Elsevier

Journal of Systems Architecture

Volume 98, September 2019, Pages 221-230
Journal of Systems Architecture

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

https://doi.org/10.1016/j.sysarc.2019.07.009Get rights and content

Highlights

  • 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.

Abstract

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).

Introduction

In recent years, with the developing of mobile networks and upgrading of the intelligent wireless devices (WDs), the rapid growth in data traffic and the massive expansion of device connectivity are new challenge of the fifth generation (5 G). At the same time, new business scenarios of 5 G networks, such as internet of vehicles, smart manufacturing, virtual reality and augmented reality, etc. And they put forward higher requirements for delay, energy consumption and reliability [1], [2]. However, the processing ability of the existing WD is difficult to satisfy the low latency and high reliability requirements of the above applications [3]. Driven by application requirements, mobile edge computing (MEC) as a new type of computing architecture has attracted widespread attention in recent years [4], [36]. MEC can provide storage and computation resources for mobile users at the edge of the network and allow WDs with limited battery capacity to offload computation task to the edge node, such as wireless access point, base station, etc. [5], [38]. MEC's characteristics can not only make up for the WD's limited computing ability but also reduce communication delay and extend the WD's battery life.

In the MEC system architecture, the energy consumed by the WD comes from its limited energy resources, such as batteries, therefore the life cycle of the WD is constrained by the limited energy. However, the emergence of wireless energy transfer (WET) technology can solve the problem of WD's energy constraints effectively. By using the WET technology, WDs can collecting energy from the radio-frequency (RF) signals provided by wireless power stations [6]. Benefiting from the development of WET technology, the joint transmission of information and energy based on wireless information transfer (WIT) has become challenging work to enhance system performance and resource reuse rate [7]. There are two main methods for achieving joint transmission: (1) Simultaneous wireless information and power transfer (SWIPT): RF can transmit energy and information synchronously through the same carrier [8]; (2) Wireless powered communication network (WPCN): WDs use the energy collected from the RF signal to transmit information [9];

A new model called wireless powered mobile edge computing (WP-MEC) is the combination of WET technology and MEC [10]. The WP-MEC consists of WDs, MEC servers, and hybrid access points (HAPs), where the HAP is integrated with the energy transmitters and signal receivers. The HAP can transfer RF power to WD and assist the MEC server to receive offloading tasks from WD. The application of WP-MEC involves many fields, such as intelligent manufacturing, agricultural monitoring and smart home, etc. (Fig. 1) Specifically, agricultural monitoring mainly includes temperature, gas and light monitoring. Monitoring sensors consume energy in the process of sensing and transmitting monitoring data. Meanwhile, monitoring devices can be charged through WET to increase working time. In this paper, the problem of joint optimization of system resources management and task computing time allocation in WP-MEC is studied. The main contributions of this paper are as follows:

  • A WP-MEC system with N WDs and one HAP is considered, where WDs and HAP are single antenna devices. TDMA protocol is applied to the task offloading process from the WDs to the MEC server, and the harvest-then-transmit protocol is used to ensure the continuous operation of the WDs.

  • The joint optimization problem of system resources management and task computing time allocation is studied. The weighted sum computation rate is used as an indicator to measure the performance of the WP-MEC system.

  • To tackle the joint optimization problem, an ADMM technique based distributed optimization method is proposed. The proposed method can decompose the optimization problem into N sub-problems, and each WD is involved in solving these sub-problems, thereby reducing the complexity of solving the optimization problem by the centralized method.

  • Numerical results show that the proposed method outperforms other benchmark methods in the performance of the weighted sum computation rate and energy consumption under the premise of time complexity O(N).

The rest of this paper is organized as follows. In Section 2, the related work is presented. In Section 3, the system model of the WP-MEC system is described and the optimization problem of weighted sum computation rate maximization is formulated. In Section 4, an ADMM technique based distributed optimization method for the optimization problem is developed. In Section 5, the simulation results are provided. In Section 6, this paper is concluded.

Section snippets

Related work

Reasonable resource allocation scheme can effectively improve the performance of WP-MEC system. The existing researches on resource allocation schemes of WP-MEC system mainly focus on the energy consumption optimization [11], [12], [13], [14], [15], [16], [17], [37] and computation rate maximization [18], [19], [20], [21], [22], [23], [24], [25], [26]. These contributions are summarized as follows.

For the existing works focus on the energy consumption optimization problem. Ji et al. [11]

WP-MEC system

In this paper, a WP-MEC system is considered. As shown in Fig. 2, this system consisting of one MEC server and N WDs. The MEC server integrates a single antenna HAP with a stable energy source, HAP can provide energy to all WD by broadcasting RF signals and assist the MEC server receiving the offloading task from WDs. The MEC server with powerful computing ability can send the computation result to WDs via HAP after processing the computation tasks from the HAP. Each WD integrates a single

A distributed optimization method based on ADMM

In this section, a distributed method based on ADMM technique is proposed. The core idea of this method is decomposing the initial problem into N sub-problems, and each sub-problem is solved in each WD respectively. However, before using the ADMM technique to solve P1, the following problems needs to be solved: (1) Traditional ADMM technique can only deal with the objective function with equality constraints. However, P1 includes inequality constraints, and hence it cannot be directly solved

benchmark schemes

In this section, the simulation experiments are performed, and performance evaluation of the proposed method. In order to test the performance of the proposed method from various aspects, the following influencing factors are considered in the experiment: the distance between AP, the number of WDs, the AP transmit power, the path-loss exponent, the bandwidth and the number of computation bits; Based on this, the performance of the weighted sum computing rate (WSCR) i=1NwiRi(ti,lti) and the

Conclusion

In this work, the problem of weighted sum computation rate maximization in multi-user WP-MEC system with TDMA offloading manner is studied, and this problem is formulated as joint optimization of system resources management and task computing time allocation. Specifically, an ADMM-based distributed optimization method is developed to solve this optimization problem. Experimental results show that under the premise of time complexity O(N), the proposed method can achieve better performance of

Conflict of interest

None.

Acknowledgment

The work was supported by the National Natural Science Foundation (NSF) under grants (no. 61873341, no. 61672397), Application Foundation Frontier Project of WuHan (no. 2018010401011290). Research Fund of Science and Technology on Parallel and Distributed Processing Laboratory, Beijing Intelligent Logistics System Collaborative Innovation Center Open Project (no. BILSCIC-2019KF-02), Beijing Youth Top-notch Talent Plan of High-Creation Plan (no. 2017000026833ZK25), Canal Plan-Leading Talent

Chunlin Li is a Professor of Computer Science in Wuhan University of Technology. She received the ME in Computer Science from Wuhan Transportation University in 2000, and PhD in Computer Software and Theory from Huazhong University of Science and Technology in 2003. Her research interests include cloud computing and distributed computing.

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    Chunlin Li is a Professor of Computer Science in Wuhan University of Technology. She received the ME in Computer Science from Wuhan Transportation University in 2000, and PhD in Computer Software and Theory from Huazhong University of Science and Technology in 2003. Her research interests include cloud computing and distributed computing.

    Mingyang Song received his BS degree in Network Engineering from Zhoukou Normal University in 2014 and MS degree in Software Engineering from Henan Normal University in 2018. He is a PhD student in School of Computer Science and Technology from Wuhan University of Technology. His research interests include cloud computing and big data.

    Hengliang Tang Associate professor, School of Information, Beijing Wuzi University, Beijing, China. He received Ph.D. degree from Beijing University of Technology in 2011. His research interest covers internet of things, logistics informatization, and computer vision.

    Youlong Luo is a vice Professor of Management at Wuhan University of Technology. He received his M.S. in Telecommunication and System from Wuhan University of Technology in 2003 and his Ph.D. in Finance from Wuhan University of Technology in 2012. His research interests include cloud computing and electronic commerce.

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