Article
Engineering, Electrical & Electronic
Lei Zhu, Zhizhong Zhang, Lilan Liu, Linlin Feng, Peng Lin, Yu Zhang
Summary: Vehicular edge computing is an emerging technology that enhances driving efficiency and traffic safety, but it faces performance bottlenecks and low resource efficiency due to load imbalance among heterogeneous edge servers. The introduction of an aerial relay station allows flexible task relay to nearby servers, and a reinforcement learning algorithm optimizes task scheduling. Simulation experiments show that the proposed algorithm improves resource utilization and significantly reduces system latency.
IEEE SENSORS JOURNAL
(2023)
Article
Telecommunications
Yanhao Zhang, Nalam Venkata Abhishek, Mohan Gurusamy
Summary: Vehicular Edge Computing (VEC) enables vehicles to offload tasks to the road side units (RSUs) to improve task performance and user experience. This study proposes a Markov Decision Process based Reinforcement Learning (RL) method to allocate resources at the RSU. The results demonstrate the effectiveness of this method.
IEEE COMMUNICATIONS LETTERS
(2022)
Article
Engineering, Electrical & Electronic
Zheng Xue, Chang Liu, Canliang Liao, Guojun Han, Zhengguo Sheng
Summary: Vehicular edge computing (VEC) is a new computing paradigm that enhances vehicular performance by introducing computation offloading and service caching. However, the dynamic topology of vehicular networks and limited caching space at edge servers require intelligent design of caching placement and computation offloading. This paper investigates a joint optimization problem by integrating service caching and computation offloading, and proposes an algorithm based on deep reinforcement learning to obtain a suboptimal solution. Simulation results show that the proposed scheme exhibits effective performance improvement in task processing delay.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2023)
Article
Telecommunications
Xumin Huang, Yupei Zhong, Yuan Wu, Peichun Li, Rong Yu
Summary: The article introduces the concept of platoon assisted vehicular edge computing and proposes an incentive mechanism. It uses a Stackelberg game and deep reinforcement learning to solve the problem, and finally verifies the effectiveness and efficiency of the scheme with numerical results.
CHINA COMMUNICATIONS
(2022)
Article
Computer Science, Information Systems
Yuwei Li, Bo Yang, Hao Wu, Qiaoni Han, Cailian Chen, Xinping Guan
Summary: This article introduces a vehicular fog-edge computing paradigm and tackles the challenges of offloading computing tasks to vehicles through a multistage Stackelberg game. By using incentive mechanisms and pricing strategies, resource coordination and utility maximization for all parties are achieved.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Engineering, Electrical & Electronic
Yaping Cui, Lijuan Du, Honggang Wang, Dapeng Wu, Ruyan Wang
Summary: The paper presents an intelligent communication and computation resource allocation strategy to reduce the total system cost by combining communication and computing resource allocation. The strategy is decomposed into three algorithms for computation offloading, collaborative vehicle selection, and multi-objective resource allocation using reinforcement learning. Simulation results show that the proposed strategy effectively reduces the total system cost compared to other strategies.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2021)
Article
Engineering, Electrical & Electronic
Liping Qian, Yuan Wu, Ningning Yu, Fuli Jiang, Haibo Zhou, Tony Q. S. Quek
Summary: In this paper, we investigate NOMA-assisted vehicular edge computing through underlay spectrum sharing to minimize the delay for vehicular users. By focusing on single-cell and multi-cell scenarios, we propose a layered algorithm for joint optimization of resources allocation and address the combinatorial nature of the pairing problem with a cross-entropy based probabilistic learning algorithm. Extensive numerical results validate the effectiveness of the proposed algorithms in reducing delay for vehicular users.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2021)
Article
Computer Science, Hardware & Architecture
Chaogang Tang, Huaming Wu
Summary: Vehicular edge computing (VEC) enables vehicles to run resource-hungry and time-sensitive applications by pushing computational resources to the logical edge of the networks; studies focus on optimizing response latency, energy consumption, but these resources are not free in VEC; a Stackelberg game model is adopted for computational resource pricing, with a distributed algorithm proposed and proven unique equilibrium exists.
JOURNAL OF SYSTEMS ARCHITECTURE
(2021)
Article
Engineering, Electrical & Electronic
Weiyang Feng, Ning Zhang, Shichao Li, Siyu Lin, Ruirui Ning, Shuzhong Yang, Yuan Gao
Summary: The paper introduces a reverse offloading framework to optimize the computation resource allocation in CVIS for reduced system latency and improved performance. By designing different strategies and algorithms, the burden on the VEC server is successfully reduced, resulting in enhanced performance outcomes.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2022)
Article
Engineering, Civil
Feng Zeng, Qiao Chen, Lin Meng, Jinsong Wu
Summary: The paper investigates the effective utilization of idle resources in volunteer vehicles to handle overloaded tasks in VEC servers, proposing a model and algorithm based on Stackelberg game. Through extensive simulations, the proposed scheme's effectiveness is demonstrated in reducing offloading costs for vehicles and improving utility for VEC servers.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2021)
Article
Engineering, Civil
Xumin Huang, Rong Yu, Shengli Xie, Yan Zhang
Summary: The proposed PV assistance scheme utilizes underutilized computing resources from parked vehicles to enhance resource capacity at the edge vehicular network, using containerization and a task-container matching market to provide efficient task processing services.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2021)
Article
Computer Science, Information Systems
Ming Tao, Kaoru Ota, Mianxiong Dong, Huaqiang Yuan
Summary: This letter investigates resource pricing and task offloading strategies in a scenario of multiple users and a single MEC server. A stackelberg game model is established and the best strategies are found using differential evolution algorithm. Simulation results show win-win situation.
IEEE WIRELESS COMMUNICATIONS LETTERS
(2022)
Article
Engineering, Electrical & Electronic
Bizheng Liang, Rongfei Fan, Han Hu, Yu Zhang, Ning Zhang, Alagan Anpalagan
Summary: The article introduces the application research of nonlinear pricing strategy in mobile edge computing, discussing the pricing mechanism and optimal solution of computing task offloading strategy between edge servers and mobile devices through Stackelberg game.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2021)
Article
Engineering, Multidisciplinary
Jianbo Du, Wenjie Cheng, Guangyue Lu, Haotong Cao, Xiaoli Chu, Zhicai Zhang, Junxuan Wang
Summary: Using blockchain in mobile systems poses challenges due to computation intensive mining tasks, but mobile edge computing (MEC) offers a solution through task offloading. To maximize rational total profit, we employ an A3C deep reinforcement learning algorithm to determine resource pricing and allocation, while striking a balance between risks and rewards using prospect theory.
IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING
(2022)
Article
Computer Science, Information Systems
Zheng Zhang, Feng Zeng
Summary: The article discusses the task offloading allocation and pricing schemes in vehicular edge computing. Using a market perspective and a game-theoretical model, the authors propose a genetic algorithm-based searching algorithm to optimize task allocation and pricing. Simulation results show that the proposed strategy outperforms other solutions in terms of task offloading delay and cost.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Chemistry, Multidisciplinary
Chunfeng Lv, Jianping Zhu, Naixue Xiong, Zhengsu Tao
Summary: This paper proposes an improved multitarget tracking method based on a PMBM filter with adaptive detection probability and adaptive newborn distribution to address the problems of unknown detection probability, random target newborn distribution, and high energy consumption in limited computational and processing capacity in sensor networks. The proposed method introduces the gamma distribution to represent the augmented state of unknown and changing target detection probability. The intensity of newborn targets is adaptively derived from the inverse gamma distribution based on this augmented state. The effectiveness of this IGGM-PMBM method is verified through comprehensive experiments, and comparisons with other multitarget tracking filters demonstrate significant improvements in tracking behaviors, especially in reducing tracking energy consumption and enhancing tracking accuracy.
APPLIED SCIENCES-BASEL
(2023)
Article
Computer Science, Information Systems
Xiaoheng Deng, Jian Yin, Peiyuan Guan, Neal N. Xiong, Lan Zhang, Shahid Mumtaz
Summary: The development of Industrial Internet of Things (IIoT) and Industry 4.0 has transformed the traditional manufacturing industry. With the mobile-edge computing (MEC) system, computation-intensive tasks can be offloaded from resource-constrained IIoT devices to nearby MEC servers, resulting in lower delay and energy consumption for better Quality of Service (QoS).
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Multidisciplinary Sciences
Xin Liu, Yang Xu, Dan Luo, Gang Xu, Neal Xiong, Xiu-Bo Chen
Summary: This paper studies the similarity problem of geometric graphics and proposes a graphic similarity security decision protocol, which has wide application value in various fields.
SCIENTIFIC REPORTS
(2023)
Editorial Material
Physics, Mathematical
Bin Wang, Naixue Xiong, Fengming Xin
ADVANCES IN MATHEMATICAL PHYSICS
(2023)
Article
Computer Science, Theory & Methods
Shaobo Zhang, Tao Guo, Qin Liu, Entao Luo, Kim-Kwang Raymond Choo, Guojun Wang
Summary: This paper proposes an accuracy-aware location privacy service, named ALPS, based on assisted regions to protect user location privacy while ensuring the accuracy of services. Two novel mechanisms (assisted regions mechanism and query obfuscation mechanism) are devised to protect user location privacy and ensure the accuracy of LSSs based on trilateration. Theoretical analysis and experimental evaluation demonstrate that our scheme can protect location privacy without compromising the accuracy of LSSs.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2023)
Article
Computer Science, Information Systems
Jianwen Xu, Kaoru Ota, Mianxiong Dong
Summary: With continuous innovation in manufacturing, UAVs have become commodities from professional equipment. Quadrotor UAVs, as a universal type, show potential for applications in multiple fields. In the field of aerial computing, UAVs have started to play a leading role in providing computing services. However, due to limitations in onboard equipment performance, relying on a single UAV for complex computing tasks is not feasible.
IEEE TRANSACTIONS ON MOBILE COMPUTING
(2023)
Article
Engineering, Electrical & Electronic
Yingkun Wen, Lei Liu, Junhuai Li, Xiangwang Hou, Ning Zhang, Mianxiong Dong, Mohammed Atiquzzaman, Kan Wang, Yan Huo
Summary: In this work, a covert jamming scheme is designed to protect the primary message from intelligent eavesdroppers in cognitive radio networks. The detection error probability is derived and used to formulate the covert jamming scheme, which is then optimized to maximize secrecy performance and effective secrecy throughput.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2023)
Article
Computer Science, Information Systems
Xuezheng Yang, Zhiwen Zeng, Anfeng Liu, Neal N. Xiong, Tian Wang, Shaobo Zhang
Summary: In this paper, a decentralized trust inference approach is proposed to improve the data collection quality for mobile crowd sensing. The approach includes trust evaluation and data filling components, which assess the trust level of workers and fill missing data using Bayesian probabilistic matrix factorization. Furthermore, a worker recruitment method based on trust prioritization and bid ratio is proposed to preferentially select reliable and low-bid workers, thereby improving data quality and reducing costs.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Information Systems
Fuhu Wu, Jian Sun, Shun Zhang, Neal Xiong, Hong Zhong
Summary: This paper proposes an efficient reversible data hiding scheme through a double-peak two-layer embedding and prediction error expansion. By utilizing the higher significant bit (HSB) plane and double prediction error peaks, the redundancy space of images can be fully utilized. Moreover, the size of the auxiliary information is reduced through pre-processing. Experimental results demonstrate that this scheme performs better in high capacity embedding scenarios and achieves a 83% higher embedding rate on real-world datasets, such as BOSSbase, BOWS2, and UCID, compared to state-of-the-art approaches.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Information Systems
Shibo He, Yuyi Sun, Yuan Wu, Mianxiong Dong, Zhiguo Shi
Summary: Wireless charging is a popular technology in IoT that eliminates the need for batteries in electronic devices. This article presents a wireless charging antenna design that extends the charging distance from 5 to 55 cm, achieving a ten-fold improvement compared to near-field commercial communication. The authors model the near-field signal propagation with higher-order factors and establish the relationship between geometry parameters and transmit power, utilizing a greedy search algorithm to optimize coil parameters and maximize charging distance.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Computer Science, Information Systems
Dongyang Xu, Lei Liu, Ning Zhang, Mianxiong Dong, Victor C. M. Leung, James A. Ritcey
Summary: Protecting the initial access of IoT devices over wireless channels is challenging, especially when malicious quantum adversaries tamper critical wireless messages. We propose a nested hash access system with post-quantum encryption to solve this issue. The system encodes and decodes preambles precisely and resiliently using random repetition coding and nested hash coding on multidomain physical-layer resources.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Computer Science, Information Systems
Ziqing Xia, Zhangyang Gao, Anfeng Liu, Neal N. Xiong
Summary: In this paper, an asymmetric quorum-based neighbor discovery (AQND) protocol is proposed to reduce delay, improve energy utilization and lifetime, and outperform previous strategies in main performance indicators.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Xiaohuan Liu, Anfeng Liu, Shaobo Zhang, Tian Wang, Neal N. Xiong
Summary: This paper proposes a delay differentiated services routing (DDSR) scheme to reduce the deployment costs for wireless sensor networks (WSNs) with wake-up radio (WuR) functionality, while meeting the delay requirement of forwarding urgent data and maintaining a long lifetime.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Artificial Intelligence
Qinghua Gu, Yan Wang, Peipei Wang, Xuexian Li, Lu Chen, Neal N. Xiong, Di Liu
Summary: This paper proposes a new ensemble clustering method that combines the influence of cluster level and the base clustering level in a unified framework. The method inserts a global weighting strategy into a local ensemble cluster learning framework, improving the robustness and stability of clustering.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
