimization method is proposed. The proposed method can decompose the optimization problem into N sub-problems, which are solved by N WDs. Experimental results show that the proposed method outperforms the benchmarks and greatly increases the weighted sum computation rate while keeping the energy consumption at a low level under the premise of time complexity O ( N ).

Resource Allocation in Wireless-Powered Mobile Edge Computing Systems for Internet of Things Applications

Wireless devices in Internet of Things (IoT) applications, such as wireless sensors and Radio Frequency Identifications (RFIDs), are faced with challenges of heavy computation tasks and limited energy, which can be solved by the importation of mobile edge computing (MEC) and wireless power transfer (WPT) techniques. As MEC can effectively enhance computation capability, and the wireless power transfer can ensure a sustainable supply of energy, it has drawn significant research interest in IoT applications. In this paper, we will study the resource allocation problem in the wireless-powered MEC system for IoT applications with one access point (AP) and many other wireless devices, and propose a Stackelberg dynamic game model to obtain the optimal allocated resource for the nodes in the IoT environment. The AP is a wireless power source that can charge wireless devices based on wireless power transfer techniques. The AP is also integrated with a MEC server that can carry out computation tasks that offload from wireless devices. The wireless devices can use the harvested energy to execute and offload computation tasks to the AP. Based on the proposed game model, the AP and wireless devices can control their optimal transmit power for energy transfer, and computation tasks offloading to the AP, respectively. The numerical simulation results show the correctness and effectiveness of the proposed model.

Joint Optimization for Residual Energy Maximization in Wireless Powered Mobile-Edge Computing Systems

Mobile Edge Computing (MEC) and Wireless Power Transfer (WPT) are both recognized as promising techniques, one is for solving the resource insufficient of mobile devices and the other is for powering the mobile device. Naturally, by integrating the two techniques, task will be capable of being executed by the harvested energy which makes it possible that less intrinsic energy consumption for task execution. However, this innovative integration is facing several challenges inevitably. In this paper, we aim at prolonging the battery life of mobile device for which we need to maximize the harvested energy and minimize the consumed energy simultaneously, which is formulated as residual energy maximization (REM) problem where the offloading ratio, energy harvesting time, CPU frequency and transmission power of mobile device are all considered as key factors. To this end, we jointly optimize the offloading ratio, energy harvesting time, CPU frequency and transmission power of mobile device to solve the REM problem. Furthermore, we propose an efficient convex optimization and sequential unconstrained minimization technique based combining method to solve the formulated multi-constrained nonlinear optimization problem. The result shows that our joint optimization outperforms the single optimization on REM problem. Besides, the proposed algorithm is more efficiency.

Joint Offloading and Computing Optimization in Wireless Powered Mobile-Edge Computing Systems

Mobile-edge computing (MEC) and wireless power transfer (WPT) have been recognized as promising techniques in the Internet of Things era to provide massive low-power wireless devices with enhanced computation capability and sustainable energy supply. In this paper, we propose a unified MEC-WPT design by considering a wireless powered multiuser MEC system, where a multiantenna access point (AP) (integrated with an MEC server) broadcasts wireless power to charge multiple users and each user node relies on the harvested energy to execute computation tasks. With MEC, these users can execute their respective tasks locally by themselves or offload all or part of them to the AP based on a time-division multiple access protocol. Building on the proposed model, we develop an innovative framework to improve the MEC performance, by jointly optimizing the energy transmit beamforming at the AP, the central processing unit frequencies and the numbers of offloaded bits at the users, as well as the time allocation among users. Under this framework, we address a practical scenario where latency-limited computation is required. In this case, we develop an optimal resource allocation scheme that minimizes the AP’s total energy consumption subject to the users’ individual computation latency constraints. Leveraging the state-of-the-art optimization techniques, we derive the optimal solution in a semiclosed form. Numerical results demonstrate the merits of the proposed design over alternative benchmark schemes.

Joint Offloading and Computing Design in Wireless Powered Mobile-Edge Computing Systems With Full-Duplex Relaying

Simultaneous wireless information and power transfer (SWIPT) and mobile-edge computing (MEC) are promising technologies in Internet of Things era, which can enhance the self-sustainability and computation capability of user equipment, respectively. In this paper, we propose a multiple-input multiple-output full-duplex (FD) relay-based SWIPT-MEC system. With MEC and SWIPT, UE can use its battery energy to execute a computation task locally or offload part or all of it to access point connected with an MEC server and refill battery while receiving the computation results. The FD mode allows relay to receive and transmit the offloaded bits simultaneously. Based on these, an energy-efficient problem is formulated to minimize the system energy consumption over a time block, while the latency constraints and the energy consumption constraints are guaranteed. Since the original problem is non-convex, we use an alternating optimization technique to convert the original problem into three subproblems. Some propositions and algorithms are proposed to solve these three subproblems. Numerical results demonstrate that the proposed optimization scheme achieves a significant performance gain by comparisons with three benchmark schemes.