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Falcon Optimization Algorithm-Based Energy Efficient Communication Protocol for Cluster-Based vehicular networks

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Computers, Materials & Continua 2024年第3期78卷 4243-4262页

作者： Youseef Alotaibi B.Rajasekar R.Jayalakshmi Surendran Rajendran Department of Software Engineering College of ComputingUmm Al-Qura UniversityMakkah21955Saudi Arabia Department of Electronics and Communication Engineering Sathyabama Institute of Science and TechnologyChennai600119India Department of Computer Science and Engineering Panimalar Engineering CollegeChennai600123India Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical ScienceChennai602105India

Rapid development in Information Technology(IT)has allowed several novel application regions like large outdoor vehicular networks for Vehicle-to-Vehicle(V2V)*** networks give a safe and more effective driving experience by presenting time-sensitive and location-aware *** communication occurs directly between V2V and Base Station(BS)units such as the Road Side Unit(RSU),named as a Vehicle to Infrastructure(V2I).However,the frequent topology alterations in VANETs generate several problems with data transmission as the vehicle velocity differs with ***,the scheme of an effectual routing protocol for reliable and stable communications is *** research demonstrates that clustering is an intelligent method for effectual routing in a mobile ***,this article presents a Falcon Optimization Algorithm-based Energy Efficient Communication Protocol for Cluster-based Routing(FOA-EECPCR)technique in *** FOA-EECPCR technique intends to group the vehicles and determine the shortest route in the *** accomplish this,the FOA-EECPCR technique initially clusters the vehicles using FOA with fitness functions comprising energy,distance,and trust *** the routing process,the Sparrow Search Algorithm(SSA)is derived with a fitness function that encompasses two variables,namely,energy and distance.A series of experiments have been conducted to exhibit the enhanced performance of the FOA-EECPCR *** experimental outcomes demonstrate the enhanced performance of the FOA-EECPCR approach over other current methods.

关键词： vehicular networks communication protocol clustering falcon optimization algorithm routing

Structural knowledge-driven meta-learning for task offloading in vehicular networks with integrated communications,sensing and computing

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Journal of Information and Intelligence 2024年第4期2卷 302-324页

作者： Ruijin Sun Yao Wen Nan Cheng Wei Wang Rong Chai Yilong Hui State Key Laboratory of ISN Xidian UniversityXi'an 710071China Chongqing Key Laboratory of Mobile Communication Technology Chongqing University of Posts and TelecommunicationsChongqing 400065China

Task offloading is a potential solution to satisfy the strict requirements of computation-intensive and latency-sensitive vehicular applications due to the limited onboard computing ***,the overwhelming upload traffic may lead to unacceptable uploading *** tackle this issue,for tasks taking environmental data as input,the data perceived by roadside units(RSU)equipped with several sensors can be directly exploited for computation,resulting in a novel task offloading paradigm with integrated communications,sensing and computing(I-CSC).With this paradigm,vehicles can select to upload their sensed data to RSUs or transmit computing instructions to RSUs during the *** optimizing the computation mode and network resources,in this paper,we investigate an I-CSC-based task offloading problem to reduce the cost caused by resource consumption while guaranteeing the latency of each *** this nonconvex problem can be handled by the alternating minimization(AM)algorithm that alternatively minimizes the divided four sub-problems,it leads to high computational complexity and local optimal *** tackle this challenge,we propose a creative structural knowledge-driven meta-learning(SKDML)method,involving both the model-based AM algorithm and neural ***,borrowing the iterative structure of the AM algorithm,also referred to as structural knowledge,the proposed SKDML adopts long short-term memory(LSTM)networkbased meta-learning to learn an adaptive optimizer for updating variables in each sub-problem,instead of the handcrafted counterpart in the AM ***,to pull out the solution from the local optimum,our proposed SKDML updates parameters in LSTM with the global loss *** results demonstrate that our method outperforms both the AM algorithm and the meta-learning without structural knowledge in terms of both the online processing time and the network performance.

关键词： Knowledge-driven meta-learning Integration of communication Sensing and computing Task offloading vehicular networks

Multi-agent reinforcement learning for edge information sharing in vehicular networks

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Digital Communications and networks 2022年第3期8卷 267-277页

作者： Ruyan Wang Xue Jiang Yujie Zhou Zhidu Li Dapeng Wu Tong Tang Alexander Fedotov Vladimir Badenko School of Communication and Information Engineering Chongqing University of Posts and TelecommunicationsChongqing400065China Advanced Network and Intelligent Connection Technology Key Laboratory of Chongqing Education Commission of China Chongqing400065China Key Laboratory of Ubiquitous Sensing and Networking in Chongqing Chongqing400065China Peter the Great St.Petersburg Polytechnic University Polytechnicheskaya29St.Petersburg195251Russia

To guarantee the heterogeneous delay requirements of the diverse vehicular services,it is necessary to design a full cooperative policy for both Vehicle to Infrastructure(V2I)and Vehicle to Vehicle(V2V)*** paper investigates the reduction of the delay in edge information sharing for V2V links while satisfying the delay requirements of the V2I ***,a mean delay minimization problem and a maximum individual delay minimization problem are formulated to improve the global network performance and ensure the fairness of a single user,respectively.A multi-agent reinforcement learning framework is designed to solve these two problems,where a new reward function is proposed to evaluate the utilities of the two optimization objectives in a unified ***,a proximal policy optimization approach is proposed to enable each V2V user to learn its policy using the shared global network *** effectiveness of the proposed approach is finally validated by comparing the obtained results with those of the other baseline approaches through extensive simulation experiments.

关键词： vehicular networks Edge information sharing Delay guarantee Multi-agent reinforcement learning Proximal policy optimization

Deep CLSTM for Predictive Beamforming in Integrated Sensing and Communication-Enabled vehicular networks

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Journal of Communications and Information networks 2022年第3期7卷 269-277页

作者： Chang Liu Xuemeng Liu Shuangyang Li Weijie Yuan Derrick Wing Kwan Ng School of Electrical Engineering and Telecommunications the University of New South WalesSydneyNSW 2052Australia School of Electrical and Information Engineering the University of SydneySydneyNSW 2006Australia Department of Electrical Electronic and Computer Engineeringthe University of Western AustraliaPerth WA 6009Australia Department of Electrical and Electronic Engineering Southern University of Science and TechnologyShenzhen 518055China

Predictive beamforming design is an essential task in realizing high-mobility integrated sensing and communication(ISAC),which highly depends on the accuracy of the channel prediction(CP),i.e.,predicting the angular parameters of ***,the performance of CP highly depends on the estimated historical channel stated information(CSI)with estimation errors,resulting in the performance degradation for most traditional CP *** further improve the prediction accuracy,in this paper,we focus on the ISAC in vehicle networks and propose a convolutional long-short term memory(CLSTM)recurrent neural network(CLRNet)to predict the angle of vehicles for the design of predictive *** the developed CLRNet,both the convolutional neural network(CNN)module and the LSTM module are adopted to exploit the spatial features and the temporal dependency from the estimated historical angles of vehicles to facilitate the angle ***,numerical results demonstrate that the developed CLRNet-based method is robust to the estimation error and can significantly outperform the state-of-the-art benchmarks,achieving an excellent sum-rate performance for ISAC systems.

关键词： integrated sensing and communication predictive beamforming deep learning convolutional longshort term neural network vehicular networks

A Blockchain-Assisted Distributed Edge Intelligence for Privacy-Preserving vehicular networks

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Computers, Materials & Continua 2023年第9期76卷 2959-2978页

作者： Muhammad Firdaus Harashta Tatimma Larasati Kyung-Hyune Rhee Department of Artificial Intelligence Convergence Pukyong National UniversityBusan48513Korea School of Electrical Engineering and Informatics Institut Teknologi BandungBandung40132Indonesia College of Information Technology and Convergence Division of Computer Engineering and AIPukyong National UniversityBusan48513Korea

The enormous volume of heterogeneous data fromvarious smart device-based applications has growingly increased a deeply interlaced cyber-physical *** order to deliver smart cloud services that require low latency with strong computational processing capabilities,the Edge Intelligence System(EIS)idea is now being employed,which takes advantage of Artificial Intelligence(AI)and Edge Computing Technology(ECT).Thus,EIS presents a potential approach to enforcing future Intelligent Transportation Systems(ITS),particularly within a context of a vehicular Network(VNets).However,the current EIS framework meets some issues and is conceivably vulnerable tomultiple adversarial attacks because the central aggregator server handles the entire ***,this paper introduces the concept of distributed edge intelligence,combining the advantages of Federated Learning(FL),Differential Privacy(DP),and blockchain to address the issues raised *** performing decentralized data management and storing transactions in immutable distributed ledger networks,the blockchain-assisted FL method improves user privacy and boosts traffic prediction ***,DP is utilized in defending the user’s private data from various threats and is given the authority to bolster the confidentiality of data-sharing *** model has been deployed in two strategies:First,DP-based FL to strengthen user privacy by masking the intermediate data during model ***,blockchain-based FL to effectively construct secure and decentralized traffic management in vehicular *** simulation results demonstrated that our framework yields several benefits for VNets privacy protection by forming a distributed EIS with privacy budget(ε)of 4.03,1.18,and 0.522,achieving model accuracy of 95.8%,93.78%,and 89.31%,respectively.

关键词： Edge intelligence federated learning differential privacy blockchain vehicular networks

A Novel Collision Supervision and Avoidance Algorithm for Scalable MAC of vehicular networks

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Chinese Journal of Electronics 2021年第1期30卷 164-170页

作者： WANG Shuai LU Yan ZHU Jie WANG Ping School of Information Science and Technology Donghua University

In order to meet low-latency and ultra-reliable requirements on safety services in vehicular networks, this paper proposes a novel Collision supervision and avoidance(CSA) algorithm for the contention based scalable media access control protocol. The two-dimensional Markov chain model of adaptive backoff state transition criterion in CSA has been built, which could efficiently match the backoff states of nodes to the dynamic changes of vehicular networks. The scalable transmissions can be achieved through supervised trend and matching backoff mechanisms with three adaptive backoff *** packet transmit probabilities for the backoff modes have been derived with the theoretical result of the enhanced throughput. The simulation results show the remarkable scalability performance such as normalized throughput > 0.92, PDR > 86% and delay < 6.5 ms even in the high-density and high-mobility environment.

关键词： vehicular networks Contention based scalable media access control Collision supervision and avoidance Adaptive backoff state transition Markov chain model

Enabling QoE-aware Mobile Cloud Video Recording over Roadside vehicular networks

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China Communications 2016年第8期13卷 63-73页

作者： Tongtong Jiang Zhaobin Deng Weiwei Huang Guoqing Zhang Department of Computer Science Sun Yat-sen University Guangdong Province Key Laboratory of Big Data Analysis and Processing Sun Yat-sen University Collaborative Innovation Center of High Performance Computing National University of Defense Technology Institute of Computing Technology Chinese Academy of Sciences

Most of previous video recording devices in mobile vehicles commonly store captured video contents locally. With the rapid development of 4G/Wi Fi networks, there emerges a new trend to equip video recording devices with wireless interfaces to enable video uploading to the cloud for video playback in a later time point. In this paper, we propose a QoE-aware mobile cloud video recording scheme in the roadside vehicular networks, which can adaptively select the proper wireless interface and video bitrate for video uploading to the cloud. To maximize the total utility, we need to design a control strategy to carefully balance the transmission cost and the achieved QoE for users. To this purpose, we investigate the tradeoff between cost incurred by uploading through cellular networks and the achieved QoE of users. We apply the optimization framework to solve the formulated problem and design an online scheduling algorithm. We also conduct extensive trace-driven simulations and our results show that our algorithm achieves a good balance between the transmission cost and user QoE.

关键词： mobile cloud video vehicular networks quality-of-experience

Mobility-Aware Partial Computation Offloading in vehicular networks: A Deep Reinforcement Learning Based Scheme

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China Communications 2020年第10期17卷 31-49页

作者： Jianfei Wang Tiejun Lv Pingmu Huang P.Takis Mathiopoulos School of Information and Communication Engineering Beijing University of Posts and Telecommunications(BUPT)Beijing 100876China Department of Informatics and Telecommunications National and Kapodistrian University of AthensAthens 15784Greece

Encouraged by next-generation networks and autonomous vehicle systems,vehicular networks must employ advanced technologies to guarantee personal safety,reduce traffic accidents and ease traffic *** leveraging the computing ability at the network edge,multi-access edge computing(MEC)is a promising technique to tackle such *** to traditional full offloading,partial offloading offers more flexibility in the perspective of application as well as deployment of such ***,in this paper,we investigate the application of partial computing offloading in-vehicle *** particular,by analyzing the structure of many emerging applications,e.g.,AR and online games,we convert the application structure into a sequential multi-component *** on shortening the application execution delay,we extend the optimization problem from the single-vehicle computing offloading(SVCOP)scenario to the multi-vehicle computing offloading(MVCOP)by taking multiple constraints into account.A deep reinforcement learning(DRL)based algorithm is proposed as a solution to this *** performance evaluation results have shown that the proposed algorithm achieves superior performance as compared to existing offloading mechanisms in deducing application execution delay.

关键词： partial offloading MEC fog computing vehicular networks D2D AR

Distributed Beamforming for Energy-Harvesting Relaying in vehicular networks

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Journal of Communications and Information networks 2020年第2期5卷 160-167页

作者： Yuxiang Liang Bing Li Rongqing Zhang Hongze Li Shengjie Zhao School of Software Engineering Tongji UniversityShanghai 201804China School of Electronics and Information Engineering Tongji UniversityShanghai 201804China

Recently,battery capacity and charging speed have been the bottlenecks of mobile communication *** harvesting(EH)relaying has become a promising solution for green 5th generation mobile communication with the advancement of wireless power transfer *** this paper,we investigate EH relaying in vehicular networks and adopt distributed beamforming(DB)to enhance the reliability and capacity of EH *** be more specific,we propose a DB solution based on the joint optimization of power-splitting(PS)*** amplify-and-forward relaying,to transform the optimization problem into a quasi-convex one,we apply the semidefinite relaxation(SDR)method so that we can effectively attain the global optimal solution,while the suboptimal DB solution with distributed optimal PS factor which only requires local channel state information is also *** decode-and-forward relaying,to get the optimal PS factors,we set a signal-to-noise ratio threshold at the relays,which can reduce the system error rate caused by the poor transmission *** results demonstrate the efficiency of our proposed DB-based EH relaying scheme in vehicular networks.

关键词： vehicular networks energy-harvesting relay SWIPT power splitting distributed beamforming