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Stackelberg Game for wireless Powered and Backscattering Enabled Sensor networks

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China communications 2024年第3期21卷 189-204页

作者： Lyu Bin Cao Yi Wang Shuai Guo Haiyan Hao Chengyao Key Laboratory of Ministry of Education in Broadband wireless communication and Sensor network technology Nanjing University of Posts and TelecommunicationsNanjing 210003China Jiangsu Engineering research Center of Novel Optical Fiber technology and communication network Suzhou 215006China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China

This paper investigates a wireless powered and backscattering enabled sensor network based on the non-linear energy harvesting model, where the power beacon(PB) delivers energy signals to wireless sensors to enable their passive backscattering and active transmission to the access point(AP). We propose an efficient time scheduling scheme for network performance enhancement, based on which each sensor can always harvest energy from the PB over the entire block except its time slots allocated for passive and active information delivery. Considering the PB and wireless sensors are from two selfish service providers, we use the Stackelberg game to model the energy interaction among them. To address the non-convexity of the leader-level problem, we propose to decompose the original problem into two subproblems and solve them iteratively in an alternating manner. Specifically, the successive convex approximation, semi-definite relaxation(SDR) and variable substitution techniques are applied to find a nearoptimal solution. To evaluate the performance loss caused by the interaction between two providers, we further investigate the social welfare maximization problem. Numerical results demonstrate that compared to the benchmark schemes, the proposed scheme can achieve up to 35.4% and 38.7% utility gain for the leader and the follower, respectively.

关键词： backscatter communication energy interaction stackelberg game wireless powered sensor network

Policy network-Based Dual-Agent Deep Reinforcement Learning for Multi-Resource Task Offloading in Multi-Access Edge Cloud networks

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China communications 2024年第4期21卷 53-73页

作者： Feng Chuan Zhang Xu Han Pengchao Ma Tianchun Gong Xiaoxue School of communications and Information Engineering Chongqing University of Posts and TelecommunicationsChongqing 400065China institute of intelligent communication and network Security Chongqing University of Posts and TelecommunicationsChongqing 400065China Key Laboratory of Big Data intelligent Computing Chongqing University of Posts and TelecommunicationsChongqing 400065China School of Information Engineering Guangdong University of TechnologyGuangzhou 510006China

The Multi-access Edge Cloud(MEC) networks extend cloud computing services and capabilities to the edge of the networks. By bringing computation and storage capabilities closer to end-users and connected devices, MEC networks can support a wide range of applications. MEC networks can also leverage various types of resources, including computation resources, network resources, radio resources,and location-based resources, to provide multidimensional resources for intelligent applications in 5/***, tasks generated by users often consist of multiple subtasks that require different types of resources. It is a challenging problem to offload multiresource task requests to the edge cloud aiming at maximizing benefits due to the heterogeneity of resources provided by devices. To address this issue,we mathematically model the task requests with multiple subtasks. Then, the problem of task offloading of multi-resource task requests is proved to be NP-hard. Furthermore, we propose a novel Dual-Agent Deep Reinforcement Learning algorithm with Node First and Link features(NF_L_DA_DRL) based on the policy network, to optimize the benefits generated by offloading multi-resource task requests in MEC networks. Finally, simulation results show that the proposed algorithm can effectively improve the benefit of task offloading with higher resource utilization compared with baseline algorithms.

关键词： benefit maximization deep reinforcement learning multi-access edge cloud task offloading

Video caching and scheduling with edge cooperation

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Digital communications and networks 2024年第2期10卷 450-460页

作者： Zhidu Li Fuxiang Li Tong Tang Hong Zhang Jin Yang 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

In this paper,we explore a distributed collaborative caching and computing model to support the distribution of adaptive bit rate video *** aim is to reduce the average initial buffer delay and improve the quality of user *** the difference between global and local video popularities and the time-varying characteristics of video popularity,a two-stage caching scheme is proposed to push popular videos closer to users and minimize the average initial buffer *** on both long-term content popularity and short-term content popularity,the proposed caching solution is decouple into the proactive cache stage and the cache update *** the proactive cache stage,we develop a proactive cache placement algorithm that can be executed in an off-peak *** the cache update stage,we propose a reactive cache update algorithm to update the existing cache policy to minimize the buffer *** results verify that the proposed caching algorithms can reduce the initial buffer delay efficiently.

关键词： Video service Distributed and collaborative caching Long-term popularity Short-term popularity

Improved decoy-state quantum key distribution with uncharacterized heralded single-photon sources

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Chinese Physics B 2024年第2期33卷 204-208页

作者：徐乐辰张春辉周星宇王琴 institute of Quantum Information and technology Nanjing 210003China Broadband wireless communication and Sensor network technology Key Laboratory of Ministry of EducationNanjing University of Posts and TelecommunicationsNanjing 210003China Telecommunication and networks National Engineering research Center Nanjing University of Posts and TelecommunicationsNanjing 210003China

Encoding system plays a significant role in quantum key distribution(QKD).However,the security and performance of QKD systems can be compromised by encoding misalignment due to the inevitable defects in realistic *** alleviate the influence of misalignments,a method exploiting statistics from mismatched basis is proposed to enable uncharacterized sources to generate secure keys in *** this work,we propose a scheme on four-intensity decoy-state quantum key distribution with uncharacterized heralded single-photon *** only requires the source states are prepared in a two-dimensional Hilbert space,and can thus reduce the complexity of practical ***,we carry out corresponding numerical simulations and demonstrate that our present four-intensity decoy-state scheme can achieve a much higher key rate compared than a three-intensity decoy-state method,and meantime it can obtain a longer transmission distance compared than the one using weak coherent sources.

关键词： quantum key distribution heralded single-photon source decoy-state method

RGB-guided hyperspectral image super-resolution with deep progressive learning

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CAAI Transactions on Intelligence technology 2024年第3期9卷 679-694页

作者： Tao Zhang Ying Fu Liwei Huang Siyuan Li Shaodi You Chenggang Yan School of Computer Science and technology Beijing Institute of TechnologyBeijingChina Satellite Information intelligent Processing and Application research Laboratory Beijing Institute of Remote SensingBeijingChina Key Laboratory of Spectral Imaging technology Xi'an Institute of Optics and Precision Mechanics of Chinese Academy of SciencesXi'anChina institute of Informatics University of AmsterdamAmsterdamNetherlands School of communication Engineering Hangzhou Dianzi UniversityHangzhouChina

Due to hardware limitations,existing hyperspectral(HS)camera often suffer from low spatial/temporal ***,it has been prevalent to super-resolve a low reso-lution(LR)HS image into a high resolution(HR)HS image with a HR RGB(or mul-tispectral)image *** approaches for this guided super-resolution task often model the intrinsic characteristic of the desired HR HS image using hand-crafted ***,researchers pay more attention to deep learning methods with direct supervised or unsupervised learning,which exploit deep prior only from training dataset or testing *** this article,an efficient convolutional neural network-based method is presented to progressively super-resolve HS image with RGB image ***-ically,a progressive HS image super-resolution network is proposed,which progressively super-resolve the LR HS image with pixel shuffled HR RGB image ***,the super-resolution network is progressively trained with supervised pre-training and un-supervised adaption,where supervised pre-training learns the general prior on training data and unsupervised adaptation generalises the general prior to specific prior for variant testing *** proposed method can effectively exploit prior from training dataset and testing HS and RGB images with spectral-spatial *** has a good general-isation capability,especially for blind HS image *** experimental results show that the proposed deep progressive learning method out-performs the existing state-of-the-art methods for HS image super-resolution in non-blind and blind cases.

关键词： computer vision deep neural networks image processing image resolution unsupervised learning

CCM-FL:Covert communication mechanisms for federated learning in crowd sensing IoT

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Digital communications and networks 2024年第3期10卷 597-608页

作者： Hongruo Zhang Yifei Zou Haofei Yin Dongxiao Yu Xiuzhen Cheng institute of intelligent Computing School of Computer Science and TechnologyShandong UniversityQingdao266237China

The past decades have witnessed a wide application of federated learning in crowd sensing,to handle the numerous data collected by the sensors and provide the users with precise and customized ***,how to protect the private information of users in federated learning has become an important research *** with the differential privacy(DP)technique and secure multiparty computation(SMC)strategy,the covert communication mechanism in federated learning is more efficient and energy-saving in training the ma-chine learning *** this paper,we study the covert communication problem for federated learning in crowd sensing Internet-of-Things *** from the previous works about covert communication in federated learning,most of which are considered in a centralized framework and experimental-based,we firstly proposes a centralized covert communication mechanism for federated learning among n learning agents,the time complexity of which is O(log n),approximating to the optimal ***,for the federated learning without parameter server,which is a harder case,we show that solving such a problem is NP-hard and prove the existence of a distributed covert communication mechanism with O(log logΔlog n)times,approximating to the optimal solution.Δis the maximum distance between any pair of learning *** analysis and nu-merical simulations are presented to show the performance of our covert communication *** hope that our covert communication work can shed some light on how to protect the privacy of federated learning in crowd sensing from the view of communications.

关键词： Covert communications Federated learning Crowd sensing SINR model

Enhancing action discrimination via category-specific frame clustering for weakly-supervised temporal action localization

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Frontiers of Information technology & Electronic Engineering 2024年第6期25卷 809-823页

作者： Huifen XIA Yongzhao ZHAN Honglin LIU Xiaopeng REN School of Computer Science and communication Engineering Jiangsu UniversityZhenjiang 212013China Jiangsu Engineering research Center of Big Data Ubiquitous Perception and intelligent Agricultural Applications Zhenjiang 212013China Changzhou Vocational institute of Mechatronic technology Changzhou 213164China

Temporal action localization (TAL) is a task of detecting the start and end timestamps of action instances and classifying them in an untrimmed video. As the number of action categories per video increases, existing weakly-supervised TAL (W-TAL) methods with only video-level labels cannot provide sufficient supervision. Single-frame supervision has attracted the interest of researchers. Existing paradigms model single-frame annotations from the perspective of video snippet sequences, neglect action discrimination of annotated frames, and do not pay sufficient attention to their correlations in the same category. Considering a category, the annotated frames exhibit distinctive appearance characteristics or clear action ***, a novel method to enhance action discrimination via category-specific frame clustering for W-TAL is proposed. Specifically,the K-means clustering algorithm is employed to aggregate the annotated discriminative frames of the same category, which are regarded as exemplars to exhibit the characteristics of the action category. Then, the class activation scores are obtained by calculating the similarities between a frame and exemplars of various categories. Category-specific representation modeling can provide complimentary guidance to snippet sequence modeling in the mainline. As a result, a convex combination fusion mechanism is presented for annotated frames and snippet sequences to enhance the consistency properties of action discrimination,which can generate a robust class activation sequence for precise action classification and localization. Due to the supplementary guidance of action discriminative enhancement for video snippet sequences, our method outperforms existing single-frame annotation based methods. Experiments conducted on three datasets (THUMOS14, GTEA, and BEOID) show that our method achieves high localization performance compared with state-of-the-art methods.

关键词： Weakly supervised Temporal action localization Single-frame annotation Category-specific Action discrimination

A Novel Anti-Collision Algorithm for Large Scale of UHF RFID Tags Access Systems

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Computers, Materials & Continua 2024年第7期80卷 897-912页

作者： Xu Zhang Yi He Haiwen Yi Yulu Zhang Yuan Li Shuai Ma Gui Li Zhiyuan Zhao Yue Liu Junyang Liu Guangjun Wen Jian Li School of Information and communication Engineering/Yibin institute University of Electronic Science and Technology of ChinaChengdu611731China Department of IoT technology research China Mobile Research InstituteBeijing100053China The 10th research institute of China Electronics technology Group Corporation Chengdu611731China College of Information and communication National University of Defense TechnologyWuhan430019China

When the radio frequency identification(RFID)system inventories multiple tags,the recognition rate will be seriously affected due to *** on the existing dynamic frame slotted Aloha(DFSA)algorithm,a sub-frame observation and cyclic redundancy check(CRC)grouping combined dynamic framed slotted Aloha(SUBF-CGDFSA)algorithm is *** algorithm combines the precise estimation method of the quantity of large-scale tags,the large-scale tags grouping mechanism based on CRC pseudo-randomcharacteristics,and the Aloha anti-collision optimization mechanism based on sub-frame *** grouping tags and sequentially identifying themwithin subframes,it accurately estimates the number of remaining tags and optimizes frame length accordingly to improve efficiency in large-scale RFID *** outcomes demonstrate that this proposed algorithmcan effectively break through the system throughput bottleneck of 36.8%,which is up to 30%higher than the existing DFSA standard scheme,and has more significant advantages,which is suitable for application in largescale RFID tags scenarios.

关键词： Radio frequency identification anti-collision tags grouping dynamic framed slotted Aloha

Passive IoT Localization technology Based on SD-PDOA in NLOS and Multi-Path Environments

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Computers, Materials & Continua 2024年第7期80卷 913-930页

作者： Junyang Liu Yuan Li Yulu Zhang Shuai Ma Gui Li Yi He Haiwen Yi Yue Liu Xiaotao Xu Xu Zhang Jinyao He Guangjun Wen Jian Li School of Information and communication Engineering/Yibin institute University of Electronic Science and Technology of ChinaChengdu611731China Department of IoT technology research China Mobile Research InstituteBeijing100053China The 10th research institute of China Electronics technology Group Corporation Chengdu611731China College of Information and communication National University of Defense TechnologyWuhan430019China

Addressing the challenges of passive Radio Frequency Identification(RFID)indoor localization technology in Non-Line-of-Sight(NLoS)and multipath environments,this paper presents an innovative approach by introducing a combined technology integrating an improved Kalman Filter with Space Domain Phase Difference of Arrival(SD-PDOA)and Received Signal Strength Indicator(RSSI).This methodology utilizes the distinct channel characteristics in multipath and NLoS contexts to effectively filter out interference and accurately extract localization information,thereby facilitating high precision and stability in passive RFID *** efficacy of this approach is demonstrated through detailed simulations and empirical tests conducted on a custom-built experimental platform consisting of passive RFID tags and an R420 *** findings are significant:in NLoS conditions,the four-antenna localization system achieved a notable localization accuracy of 0.25 m at a distance of 5 *** complex multipath environments,this system achieved a localization accuracy of approximately 0.5 m at a distance of 5 *** compared to conventional passive localization methods,our proposed solution exhibits a substantial improvement in indoor localization accuracy under NLoS and multipath *** research provides a robust and effective technical solution for high-precision passive indoor localization in the Internet of Things(IoT)system,marking a significant advancement in the field.

关键词： Indoor localization multipath and NLoS environments combined SD-PDOA and RSSI technology