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4th International Conference on Digital Technologies and Applications (ICDTA)

作者： Temghart, Abdelkarim Ait Marwan, Mbarek Ouaskou, Mohamed Baslam, Mohamed Sultan Moulay Slimane Univ TIAD Lab FST Beni Mellal Morocco Mohammed V Univ ENSIAS Rabat Morocco

ISBN: (纸本)9783031686740;9783031686757

Multi-access Edge Computing (MEC) is an emerging concept aimed at fulfilling the demands of low-latency and highly reliable applications in vehicular networks. computation offloading from vehicles to MEC servers allows for the execution of computation-intensive and time-sensitive applications. However, the resources available in MEC servers are not limitless. To achieve optimal performance, it is necessary for vehicles to make real-time decisions regarding their task offloading strategies, considering the dynamic network environment. A new approach is presented in this paper by proposing a multi-user non-cooperative computational offloading game within vehicular MEC networks. The game adjusts the offloading probability for each vehicle, based on factors such as the distance between the MEC and vehicles, communication model, and the competition for MEC resources among multiple vehicles. Furthermore, a best-response learning algorithm is assigned based on the computational offloading game. It aims to maximize each vehicle's utility and guarantees convergence to a unique equilibrium under a set of conditions. To validate the effectiveness and performance of the proposed approach, a series of experiments are conducted.

关键词： Game theory computation offloading Multi-access Edge Computing Vehicle communication Nash equilibrium

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Reliable Function computation offloading in Cloud-Edge Collaborative Network 23rd

Reliable Function Computation Offloading in Cloud-Edge Colla...

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23rd International Conference on Algorithms and Architectures for Parallel Processing (ICA3PP)

作者： Li, Shaonan Xie, Yongqiang Li, Zhongbo Qi, Jin Tian, Yumeng Acad Mil Sci Inst Syst Engn Beijing 100141 Peoples R China

ISBN: (纸本)9789819708000;9789819708017

In this paper, we focus on the cloud-edge collaborative network, where a task is decomposed into a set of functions and could be offloaded to different computing nodes, which is referred to as Function computation offloading (FCO). One of the most important problems in FCO is to schedule the functions in computing nodes to achieve low latency and high reliability. We formulate FCO scheduling in the Cloud-edge Collaborative Network as mixed-integer nonlinear programming. The objective is to minimise the end-to-end delay of a task while satisfying the latency and reliability constraints. To solve the problem, we propose an efficient mechanism to decide the redundancy of functions according to the reliability requirements. Then, we deploy the non-redundant functions on the computing nodes. Finally, we present a Reinforcement Learning (RL) to learn the scheduling policy of the redundant functions to further reduce the end-to-end delay of the task. Simulation results show that our proposed algorithm can significantly reduce tasks' completion time by about 13-26% with fewer iterations compared with other alternatives.

关键词： computation offloading Reliability Task Decomposition Reinforcement learning Cloud-edge Collaboration

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Distributed computation offloading and Trajectory Optimization in Multi-UAV-Enabled Edge Computing

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IEEE INTERNET OF THINGS JOURNAL 2022年第20期9卷 20096-20110页

作者： Chen, Xiangyi Bi, Yuanguo Han, Guangjie Zhang, Dongyu Liu, Minghan Shi, Han Zhao, Hai Li, Fengyun Northeastern Univ Sch Comp Sci & Engn Shenyang 110169 Peoples R China Minist Educ Engn Res Ctr Secur Technol Complex Network Syst Shenyang 110169 Peoples R China Hohai Univ Dept Informat & Commun Syst Changzhou 213022 Peoples R China Chinese Acad Sci Inst Acoust State Key Lab Acoust Beijing 100190 Peoples R China

The Internet of Things (IoT) technology has expanded network space by interconnected devices, which has been widely used in various fields, such as environmental monitoring, object tracking, risk warning, etc. Due to insufficient computing capacity, limited battery life, and unreliable communication environment in IoT, unmanned aerial vehicle (UAV)-enabled edge computing has been recently utilized to provide enhanced coverage and efficient computational support in the scenarios with sparse or unreliable ground infrastructure, such as disaster rescue, emergency response, military fields, etc. However, UAV-enabled edge computing faces many challenges, such as low offloading efficiency, high energy consumption, high complexity, etc. In this article, a distributed computation offloading scheme is proposed to provide computational support to large-scale IoT nodes and optimize the energy efficiency of multiple UAVs. First, to provide accurate and efficient computational support, a real-time intelligent positioning algorithm is designed to obtain the precise location information of IoT nodes. Then, a distributed computation offloading and path planning algorithm is presented, which jointly optimizes the computation offloading of large-scale IoT nodes and trajectory planning of multiple UAVs to reduce the energy consumption of UAVs. Furthermore, we develop a closed-form theoretical analysis model to demonstrate that the algorithm enables a performance guarantee related to energy efficiency. Finally, extensive simulations have been conducted and show that the proposed scheme can greatly improve the system utility and energy efficiency.

关键词： Internet of Things Task analysis Optimization Servers Edge computing computational modeling Energy consumption computation offloading edge computing Internet of Things (IoT) trajectory optimization

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Joint computation offloading and Resource Allocation in Covert VEC Networks 1

Joint Computation Offloading and Resource Allocation in Cove...

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1st ACM International Workshop on Radio Frequency (RF) Computing (RFCom)

作者： Jiang, Anguo Yang, Jia Meng, Deng Zhao, Liang Zhou, Huan China Three Gorges Univ Yichang Peoples R China Wuhan Univ Wuhan Peoples R China Northwestern Polytech Univ Xian Peoples R China

ISBN: (纸本)9798400712982

Covert communication can provide higher security by protecting communication behavior. In this paper, we utilize covert communication to protect the communication behavior in vehicular edge computing (VEC) networks, that is covert VEC networks, where multiple task vehicles (TVs) secretly initiate the transmission of sensitive tasks without being detected by attacker Willie. Specifically, TVs offload tasks to the neighboring roadside unit (RSU) subject to their limited abilities. Meanwhile, RSU transmits artificial noise to against Willie's eavesdropping attack. Then, we consider an optimization problem for joint computing offloading and resource allocation of the covert VEC networks conceived. A soft actor-critic (SAC)-based algorithm is proposed for minimizing total latency under given covert constraints in computation offloading and resource allocation. Simulation results demonstrate the superior performance of our proposed algorithm compared to similar benchmarks, which explores the possibility of achieving covert transmission in VEC networks.

关键词： VEC networks RSU covert communication SAC computation offloading resource allocation

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Blockchain-Inspired Secure computation offloading in a Vehicular Cloud Network

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IEEE INTERNET OF THINGS JOURNAL 2022年第16期9卷 14723-14740页

作者： Xu, Shilin Guo, Caili Hu, Rose Qingyang Qian, Yi Beijing Univ Posts & Telecommun Sch Informat & Commun Engn Beijing 100876 Peoples R China Utah State Univ Dept Elect & Comp Engn Logan UT 84322 USA Beijing Univ Posts & Telecommun Sch Informat & Commun Engn Beijing Key Lab Network Syst Architecture & Conve Beijing 100876 Peoples R China Univ Nebraska Lincoln Dept Elect & Comp Engn Omaha NE 68182 USA

With the emergence of computation-intensive vehicular applications, computation offloading based on mobile-edge computing (MEC) has become a promising paradigm in resource-constrained vehicular cloud networks (VCNs). However, when doing computation offloading in a VCN, malicious service providers can cause serious security concerns on the content offloading. To address that in this article, a blockchain-based secure computation offloading scheduling scheme is proposed. It embraces the blockchain-based trust management paradigm and smart contract-enabled deep reinforcement learning (DRL) algorithm. As for the trust management, the long-term reputation and short-term trust variability are jointly considered. Specifically, a novel three-valued subjective logic (3VSL) scheme is adopted to obtain a more comprehensive reputation, and the statistics of behavioral transitions can provide a short-term trust variability to timely capture the malicious behaviors. In addition, to securely update, validate, and store the trust information, we propose a hierarchical blockchain framework that comprises vehicular blockchain, roadside unit (RSU) blockchain, and cloud blockchain. Furthermore, a smart contract-enabled DRL algorithm is proposed to implement the secure and intelligent computation offloading scheduling in a VCN. Simulations are conducted to verify the effectiveness of the proposed scheme.

关键词： Blockchain Trust management Smart contracts computational modeling Task analysis Cloud computing Wireless communication Blockchain computation offloading deep reinforcement learning (DRL) reputation smart contract vehicular cloud networks (VCNs)

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Signaling-Based Incentive Mechanism for D2D computation offloading

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IEEE INTERNET OF THINGS JOURNAL 2022年第6期9卷 4639-4649页

作者： Chen, Min Wang, Haibo Han, Dafeng Chu, Xiaoli Beijing Jiaotong Univ Res Inst Broadband Wireless Mobile Commun Beijing 100044 Peoples R China Univ Sheffild Dept Elect & Elect Engn Sheffild S1 3JD England

Due to the limited computing capacity in mobile devices, device-to-device (D2D) computation offloading has been proposed as a promising solution to improving the quality of service in the Internet of Things (IoT) networks, by allowing mobile devices to exploit spare computing resources in nearby user devices. However, a major challenge to realizing this new paradigm is how to effectively motivate user devices to participate as computation providers (CPs) for computation requesters (CRs), which is further exacerbated by the fact that user incentives are usually coupled with information asymmetry between the network operator and user devices. This has not been sufficiently studied for D2D computation offloading. In this article, we propose a signaling-based incentive mechanism that leverages contract theory to address information asymmetry for D2D computation offloading. Based on the proposed contract-based incentive mechanism, we also solve the many-to-many CP-CR pairing problem by devising a polynomial-complexity matching scheme. Simulation results show that our proposed algorithm can effectively motivate user devices to participate in D2D computation offloading and select the most appropriate CPs to perform the computation tasks for corresponding CRs.

关键词： Device-to-device communication Task analysis Contracts computational modeling Internet of Things Delays Servers computation offloading device to device (D2D) incentive mechanism information asymmetry matching signaling game

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Cost-Efficient computation offloading in VEC Using Deep Reinforcement Learning Techniques 20

Cost-Efficient Computation Offloading in VEC Using Deep Rein...

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20th IEEE International Wireless Communications and Mobile Computing (IEEE IWCMC) - Green and Intelligent Communications

作者： Wang, Bingxin Tu, Dan Wang, Jie China Telecom Corp Ltd Beijing Res Inst Beijing Peoples R China

ISBN: (纸本)9798350361261;9798350361278

The rapid progression in autonomous driving and vehicle networking technologies has catalyzed the emergence of advanced vehicle applications, aiming to augment traffic safety and the driving experience. This advancement, however, is challenged by the limited computational and storage capacities inherent in on-board vehicle systems. To address this, Vehicle Edge Computing (VEC) emerges as a pivotal solution, enhancing vehicular computational capabilities. In this context, we introduce a novel VEC task offloading model utilizing Deep Reinforcement Learning (DRL). This model leverages the otherwise idle computational resources available in vehicles to facilitate efficient edge computing offloading within heterogeneous networks. A key innovation in our approach is the integration of Reinforcement Learning (RL) with Deep Learning (DL), significantly improving the convergence efficiency of the system. We also introduce an enhanced Q-learning algorithm tailored to jointly address the task offloading and processing challenges in VEC. This algorithm is adept at making optimal offloading decisions, aiming to minimize the overall system cost, encompassing both latency and energy consumption. Through rigorous simulation, our results demonstrate that this improved Q-learning approach substantially reduces total system costs while concurrently improving the quality of service in VEC environments. Our study not only offers a robust framework for computation offloading in vehicular networks but also paves the way for future research in AI-driven vehicular technology optimizations.

关键词： Vehicle Edge Computing Autonomous Driving Heterogeneous Networks computation offloading Q-learning Algorithm System Cost Minimization Quality of Service Vehicular Networking AI in Transportation Edge Computing Efficiency

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Joint computation offloading and Resource Allocation for Hybrid Cloud and Edge Computing in Satellite-assisted Unmanned System 39

Joint Computation Offloading and Resource Allocation for Hyb...

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39th Youth Academic Annual Conference of Chinese-Association-of-Automation (YAC)

作者： Wu, Yeyu Fan, Huili Yu, Jian Yang, Li Huang, Qilong Qi, Yaowen Nanjing Univ Sci & Technol Sch Automat Nanjing Peoples R China China Ship Dev & Design Ctr Wuhan Peoples R China

ISBN: (纸本)9798350390780;9798350379228

Unmanned systems such as unmanned surface vehicle (USV) and unmanned aerial vehicle (UAV) are difficult to undertake complex computational tasks on their own due to their limited computational capabilities. The existing method of sending the computation tasks to the ground terminal for processing will bring large transmission delay and reduce the real-time performance of task execution. Inspired by terrestrial mobile edge computing (MEC), deploying edge computing servers on low-orbit satellites (LEO) allows unmanned system to offload tasks to the satellite to enhance the task processing efficiency. However, owing to restricted computing and communication resources of satellite, designing appropriate computational offloading and resource allocation strategies is of great relevance. In this article, we propose a partial computation offloading strategy for a three-tier edge computing architecture consisting of unmanned system, satellites and cloud. We consider the energy optimization of the system under resource constraints while meeting the requirements of tasks. To address this issue, we propose a computational offloading method based on Deep Deterministic Policy Gradient (DDPG). This method can simultaneously decide the optimal offloading ratio, offloading location and resource allocation ratio under the dynamic environment with multi-task concurrency. The proposed method meets task's requirements while effectively decreasing the total energy consumption of system. Finally, experimental results prove the effectiveness of the formulated method.

关键词： Unmanned system computation offloading Cloud and edge computing Deep deterministic policy gradient Resource allocation

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Intelligent Terminal computation offloading Method Based on the Incentive of Reputation Value

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IEEE ACCESS 2022年 10卷 115503-115512页

作者： Zhi, Hui Wu, Yinliang Zhang, Xinyu Yang, Lixia Anhui Univ Key Lab Intelligent Comp & Signal Proc Minist Edu Hefei 230601 Peoples R China Anhui Univ Sch Comp Sci & Technol Hefei 230601 Peoples R China Anhui Univ Sch Elect & Informat Engn Hefei 230601 Peoples R China

Existing researches that apply blockchain into computation offloading do not consider the enthusiasm of terminals to participate in computation offloading, the fairness of terminals to obtain bookkeeping right and the consensus mechanism that applicable to computation offloading between terminals. So, in this paper, a computation offloading method based on reputation value incentive is proposed, and the detailed workflow of this method is designed. In the workflow, a computation resource allocation method based on dynamic adjustment auction, a mining difficulty setting method based on intelligent terminal devices (ITDs) clustering and an improved POW consensus mechanism based on reputation value (RVPOW) are proposed to ensure the fairness of ITDs to obtain bookkeeping right and improve the enthusiasm of ITDs to participate in computation offloading and blockchain consensus. Simulation results show that the proposed computation offloading method can encourage ITDs to participate in computation offloading, and the RVPOW consensus mechanism can achieve fast consensus.

关键词： Consensus protocol Clustering methods Resource management Heuristic algorithms computational modeling Servers Data security Blockchains computation offloading blockchain K-means clustering reputation value

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Distributionally Robust Optimization for computation offloading in Aerial Access Networks

Distributionally Robust Optimization for Computation Offload...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Jiang, Guanwang Jia, Ziye He, Lijun Dong, Chao Wu, Qihui Han, Zhu Nanjing University of Aeronautics and Astronautics Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Ministry of Industry and Information Technology Nanjing211106 China Southeast University National Mobile Communications Research Laboratory Nanjing211111 China Northwestern Polytechnical University School of Software Xi'an710072 China University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States

ISBN: (纸本)9798350351255

With the rapid increment of multiple users for data offloading and computation, it is challenging to guarantee the quality of service (QoS) in remote areas. To deal with the challenge, it is promising to combine aerial access networks (AANs) with multi-access edge computing (MEC) equipments to provide computation services with high QoS. However, as for uncertain data sizes of tasks, it is intractable to optimize the offloading decisions and the aerial resources. Hence, in this paper, we consider the AAN to provide MEC services for uncertain tasks. Specifically, we construct the uncertainty sets based on historical data to characterize the possible probability distribution of the uncertain tasks. Then, based on the constructed uncertainty sets, we formulate a distributionally robust optimization problem to minimize the system delay. Next, we relax the problem and reformulate it into a linear programming problem. Accordingly, we design a MEC-based distributionally robust latency optimization algorithm. Finally, simulation results reveal that the proposed algorithm achieves a superior balance between reducing system latency and minimizing energy consumption, as compared to other benchmark mechanisms in the existing literature. © 2024 IEEE.

关键词： computation offloading

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