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Prof. Anfeng Liu
School of Information Science and Engineering, Central South University, Changsha 410083, China

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Research Keywords & Expertise

0 Internet of Things
0 Routing Protocols
0 Wireless Sensor Networks
0 Mobile Crowdsourcing
0 Network security, trust and privacy

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Wireless Sensor Networks
Internet of Things
Mobile Crowdsourcing
Network security, trust and privacy

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Journal article
Published: 30 August 2021 in IEEE Transactions on Industrial Informatics
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A Cloud-Assisted Reliable Trust Computing Scheme(CRTC) is proposed to identify the trust of ISDs and ensure secure data collection in IoT. The CRTC scheme includes the following parts: (a) A reliable approach for identifying the trust of ISDs is proposed in which ISDs use a fitting function to fit forwarding packets information to Inspection Information (II) with a small amount of data and then send II to Inspection Nodes (INs), effectively reducing inspection information cost of routing. Then, an effective trust reasoning approach is given to compute the trust of ISDs based on the data and II collected by UAV. Last, the aggregators are selected from high-trust ISDs to ensure secure data collection. (b) A trajectory optimization algorithm for UAV is proposed to collect data as well as II from aggregator and INs. Theoretical analysis shows that the proposed URTC scheme is superior to previous strategies.

ACS Style

Wen Mo; Wei Liu; Guosheng Huang; Naixue Xiong; Anfeng Liu; Shaobo Zhang. A Cloud-Assisted Reliable Trust Computing Scheme for Data Collection in Internet of Things. IEEE Transactions on Industrial Informatics 2021, PP, 1 -1.

AMA Style

Wen Mo, Wei Liu, Guosheng Huang, Naixue Xiong, Anfeng Liu, Shaobo Zhang. A Cloud-Assisted Reliable Trust Computing Scheme for Data Collection in Internet of Things. IEEE Transactions on Industrial Informatics. 2021; PP (99):1-1.

Chicago/Turabian Style

Wen Mo; Wei Liu; Guosheng Huang; Naixue Xiong; Anfeng Liu; Shaobo Zhang. 2021. "A Cloud-Assisted Reliable Trust Computing Scheme for Data Collection in Internet of Things." IEEE Transactions on Industrial Informatics PP, no. 99: 1-1.

Journal article
Published: 12 August 2021 in Pervasive and Mobile Computing
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The mobile charging scheme is a promising solution to extending the lifetime of the network by replenishing the energy for the sensing nodes, which has attracted more and more attention from the researchers. However, due to the limitation of energy storage both for sensing nodes and mobile chargers, not all the sensing nodes can be recharged in time by mobile chargers. Therefore, how to select appropriate sensing nodes and design the path for the mobile charger are the key to improve the system utility. This paper proposes an Intelligent Charging scheme Maximizing the Quality Utility (ICMQU) to design the charging path for the mobile charger. Comparing to the previous studies, we consider not only the utility of the data collected from the environment, but also the impact of sensing nodes with different quality. Quality Utility is proposed to optimize the charging path design. Besides, ICMQU designs the charging scheme for a single mobile charger and multiple mobile chargers simultaneously. For the charging scheme with multiple mobile chargers, the workload balance among different mobile chargers is also considered as well as the utility of the system. Extensive simulation results are provided, which demonstrates the proposed ICMQU scheme can significantly improve the utility of the system.

ACS Style

Yingying Ren; Anfeng Liu; Xingliang Mao; Fangfang Li. An intelligent charging scheme maximizing the utility for rechargeable network in smart city. Pervasive and Mobile Computing 2021, 77, 101457 .

AMA Style

Yingying Ren, Anfeng Liu, Xingliang Mao, Fangfang Li. An intelligent charging scheme maximizing the utility for rechargeable network in smart city. Pervasive and Mobile Computing. 2021; 77 ():101457.

Chicago/Turabian Style

Yingying Ren; Anfeng Liu; Xingliang Mao; Fangfang Li. 2021. "An intelligent charging scheme maximizing the utility for rechargeable network in smart city." Pervasive and Mobile Computing 77, no. : 101457.

Journal article
Published: 31 May 2021 in Computer Networks
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Convergence of Augmented Reality (AR) and Next Generation Internet-of-Things (NG-IoT) can create new opportunities in many emerging areas, where the real-time data can be visualized on the devices. Integrated NG-IoT network, AR can improve efficiency in many fields such as mobile computing, smart city, intelligent transportation and telemedicine. However, limited by capability of mobile device, the reliability and latency requirements of AR applications is difficult to meet by local processing. To solve this problem, we study a binary offloading scheme for AR edge computing. Based on the proposed model, the parts of AR computing can offload to edge network servers, which is extend the computing capability of mobile AR devices. Moreover, a deep reinforcement learning offloading model is considered to acquire B5G network resource allocation and optimally AR offloading decisions. First, this offloading model does not need to solve combinatorial optimization, which is greatly reduced the computational complexity. Then the wireless channel gains and binary offloading states is modeled as a Markov decision process, and solved by deep reinforcement learning. Numerical results show that our scheme can achieve better performance compared with existing optimization methods.

ACS Style

Miaojiang Chen; Wei Liu; Tian Wang; Anfeng Liu; Zhiwen Zeng. Edge intelligence computing for mobile augmented reality with deep reinforcement learning approach. Computer Networks 2021, 195, 108186 .

AMA Style

Miaojiang Chen, Wei Liu, Tian Wang, Anfeng Liu, Zhiwen Zeng. Edge intelligence computing for mobile augmented reality with deep reinforcement learning approach. Computer Networks. 2021; 195 ():108186.

Chicago/Turabian Style

Miaojiang Chen; Wei Liu; Tian Wang; Anfeng Liu; Zhiwen Zeng. 2021. "Edge intelligence computing for mobile augmented reality with deep reinforcement learning approach." Computer Networks 195, no. : 108186.

Journal article
Published: 30 April 2021 in Computer Communications
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Recently, in order to distribute computing, networking resources, services, near terminals, mobile fog is gradually becoming the mobile edge computing (MEC) paradigm. In a mobile fog environment, the quality of service affected by offloading speeds and the fog processing, however the traditional fog method to solve the problem of computation resources allocation is difficult because of the complex network states distribution environment (that is, F-AP states, AP states, mobile device states and code block states). In this paper, to improve the fog resource provisioning performance of mobile devices, the learning-based mobile fog scheme with deep deterministic policy gradient (DDPG) algorithm is proposed. An offloading block pulsating discrete event system is modeled as a Markov Decision Processes (MDPs), which can realize the offloading computing without knowing the transition probabilities among different network states. Furthermore, the DDPG algorithm is used to solve the issue of state spaces explosion and learn an optimal offloading policy on distributed mobile fog computing. The simulation results show that our proposed scheme achieves 20%, 37%, 46% improvement on related performance compared with the policy gradient (PG), deterministic policy gradient (DPG) and actor–critic (AC) methods. Besides, compared with the traditional fog provisioning scheme, our scheme shows better cost performance of fog resource provisioning under different locations number and different task arrival rates.

ACS Style

Miaojiang Chen; Tian Wang; Shaobo Zhang; Anfeng Liu. Deep reinforcement learning for computation offloading in mobile edge computing environment. Computer Communications 2021, 175, 1 -12.

AMA Style

Miaojiang Chen, Tian Wang, Shaobo Zhang, Anfeng Liu. Deep reinforcement learning for computation offloading in mobile edge computing environment. Computer Communications. 2021; 175 ():1-12.

Chicago/Turabian Style

Miaojiang Chen; Tian Wang; Shaobo Zhang; Anfeng Liu. 2021. "Deep reinforcement learning for computation offloading in mobile edge computing environment." Computer Communications 175, no. : 1-12.

Journal article
Published: 11 February 2021 in IEEE Transactions on Network Science and Engineering
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Artificial Intelligence (AI) technology has been widely applied to Internet of Thing (IoT) and one of key applications is intelligence data collection from billions of IoT devices. However, many AI based data collection approaches lack security considerations leading to availability restricted. In this paper, an Intelligent Trust Collaboration Network System (ITCN) is established to collect data through collaboration with mobile vehicles and Unmanned Aerial Vehicle (UAV) for IIoT. The first, a deadline-aware data collection collaboration network framework is proposed by collaboration with mobile vehicles and UAV. The second, an active and verifiable trust evaluation approach is proposed to obtain the trust of the data participants, which ensures the security and privacy of the system. The last, a trust joint AI based UAV trajectory optimization algorithm is proposed to collect as much baseline data as possible, so more trust of data participants can be accurately evaluated and the data can be collected before deadline at low cost. The simulation results show that ITCN reduces the cost by 35.08% compared with using the UAV only, saves the collection time by 58.32% and increases the accuracy by 33.34% on average compared with the previous strategies only using vehicles.

ACS Style

Jialin Guo; Anfeng Liu; Kaoru Ota; Mianxiong Dong; Xiaoheng Deng; Naixue Xiong. ITCN: An Intelligent Trust Collaboration Network System in IoT. IEEE Transactions on Network Science and Engineering 2021, PP, 1 -1.

AMA Style

Jialin Guo, Anfeng Liu, Kaoru Ota, Mianxiong Dong, Xiaoheng Deng, Naixue Xiong. ITCN: An Intelligent Trust Collaboration Network System in IoT. IEEE Transactions on Network Science and Engineering. 2021; PP (99):1-1.

Chicago/Turabian Style

Jialin Guo; Anfeng Liu; Kaoru Ota; Mianxiong Dong; Xiaoheng Deng; Naixue Xiong. 2021. "ITCN: An Intelligent Trust Collaboration Network System in IoT." IEEE Transactions on Network Science and Engineering PP, no. 99: 1-1.

Journal article
Published: 05 January 2021 in IEEE Internet of Things Journal
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With billions of sensing devices are deployed in smart cities to monitor regions of interests and collect large sensing data, the Internet of Things (IoT) applications are being widely used in various fields and empower the intelligent smart cities. Due to the smart decision made by IoT applications depends on the reliability of data collection, it is pivotal to collect data from the trust sensing devices. However, how to identify the credibility of sensor nodes to ensure the credibility of data collection is a challenge issue. In this paper, an Active and Trace-able Trust based Data Collection (ATTDC) scheme is proposed to collect trust data in Internet of Thing. The main contribution of this paper are as follow: (1) An active trust framework is proposed to quickly obtain the trustworthiness of sensor nodes by using Unmanned Aerial Vehicles (UAV) with a piggybacking method; (2) In order to accurately obtain the trust degree of the sensor nodes, a trace-able trust method of obtaining is proposed in which nodes in the network send data packets by digital signature, Tracing suspicious nodes according to data routing paths to obtain active trust, therefore, the acquisition cost of the network can be effectively reduced. (3) In order to reduce the acquisition cost of UAV, an ant colony algorithm-based flight path algorithm is designed to reduce the flight path of UAV, and obtain the credibility evaluation of as many nodes as possible. The experimental results show that the ATTDC scheme proposed in this paper can identify the trust of the sensing nodes faster and more accurately, ensuring the credibility of data collection.

ACS Style

Mengqiu Shen; Anfeng Liu; Guosheng Huang; Neal N. Xiong; Huimin Lu. ATTDC: An Active and Traceable Trust Data Collection Scheme for Industrial Security in Smart Cities. IEEE Internet of Things Journal 2021, 8, 6437 -6453.

AMA Style

Mengqiu Shen, Anfeng Liu, Guosheng Huang, Neal N. Xiong, Huimin Lu. ATTDC: An Active and Traceable Trust Data Collection Scheme for Industrial Security in Smart Cities. IEEE Internet of Things Journal. 2021; 8 (8):6437-6453.

Chicago/Turabian Style

Mengqiu Shen; Anfeng Liu; Guosheng Huang; Neal N. Xiong; Huimin Lu. 2021. "ATTDC: An Active and Traceable Trust Data Collection Scheme for Industrial Security in Smart Cities." IEEE Internet of Things Journal 8, no. 8: 6437-6453.

Journal article
Published: 18 December 2020 in IEEE Transactions on Industrial Informatics
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As an emerging technology, Industrial In-ternet of Things (IIoT) connects massive sensors and actuators to empower industrial sectors being smart, autonomous, efficient, and safety. However, due the large number of build-in sensors of IIoT smart devices, the IIoT systems are vulnerable to side-channel attack. In this paper, a novel side-channel-based passwords cracking system, namely MAGLeak, is proposed to recognize the victim's passwords by leveraging accelerometer, gyroscope, and magnetometer of IIoT touch-screen smart device. Specifically, an event-driven data collection method is proposed to ensure that the user's keystroke behavior can be reflected accurately by the obtained measurements of three sensors. Moreover, random forest algorithm is leveraged for the recognition module, followed by a data preprocessing process. Extensive experimental results demonstrate that MAGLeak achieves a high recognition accuracy under small training dataset, e.g., achieving recognition accuracy 98% of each single key for 2000 training samples.

ACS Style

Dajiang Chen; Zihao Zhao; Xue Qin; Yaohua Luo; Mingsheng Cao; Hua Xu; Anfeng Liu. MAGLeak: A Learning-based Side-channel Attack for Password Recognition with Multiple Sensors in IIoT Environment. IEEE Transactions on Industrial Informatics 2020, PP, 1 -1.

AMA Style

Dajiang Chen, Zihao Zhao, Xue Qin, Yaohua Luo, Mingsheng Cao, Hua Xu, Anfeng Liu. MAGLeak: A Learning-based Side-channel Attack for Password Recognition with Multiple Sensors in IIoT Environment. IEEE Transactions on Industrial Informatics. 2020; PP (99):1-1.

Chicago/Turabian Style

Dajiang Chen; Zihao Zhao; Xue Qin; Yaohua Luo; Mingsheng Cao; Hua Xu; Anfeng Liu. 2020. "MAGLeak: A Learning-based Side-channel Attack for Password Recognition with Multiple Sensors in IIoT Environment." IEEE Transactions on Industrial Informatics PP, no. 99: 1-1.

Journal article
Published: 17 December 2020 in IEEE Transactions on Intelligent Transportation Systems
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The rapid and convenient travel of people and the timely transportation of goods depend on the correct decision of the Intelligent Transportation Systems (ITS). Due to the decision-making of ITS requires a large amount of data to support, UAV-enabled periodic data collection is an effective method. However, due to the limited resources of UAV, UAV cannot directly collect data from all storage devices, resulting in unfair data collection. Therefore, we propose a UAV Speed Control based Fairness Data Collection (USCFDC) scheme. First, since the fairness of data collection will affect the decision-making of ITS, a framework for controlling the flight speed of the UAV is proposed to improve the fairness of data collection. The flight speed of UAV will slow down in areas with a large number of nodes, thereby improving the fairness of data collection. Second, a novel method is proposed to maximize the amount of data collected by UAV from each node. With this method, the value of the amount of data will be used as the dichotomous value in the dichotomy algorithm, and the UAV must collect a certain amount of data from each node. The upper and lower limits of the dichotomy algorithm are adjusted according to the time duration for UAV to collect data. Compared with previous schemes, the fairness of data collection can be improved by a maximum of 15.89% under the same flight time of UAV. Besides, the energy consumption is reduced by 49.31%-52.55% and the flight time of the UAV is reduced by 48%-62.38% when the amount of collected data is the same.

ACS Style

Xiong Li; Jiawei Tan; Anfeng Liu; Pandi Vijayakumar; Neeraj Kumar; Mamoun Alazab. A Novel UAV-Enabled Data Collection Scheme for Intelligent Transportation System Through UAV Speed Control. IEEE Transactions on Intelligent Transportation Systems 2020, 22, 2100 -2110.

AMA Style

Xiong Li, Jiawei Tan, Anfeng Liu, Pandi Vijayakumar, Neeraj Kumar, Mamoun Alazab. A Novel UAV-Enabled Data Collection Scheme for Intelligent Transportation System Through UAV Speed Control. IEEE Transactions on Intelligent Transportation Systems. 2020; 22 (4):2100-2110.

Chicago/Turabian Style

Xiong Li; Jiawei Tan; Anfeng Liu; Pandi Vijayakumar; Neeraj Kumar; Mamoun Alazab. 2020. "A Novel UAV-Enabled Data Collection Scheme for Intelligent Transportation System Through UAV Speed Control." IEEE Transactions on Intelligent Transportation Systems 22, no. 4: 2100-2110.

Journal article
Published: 08 December 2020 in IEEE Transactions on Wireless Communications
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Media access control (MAC) plays a pivotal role in ensuring proper operation in wireless body area networks (WBANs). However, current solutions still cannot satisfy the stringent requirements of low power and low delay for emergency data reporting. In this paper, we propose an energy-efficient and emergency-aware MAC (EEEA-MAC) protocol for meeting such a rigorous demand. First, we design a node-different channel access scheme, in which source nodes use the CSMA/CA pattern while relay nodes adopt the hybrid CSMA/CA-TDMA pattern. Second, we devise an emergency-first time-slot allocation scheme, in which channel sensing is performed and the emergency data is handled by relay nodes according to different cases. EEEA-MAC has two striking features. One is that, source nodes adopt the CSMA/CA scheme instead of the conventional CSMA/CA-TDMA scheme, ensuring the requirement because there are almost no collisions and no confirmation messages in this scheme. The other is that, relay nodes use a sensing-based emergency data handling mechanism instead of the traditional empty-slot occupying mechanism, further guaranteeing the requirement owing to the immediate handling of emergency data and the short time of channel sensing. Extensive simulations demonstrate the advantages of EEEA-MAC in terms of energy dissipation and latency.

ACS Style

Baowen Liang; Xuxun Liu; Huan Zhou; Victor C. M. Leung; Anfeng Liu; Kaikai Chi. Channel Resource Scheduling for Stringent Demand of Emergency Data Transmission in WBANs. IEEE Transactions on Wireless Communications 2020, 20, 2341 -2352.

AMA Style

Baowen Liang, Xuxun Liu, Huan Zhou, Victor C. M. Leung, Anfeng Liu, Kaikai Chi. Channel Resource Scheduling for Stringent Demand of Emergency Data Transmission in WBANs. IEEE Transactions on Wireless Communications. 2020; 20 (4):2341-2352.

Chicago/Turabian Style

Baowen Liang; Xuxun Liu; Huan Zhou; Victor C. M. Leung; Anfeng Liu; Kaikai Chi. 2020. "Channel Resource Scheduling for Stringent Demand of Emergency Data Transmission in WBANs." IEEE Transactions on Wireless Communications 20, no. 4: 2341-2352.

Journal article
Published: 26 November 2020 in IEEE Internet of Things Journal
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The development of the Internet of Things (IoT) and intelligent vehicles bring a comfortable environment for users. Various emerging vehicular applications using artificial intelligence (AI) technologies are expected to enrich users’ daily life. However, how to execute computation-intensive applications on resource-constrained vehicles based on AI still faces great challenges. In this paper, we consider the vehicular computation offloading problem in mobile edge computing (MEC), in which multiple mobile vehicles select nearby MEC servers to offload their computing tasks. We propose a multi-agent deep reinforcement learning (DRL) based computation offloading scheme, in which the uncertainty of a multi-vehicle environment is considered so that the vehicles can make offloading decisions to achieve an optimal long-term reward. Firstly, we formalize a formula for the computation offloading problem. The goal of this paper is to determine the optimal offloading decision to MEC server under each observed system state, so as to minimize the total task processing delay in a long-term period. Then, we use a multi-agent DRL algorithm to learn an effective solution to the vehicular task offloading problem. To evaluate the performance of the proposed offloading scheme, a large number of simulations are carried out. The simulation results verify the effectiveness and superiority of the proposed scheme.

ACS Style

Xiaoyu Zhu; Yueyi Luo; Anfeng Liu; Zakirul Alam Bhuiyan; Shaobo Zhang. Multiagent Deep Reinforcement Learning for Vehicular Computation Offloading in IoT. IEEE Internet of Things Journal 2020, 8, 9763 -9773.

AMA Style

Xiaoyu Zhu, Yueyi Luo, Anfeng Liu, Zakirul Alam Bhuiyan, Shaobo Zhang. Multiagent Deep Reinforcement Learning for Vehicular Computation Offloading in IoT. IEEE Internet of Things Journal. 2020; 8 (12):9763-9773.

Chicago/Turabian Style

Xiaoyu Zhu; Yueyi Luo; Anfeng Liu; Zakirul Alam Bhuiyan; Shaobo Zhang. 2020. "Multiagent Deep Reinforcement Learning for Vehicular Computation Offloading in IoT." IEEE Internet of Things Journal 8, no. 12: 9763-9773.

Journal article
Published: 29 October 2020 in IEEE Transactions on Network Science and Engineering
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The rapid development of Internet of Things (IoT) can promote the establishment of the smart connected world by using numerous devices and sensors. Collecting the data perceived by sensing devices in a fast and energy-saving style is critical for building a stable network in IoT. In this paper, we propose an Early Message Ahead Join Adaptive Data Aggregation (E-ADA) scheme for IoT. Firstly, an advance notification mechanism based on early message is introduced to improve the probability of data aggregation, thus optimizing energy consumption and network lifetime. In this mechanism, the routing of early message is faster than that of the data packet. Nodes with data packet send an early message forward to notice, and other data packets which monitor the early message can wait for that to aggregate under the delay deadline constraints. Secondly, we propose a Delay-optimized Convergence Routing based E-ADA (DOCR-E-ADA) data collection scheme which combines advance notification mechanism and convergence routings. The duty cycle of nodes in convergence routing is adaptively adjusted, which greatly reduces transmission latency. Finally, extensive experimental results demonstrate that our DOCR-E-ADA outperforms the existing schemes in terms of network delay and lifetime.

ACS Style

Yan Ouyang; Anfeng Liu; Naixue Xiong; Tian Wang. An Effective Early Message Ahead Join Adaptive Data Aggregation Scheme for Sustainable IoT. IEEE Transactions on Network Science and Engineering 2020, 8, 201 -219.

AMA Style

Yan Ouyang, Anfeng Liu, Naixue Xiong, Tian Wang. An Effective Early Message Ahead Join Adaptive Data Aggregation Scheme for Sustainable IoT. IEEE Transactions on Network Science and Engineering. 2020; 8 (1):201-219.

Chicago/Turabian Style

Yan Ouyang; Anfeng Liu; Naixue Xiong; Tian Wang. 2020. "An Effective Early Message Ahead Join Adaptive Data Aggregation Scheme for Sustainable IoT." IEEE Transactions on Network Science and Engineering 8, no. 1: 201-219.

Journal article
Published: 07 October 2020 in IEEE Transactions on Intelligent Transportation Systems
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Mobile crowdsensing is an emerging paradigm that selects users to complete sensing tasks. Recently, mobile vehicles are adopted to perform sensing data collection tasks in the urban city due to their ubiquity and mobility. In this article, we study how mobile vehicles can be optimally selected in order to collect maximum data from the urban environment in a future period of tens of minutes. We formulate the recruitment of vehicles as a maximum data limited budget problem. The application scenario is generalized to a realistic online setting where vehicles are continuously moving in real-time and the data center decides to recruit a set of vehicles immediately. A deep learning-based scheme through mobile vehicles (DLMV) is proposed to collect sensing data in the urban environment. We first propose a deep learning-based offline algorithm to predict vehicle mobility in a future time period. Furthermore, we propose a greedy online algorithm to recruit a subset of vehicles with a limited budget for the NP-Complete problem. Extensive experimental evaluations are conducted on the real mobility dataset in Rome. The results have not only verified the efficiency of our proposed solution but also validated that DLMV can improve the quantity of collected sensing data compared with other algorithms.

ACS Style

Xiaoyu Zhu; Yueyi Luo; Anfeng Liu; Wenjuan Tang; Zakirul Alam Bhuiyan. A Deep Learning-Based Mobile Crowdsensing Scheme by Predicting Vehicle Mobility. IEEE Transactions on Intelligent Transportation Systems 2020, 22, 4648 -4659.

AMA Style

Xiaoyu Zhu, Yueyi Luo, Anfeng Liu, Wenjuan Tang, Zakirul Alam Bhuiyan. A Deep Learning-Based Mobile Crowdsensing Scheme by Predicting Vehicle Mobility. IEEE Transactions on Intelligent Transportation Systems. 2020; 22 (7):4648-4659.

Chicago/Turabian Style

Xiaoyu Zhu; Yueyi Luo; Anfeng Liu; Wenjuan Tang; Zakirul Alam Bhuiyan. 2020. "A Deep Learning-Based Mobile Crowdsensing Scheme by Predicting Vehicle Mobility." IEEE Transactions on Intelligent Transportation Systems 22, no. 7: 4648-4659.

Special issue article
Published: 17 September 2020 in Transactions on Emerging Telecommunications Technologies
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With billions of sensor‐based devices connected to the Internet of Things (IoT), it is a pivotal issue to design an effective task scheduling scheme when the resource of sensor nodes is limited. In the past, Q‐learning based task scheduling scheme which only focuses on the node angle led to poor performance of the whole network. Thus, a Q‐learning based flexible task scheduling with global view (QFTS‐GV) scheme is proposed to improve task scheduling success rate, reduce delay, and extend lifetime for the IoT. First, the Q‐learning framework, including state set, action set, and rewards function is defined in a global view so as to forms the basis of the QFTS‐GV scheme. Then, a task scheduling policy is established with distinguishing rewards for nodes in different areas of the network, so the energy‐strained nodes can be protected to ensure a high lifetime, and the energy‐relaxed nodes can increase their transmission power to promote the benefits of the whole network. Finally, experimental results demonstrate that the QFTS‐GV scheme can achieve a higher task scheduling success rate, lower delay, and less energy consumption. Compared with the Q‐learning based task scheduling scheme, the QFTS‐GV improves the task scheduling success rate by 1.42% to 7.13%, reduces the delay by 24.60% to 42.56%, and saves energy by 21.18% to 36.60%.

ACS Style

Junxiao Ge; Bin Liu; Tian Wang; Qiang Yang; Anfeng Liu; Ang Li. Q‐learning based flexible task scheduling in a global view for the Internet of Things. Transactions on Emerging Telecommunications Technologies 2020, 32, 1 .

AMA Style

Junxiao Ge, Bin Liu, Tian Wang, Qiang Yang, Anfeng Liu, Ang Li. Q‐learning based flexible task scheduling in a global view for the Internet of Things. Transactions on Emerging Telecommunications Technologies. 2020; 32 (8):1.

Chicago/Turabian Style

Junxiao Ge; Bin Liu; Tian Wang; Qiang Yang; Anfeng Liu; Ang Li. 2020. "Q‐learning based flexible task scheduling in a global view for the Internet of Things." Transactions on Emerging Telecommunications Technologies 32, no. 8: 1.

Journal article
Published: 07 August 2020 in IEEE Transactions on Network Science and Engineering
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Billions of sensors and devices are connecting to Internet of Thing (IoT) and generating massive data which are benefit for smart network systems. However, low-cost, secure, reliable and efficient data collection from billions of IoT devices in smart city is a huge challenge. Recruiting mobile vehicles (MVs) has been proved to be an effective data collection method. However, the previous approaches rarely considered the credibility. In this paper, a novel Baseline Data based Verifiable Trust Evaluation (BD-VTE) scheme is proposed to guarantee credibility at a low cost. BD-VTE scheme includes Verifiable Trust Evaluation (VTE) mechanism, Effectiveness-based Incentive (EI) mechanism, and Secondary Path Planning (SPP) strategy, which are respectively used for reliable trust evaluation, reasonable reward, and efficient path adjustment. Among them, an active trust verification mechanism is innovatively proposed in the VTE mechanism, which evaluates the trust of MVs by sending UAVs to perceive IoT devices data as baseline data. This is a fundamental change to the previous passive and unverifiable trust mechanism. The simulation results show that BD-VTE scheme reduces the cost by at least 25.12%~38.03%, improves the collection rate by 0.91%~9.65% and increases the accuracy by 10.28% on average compared with the previous strategies.

ACS Style

Shaobo Huang; Anfeng Liu; Tian Wang; Naixue Xiong. BD-VTE: A Novel Baseline Data based Verifiable Trust Evaluation Scheme for Smart Network Systems. IEEE Transactions on Network Science and Engineering 2020, PP, 1 -1.

AMA Style

Shaobo Huang, Anfeng Liu, Tian Wang, Naixue Xiong. BD-VTE: A Novel Baseline Data based Verifiable Trust Evaluation Scheme for Smart Network Systems. IEEE Transactions on Network Science and Engineering. 2020; PP (99):1-1.

Chicago/Turabian Style

Shaobo Huang; Anfeng Liu; Tian Wang; Naixue Xiong. 2020. "BD-VTE: A Novel Baseline Data based Verifiable Trust Evaluation Scheme for Smart Network Systems." IEEE Transactions on Network Science and Engineering PP, no. 99: 1-1.

Journal article
Published: 06 August 2020 in Information Sciences
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Because of high mobility, large number of vehicles are utilized to achieve timely and quality-based information in the smart Internet of Things, which has formulated into a dynamic Distributed Networked Systems (DNS). However, designing a vehicular recruitment scheme to enhance a security-based DNS is challenging since it is hard to judge trustworthiness values of vehicular sensors. Therefore, in this paper, a novel vehicular trust evaluation scheme is proposed to analyze and supervise the data collected by vehicular sensors with a trust and low-cost style. To obtain vehicular trusts, the proposed scheme that considers time factor and gap between sensed data and real data is designed to calculate trustworthiness of vehicles. Moreover, sensing data in the vehicle sparse regions has more contributions because of its rareness. Thus, to inspire vehicles to sense data in the vehicle sparse regions, a trustworthiness-based gradient pricing method is designed to pay rewards for the vehicular sensors. Finally, with real vehicular GPS datasets, simulation results demonstrate that the proposed scheme can improve accuracy rate of data sensing by 37.72% and can improve data quality by 76.95%. By incentive pricing method, coverage ratio of data sensing is improved by 13.1%. In general, performances of the proposed scheme can be improved by 19. 39% to 22.32% approximately. Future works focus on improving information security by advanced machine learning methods in the dynamic DNS.

ACS Style

Ting Li; Anfeng Liu; Neal N. Xiong; Shaobo Zhang; Tian Wang. A trustworthiness-based vehicular recruitment scheme for information collections in Distributed Networked Systems. Information Sciences 2020, 545, 65 -81.

AMA Style

Ting Li, Anfeng Liu, Neal N. Xiong, Shaobo Zhang, Tian Wang. A trustworthiness-based vehicular recruitment scheme for information collections in Distributed Networked Systems. Information Sciences. 2020; 545 ():65-81.

Chicago/Turabian Style

Ting Li; Anfeng Liu; Neal N. Xiong; Shaobo Zhang; Tian Wang. 2020. "A trustworthiness-based vehicular recruitment scheme for information collections in Distributed Networked Systems." Information Sciences 545, no. : 65-81.

Journal article
Published: 19 June 2020 in IEEE Transactions on Intelligent Transportation Systems
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Smart cities can manage assets and resources efficiently by using different types of electronic data collection sensors, devices and vehicles. However, growing complexity of systems and heterogeneous networking also enlarge the destructive effect of compromised or malicious sensor nodes. In this paper, we introduce electric vehicles to conduct trust evaluation for heterogeneous vehicle network in smart cities. Compared with traditional trust evaluation mechanism, mobility-based trust evaluation owns the advantages of low energy consumption and high evaluation accuracy. Meanwhile, we investigate the problem of minimizing transmission hops of trust evaluation and refers to this as the mobile trust evaluation problem (MTEP). We first formalize the MTEP into an optimization problem and present a heuristic moving strategy of single electric vehicle. Then, we consider the MTEP with multiple electric vehicles. By scheduling the electric vehicles to access the nodes on spanning tree with maximum neighbor distance ratio, the algorithm can improve the efficiency of trust evaluation. In experiments, we compare moving strategy of single electric vehicle and multiple electric vehicles with existing methods respectively. The results demonstrate that the proposed algorithms are able to effectively reduce the entire transmission hops of trust evaluation and thus prolong the life of the network.

ACS Style

Tian Wang; Hao Luo; Xiangxiang Zeng; Zhiyong Yu; Anfeng Liu; Arun Kumar Sangaiah. Mobility Based Trust Evaluation for Heterogeneous Electric Vehicles Network in Smart Cities. IEEE Transactions on Intelligent Transportation Systems 2020, 22, 1797 -1806.

AMA Style

Tian Wang, Hao Luo, Xiangxiang Zeng, Zhiyong Yu, Anfeng Liu, Arun Kumar Sangaiah. Mobility Based Trust Evaluation for Heterogeneous Electric Vehicles Network in Smart Cities. IEEE Transactions on Intelligent Transportation Systems. 2020; 22 (3):1797-1806.

Chicago/Turabian Style

Tian Wang; Hao Luo; Xiangxiang Zeng; Zhiyong Yu; Anfeng Liu; Arun Kumar Sangaiah. 2020. "Mobility Based Trust Evaluation for Heterogeneous Electric Vehicles Network in Smart Cities." IEEE Transactions on Intelligent Transportation Systems 22, no. 3: 1797-1806.

Article
Published: 05 June 2020 in Peer-to-Peer Networking and Applications
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The mode selection and resource allocation are important issues in Device-to-Device (D2D) communication, which have been investigated in single Base Station (BS) cellular network. However, the joint problem of the base station selection, mode selection and resource allocation is a challenging issue. Because the mode selection, base station and resource allocation are inter-coupled with each other. The joint problem of D2D User Equipments (DUEs) mode selection, base station selection, channel allocation and power allocation remains an open problem. In this paper, the scenario with multiple BSs in D2D heterogeneous networks is considered, and the joint problem of DUEs mode selection, base station selection, channel allocation and power allocation is studied. The objective is to maximize the system energy efficiency. We consider the DUEs multiplex the cellular user uplink resource. Meanwhile, the constraint of the total transmission power of the DUEs, and the constraint of the load balancing of the BSs are considered. The joint problem of mode selection, base station selection and resource allocation is formulated. The formulated problem is a non-convex mixed-integer optimization problem. In order to handle the formulated problem, a joint mode selection, base station selection and resource allocation algorithm based on particle swarm optimization is proposed. Numerical results demonstrate that the proposed algorithm can improve the system energy efficiency and obtain the desired target.

ACS Style

Zhufang Kuang; Gongqiang Li; Libang Zhang; Huibin Zhou; Changyun Li; Anfeng Liu. Energy Efficient Mode Selection, Base Station Selection and Resource Allocation Algorithm in D2D Heterogeneous Networks. Peer-to-Peer Networking and Applications 2020, 13, 1 -16.

AMA Style

Zhufang Kuang, Gongqiang Li, Libang Zhang, Huibin Zhou, Changyun Li, Anfeng Liu. Energy Efficient Mode Selection, Base Station Selection and Resource Allocation Algorithm in D2D Heterogeneous Networks. Peer-to-Peer Networking and Applications. 2020; 13 (5):1-16.

Chicago/Turabian Style

Zhufang Kuang; Gongqiang Li; Libang Zhang; Huibin Zhou; Changyun Li; Anfeng Liu. 2020. "Energy Efficient Mode Selection, Base Station Selection and Resource Allocation Algorithm in D2D Heterogeneous Networks." Peer-to-Peer Networking and Applications 13, no. 5: 1-16.

Journal article
Published: 30 March 2020 in IEEE Access
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Artificial intelligence-empowered path selection plays an important role in wireless sensor networks (WSNs), because it can exceed the cognitive performance of humans and determine multiple aspects of the network performance. Ant colony optimization (ACO) is an effective intelligence algorithm which succeeds in addressing several issues of WSNs, including data transmission, node deployment, etc. There exist several ACO-based transmission strategies for WSNs, but the summary and comparison of such works are very limited. This paper provides a comprehensive overview of ACO-based transmission strategies for static and mobile WSNs. First, we provide a classification of existing ACO-based transmission methods, which distinguishes itself from other works in network types. Second, the highly typical ACO-based transmission strategies for WSNs are illustrated and discussed. Finally, we summarize the paper and present several open issues concerning the design of such networks. This survey contributes to system design guidance and network performance improvement.

ACS Style

Xiaowei Chen; Lei Yu; Tian Wang; Anfeng Liu; Xiaofeng Wu; Benhong Zhang; Zhiguo Lv; Zeyu Sun. Artificial Intelligence-Empowered Path Selection: A Survey of Ant Colony Optimization for Static and Mobile Sensor Networks. IEEE Access 2020, 8, 71497 -71511.

AMA Style

Xiaowei Chen, Lei Yu, Tian Wang, Anfeng Liu, Xiaofeng Wu, Benhong Zhang, Zhiguo Lv, Zeyu Sun. Artificial Intelligence-Empowered Path Selection: A Survey of Ant Colony Optimization for Static and Mobile Sensor Networks. IEEE Access. 2020; 8 (99):71497-71511.

Chicago/Turabian Style

Xiaowei Chen; Lei Yu; Tian Wang; Anfeng Liu; Xiaofeng Wu; Benhong Zhang; Zhiguo Lv; Zeyu Sun. 2020. "Artificial Intelligence-Empowered Path Selection: A Survey of Ant Colony Optimization for Static and Mobile Sensor Networks." IEEE Access 8, no. 99: 71497-71511.

Journal article
Published: 16 March 2020 in IEEE Internet of Things Journal
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With the widespread application of infotainment services in intelligent connected vehicles (ICV), network traffic has grown exponentially, bringing huge burden and energy consumption to ICV network. Edge caching, which enables edges (e.g., vehicles or roadside units) with cache storages, is a promising technology to alleviate this problem. In this paper, in terms of the hybrid communication mode of vehicle to vehicle (V2V) and vehicle to roadside unit (V2R), an energy aware caching scheme for infotainment services is proposed. Considering the geographical distribution of vehicles and roadside units as well as the size of transmission content, the energy consumption model in ICV network is formulated to implement the optimal selection of cache nodes. Then the selection of cache node in ICV network is transformed into the optimal stopping problem and solved by the optimal stopping theory. Finally, we propose a new algorithm for optimal energy efficiency cache node selection (OEECS). Simulation results show that the proposed OEECS can obtain higher energy saving and lower average access latency than other baseline schemes.

ACS Style

Hongjia Wu; Jiao Zhang; Zhiping Cai; Fang Liu; Yangyang Li; Anfeng Liu. Toward Energy-Aware Caching for Intelligent Connected Vehicles. IEEE Internet of Things Journal 2020, 7, 8157 -8166.

AMA Style

Hongjia Wu, Jiao Zhang, Zhiping Cai, Fang Liu, Yangyang Li, Anfeng Liu. Toward Energy-Aware Caching for Intelligent Connected Vehicles. IEEE Internet of Things Journal. 2020; 7 (9):8157-8166.

Chicago/Turabian Style

Hongjia Wu; Jiao Zhang; Zhiping Cai; Fang Liu; Yangyang Li; Anfeng Liu. 2020. "Toward Energy-Aware Caching for Intelligent Connected Vehicles." IEEE Internet of Things Journal 7, no. 9: 8157-8166.

Journal article
Published: 12 February 2020 in IEEE Internet of Things Journal
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Mobile edge nodes, as an efficient approach to the performance improvement of wireless sensor networks (WSNs), play an important role in edge computing. However, existing works only focus on connected networks and suffer from high calculational cost. In this paper, we propose a rendezvous selection strategy for data collection of disjoint WSNs with mobile edge nodes. The goal is to achieve full network connectivity and minimize the path length. From the perspective of application scenario, our wok is distinctive in two aspects. On one hand, it is especially designed for partitioned networks which are much more complex than conventional connected scenarios. On other hand, our work is especially designed for delay-harsh applications rather than usual energy-oriented scenarios. From the viewpoint of implementation method, a simplified ant colony optimization (ACO) algorithm is performed and displays two characteristics. The first one is the path segmenting mechanism, simplifying the path construction of each part and consequently reducing the computational cost. The second one is the candidate grouping mechanism, reducing the search space and accordingly speeding up the convergence speed. Simulation results demonstrate the feasibility and advantages of this approach.

ACS Style

Xuxun Liu; Tie Qiu; Bin Dai; Lei Yang; Anfeng Liu; Jiangtao Wang. Swarm-Intelligence-Based Rendezvous Selection via Edge Computing for Mobile Sensor Networks. IEEE Internet of Things Journal 2020, 7, 9471 -9480.

AMA Style

Xuxun Liu, Tie Qiu, Bin Dai, Lei Yang, Anfeng Liu, Jiangtao Wang. Swarm-Intelligence-Based Rendezvous Selection via Edge Computing for Mobile Sensor Networks. IEEE Internet of Things Journal. 2020; 7 (10):9471-9480.

Chicago/Turabian Style

Xuxun Liu; Tie Qiu; Bin Dai; Lei Yang; Anfeng Liu; Jiangtao Wang. 2020. "Swarm-Intelligence-Based Rendezvous Selection via Edge Computing for Mobile Sensor Networks." IEEE Internet of Things Journal 7, no. 10: 9471-9480.