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UAV-Assisted computation offloading Toward Energy-Efficient Blockchain Operations in Internet of Things

IEEE WIRELESS COMMUNICATIONS LETTERS 2023年第8期12卷 1469-1473页

作者： Lan, Xunqiang Tang, Xiao Zhang, Ruonan Lin, Wensheng Han, Zhu Northwestern Polytechin Univ Sch Elect & Informat Xian 710072 Peoples R China Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China Univ Houston Dept Elect & Comp Engn Houston TX 77004 USA Kyung Hee Univ Dept Comp Sci & Engn Seoul 446701 South Korea

The advances in the Internet of Things (IoT) have provided generous opportunities for various applications, yet the data security concerns need to be specially addressed due to the limited capability of IoT devices. Toward this issue, we propose to deploy a blockchain system with practical Byzantine fault tolerance (PBFT) consensus for IoT data security provisioning. In particular, we conduct profound analyses regarding the PBFT procedure and elaborate on the computation burden therein. Then, an unmanned aerial vehicle (UAV) is leveraged to offload the computation and assist the blockchain operations. The system energy efficiency is introduced as the ratio of the blockchained IoT data amount and energy consumption for the computation and communication in the network. Further, we maximize the system energy efficiency by the joint design of offloading and transmission strategies, as well as the UAV deployment, within a reinforcement learning framework. Finally, simulation results are provided to corroborate the effectiveness of our proposal.

关键词： Internet of Things Blockchains Task analysis Servers Autonomous aerial vehicles Energy consumption computational modeling blockchain computation offloading unmanned aerial vehicle deep reinforcement learning

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Non-cooperative game algorithms for computation offloading in mobile edge computing environments

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JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING 2023年 172卷 18-31页

作者： Chen, Jianguo Deng, Qingying Yang, Xulei Sun Yat sen Univ Sch Software Engn Guangzhou 519082 Peoples R China Xiangtan Univ Sch Math & Computat Sci Xiangtan 411105 Peoples R China Agcy Sci Technol & Res Inst Infocomm Res Singapore 138632 Singapore

Mobile Edge Computing (MEC) has become a promising technology for 5G networks. computation offloading is an essential issue of MEC, which enables mobile User Equipment (UE) to enjoy rich wireless resources and huge computing power anywhere. This paper considers the Quality-of-Experience (QoE) of UEs in 5G MEC systems and presents a dynamic non-cooperative game (QCOG-DG) algorithm and a static non-cooperative game (QCOG-SG) algorithm for computation offloading of MEC applications. We establish an MEC computation offloading model by considering the QoE requirements of UEs, and discuss the communication overheads, computation cost, and energy consumption models to minimize the energy consumption and time delay of each UE. Considering that there are multiple UEs who want to offload their computation tasks to a resource-constrained MEC server, and each UE is selfish and competitive, we formulate the problem of computation offloading decision as a non-cooperative game model. We prove the existence of a Nash Equilibrium (NE) solution for the proposed game model. In addition, we propose an algorithm that jointly optimizes energy consumption and time delay under QoE preferences to achieve optimal offloading benefits for each UE. Moreover, we respectively propose a dynamic non-cooperative game (QCOG-DG) algorithm and a static non-cooperative game (QCOG-SG) algorithm to efficiently find the NE solution. Extensive simulation experiments are conducted to verify the effectiveness of the proposed MEC computation offloading model and the QCOG-DG and QCOG-SG algorithms. Simulation results show that the proposed QCOG-DG algorithm can efficiently find the NE solutions in the MEC scenarios with UEs of different sizes. (c) 2022 Elsevier Inc. All rights reserved.

关键词： computation offloading Dynamic game 5G networks Mobile edge computing Non-cooperative game

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Distributed and Collective Intelligence for computation offloading in Aerial Edge Networks

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2023年第7期24卷 7516-7526页

作者： Su, Jian Yu, Shiming Li, Bin Ye, Yinghui Nanjing Univ Informat Sci & Technol Sch Comp & Software Nanjing 210044 Peoples R China Xian Univ Posts & Telecommun Shaanxi Key Lab Informat Commun Network & Secur Xian 710121 Shaanxi Peoples R China Xian Univ Posts & Telecommun Shaanxi Key Lab Informat Commun Network & Secur Xian 710061 Peoples R China

Unmanned aerial vehicles (UAVs) with integrated computing platforms can be used to provide computing offloading services for ground user equipments (UEs) with limited local computing capabilities, especially in remote areas. In this paper, we focus on the task offloading in an aerial edge network (AEN) assisted by a UAV. We aim at minimizing the sum energy consumption of all UEs by the joint optimization of the task offloading decisions and the UAV position under the constraints of the latency and the total energy of UAV. The formulated optimization problem is a mixed-integer nonconvex problem and involves coupling of many optimization variables. To address this challenge, we first transform the original optimization problem into a linear convex optimization problem via reformulation linearization technology, and then the alternating direction method of multipliers (ADMM) algorithm is proposed to achieve the approximate optimal solution. Numerical results confirm that the proposed ADMM algorithm can effectively reduce the total of energy consumption of UEs and ensure the continuous operation of the UEs.

关键词： Optimization Task analysis Autonomous aerial vehicles Energy consumption Servers computational modeling Resource management Mobile edge computing unmanned aerial vehicle computation offloading alternating direction method of multipliers

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Online computation offloading with double reinforcement learning algorithm in mobile edge computing

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JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING 2023年 171卷 28-39页

作者： Liao, Linbo Lai, Yongxuan Yang, Fan Zeng, Wenhua Xiamen Univ Shenzhen Res Inst Sch Informat Xiamen Shenzhen Peoples R China Longyan Univ Sch Math & Informat Engn Longyan Peoples R China Xiamen Univ Sch Aerosp Engn Dept Automat Xiamen Peoples R China

Smart mobile devices have recently emerged as a promising computing platform for computation tasks. However, the task performance is restricted by the computing power and battery capacity of mobile devices. Mobile edge computing, an extension of cloud computing, solves this problem well by providing computational support to mobile devices. In this paper, we discuss a mobile edge computing system with a server and multiple mobile devices that need to perform computation tasks with priorities. The limited resources of the mobile edge computing server and mobile device make it challenging to develop an offloading strategy to minimize both delay and energy consumption in the long term. To this end, an online algorithm is proposed, namely, the double reinforcement learning computation offloading (DRLCO) algorithm, which jointly decides the offloading decision, the CPU frequency, and transmit power for computation offloading. Concretely, we first formulate the power scheduling problem for mobile users to minimize energy consumption. Inspired by reinforcement learning, we solve the problem by presenting a power scheduling algorithm based on the deep deterministic policy gradient (DDPG). Then, we model the task offloading problem to minimize the delay of tasks and propose a double Deep Q -networks (DQN) based algorithm. In the decision-making process, we fully consider the influence of task queue information, channel state information, and task information. Moreover, we propose an adaptive prioritized experience replay algorithm to improve the model training efficiency. We conduct extensive simulations to verify the effectiveness of the scheme, and the simulation results show that compared with the conventional schemes, our method reduces the delay by 48% and the energy consumption by 53%.& COPY;2022 Elsevier Inc. All rights reserved.

关键词： Mobile edge computing Power control computation offloading Deep deterministic policy gradient Double Deep Q -Networks

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Multi-Agent Reinforcement Learning Aided computation offloading in Aerial Computing for the Internet-of-Things

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IEEE TRANSACTIONS ON SERVICES COMPUTING 2023年第3期16卷 1976-1986页

作者： Qin, Zeyu Yao, Haipeng Mai, Tianle Wu, Di Zhang, Ni Guo, Song Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China China Elect Corp Res Inst 6 Beijing 100846 Peoples R China Hong Kong Polytech Univ Dept Comp Hung Hom Hong Kong Peoples R China

LEO satellite networks have become a necessary supplement to terrestrial networks aiming to provide worldwide, ubiquitous connectivity, especially in complicated areas (e.g., mountains, oceans, and disaster areas) where terrestrial network infrastructures are typically sparingly distributed or unavailable. However, the increasing computation-intensive Internet-of-Things (IoT) applications (e.g., real-time remote monitoring, intelligent transportation) require not only efficient and reliable communication but also massive computing capabilities. Constrained by the battery and computing resources, the computing tasks and data of applications have to be transmitted to remote cloud servers. This bandwidth limitation and high transmission delay in LEO networks will reduce the quality-of-service (QoS) of IoT applications. Recently, the combination of LEO networks and edge computing (i.e., Satellite Mobile Edge Computing, SMEC) offers significant opportunities to address these problems. The IoT devices can directly get the computing resources directly from satellites rather than remote servers, thus avoiding long-distance transmission. Considering the resource constraints on satellites, offloading policy plays a crucial role in whole system performance. In this paper, we design a hybrid offloading architecture, which applies a centralized training and distributed execution framework. Also, we propose a multi-agent actor-critic reinforcement learning algorithm, where a centralized "critic" is augmented with the global network state to ease the training procedure of distributed user equipments (UE) by evaluating the benefits of their decisions, while the UEs can adjust their policies according to the critic's evaluation and choose their own decisions relying on their observations.

关键词： Satellites Task analysis Servers Delays Low earth orbit satellites Computer architecture computational modeling Aerial computing computation offloading deep reinforcement learning mobile edge computing multi-agent system

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New Bridge to Cloud: An Ultra-Dense LEO Assisted Green computation offloading Approach

IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND NETWORKING

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IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND NETWORKING 2023年第2期7卷 552-564页

作者： Tang, Zhixuan Yu, Kai Yang, Guannan Cai, Lin X. Zhou, Haibo Nanjing Univ Sch Elect Sci & Engn Nanjing 210023 Peoples R China Nanjing Univ Finance & Econ Coll Informat Engn Nanjing 210046 Peoples R China IIT Dept Elect & Comp Engn Chicago IL 60616 USA

Mobile edge computing and cloud computing have emerged as effective technologies to alleviate the increasing computational workload of mobile devices. As a promising enabling 6G technology, the ultra-dense (UD) low earth orbit (LEO) satellite network with low communication latency and high throughput is considered a new bridge for cloud computation offloading. In this paper, we investigate energy-efficient cloud and edge computing in UD-LEO-assisted terrestrial-satellite networks. An optimization problem aiming at minimizing the energy consumption of the computation tasks is formulated. The optimization problem is a mixed-integer non-linear programming problem. To solve this problem, we decompose it into two subproblems, i.e., a joint user association and task scheduling subproblem, and an adaptive computation resource allocation subproblem. For the first subproblem, we model the input of a forward neural network (NN) as the large-scale information (i.e., channel gain and task arrival rates) and obtain the optimal solution by transforming the direct output of the NN. For the second subproblem, we introduce a successive convex approximation method to optimize it iteratively. The simulation results show that our proposed user association and task scheduling strategy outperforms two benchmark algorithms in terms of energy consumption under a strict delay bound and high user density.

关键词： Ultra-dense LEO terrestrial-satellite network computation offloading user association

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Joint Detection and computation offloading With Age of Information in Mobile Edge Networks

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2023年第3期10卷 1417-1430页

作者： Feng, Jialiang Gong, Jie Sun Yat Sen Univ Sch Comp Sci & Engn Guangdong Key Lab Informat Secur Technol Guangzhou 510006 Peoples R China

As a promising computing paradigm, mobile edge computing can sink the computing resources from the cloud center into the edge of the network, consequently enhancing the ability of data processing. To meet the real-time requirements of some computation-intensive tasks in the end devices, e.g., face recognition, video analysis, etc., the resource-constrained end devices require to offload the tasks to the edge nodes with more computing resources. In order to optimize the offloading decisions, the status information defined as status of the available computation capacity held by the end devices becomes critical. If the status held by the end devices is stale, i.e., the Age-of-Information (AoI) is high, it is impossible to make effective offloading decisions. In this paper, we formulate a problem of maximizing the average computation rate of the edge system. It is decomposed into several sub-problems, including detection action optimization and offloading action optimization. A node sorting algorithm based on status and AoI is proposed to optimize the detection sequence. Then, a threshold-based nodes' status detection algorithm is devised. In addition, we propose an AoI-aware detected proximal policy optimization framework (ADPPO) to optimize the offloading decisions based on status information and AoI. The numerical results show that the proposed framework outperforms other offloading algorithms in terms of the average computation rate.

关键词： Image edge detection Task analysis Real-time systems Edge computing Wireless communication Optimization Heuristic algorithms Age of information computation offloading deep reinforcement learning edge computing nodes detection

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Joint computation offloading and Resource Allocation for D2D-Assisted Mobile Edge Computing

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IEEE TRANSACTIONS ON SERVICES COMPUTING 2023年第3期16卷 1949-1963页

作者： Jiang, Wei Feng, Daquan Sun, Yao Feng, Gang Wang, Zhenzhong Xia, Xiang-Gen Shenzhen Univ Coll Elect & Informat Engn Shenzhen Key Lab Digital Creat Technol Guangdong Prov Engn Lab Digital Creat TechnolGuan Shenzhen 518060 Guangdong Peoples R China Univ Glasgow James Watt Sch Engn Glasgow G12 8QQ Scotland Univ Elect Sci & Technol China Yangtze Delta Reg Inst Huzhou Huzhou 313001 Peoples R China Univ Elect Sci & Technol China Natl Key Lab Sci & Technol Commun Chengdu 611731 Peoples R China China Media Grp Tech Management Ctr Beijing 100020 Peoples R China Univ Delaware Dept Elect & Comp Engn Newark DE 19716 USA

computation offloading via device-to-device communications can improve the performance of mobile edge computing by exploiting the computing resources of user devices. However, most proposed optimization-based computation offloading schemes lack self-adaptive abilities in dynamic environments due to time-varying wireless environment, continuous-discrete mixed actions, and coordination among devices. The conventional reinforcement learning based approaches are not effective for solving an optimal sequential decision problem with continuous-discrete mixed actions. In this paper, we propose a hierarchical deep reinforcement learning (HDRL) framework to solve the joint computation offloading and resource allocation problem. The proposed HDRL framework has a hierarchical actor-critic architecture with a meta critic, multiple basic critics and actors. Specifically, a combination of deep Q-network (DQN) and deep deterministic policy gradient (DDPG) is exploited to cope with the continuous-discrete mixed action spaces. Furthermore, to handle the coordination among devices, the meta critic acts as a DQN to output the joint discrete action of all devices and each basic critic acts as the critic part of DDPG to evaluate the output of the corresponding actor. Simulation results show that the proposed HDRL algorithm can significantly reduce the task computation latency compared with baseline offloading schemes.

关键词： Task analysis Servers Resource management Device-to-device communication Wireless communication Mobile handsets Energy consumption computation offloading deep reinforcement learning Index Terms device-to-device mobile edge computing resource allocation

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Energy-Efficient Joint computation offloading and Resource Allocation Strategy for ISAC-Aided 6G V2X Networks

IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND NETWORKING

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IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND NETWORKING 2023年第1期7卷 413-423页

作者： Liu, Qian Luo, Rui Liang, Hairong Liu, Qilie Chongqing Univ Posts & Telecommun Sch Commun & Informat Engn Chongqing Key Lab Mobile Commun Technol Chongqing 400065 Peoples R China Chongqing Univ Posts & Telecommun Mobile Commun Technol Key Lab Minist Educ Chongqing 400065 Peoples R China Minist Educ Postdoctoral Res Workstat Engn Res Ctr Mobile Comm Chongqing 400065 Peoples R China

Integrated sensing and communications (ISAC) is a pillar technology of 6G to enable intelligent environment awareness and hardware cost efficiencies. In ISAC-aided 6G vehicle-to-everything (V2X) networks, the perception data fusion from multiple sources can make a reliable and efficient data sharing to guarantee driving safety. However, the contradiction between task delay requirement and energy consumption becomes more and more prominent with the growth of computing task amount. Therefore, this paper proposes a joint computation offloading and resource allocation strategy to build greener V2X networks with mobile-edge computing (MEC) and ISAC technologies. A data fusion architecture for cooperative perception is first introduced to support fusing massive perception data from wireless infrastructures and vehicles. Then, a minimization problem of queuing latency is formulated with long-term latency and energy consumption constraints for data fusion computing tasks. The problem is further reformulated by the Lyapunov optimization method to transfer delay and energy constraints into queue stability problems. Finally, a joint computation offloading and resource allocation (JCORA) scheme is proposed to obtain the optimal computation offloading and resource allocation decision, achieving the balance between task delay and energy consumption. Extensive simulations validate the effectiveness of the proposed strategy compared with other baseline schemes.

关键词： Task analysis Resource management Sensors Vehicle-to-everything Data integration Delays computational modeling Integrated sensing and communications vehicle-to-everything networks computation offloading resource allocation energy efficiency

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Profit-Optimized computation offloading With Autoencoder-Assisted Evolution in Large-Scale Mobile-Edge Computing

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IEEE INTERNET OF THINGS JOURNAL 2023年第13期10卷 11896-11909页

作者： Yuan, Haitao Hu, Qinglong Bi, Jing Lu, Jinhu Zhang, Jia Zhou, Mengchu Beihang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China Beijing Univ Technol Sch Software Engn Fac Informat Technol Beijing 100124 Peoples R China Southern Methodist Univ Dept Comp Sci Dallas TX 75206 USA New Jersey Inst Technol Dept Elect & Comp Engn Newark NJ 07102 USA

Cloud-edge hybrid systems are known to support delay-sensitive applications of contemporary industrial Internet of Things (IoT). While edge nodes (ENs) provide IoT users with real-time computing/network services in a pay-as-you-go manner, their resources incur cost. Thus, their profit maximization remains a core objective. With the rapid development of 5G network technologies, an enormous number of mobile devices (MDs) have been connected to ENs. As a result, how to maximize the profit of ENs has become increasingly more challenging since it involves massive heterogeneous decision variables about task allocation among MDs, ENs, and a cloud data center (CDC), as well as associations of MDs to proper ENs dynamically. To tackle such a challenge, this work adopts a divide-and-conquer strategy that models applications as multiple subtasks, each of which can be independently completed in MDs, ENs, and a CDC. A joint optimization problem is formulated on task offloading, task partitioning, and associations of users to ENs to maximize the profit of ENs. To solve this high-dimensional mixed-integer nonlinear program, a novel deep-learning algorithm is developed and named as a Genetic Simulated-annealing-based Particle-swarm-optimizer with Stacked Autoencoders (GSPSA). Real-life data-based experimental results demonstrate that GSPSA offers higher profit of ENs while strictly meeting latency needs of user tasks than state-of-the-art algorithms.

关键词： Autoencoders computation offloading high-dimensional optimization algorithms mobile-edge computing (MEC) particle swarm optimization

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