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mobile-edge computing: Partial Computation Offloading Using Dynamic Voltage Scaling

IEEE TRANSACTIONS ON COMMUNICATIONS 2016年第10期64卷 4268-4282页

作者： Wang, Yanting Sheng, Min Wang, Xijun Wang, Liang Li, Jiandong Xidian Univ State Key Lab Integrated Serv Networks Inst Informat Sci Xian 710071 Peoples R China Shannxi Normal Univ Sch Comp Sci Xian 710119 Peoples R China

The incorporation of dynamic voltage scaling technology into computation offloading offers more flexibilities for mobile edge computing. In this paper, we investigate partial computation offloading by jointly optimizing the computational speed of smart mobile device (SMD), transmit power of SMD, and offloading ratio with two system design objectives: energy consumption of SMD minimization (ECM) and latency of application execution minimization (LM). Considering the case that the SMD is served by a single cloud server, we formulate both the ECM problem and the LM problem as nonconvex problems. To tackle the ECM problem, we recast it as a convex one with the variable substitution technique and obtain its optimal solution. To address the nonconvex and nonsmooth LM problem, we propose a locally optimal algorithm with the univariate search technique. Furthermore, we extend the scenario to a multiple cloud servers system, where the SMD could offload its computation to a set of cloud servers. In this scenario, we obtain the optimal computation distribution among cloud servers in closed form for the ECM and LM problems. Finally, extensive simulations demonstrate that our proposed algorithms can significantly reduce the energy consumption and shorten the latency with respect to the existi ofngfloading schemes.

关键词： Partial computation offloading dynamic voltage scaling mobile-edge computing collaboration between communication and computation resources

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Energy-Optimized Partial Computation Offloading in mobile-edge computing With Genetic Simulated-Annealing-Based Particle Swarm Optimization

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IEEE INTERNET OF THINGS JOURNAL 2021年第5期8卷 3774-3785页

作者： Bi, Jing Yuan, Haitao Duanmu, Shuaifei Zhou, MengChu Abusorrah, Abdullah Beijing Univ Technol Sch Software Engn Fac Informat Technol Beijing 100124 Peoples R China Beihang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China New Jersey Inst Technol Dept Elect & Comp Engn Newark NJ 07102 USA King Abdulaziz Univ Ctr Res Excellence Renewable Energy & Power Syst Jeddah 21481 Saudi Arabia King Abdulaziz Univ Dept Elect & Comp Engn Fac Engn Jeddah 21481 Saudi Arabia

Smart mobile devices (SMDs) can meet users' high expectations by executing computational intensive applications but they only have limited resources, including CPU, memory, battery power, and wireless medium. To tackle this limitation, partial computation offloading can be used as a promising method to schedule some tasks of applications from resource-limited SMDs to high-performance edge servers. However, it brings communication overhead issues caused by limited bandwidth and inevitably increases the latency of tasks offloaded to edge servers. Therefore, it is highly challenging to achieve a balance between high-resource consumption in SMDs and high communication cost for providing energy-efficient and latency-low services to users. This work proposes a partial computation offloading method to minimize the total energy consumed by SMDs and edge servers by jointly optimizing the offloading ratio of tasks, CPU speeds of SMDs, allocated bandwidth of available channels, and transmission power of each SMD in each time slot. It jointly considers the execution time of tasks performed in SMDs and edge servers, and transmission time of data. It also jointly considers latency limits, CPU speeds, transmission power limits, available energy of SMDs, and the maximum number of CPU cycles and memories in edge servers. Considering these factors, a nonlinear constrained optimization problem is formulated and solved by a novel hybrid metaheuristic algorithm named genetic simulated annealing-based particle swarm optimization (GSP) to produce a close-to-optimal solution. GSP achieves joint optimization of computation offloading between a cloud data center and the edge, and resource allocation in the data center. Real-life data-based experimental results prove that it achieves lower energy consumption in less convergence time than its three typical peers.

关键词： Servers Task analysis Optimization Energy consumption edge computing Bandwidth mobile handsets Computation offloading energy optimization genetic algorithm (GA) machine learning mobile-edge computing particle swarm optimization (PSO) simulated annealing (SA)

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Secure and Efficient Biometric-Based Anonymous Authentication Scheme for mobile-edge computing

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IEEE INTERNET OF THINGS JOURNAL 2024年第20期11卷 33604-33623页

作者： Zhu, Qiang You, Lin Hu, Gengran Wang, Shengguo Hangzhou Dianzi Univ Sch Cyberspace Secur Hangzhou 310018 Peoples R China Hangzhou Hikvis Digital Technol Co Ltd Hardware R&D Ctr Hangzhou 310050 Peoples R China

Currently, the biometric-based authentication schemes have been widely deployed in the mobile edge computing environment to ensure the authenticity of the edge nodes' identities. While some solutions for the mobile edge computing adopting a tripartite architecture employ anonymous authentication to protect the edge nodes' sensitive biometric data and transaction information, these solutions come with their own set of challenges. Specifically, the service provider is unable to hold the edge nodes accountable for any violation committed during the transactions. Moreover, the edge nodes are unable to revoke their identity information stored in the registration center. To address these issues, in this work, we introduce a secure and efficient biometric-based anonymous authentication scheme for the mobile edge computing. Our approach utilizes noninteractive zero-knowledge (NIZK) arguments and linear encryption to achieve anonymity and traceability of the edge nodes' identities, while also using accumulators to implement the revocability of the edge nodes' identities, ensuring that the edge nodes with revoked identities cannot pass the verification of the service provider. Additionally, we design an efficient batch verification protocol to handle anonymous authentication messages for the large-scale edge nodes. The feasibility of the proposed scheme could be proven positively by both of the experimental results and the security analysis. The experimental results show that our approach requires up to 418 ms to create an anonymous identity and 16.2 ms to verify it. The time consumption for batch verification of the anonymous identities of the 1200 edge nodes is only 54.4 ms.

关键词： Biometrics (access control) Authentication Security Multi-access edge computing Encryption Regulators Registers Anonymity authentication biometric mobile-edge computing privacy-preserving revocability traceability

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Joint Offloading and computing Optimization in Wireless Powered mobile-edge computing Systems

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2018年第3期17卷 1784-1797页

作者： Wang, Feng Xu, Jie Wang, Xin Cui, Shuguang Guangdong Univ Technol Sch Informat Engn Guangzhou 510006 Guangdong Peoples R China Fudan Univ Shanghai Inst Adv Commun & Data Sci Dept Commun Sci & Engn Key Lab Informat Sci Electromagnet WavesMoE Shanghai 200433 Peoples R China Univ Calif Davis Dept Elect & Comp Engn Davis CA 95616 USA Shenzhen Res Inst Big Data Shenzhen 518172 Peoples R China

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.

关键词： mobile-edge computing wireless power transfer computation offloading energy beamforming convex optimization

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Adaptive Bitrate Video Caching and Processing in mobile-edge computing Networks

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IEEE TRANSACTIONS ON mobile computing 2019年第9期18卷 1965-1978页

作者： Tuyen X Tran Pompili, Dario Rutgers State Univ Dept Elect & Comp Engn New Brunswick NJ 08854 USA

mobile-edge computing (MEC) is a promising paradigm that provides storage and computation resources at the network edge in order to support low-latency and computation-intensive mobile applications. In this article, we propose a joint collaborative caching and processing framework that supports Adaptive Bitrate (ABR)-video streaming in MEC networks. We formulate an Integer Linear Program (ILP) that determines the placement of video variants in the caches and the scheduling of video requests to the cache servers so as to minimize the expected delay cost of video retrieval. The considered problem is challenging due to its NP-completeness and to the lack of a-priori knowledge about video request arrivals. Our approach decomposes the original problem into a cache placement problem and a video request scheduling problem while preserving the interplay between the two. We then propose practically efficient solutions, including: (i) a novel heuristic ABR-aware proactive cache placement algorithm when video popularity is available, and (ii) an online low-complexity video request scheduling algorithm that performs very closely to the optimal solution. Simulation results show that our proposed solutions achieve significant increase in terms of cache hit ratio and decrease in backhaul traffic and content access delay compared to the traditional approaches.

关键词： Collaborative caching adaptive bitrate streaming multi-bitrate video mobile-edge computing

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Joint Multi-UAV Path Planning and LoS Communication for mobile-edge computing in IoT Networks With RISs

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IEEE INTERNET OF THINGS JOURNAL 2023年第3期10卷 2720-2727页

作者： Savkin, Andrey V. V. Huang, Chao Ni, Wei Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW 2052 Australia Hong Kong Polytech Univ Dept Ind & Syst Engn Hong Kong Peoples R China CSIRO Data61 Canberra ACT 2601 Australia

This article addresses a joint path planning and communication scheduling problem for a team of unmanned aerial vehicles (UAVs) equipped with central processing units and performing computing tasks for Internet of Things (IoT) devices located on uneven terrain. On such terrains, the Line of Sight (LoS) between a UAV and a ground IoT device may be blocked by buildings or mountains. In this article, reconfigurable intelligent surfaces (RISs) are deployed on the ground to improve wireless communication between UAVs and IoT devices. computing tasks and their results can be sent to and from UAVs either directly or via RIS-reflected paths. The objective is to develop a joint multi-UAV path planning/transmission scheduling algorithm that maximizes the number of computing tasks successfully and timely performed by the UAVs and transmitted back to the IoT ground devices and minimizes the total energy consumption of the UAVs. An effective path planning algorithm for this optimization problem is proposed. A mathematically rigorous proof of its asymptotic optimality is given. The developed algorithm is computationally effective and easily implementable in real time. Computer simulations and comparisons with other effective methods prove the effectiveness of the developed approach.

关键词： Internet of Things Task analysis Multi-access edge computing Autonomous aerial vehicles Navigation Buildings Trajectory Central processing unit (CPU)-equipped unmanned aerial vehicles (UAVs) Internet of Things (IoT) IoT networks mobile-edge computing navigation path planning reconfigurable intelligent surfaces (RIS) RIS-reflected paths transmission scheduling uneven terrains unmanned aerial vehicles

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Physical-Layer Assisted Secure Offloading in mobile-edge computing

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2020年第6期19卷 4054-4066页

作者： He, Xiaofan Jin, Richeng Dai, Huaiyu Wuhan Univ Sch Elect Informat Wuhan 430072 Peoples R China North Carolina State Univ Dept Elect & Comp Engn Raleigh NC 27695 USA

The wireless offloading feature of the recently advocated mobile-edge computing (MEC) imposes a risk of disclosing private user data to eavesdroppers. Physical-layer security approaches that are built on information theoretic methods can be applied to defend eavesdropping in MEC. Nonetheless, directly incorporating existing physical-layer security technique may introduce extra energy and delay costs to the resource-limited mobile device and thus substantially disrupt the users' offloading decisions. To fulfill effective secure offloading in MEC, there is a compelling need to properly optimize existing physical-layer security techniques and develop new offloading schemes accordingly. With this consideration, a novel physical-layer assisted secure offloading scheme is proposed in this work, in which the edge server proactively broadcasts jamming signals to impede eavesdropping and leverages full-duplex communication technique to effectively suppress the self-interference. Finding the optimal jamming signal and the corresponding optimal offloading ratio turns out to be a challenging bilevel optimization problem. The special structure of the secure offloading problem is exploited to develop efficient offloading algorithms. Numerical results are presented to validate the effectiveness of the proposed scheme.

关键词： Servers Jamming Security mobile handsets Wireless communication Task analysis Interference cancellation mobile-edge computing secure offloading physical-layer

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Joint Offloading and computing Design in Wireless Powered mobile-edge computing Systems With Full-Duplex Relaying

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IEEE ACCESS 2018年 6卷 72786-72795页

作者： Wen, Zhigang Yang, Kaixi Liu, Xiaoqing Li, Shan Zou, Junwei Beijing Univ Posts & Telecommun Commun & Network Ctr Sch Elect Engn Beijing 100876 Peoples R China

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 multipleoutput 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.

关键词： mobile-edge computing simultaneous wireless information and power transfer full duplex offload alternating optimization technique

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Communication-Efficient Offloading for mobile-edge computing in 5G Heterogeneous Networks

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IEEE INTERNET OF THINGS JOURNAL 2021年第13期8卷 10237-10247页

作者： Zhou, Ping Shen, Ke Kumar, Neeraj Zhang, Yin Hassan, Mohammad Mehedi Hwang, Kai Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430074 Peoples R China Thapar Inst Engn & Technol Dept Comp Sci & Engn Patiala 147004 Punjab India Asia Univ Dept Comp Sci & Informat Engn Taichung 41354 Taiwan Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Peoples R China King Saud Univ Coll Comp & Informat Sci Riyadh 11543 Saudi Arabia King Saud Univ Res Chair Smart Technol Riyadh 11543 Saudi Arabia Chinese Univ Hong Kong Shenzhen Sch Data Sci Shenzhen 518172 Peoples R China Chinese Univ Hong Kong Shenzhen Shenzhen Inst Artificial Intelligence & Robot Soc Shenzhen 518172 Peoples R China

The unified management of IoT devices with interoperability can be inspired by cloud computing. In addition, sinking the 5G core network to the edge brings chances for the deployment of end-to-end ultralow-latency services. However, the resource efficiency brought by heterogeneous computing devices in 5G spectrum multiplexing environments has encountered challenges. To discuss this issue from a comprehensive perspective, this article first proposes an ultralow-latency service deployment architecture in 5G heterogeneous networks, and three cognitive engines are the key components for efficient service communication across the terminal/edge/cloud computing structure. Then we give an analysis of application task model in the proposed architecture, and following the service response time models are established. In addition, it is efficient to deploy multiuser tasks with constraint resources when the differentiated user requirements are met. Finally, we conducted some experiments and the result statistics are up to our expectations. The first one is the system performance under two microcloud covered cells, and the second one is the performance comparison of the proposed solution with three single scenes of terminal computing, edge computing and cloud computing.

关键词： Engines Cloud computing Quality of experience Task analysis Computer architecture Resource management Internet of Things 5G heterogeneous networks computation offloading mobile-edge computing quality of experience quality of service service response time

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Multiobjective Optimized Cloudlet Deployment and Task Offloading for mobile-edge computing

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IEEE INTERNET OF THINGS JOURNAL 2021年第20期8卷 15582-15595页

作者： Zhu, Xiaojian Zhou, MengChu Changshu Inst Technol Sch Comp Sci & Engn Changshu 215500 Jiangsu Peoples R China New Jersey Inst Technol Dept Elect & Comp Engn Newark NJ 07102 USA St Petersburg State Marine Tech Univ Dept Cyber Phys Syst St Petersburg 198262 Russia

mobile-edge computing provides an effective approach to reducing the workload of smart devices and the network delay induced by data transfer through deploying computational resources in the proximity of the devices. In a mobile-edge computing system, it is of great importance to improve the quality of experience of users and reduce the deployment cost for service providers. This article investigates a joint cloudlet deployment and task offloading problem with the objectives of minimizing energy consumption and task response delay of users and the number of deployed cloudlets. Since it is a multiobjective optimization problem, a set of tradeoff solutions ought to be found. After formulating this problem as a mixed-integer nonlinear program and proving its NP-completeness, we propose a modified guided population archive whale optimization algorithm to solve it. The superiority of our devised algorithm over other methods is confirmed through extensive simulations.

关键词： Task analysis Cloud computing Delays Energy consumption Internet of Things Optimization edge computing Communication Internet of Things (IoT) mobile-edge computing multiobjective optimization offloading

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