Article
Computer Science, Information Systems
Yuhe Chen, Xuying Zhou, Wei Wang, Huiqiong Wang, Zhi Zhang, Zhaoyang Zhang
Summary: The letter proposes a delay-optimal multi-destination computation offloading system by optimizing task assignment and offloading scheduling, which reduces the delay by up to 62.4% compared to non-scheduling offloading method.
IEEE WIRELESS COMMUNICATIONS LETTERS
(2021)
Article
Telecommunications
Mingzhi Wang, Tao Wu, Tao Ma, Xiaochen Fan, Mingxing Ke
Summary: In this paper, the authors propose a delay sensitivity-aware computation offloading method in Mobile Edge Computing (MEC) systems. They define the latency sensitivity of task offloading based on delay distribution and devise a scoring mechanism and a Centralized Iterative Redirection Offloading (CIRO) algorithm to optimize the offloading strategy. Simulation results show that their method significantly improves the utility of computation offloading in MEC systems and has lower time complexity than existing algorithms.
DIGITAL COMMUNICATIONS AND NETWORKS
(2022)
Article
Computer Science, Information Systems
Laizhong Cui, Ziteng Chen, Shu Yang, Zhongxing Ming, Qi Li, Yipeng Zhou, Shiping Chen, Qinghua Lu
Summary: CUTE is a containerized edge computing platform designed to provide low-latency computation services for the Internet of Vehicles, integrated with blockchain for enhanced network security. By developing a heuristic container scheduling algorithm, CUTE optimizes the scheduling efficiency for vehicle-submitted tasks based on DAG structures.
IEEE INTERNET OF THINGS JOURNAL
(2021)
Article
Chemistry, Multidisciplinary
Suzhen Wang, Zhongbo Hu, Yongchen Deng, Lisha Hu
Summary: Task offloading and resource allocation are crucial in edge computing, as they can reduce processing time and energy consumption. Current studies mainly focus on resource allocation between terminals and edge servers, disregarding the computing resources in the cloud center. To address this, we propose a coarse-grained task offloading strategy and intelligent resource matching scheme that leverages both cloud and edge server resources. Our approach considers mobile device heterogeneity and inter-channel interference, and maximizes system utility through a game-theory-based task migration model. Experimental results demonstrate the superiority of our scheme in terms of latency, energy consumption, and scalability.
APPLIED SCIENCES-BASEL
(2022)
Article
Computer Science, Information Systems
Sladana Josilo, Gyorgy Dan
Summary: This study addresses the computation offloading problem in an edge computing system, developing game theoretical models and proposing efficient decentralized algorithms. The simulation results demonstrate that the cost minimizing resource allocation policy can achieve significantly lower completion times, with algorithm convergence time approximately linear in the number of devices.
IEEE TRANSACTIONS ON CLOUD COMPUTING
(2021)
Review
Computer Science, Information Systems
Shuchen Zhou, Waqas Jadoon, Iftikhar Ahmed Khan
Summary: With the rise of IoT and the advancement of 5G, new services are emerging and mobile data traffic is growing exponentially. Mobile edge computing (MEC) has become a popular computing model to meet QoS requirements. This paper provides an overview of task offloading in MEC, including its concepts, application scenarios, and research progress. The paper also identifies key technologies, schemes, and objectives in the industry, and suggests future research directions for computational offloading techniques.
Article
Computer Science, Information Systems
Mingyu Chen, Xiaowen Gong, Yang Cao
Summary: This article explores the correlation between computation workloads and communication workloads in distributed edge computation offloading, aiming to minimize the total completion time. The authors tackle challenges such as precedence constraints, interference constraints, and workload correlation in order to devise efficient policies that approach the optimal solution. Their results provide valuable insights for the computation-communication co-design of distributed edge computation offloading.
IEEE TRANSACTIONS ON MOBILE COMPUTING
(2023)
Article
Computer Science, Theory & Methods
Xiaojie Wang, Zhaolong Ning, Song Guo
Summary: Pervasive edge computing is a decentralized computing approach where users schedule based on their own utility, but ensuring fairness among devices is a challenge. Researchers propose an algorithm based on game theory and imitation learning to reduce task completion time and achieve significant advantages.
IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Miaojiang Chen, Wei Liu, Tian Wang, Shaobo Zhang, Anfeng Liu
Summary: This paper proposes a polling callback energy-saving offloading strategy, and simulation results show that the proposed algorithm performs better than DDQN, DQN, and BCD-based optimal methods.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
S. Premkumar, A. N. Sigappi
Summary: This study discusses the challenges of handling complex tasks in IoT applications and emphasizes the need to offload tasks to resource-rich edge computing and the cloud. By developing a dynamic decision model, the optimal offloading mechanism can be determined based on processing capacity, while considering factors such as energy consumption and network time. The paper compares different offloading strategies through simulations and evaluates their performance in various applications.
COMPUTATIONAL INTELLIGENCE
(2022)
Article
Telecommunications
Wanneng Shu, Yan Li
Summary: This paper presents a joint offloading strategy based on quantum particle swarm optimization for mobile edge computing (MEC) enabled vehicular networks to reduce task execution delay cost and energy consumption. Simulation results show that the proposed strategy effectively reduces system overhead and task completion delay.
DIGITAL COMMUNICATIONS AND NETWORKS
(2023)
Article
Computer Science, Information Systems
Quyuan Luo, Shihong Hu, Changle Li, Guanghui Li, Weisong Shi
Summary: With the increasing demand for data communications and computing, edge computing has emerged as a paradigm shift by providing powerful communication, storage, networking, and computing capacity closer to users. Resource scheduling is crucial for the success of edge computing systems, attracting growing research interest. Current research focuses on various resource scheduling techniques and application scenarios.
IEEE COMMUNICATIONS SURVEYS AND TUTORIALS
(2021)
Article
Computer Science, Information Systems
Tong Liu, Yameng Zhang, Yanmin Zhu, Weiqin Tong, Yuanyuan Yang
Summary: This article introduces an optimized task offloading strategy based on mobile-edge computing in an ultradense network. A double deep Q network (DDQN) approach is proposed using reinforcement learning, along with a context-aware attention mechanism. Extensive simulations demonstrate the effectiveness of the proposed method.
IEEE INTERNET OF THINGS JOURNAL
(2021)
Article
Computer Science, Artificial Intelligence
Junyao Yang, Yan Wang, Zijian Li
Summary: This paper addresses the challenge of designing an efficient offloading strategy for edge computing by proposing a two-step offloading framework and utilizing the Deep Deterministic Policy Gradient Algorithm and genetic algorithm to optimize resource allocation and decision-making. The proposed algorithm achieves a high-quality offloading environment.
APPLIED SOFT COMPUTING
(2022)
Article
Chemistry, Analytical
Xinwang Yuan, Zhidong Xie, Xin Tan
Summary: This paper studies an efficient computing resource offloading mechanism for UAV-enabled edge computing. It comprehensively considers the interests of different roles and factors to improve overall system performance through a Stackelberg game model.
Article
Computer Science, Information Systems
Gang Li, Jun Cai, Xianfu Chen, Zhou Su
Summary: This paper studies task offloading in edge computing systems and proposes an incentive mechanism design problem considering the unique characteristics of these systems. It introduces a novel online incentive mechanism called Integrate Rounding Scheme based Maxima-in-distributional Range (IRSM) and verifies its effectiveness through theoretical analysis and comprehensive simulations.
IEEE TRANSACTIONS ON MOBILE COMPUTING
(2023)
Article
Engineering, Electrical & Electronic
Gang Li, Jun Cai, Shuang Ni
Summary: This paper studies collaborative task offloading in edge computing, proposes a truthful mechanism to incentivize smartphone users, and introduces a new approach to tackle the high computational complexity.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2022)
Article
Computer Science, Information Systems
Xiaolong Li, Jun Cai, Rongyang Zhao, Chuang Li, Chengwen He, Dian He
Summary: This article discusses the optimal anchor node deployment problem for fingerprint localization in wireless indoor positioning systems using off-the-shelf communication chips. Two deployment algorithms, ANDAs and WANDA, are proposed to minimize the number of anchor nodes while satisfying adaptive localization precision, unique fingerprint, and other requirements. Simulation and experimental results show the effectiveness and superiority of ANDA and WANDA.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Computer Science, Information Systems
You Shi, Changyan Yi, Bing Chen, Chenze Yang, Kun Zhu, Jun Cai
Summary: Edge-cloud collaboration is critical in the IIoT for computation-intensive tasks. This article proposes an energy-efficient resource management framework that optimizes sensors' sampling rate, edge servers' preprocessing mode, and edge-cloud communication and computing resource allocation to minimize energy consumption while ensuring service delay and data processing accuracy. A low-complexity online algorithm based on Lyapunov optimization and Markov approximation is introduced. Simulation results demonstrate its feasibility and improvement in energy consumption and service delay compared to benchmark schemes.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Computer Science, Information Systems
Linqing Gui, Chunzhe Ma, Biyun Sheng, Zhengxin Guo, Jun Cai, Fu Xiao
Summary: This article proposes a new system based on channel-state information of domestic WiFi network to monitor both turnover activities and breathing rate of sleepers. The system improves recognition accuracy of sleep turnover activities, sleep postures, and estimation accuracy of breathing rate, especially in tough scenarios.
IEEE SYSTEMS JOURNAL
(2023)
Article
Computer Science, Information Systems
Changyan Yi, Jun Cai, Tong Zhang, Kun Zhu, Bing Chen, Qiang Wu
Summary: This paper addresses a long-term workload management problem in multi-server edge computing with server collaboration. A cooperative queueing game approach is proposed to solve the joint optimization problem of workload allocation, compensation price determination, and computing speed selection for each edge server. The proposed solution is evaluated through theoretical analyses and extensive simulations, which demonstrate its superiority over existing counterparts.
IEEE TRANSACTIONS ON MOBILE COMPUTING
(2023)
Article
Engineering, Electrical & Electronic
Jiayuan Chen, Changyan Yi, Ran Wang, Kun Zhu, Jun Cai
Summary: This paper studies the problem of joint sensor activation and mobile charging vehicle scheduling in wireless rechargeable sensor networks for industrial Internet of Things. The proposed framework collaboratively executes heterogeneous industrial tasks by selecting an optimal sensor set, meeting each task's quality-of-monitoring requirements. A mobile charging vehicle is scheduled to recharge sensors before their charging deadlines, aiming to prevent service interruptions. The goal is to minimize the system energy consumption by jointly optimizing the sensor activation and mobile charging vehicle scheduling, while considering task requirements, sensor charging deadlines, and the energy capacity of the vehicle. To solve this nontrivial problem, a novel scheme integrating reinforcement learning and marginal product based approximation algorithms is designed, which is computationally efficient and theoretically bounded. Simulation results demonstrate the feasibility and superiority of the proposed scheme.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2023)
Article
Engineering, Electrical & Electronic
Chen Ying, Zhen Zhao, Changyan Yi, You Shi, Jun Cai
Summary: The freshness of system information is a critical QoS indicator in industrial wireless sensor networks (IWSNs). Existing freshness metrics only consider single-type data, which cannot accurately measure the freshness of multiple types of data. Therefore, we propose a task-oriented information age metric that optimizes access selections and sampling frequencies to minimize the long-term age of information in IWSNs applications.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2023)
Article
Engineering, Electrical & Electronic
Huabing Lu, Xianzhong Xie, Zhaoyuan Shi, Hongjiang Lei, Helin Yang, Jun Cai
Summary: Non-orthogonal multiple access (NOMA) assisted semi-grant-free (SGF) transmission is a promising solution for the massive access problem in 5G and beyond networks. This paper studies the outage performance of greedy best user scheduling SGF scheme (BU-SGF) and proposes a fair SGF scheme using cumulative distribution function (CDF)-based scheduling (CS-SGF) to address the admission fairness problem. The theoretical analysis shows that both BU-SGF and CS-SGF schemes can achieve full diversity orders, but the rate performance is limited when the served users' data rate is capped. To overcome this limitation, a distributed power control strategy is proposed. Simulation results demonstrate the fairness performance of CS-SGF scheme and the effectiveness of the power control strategy.
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS
(2023)
Article
Engineering, Electrical & Electronic
Ran Wang, Hui Wang, Kun Zhu, Changyan Yi, Ping Wang, Dusit Niyato
Summary: Electric vehicles (EVs) are important for sustainable transportation, but their limited battery capacity and long charging time hinder widespread adoption. To address this issue, mobile charging services (MCSs) using mobile charging vehicles (MCVs) are investigated as supplemental charging methods. This article examines the advantages of MCSs under different charging scenarios and discusses future research and possible methodologies.
IEEE VEHICULAR TECHNOLOGY MAGAZINE
(2023)
Article
Computer Science, Information Systems
Jialiuyuan Li, Changyan Yi, Jiayuan Chen, Kun Zhu, Jun Cai
Summary: This article studies an energy-efficient scheduling problem for multiple UAV-assisted MEC. It formulates a joint optimization problem of UAVs' trajectory planning, energy renewal, and application placement. It proposes a TLRL approach to reach equilibriums in the optimization problem and analyzes the convergence and complexity of the proposed solution. Simulations demonstrate the superiority of the TLRL approach over counterparts.
IEEE INTERNET OF THINGS JOURNAL
(2023)
