Article
Engineering, Chemical
Jixian Zhang, Mingyi Zong, Weidong Li
Summary: This study addresses the problem of allocating wireless resources for multiple base stations in the construction of a metaverse. It proposes an integer linear programming constraint model and an OPT-VCGRA mechanism to optimize resource allocation. Additionally, a MDTRAP mechanism based on monotonic allocation and key value theory is designed for optimal allocation and pricing. Experimental results show that the MDTRAP mechanism effectively maximizes social welfare and outperforms the OPT-VCGRA mechanism in terms of execution time and total payment.
Article
Computer Science, Information Systems
Ao Zhou, Xiao Ma, Siyi Gao, Shangguang Wang
Summary: The article discusses the feasibility of using parked-vehicle-assisted mobile edge computing to address computation offloading issues, and explores challenges related to privacy, data security, and heterogeneous environments. Solutions are proposed and verified through simulations with algorithms.
ACM TRANSACTIONS ON INTERNET TECHNOLOGY
(2022)
Article
Engineering, Electrical & Electronic
Dara Ron, Jung-Ryun Lee
Summary: This paper investigates the use of transmit power control algorithm to minimize interference between cellular users and vehicles in V2V communication. The proposed algorithm, trained using a deep neural network, achieves better performance with lower computation complexity compared to other methods.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2022)
Article
Computer Science, Information Systems
Jianbin Xue, Qi Wang, Han Zhang, Na An, Chengbin An
Summary: This paper proposes a model that uses idle-parked vehicles to assist Vehicular Edge Computing (VEC) servers in offloading computing tasks. A more flexible dynamic offloading scheme is proposed based on a comprehensive consideration of the selection strategy and pricing strategy. Through game analysis, it is proven that there is a unique Nash equilibrium in the interaction between the VEC server and the idle-parked vehicle. An improved joint selection decision and pricing decision algorithm based on the Branch and Bound (JSPBB) algorithm is also proposed. The simulation results show that the proposed algorithm effectively solves the problem and achieves good performance, enabling the system to obtain higher benefits.
Article
Computer Science, Artificial Intelligence
Zhiyang Chen, Jiapeng You, Hongwei Jiang, Xinguo Ming, Poly Z. H. Sun
Summary: Efficient order allocation is crucial for third-party vehicle logistics platforms. However, current allocation methods often suffer from low transportation efficiency and difficulty in obtaining accurate information. This paper proposes a new allocation method based on the auction mechanism to address these issues more effectively.
ADVANCED ENGINEERING INFORMATICS
(2023)
Article
Economics
Rohan Best
Summary: A major challenge for policymakers is determining appropriate subsidy amounts for household energy investments, which can lead to inequitable support. Equitable reverse auctions provide a novel approach to address this issue, allowing households to bid for government subsidies needed for energy investments. These auctions promote equity by targeting specific socio-economic groups, resulting in lower inequality and cost-effective government support compared to equal subsidies. Although equitable reverse auctions may have reduced cost-effectiveness compared to standard reverse auctions, they offer flexibility within socio-economic groups and help reduce non-additional subsidy spending.
Article
Computer Science, Information Systems
Wenyuan Xie, Liming Lin, Ting Lyu, Haitao Xu
Summary: In this paper, the resource allocation problem in ocean areas for multi-access edge computing (MEC) assisted satellite networks is studied. An online multi-round auction-based resource allocation (OMARA) approach is proposed to address the demand heterogeneity of users and limited resources of satellites. The proposed approach enables effective determination of resource trading prices and matching service relationships between satellites and users. Simulation results demonstrate that the proposed approach outperforms existing algorithms in maximizing service satisfaction.
Article
Computer Science, Information Systems
Vibha Jain, Bijendra Kumar
Summary: With the rise of terminal devices, network traffic has increased rapidly, leading to the introduction of fog computing to provide proximate and spot-on services. However, due to the decentralized and untrusted behavior of fog nodes, sensitive trade and pricing information may be tampered with. This paper proposes a joint resource allocation and pricing scheme using blockchain employed smart contract to prevent malicious node tampering and enable fair and secure payment between user nodes and fog nodes.
PEER-TO-PEER NETWORKING AND APPLICATIONS
(2021)
Article
Engineering, Electrical & Electronic
Wenhao Fan, Jie Liu, Mingyu Hua, Fan Wu, Yuan'an Liu
Summary: This paper proposes a joint task offloading and resource allocation scheme for a parked-and-moving-vehicles-assisted MEC scenario consisting of multiple devices, parked vehicles, and moving vehicles.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2022)
Article
Engineering, Electrical & Electronic
Xumin Pu, Tiantian Lei, Wanli Wen, Wenting Feng, Zhengqiang Wang, Qianbin Chen, Shi Jin
Summary: We investigate a mobile edge computing system that supports collaborative task offloading. To address challenges including limited battery capacity and complex energy expenditure, we propose a resource sharing auction algorithm and a low-complexity resource allocation algorithm. The numerical results demonstrate the effectiveness of the proposed mechanism and algorithms.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2023)
Article
Engineering, Electrical & Electronic
Xuan-Qui Pham, Thien Huynh-The, Eui-Nam Huh, Dong-Seong Kim
Summary: This article investigates a partial offloading strategy for multi-user PVMEC and proposes a low-complexity distributed offloading scheme using game theory, which can significantly improve the system utility.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2022)
Article
Computer Science, Information Systems
Feng Zeng, Ranran Rou, Qi Deng, Jinsong Wu
Summary: This paper proposes a vehicular edge computing (VEC) scheme that utilizes idle resources in parked vehicles to process computing tasks and introduces incentive mechanisms to encourage parked vehicles to participate in VEC. A system architecture for task offloading is designed, where an edge crowdsourcing platform (ECP) manages and schedules the resources of parked vehicles. Based on Stackelberg game, the interactions between VEC participants are formulated as a task allocation optimization problem, and a price model is established to maximize utility for each participant. The simulation results show that the proposed scheme outperforms traditional methods.
PEER-TO-PEER NETWORKING AND APPLICATIONS
(2023)
Article
Computer Science, Theory & Methods
R. Anantha Kumar, K. Kartheeban
Summary: The proposed adaptive scheduling algorithm called Dynamic pricing based Combinatorial Auction aims to increase resource utilization and user satisfaction through dynamic pricing with combinatorial auction.
JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING
(2021)
Article
Computer Science, Theory & Methods
Xuyang Ma, Du Xu, Katinka Wolter
Summary: This paper proposes a distributed Feedback-based Combinatorial Multi-unit Double Auction mechanism backed by blockchain to establish a cloud resource market that not only produces high social welfare but also motivates participants to provide high-quality service.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2022)
Article
Computer Science, Information Systems
Huan Zhou, Tong Wu, Xin Chen, Shibo He, Deke Guo, Jie Wu
Summary: This article proposes a novel Reverse Auction-based Computation Offloading and Resource Allocation Mechanism, named RACORAM, for mobile Cloud-Edge computing. RACORAM uses reverse auction to stimulate edge server owners to participate in the offloading process, aiming to minimize the cost of the Cloud Service Center (CSC). The article also presents low-complexity algorithms to solve the optimization problems.
IEEE TRANSACTIONS ON MOBILE COMPUTING
(2023)
Article
Computer Science, Information Systems
Peng Qin, Miao Wang, Xiongwen Zhao, Suiyan Geng
Summary: Due to the limited coverage of the existing terrestrial fifth-generation (5G) network, meeting the increasing demand for seamless network connection is difficult. The current network resource allocation methods mainly focus on improving system performance from the perspective of resource utilization without considering users' specific needs for network content. This poses challenges to efficient network service and flexible resource allocation. To address this, we propose a content service-oriented resource allocation model for space-air-ground integrated sixth-generation networks (SAGIN 6G). We formulate a three-sided matching issue among SAGINE, content sources, and users, and develop algorithms for resource allocation in a distributed manner. Extensive simulations show that our approach outperforms traditional benchmark schemes in terms of system throughput, CSP revenue, and user experience.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Engineering, Electrical & Electronic
Zihao Fu, Fei Du, Xiongwen Zhao, Suiyan Geng, Yu Zhang, Peng Qin
Summary: Taking advantage of deep neural network (DNN) and long short-term memory (LSTM), a novel approach is proposed to accurately predict millimeter-wave (mmWave) channel features. This approach considers both historical states of channel features and position information, and utilizes a unique DNN-LSTM structure for prediction. Experimental results conducted in a railway station demonstrate that the proposed approach outperforms existing methods by achieving more than 4.5% improvement in accuracy.
IEEE ANTENNAS AND WIRELESS PROPAGATION LETTERS
(2023)
Article
Telecommunications
Zhenyu Zhou, Xinyi Chen, Haijun Liao, Zhong Gan, Fei Xiao, Qi Tu, Wenwen Sun, Yun Liu, Shahid Mumtaz, Mohsen Guizani
Summary: This paper proposes a multi-timescale VNF Embedding and floW Scheduling algorithm named NEWS to maximize throughput while reducing VNF embedding cost and energy consumption. The joint optimization problem is transformed into three subproblems, including large-timescale VNF embedding, small-timescale admission control, and small-timescale route selection and computation resource allocation. Simulations demonstrate that NEWS performs superior in terms of throughput, embedding cost, and energy consumption.
IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND NETWORKING
(2023)
Article
Engineering, Electrical & Electronic
Haijun Liao, Zhenyu Zhou, Zehan Jia, Yiling Shu, Muhammad Tariq, Jonathan Rodriguez, Valerio Frascolla
Summary: This paper proposes a novel information timeliness metric called ultra-low AoI (ULAoI) that guarantees reliable information timeliness by considering extreme events and higher-order statistical characteristics. The proposed algorithm achieves coordinated resource allocation through joint allocation of multi-dimensional resources and the use of ULAoI-DT priority DQN. Simulation results demonstrate the superior performance of the algorithm in terms of global loss function, ULAoI guarantee, and energy management.
IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS
(2023)
Article
Engineering, Electrical & Electronic
Yiling Shu, Haijun Liao, Zijia Yao, Zhenyu Zhou, Xiaoyan Wang, Muhammad Tariq
Summary: This article proposes a method for corona detection in high-voltage substations using unmanned aerial vehicles (UAVs). The method involves joint optimization of sensing, communication, and computing to maximize data sensed by the UAV. The proposed algorithm, DESCANT, utilizes deep reinforcement learning and dynamic resource management for energy awareness. Simulation results confirm the superiority of DESCANT compared to state-of-the-art algorithms.
IEEE SENSORS JOURNAL
(2023)
Article
Computer Science, Information Systems
Peng Qin, Shuo Wang, Zhou Lu, Yuanbo Xie, Xiongwen Zhao
Summary: With the popularization of IoT, the need for efficient communication and computing service in emerging applications poses challenges to terminal equipment. The ground-based 5G system cannot provide seamless service, especially in hotspot and remote areas. To address these issues, an air ground integrated heterogeneous networks model is proposed, consisting of UAVs and GBSs equipped with edge servers. A comprehensive approach is presented, considering terminal power control, computing resource allocation, and task offloading decision. The proposed approach achieves superior performance in terms of energy consumption and convergence speed compared to benchmark methods.
IEEE SYSTEMS JOURNAL
(2023)
Article
Engineering, Electrical & Electronic
Peng Qin, Yang Fu, Yuanbo Xie, Kui Wu, Xianchao Zhang, Xiongwen Zhao
Summary: This paper investigates the application of UAV-based air-ground integrated computing networks (AGIN) in remote areas for the Power Internet of Things (PIoT). The goal is to optimize task offloading and UAV trajectory to minimize system energy consumption while meeting long-term queue delay constraints. The authors propose a multi-agent deep reinforcement learning-based algorithm called AGIN-MADDPG for task offloading and a greedy solution for aerial edge resource allocation. Experimental results show that their approach outperforms other benchmark methods in terms of power consumption, task backlog, queue delay, and system throughput.
IEEE TRANSACTIONS ON COMMUNICATIONS
(2023)
Article
Engineering, Civil
Chao Pan, Zhao Wang, Haijun Liao, Zhenyu Zhou, Xiaoyan Wang, Muhammad Tariq, Sattam Al-Otaibi
Summary: This paper presents an ASTEROID algorithm that utilizes asynchronous federated DQN and URLLC awareness to achieve task offloading and computation resource allocation in vehicular networks, aiming to satisfy URLLC constraints and maximize throughput. Simulation results demonstrate the superior performances of ASTEROID.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Engineering, Electrical & Electronic
Pengju Liu, Sunxun Zhang, Zhenyu Zhou, Lei Lv, Lin Huang, Jiayu Liu
Summary: This paper presents a multi-clock source time synchronization model and formulates a problem to minimize synchronization error and delay by jointly optimizing large-timescale clock source selection and small-timescale weight selection. A reinforcement learning-based multi-timescale multi-clock source time synchronization algorithm named RL-M2 is proposed to solve the formulated problem from a learning perspective. Additionally, a lossless switching method is proposed to address the switching problem for multiple clock sources.
IET COMMUNICATIONS
(2023)
Article
Automation & Control Systems
Peng Qin, Honghao Zhao, Yang Fu, Suiyan Geng, Zhiyu Chen, Hongxi Zhou, Xiongwen Zhao
Summary: With the construction of renewable energy systems, traditional terrestrial 5G communication is no longer sufficient for the communication needs of industrial power IoT in remote areas. Therefore, a new model combining satellites and UAVs is proposed, and NOMA technology is used to improve system throughput. Through mathematical modeling and decomposed optimization, it is proven that this method outperforms traditional methods.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Engineering, Multidisciplinary
Peng Qin, Xue Wu, Ziyuan Cai, Xiongwen Zhao, Yang Fu, Miao Wang, Suiyan Geng
Summary: This study proposes a solution for constructing an Air-Ground Integrated Network (AGIN) using unmanned aerial vehicles (UAVs) and high altitude platform stations (HAPSs) to achieve seamless network coverage for remote IoT devices in the future 6G era. The proposed solution utilizes clustered-NOMA (C-NOMA) technology for improved system throughput and terminal complexity. The study also addresses the challenge of system energy efficiency (EE) and provides algorithms for optimizing UAV trajectory and resource allocation to maximize EE. Extensive experiments demonstrate the superiority of the proposed approach in terms of EE and spectrum efficiency.
IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING
(2023)
