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Energy-Sensitive binary offloading for Reconfigurable-Intelligent-Surface-Assisted Wireless-Powered Mobile-Edge Computing

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IEEE INTERNET OF THINGS JOURNAL 2024年第7期11卷 11593-11605页

作者： Yang, Yizhen Gong, Yi Wu, Yik-Chung Univ Hong Kong Dept Elect & Elect Engn Hong Kong Peoples R China Southern Univ Sci & Technol Dept Elect & Elect Engn Shenzhen 518055 Peoples R China

Wireless power transfer (WPT) is recognized as a promising technique to alleviate the energy limitation of wireless devices (WDs) under mobile-edge computing (MEC) scenario in the upcoming Internet of Things (IoT) era. In wireless-powered MEC networks, WDs can utilize the harvested energy to handle the computation tasks. Furthermore, reconfigurable intelligent surface (RIS) can also play a significant role in MEC systems due to its capability to enhance the channel quality. In this article, we investigate an RIS-assisted wireless-powered MEC system where each WD follows the binary offloading policy. The objective is to minimize the total energy consumption of WDs by jointly optimizing the WPT time, the RIS phase shifts for WPT, the binary mode selection, the CPU frequencies for local computation, the RIS phase shifts for offloading, and the offloading times and powers. A gradient ascent-based algorithm with linear complexity with respect to the number of RIS reflecting elements is proposed to optimize the transmit power and RIS phases shifts design. On the other hand, a penalty-based algorithm with linear complexity with respect to the number of WDs is proposed to solve the offloading decision and time allocation. Numerical results are presented to demonstrate the effectiveness of the proposed system and algorithms.

关键词： Wireless communication Wireless sensor networks Task analysis Communication system security Servers Internet of Things Energy consumption binary offloading energy minimization mobileedge computing (MEC) reconfigurable intelligent surface (RIS) wireless power transfer (WPT)

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Energy Minimization for Cellular-Connected Aerial Edge Computing System With binary offloading

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IEEE INTERNET OF THINGS JOURNAL 2024年第6期11卷 9558-9571页

作者： Han, Hangcheng Zhan, Cheng Lv, Jian Xu, Changyuan Beijing Inst Technol Sch Integrated Circuits & Elect Beijing 100081 Peoples R China Southwest Univ Sch Comp & Informat Sci Chongqing 400715 Peoples R China

Due to the characteristic of wide coverage, flexible deployment, and low cost, unmanned aerial vehicles (UAVs) have been employed to provide mobile crowdsensing and edge computing. However, the limited computation and onboard battery capacities of UAVs impose a changeling for timely computation and endurance. In this article, we consider an aerial edge computing system where multiple cellular-connected UAVs are employed to perform sensing and computation tasks over target subregions, and the UAVs can offload their computation tasks to the ground base station (BS) with the binary offloading scheme. We aim to minimize the maximum energy consumption of all UAVs by optimizing 3-D UAV trajectories jointly with the binary offloading indicator as well as computation resource allocation, subject to the target sensing constraints and the computation completion time constraints. The optimization problem we formulated is nonconvex and involves binary design variables, making it difficult to find the optimal solution. To address this challenge, we propose an efficient alternating optimization algorithm that can obtain a high-quality suboptimal solution, where the exact penalty method with equilibrium constraints is adopted to tackle the binary constraints. To tackle the nonconvexity of the optimization subproblems, we utilize the successive convex approximation approach to obtain a suboptimal solution. Extensive simulations are conducted and the results demonstrate that the proposed design significantly reduces the energy consumption of the UAVs over several baseline methods.

关键词： Autonomous aerial vehicles Task analysis Three-dimensional displays Trajectory Energy consumption Optimization Resource management binary offloading cellular-connected unmanned aerial vehicle (UAV) energy minimization mobile edge computing (MEC)

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IRS Aided MEC Systems With binary offloading: A Unified Framework for Dynamic IRS Beamforming

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IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 2023年第2期41卷 349-365页

作者： Chen, Guangji Wu, Qingqing Liu, Ruiqi Wu, Jingxian Fang, Chao Shanghai Jiao Tong Univ Inst Signal Proc & Syst Shanghai 200240 Peoples R China Univ Macau State Key Lab Internet Things Smart City Macau 999078 Peoples R China Shanghai Jiao Tong Univ Dept Elect Engn Shanghai 200240 Peoples R China ZTE Corp State Key Lab Mobile Network & Mobile Multimedia T Shenzhen 518057 Peoples R China Univ Arkansas Dept Elect Engn Fayetteville AR 72701 USA Beijing Univ Technol Fac Informat Technol Beijing 100081 Peoples R China

In this paper, we develop a unified dynamic intelligent reflecting surface (IRS) beamforming framework to boost the sum computation rate of an IRS-aided mobile edge computing (MEC) system, where each device follows a binary offloading policy. Specifically, the task of each device has to be either executed locally or offloaded to MEC servers as a whole with the aid of given number of IRS beamforming vectors available. By flexibly controlling the number of times for IRS reconfiguring phase-shifts, the system can achieve a balance between the performance and associated signalling overhead. We aim to maximize the sum computation rate by jointly optimizing the computational mode selection for each device, offloading time allocation, and IRS beamforming vectors across time. Since the resulting optimization problem is non-convex and NP-hard, there are generally no standard methods to solve it optimally. To tackle this problem, we first propose a penalty-based successive convex approximation algorithm, where all the associated variables in the inner-layer iterations are optimized simultaneously and the obtained solution is guaranteed to be locally optimal. Then, we further derive the offloading activation condition for each device by deeply exploiting the intrinsic structure of the original optimization problem. According to the offloading activation condition, a low-complexity algorithm based on the successive refinement method is proposed to obtain high-quality suboptimal solutions, which are more appealing for practical systems with a large number of devices and IRS elements. Moreover, the optimal condition for the proposed low-complexity algorithm is revealed. The effectiveness of the proposed algorithms is demonstrated through numerical examples. In addition, the results illustrate the practical significance of the IRS in MEC systems for achieving coverage extension and supporting multiple energy-limited devices for task offloading, and also unveil the fundamental perf

关键词： Array signal processing Task analysis Internet of Things Optimization Servers Resource management Cloud computing Intelligent reflecting surface (IRS) mobile edge computing dynamic beamforming resource allocation binary offloading computation rate

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Intelligent-Reflecting-Surface-Aided Mobile Edge Computing With binary offloading: Energy Minimization for IoT Devices

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IEEE INTERNET OF THINGS JOURNAL 2022年第15期9卷 12973-12983页

作者： Yang, Yizhen Gong, Yi Wu, Yik-Chung Univ Hong Kong Dept Elect & Elect Engn Hong Kong Peoples R China Southern Univ Sci & Technol Dept Elect & Elect Engn Shenzhen 518055 Peoples R China

Mobile edge computing (MEC) is envisioned as a promising technique to support computation-intensive and time-critical applications in future Internet of Things (IoT) era. However, the uplink transmission performance will be highly impacted by the hostile wireless channel, the low bandwidth, and the low transmission power of IoT devices. Recently, intelligent reflecting surface (IRS) has drawn much attention because of its capability to control the wireless environments so as to enhance the spectrum and energy efficiencies of wireless communications. In this article, we consider an IRS-aided multidevice MEC system where each IoT device follows the binary offloading policy, i.e., a task has to be computed as a whole either locally or remotely at the edge server. We aim to minimize the total energy consumption of devices by jointly optimizing the binary offloading modes, the CPU frequencies, the offloading powers, the offloading times, and the IRS phase shifts for all devices. Two algorithms, which are greedy based and penalty based, are proposed to solve the challenging nonconvex and discontinuous problem. It is found that the penalty-based method has only linear complexity with respect to the number of devices, but it performs close to the greedy-based method with cubic complexity with respect to the number of devices. Furthermore, binary offloading via IRS indeed saves more energy compared to the case without IRS.

关键词： Wireless communication Servers Energy consumption Internet of Things Task analysis Time-frequency analysis Minimization binary offloading energy minimization intelligent reflecting surface (IRS) Internet of Things (IoT) devices mobile edge computing (MEC)

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LBA: Matching Theory Based Latency-Sensitive binary offloading in IoT-Fog Networks 16

LBA: Matching Theory Based Latency-Sensitive Binary Offloadi...

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16th International Conference on COMmunication Systems and NETworkS, COMSNETS 2024

作者： Soni, Priyanka Deshlahre, Omkar Chand Satpathy, Anurag Addya, Sourav Kanti National Institute of Technology Karnataka Cloud and Smart System Services Lab Department of Computer Science and Engineering India Missouri University of Science and Technology RollaMO United States

ISBN: (纸本)9798350383119

The Internet of Things (IoT) is growing more popular with applications like healthcare services, traffic monitoring, video streaming, smart homes, etc. These applications produce an enormous amount of data, so a realistic option in this instance is to offload computational tasks to their proximity fog nodes (FNs) instead of the remote cloud. However, a negligent offloading strategy may cause anomalous computational traffic load at the FNs, causing congestion that may adversely affect the latency. However, the latency of task flows from IoT devices comprises communications latency at BS and computational latency at FNs. Therefore, designing offloading algorithms to distribute the computational load at FN evenly and efficiently utilize the FN resources is crucial. To solve this problem, we proposed LBA in a fog network with a binary offloading strategy using the matching theory-based approach. We utilize the Analytic Hierarchy Process (AHP) to generate the preference list. Furthermore, the binary offloading technique follows the deferred acceptance algorithm (DAA) to produce a stable assignment, and the complete offloading problem is modeled as a one-to-many matching game. Comprehensive simulations ensure that LBA can accomplish a better-balanced assignment for homogeneous and heterogeneous input concerning all the baseline algorithms. © 2024 IEEE.

关键词： Analytic Hierarchy Process (AHP) binary offloading Fog computing IoT Load balancing Matching Theory

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Distributed Design of Wireless Powered Fog Computing Networks With binary Computation offloading

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2023年第4期22卷 2084-2099页

作者： Li, Han Xiong, Ke Lu, Yang Gao, Bo Fan, Pingyi Letaief, Khaled Ben Sch Comp & Informat Technol Beijing 100044 Peoples R China Beijing Key Lab Traff Data Anal & Min Beijing 100044 Peoples R China Beijing Jiaotong Univ Frontiers Sci Ctr Smart High Speed Railway Syst Beijing 100044 Peoples R China Beijing Natl Res Ctr Informat Sci & Technol Beijing 100084 Peoples R China Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China Hong Kong Univ Sci & Technol HKUST Dept Elect & Comp Engn Hong Kong Peoples R China Pengcheng Lab Shenzhen 518055 Guangdong Peoples R China

This paper investigates a multi-user wireless powered fog computing (FC) network, where multiple energy-limited wireless sensor devices (WSDs) first harvest energy from a nearby hybrid access point (HAP), and then compute their tasks locally (i.e., the local computing (LC) mode) or offload the tasks to the HAP (i.e., the FC mode) via a binary offloading policy. In order to pursue the green computing network design, an optimization problem is formulated to minimize the transmit power at the HAP by jointly optimizing the time allocation ratio and the computing mode selection vector, under the energy causality constraints and the WSDs' computing rate requirements constraints. To efficiently solve the formulated non-convex problem in a distributed manner, it is first transformed into an approximate form, and then an alternating direction method of multipliers (ADMM)-based algorithm is designed to solve the transformed problem, based on which the successive convex approximation (SCA) is adopted to improve the approximating precision in an iterative way. With the proposed ADMM-based distributed algorithm, each WSD is able to optimize its computing mode and offloading time with local channel state information (CSI), which thus is more suitable for large-scale networks. For comparison, a channel-sorting-based (CSB) centralized algorithm with global CSI is also presented, and the computational complexities of the proposed ADMM-based algorithm and the CSB algorithm are analyzed. Simulation results show that the proposed distributed algorithm achieves a comparable performance with the CSB centralized algorithm and the exhaustive search method. It is also observed that to minimize the transmit power at the HAP, the WSDs with the better channel quality are inclined to select the LC mode, which is much different from traditional sum-computation-rate maximization design.

关键词： Wireless communication Task analysis Servers Optimization Distributed algorithms Approximation algorithms Resource management Fog computing binary offloading wireless power transfer power minimization successive convex approximation alternating direction method of multipliers

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A Customized Reinforcement Learning based binary offloading in Edge Cloud 26

A Customized Reinforcement Learning based Binary Offloading ...

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26th IEEE International Conference on Parallel and Distributed Systems (IEEE ICPADS)

作者： Li, Yuepeng Chen, Lvhao Zeng, Deze Gu, Lin China Univ Geosci Sch Comp Sci Wuhan Peoples R China Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan Peoples R China

ISBN: (纸本)9781728190747

To tackle the computation, resource poorness on the end devices, task offloading is developed to reduce the task completion time and improve the Quality-of-Service (QoS). Edge cloud facilitates such offloading by provisioning resources at the proximity of the end devices. Modern applications are usually deployed as a chain of subtasks (e.g., microservices) where a special offloading strategy, referred as binary of loading, shall be applied. binary offloading divides the chain into two parts, which will be executed on end device and the edge cloud, respectively. The offloading point in the chain therefore is critical to the QoS in terms of task completion time. Considering the system dynamics and algorithm sensitivity, we apply Q-learning to address this problem. In order to deal with the late feedback problem, a reward rewind match strategy is proposed to customize Q-learning. Trace-driven simulation results show that our customized Q-learning based approach is able to achieve significant reduction on the total execution time, outperforming traditional offloading strategies and non-customized Q-learning.

关键词： binary offloading edge computing reinforcement learning request scheduling

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TReB: Task dependency aware-Resource allocation for Internet of Things using binary offloading

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Ad Hoc Networks 2025年 177卷

作者： Priyanka Soni Ajay Gajanan Hajare Keerthan Kumar T.G. Sourav Kanti Addya Cloud & Smart System Services Lab Department of Computer Science & Engineering National Institute of Technology Karnataka Surathkal India Department of Information Science & Engineering Siddaganga Institute of Technology Tumkur India

The rapid growth of Internet of Things (IoT) applications in domains such as healthcare, smart homes, and autonomous vehicles has led to an exponential increase in data generated by compute intensive tasks. Efficiently offloading these tasks to nearby computational resources in fog environments remains a significant challenge due to the inherent heterogeneity and constrained resources of Fog Nodes (FNs). Most of the existing approaches fail to address the trade-offs between latency, energy, and resource utilization, particularly when managing dependent and independent task workloads. Moreover, establishing an offloading strategy within a densely interconnected IoT network is known to be N P -hard. To overcome these limitations, in this work, we propose a T ask dependency-Aware Re source allocation for IoT using B inary offloading ( TReB ) framework by considering both independent and dependent tasks of IoT applications. The TReB utilizes the Analytic Hierarchy Process (AHP) technique to generate the preferences of FNs and tasks by considering diverse attributes. With preferences established, a binary offloading is handled through a one-to-many matching procedure, utilizing a Deferred Acceptance Algorithm (DAA). It allows TReB to jointly minimize system energy consumption, latency, and the number of outages in an IoT network. We evaluated the effectiveness of TReB through simulation experiments, and results show that the proposed approach achieves a 49.1%, 62.4%, and 41.7% minimization in overall system latency, energy, and outages compared to the existing baselines.

关键词： Analytic Hierarchy Process binary offloading Deferred Acceptance Algorithm Internet of Things Matching theory Resource allocation

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Distributed user pairing and effective computation offloading in aerial edge networks

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Chinese Journal of Aeronautics 2024年第4期37卷 378-390页

作者： Wei LIANG Shuhui WEN Lixin LI Jingjing CUI School of Electronics and Information Northwestern Polytechnical UniversityXi’an CN710072China The State Key Laboratory of Integrated Services Networks Xidian UniversityXi’an CN710126China School of Electronics and Computer Science University of SouthamptonSouthampton SO167NSUK

Future Sixth-Generation (6G) mobile communication networks extremely require the global connectivity and the ground Base Stations (BSs) are difficult to develop in some specific areas,such as mountainous ***,the aerial networks are the key framework for the future communications,in which the aerial vehicle could act as the access ***,Mobile Edge Computing (MEC) is also essential to enhance the data processing capabilities of aerial *** this paper,we study a comprehensive communication-computation resource management problem for jointly optimizing user pairing,power and time allocation in the MEC aided Cognitive Radio (CR) aerial networks,namely CR-MEC aerial ***,this multilevel optimization problem could be decomposed into the user pairing and power allocation as well as time allocation *** the conceived CR-MEC aerial networks,we propose a User Pairing and Computation offloading Management Algorithm (UPCOMA) based on three-sided matching theory,aiming to minimize the system’s energy *** the first step of UPCOMA,multiple Primary Users (PUs) and Cognitive Users (CUs) on the ground negotiate to each other with the suitable power allocation coefficients and construct the stable user ***,the stable user pairs would match to a high altitude platform who act as the base station,which is for appropriately allocating Transmission Time Slots (TSs) at the second step of ***,a hybrid offloading mode is proposed in our conceived networks in order to support ground users to offload their tasks adaptively according to their individual ***,the simulation results reveal that the relative performance of UPCOMA is close to that of the Brute-Force Search Algorithm (BFSA) with low ***,the hybrid offloading mode with less energy consumption supports much more ground user pairs to offload tasks compared to the binary mode.

关键词： Aerial edge computing binary offloading Cognitive radio Hybrid offloading Resource management

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SCMA-Enabled Multi-Cell Edge Computing Networks: Design and Optimization

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2023年第6期72卷 7987-8003页

作者： Liu, Pengtao An, Kang Lei, Jing Liu, Wei Sun, Yifu Zheng, Gan Chatzinotas, Symeon Natl Univ Def Technol Coll Elect Sci & Technol Changsha 410073 Peoples R China Natl Univ Def Technol Res Inst 63 Nanjing 210007 Peoples R China Univ Warwick Sch Engn Coventry CV4 7AL England NCSR Demokritos Inst Informat & Telecommun Athens 15341 Greece

Multi-access edge computing (MEC) is regarded as a promising approach for providing resource-constrained mobile devices with computing resources through task offloading. Sparse code multiple access (SCMA) is a code-domain non-orthogonal multiple access (NOMA) scheme that can meet the demands of multi-cell MEC networks for high data transmission rates and massive connections. In this paper, we propose an optimization framework for SCMA-enabled multi-cell MEC networks. The joint resource allocation and computation offloading problem is formulated to minimize the system cost, which is defined as the weighted energy cost and latency. Due to the nonconvexity of the proposed optimization problem induced by the coupled optimization variables, we first propose an algorithm based on the block coordinate descent (BCD) method to iteratively optimize the transmit power and edge computing resources allocation by deriving closed-form solutions, and further develop an improved low-complexity simulated annealing (SA) algorithm to solve the computation offloading and multi-cell SCMA codebook allocation problem. To solve the problem of partial state observation and timely decision-making in long-term optimization environment, we put forward a multiagent deep deterministic policy gradient (MADDPG) algorithm with centralized training and distributed execution. Furthermore, we extend the framework to the partial offloading case and propose an algorithm based on alternating convex search for solving the task offloading ratio. Numerical results show that the proposed multi-cell SCMA-MEC scheme achieves lower energy consumption and system latency in comparison to the orthogonal frequency division multiple access (OFDMA) and power-domain (PD) NOMA techniques.

关键词： Internet of things sparse code multiple access (SCMA) multi-access edge computing (MEC) binary offloading partial offloading resource management

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