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21st International Conference on Service-Oriented Computing (ICSOC)

作者： Xu, Daliang Li, Qing Xu, Mengwei Huang, Kang Huang, Gang Wang, Shangguang Jin, Xin Ma, Yun Liu, Xuanzhe Peking Univ Sch Comp Sci Key Lab High Confidence Software Technol Minist Educ Beijing Peoples R China Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing Peoples R China Linggui Tech Co Beijing Peoples R China Peking Univ Inst Artificial Intelligence Beijing Peoples R China Natl Key Lab Data Space Technol & Syst Beijing Peoples R China

ISBN: (纸本)9783031484209;9783031484216

Intelligent applications heavily rely on deep neural network (DNN) inference services executed on edge devices to fulfill functional prerequisites while safeguarding user data privacy. However, the execution of such DNN services on resource-constrained edge devices poses a significant challenge: low throughput of inference tasks. To this end, this paper proposes Niagara, a novel system designed to maximize system throughput by judiciously scheduling DNN inference services on heterogeneous processors available on edge devices. Niagara faces two critical challenges: uncertain workload dynamics and high scheduling complexity. To effectively address these challenges, Niagara employs a predictive model to anticipate incoming workload patterns and orchestrates the allocation of services across heterogeneous processors through a combination of offline scheduling optimization and online service dispatching strategies. We have implemented Niagara and conducted thorough experiments. The results demonstrate that Niagara surpasses state-of-the-art approaches by elevating DNN inference throughput by up to 4.67x, all while satisfying the same stringent inference latency requirements. Furthermore, Niagara has been successfully deployed in realworld power supply substations to detect violations, ensuring uninterrupted, accident-free operation during its six-month deployment period.

关键词： Edge Computing Heterogeneous Processors DNN Inference Service

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A Game-Theoretic Analysis of Joint Mobile Edge Caching and Peer Content Sharing

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2023年第3期10卷 1445-1461页

作者： Jiang, Changkun Gao, Lin Luo, Jingjing Zhou, Pan Li, Jianqiang Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China Harbin Inst Technol Sch Elect & Informat Engn Shenzhen 518055 Peoples R China Harbin Inst Technol Guangdong Prov Key Lab Aerosp Commun & Networking Shenzhen 518055 Peoples R China Robot Soc Shenzhen Inst Artificial Intelligence Harbin Inst Technol Shenzhen 518055 Peoples R China Huazhong Univ Sci & Technol Hubei Engn Res Ctr Big Data Secur Sch Cyber Sci & Engn Hubei Key Lab Distributed Syst Secur Wuhan Peoples R China

Mobile edge caching allows edge devices to cache popular contents and deliver them to end-users directly, and hence can effectively alleviate the increasingly heavy backbone loads and improve the quality-of-service of end-users. Peer content sharing enables edge devices to share the cached contents with each other, and can further increase the caching capability and efficiency. While lots of research efforts have been made to edge caching or content sharing, it remains largely open to devise a joint caching and sharing framework and study the complicated technical and economic interplay between both technologies. In this paper, we propose a joint framework for mobile edge caching and peer content sharing, and focus on studying the strategic behaviors and interactions of edge devices in the joint framework. Specifically, we model their interactions as a non-cooperative game, where each edge device (player) can choose to be an agent (who caches and shares contents with others) or a requester (who doesn't cache but requests contents from other devices) of each content. We characterize the game equilibrium under a generic usage-based pricing scheme (for content sharing) and analyze its existence and uniqueness systematically. We further design a best response based iterative learning algorithm for players to update their behaviors and reach the equilibrium in a self-enforcing manner. Moreover, we analyze the Price of Anarchy (PoA) of the game equilibrium to measure the system performance degradation due to the selfish strategic behaviors of game players, based on which we further design a pricing scheme (for content sharing) to reduce such performance degradation. Simulations show that our framework can reduce the total system cost by up to 60%, comparing with the benchmark pure caching system without peer content sharing.

关键词： Games Degradation Costs Behavioral sciences Pricing Servers Quality of service Game theory mobile edge caching network economics peer content sharing

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Digital Twin Assisted Task Offloading for Aerial Edge Computing and Networks

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2022年第10期71卷 10863-10877页

作者： Li, Bin Liu, Yufeng Tan, Ling Pan, Heng Zhang, Yan Nanjing Univ Informat Sci & Technol Sch Comp & Software Nanjing 210044 Peoples R China Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Zhongyuan Univ Technol Zhengzhou 450007 Peoples R China Henan Int Joint Lab Blockchain Data Sharing Zhengzhou 450007 Peoples R China Univ Oslo N-0315 Oslo Norway Simula Metropolitan Ctr Digital Engn N-0167 Oslo Norway

Considering the user mobility and unpredictable mobile edge computing (MEC) environments, this paper studies the intelligent task offloading problem in unmanned aerial vehicle (UAV)-enabled MEC with the assistance of digital twin (DT). We aim at minimizing the energy consumption of the entire MEC system by jointly optimizing mobile terminal users (MTUs) association, UAV trajectory, transmission power distribution and computation capacity allocation while respecting the constraints of mission maximum processing delays. Specifically, double deep Q-network (DDQN) algorithm stemming from deep reinforcement learning is first proposed to effectively solve the problem of MTUs association and UAV trajectory. Then, the closed-form expression is employed to handle the problem of transmission power distribution and the computation capacity allocation problem is further addressed via an iterative algorithm. Numerical results show that our proposed scheme is able to converge and significantly reduce the total energy consumption of the MEC system compared to the benchmark schemes.

关键词： Task analysis Autonomous aerial vehicles Energy consumption Digital twins Trajectory Servers Resource management Digital twin unmanned aerial vehicle mobile edge computing user mobility deep reinforcement learning

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Cooperative Sensing and Heterogeneous Information Fusion in VCPS: A Multi-Agent Deep Reinforcement Learning Approach

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第6期25卷 4876-4891页

作者： Xu, Xincao Liu, Kai Dai, Penglin Xie, Ruitao Cao, Jingjing Luo, Jiangtao Univ Elect Sci & Technol China Shenzhen Inst Adv Study Shenzhen 518110 Peoples R China Chongqing Univ Coll Comp Sci Chongqing 400040 Peoples R China Southwest Jiaotong Univ Sch Comp & Artificial Intelligence Chengdu 611756 Peoples R China Natl Engn Lab Integrated Transportat Big Data Appl Chengdu 611756 Peoples R China Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China Wuhan Univ Technol Sch Transportat & Logist Engn Wuhan 430063 Hubei Peoples R China Chongqing Univ Posts & Telecommun Elect Informat & Networking Res Inst Chongqing 400065 Peoples R China

Cooperative sensing and heterogeneous information fusion are critical to realize vehicular cyber-physical systems (VCPSs). This paper makes the first attempt to quantitatively measure the quality of VCPS by designing a new metric called Age of View (AoV). Specifically, we first present the system architecture where heterogeneous information can be cooperatively sensed and uploaded via vehicle-to-infrastructure (V2I) communications in vehicular edge computing (VEC). Logical views are constructed by fusing the heterogeneous information at edge nodes. Further, we formulate the problem by deriving a cooperative sensing model based on the multi-class M/G/1 priority queue, and defining the AoV by modeling the timeliness, completeness and consistency of the logical views. On this basis, a multi-agent difference reward based actor-critic with V2I bandwidth allocation (MDRAC-VBA) solution is proposed. In particular, the system state includes vehicle sensed information, edge cached information and view requirements. The vehicle action space consists of the sensing frequencies and uploading priorities of information. A difference-reward-based credit assignment is designed to divide the system reward, which is defined as the VCPS quality, into the difference reward for vehicles. Edge node allocates V2I bandwidth to vehicles based on predicted vehicle trajectories and view requirements. Finally, we build the simulation model and give a comprehensive performance evaluation, which conclusively demonstrates the superiority of MDRAC-VBA.

关键词： Vehicular cyber-physical system edge computing cooperative sensing heterogeneous information fusion multi-agent deep reinforcement learning

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Joint Node Selection and Resource Allocation Optimization for Cooperative Sensing with a Shared Wireless Backhaul

arXiv

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arXiv 2024年

作者： Chen, Mingxin Zhao, Ming-Min Liu, An Li, Min Shi, Qingjiang College of Information Science and Electronic Engineering Zhejiang University Zhejiang Provincial Key Laboratory of Information Processing Communication and Networking Hangzhou310027 China School of Software Engineering Tongji University Shanghai201804 China Shenzhen Research Institute of Big Data Shenzhen518172 China

In this paper, we consider a cooperative sensing framework in the context of future multi-functional network with both communication and sensing ability, where one base station (BS) serves as a sensing transmitter and several nearby BSs serve as sensing receivers. Each receiver receives the sensing signal reflected by the target and communicates with the fusion center (FC) through a wireless multiple access channel (MAC) for cooperative target localization. To improve the localization performance, we present a hybrid information-signal domain cooperative sensing (HISDCS) design, where each sensing receiver transmits both the estimated time delay/effective reflecting coefficient and the received sensing signal sampled around the estimated time delay to the FC. Then, we propose to minimize the number of channel uses by utilizing an efficient Karhunen-Loéve transformation (KLT) encoding scheme for signal quantization and proper node selection, under the Cramér-Rao lower bound (CRLB) constraint and the capacity limits of MAC. A novel matrix-inequality constrained successive convex approximation (MCSCA) algorithm is proposed to optimize the wireless backhaul resource allocation, together with a greedy strategy for node selection. Despite the high non-convexness of the considered problem, we prove that the proposed MCSCA algorithm is able to converge to the set of Karush-Kuhn-Tucker (KKT) solutions of a relaxed problem obtained by relaxing the discrete variables. Besides, a low-complexity quantization bit reallocation algorithm is designed, which does not perform explicit node selection, and is able to harvest most of the performance gain brought by HISDCS. Finally, numerical simulations are presented to show that the proposed HISDCS design is able to significantly outperform the baseline schemes. Copyright © 2024, The Authors. All rights reserved.

关键词： Resource allocation

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Federated-Learning-Empowered Collaborative data Sharing for Vehicular Edge Networks

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IEEE NETWORK 2021年第3期35卷 116-124页

作者： Li, Xiuhua Cheng, Luxi Sun, Chuan Lam, Kwok-Yan Wang, Xiaofei Li, Feng Chongqing Univ Sch Big Data & Software Engn Key Lab Dependable Serv Comp Cyber Phys Soc Educ Minist Chongqing Peoples R China Chongqing Univ Inst Intelligent Software & Serv Comp Key Lab Dependable Serv Comp Cyber Phys Soc Educ Minist Chongqing Peoples R China Chongqing Univ Sch Big Data & Software Engn Chongqing Peoples R China Chongqing Innovat Ctr Ind Big Data Co Ltd Chongqing Peoples R China Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore Tianjin Univ Tianjin Key Lab Adv Networking Sch Comp Sci & Technol Tianjin Peoples R China Zhejiang Gongshang Univ Sch Informat & Elect Engn Hangzhou Peoples R China

The Internet of Vehicles connects all vehicles and shares dynamic vehicular data via wireless communications to effectively control vehicles and improve traffic efficiency. However, due to vehicular movement, vehicular data sharing based on conventional cloud computing can hardly realize real-time and dynamic updates. To address these challenges, artificial intelligence (AI)-empowered mobile/multi-access edge computing (MEC) has been regarded as a promising technique for intelligently supporting various vehicular services and applications in proximity of vehicles. In this article, we investigate the issue of collaborative data sharing in vehicular edge networks (VENs) with the deployment of AI-empowered MEC servers. Furthermore, we present a specific mode for collaborative data sharing. Then we propose a novel collaborative data sharing scheme with deep Q-network and federated learning to ensure efficient and secure data sharing in the VEN. Evaluation results demonstrate the effectiveness of the proposed scheme on reducing latency of vehicular data sharing. Finally, we discuss several open issues and future challenges of the AI-empowered VEN.

关键词： Wireless communication Training Cloud computing Heuristic algorithms Collaboration Training data Collaborative work

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CroApp: A CNN-Based Resource Optimization Approach in Edge Computing Environment

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2022年第9期18卷 6300-6307页

作者： Jia, Yongzhe Liu, Bowen Dou, Wanchun Xu, Xiaolong Zhou, Xiaokang Qi, Lianyong Yan, Zheng Nanjing Univ Dept Comp Sci & Technol State Key Lab Novel Software Technol Nanjing 210023 Peoples R China Nanjing Univ Informat Sci & Technol Sch Comp & Software Jiangsu Collaborat Innovat Ctr Atmospher Environm Nanjing 210044 Peoples R China Shiga Univ Fac Data Sci Hikone 5228522 Japan RIKEN Ctr Adv Intelligence Project Tokyo 1030027 Japan Qufu Normal Univ Sch Comp Sci Rizhao 276826 Peoples R China Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China Xidian Univ Sch Cyber Engn Xian 710071 Peoples R China Aalto Univ Dept Commun & Networking Espoo 02150 Finland

With the emergence of various convolutional neural network (CNN)-based applications and the rapid growth of CNN model scale, the resource-constricted end devices can hardly deploy CNN-based applications. Current work optimizes the CNN model on edge servers and deploys the optimized model on devices in an edge computing environment. However, most of them only optimize the resource consumption within or across models solely, whereas neglecting the other side. In this article, we propose a novel CNN-based resource optimization approach (CroApp) that not only optimizes the resource consumption within the CNN model but also pays attention to resource optimization across the applications. Specifically, we adopt model compression as the "inner-model" optimization method, as well as computation sharing as the "intermodel" optimization method. First, during "inner-model" optimization, the CroApp prunes unnecessary parameters within the model on edge servers to reduce the scale of the model. Then, during "intermodel" optimization, the CroApp trains a set of shareable models based on the pruned model and sends these shareable models to end devices. Finally, the CroApp adaptively adjusts the shared models to reduce resource consumption. The experimental results show that the CroApp outperforms the state-of-the-art approaches in terms of resource reduction, scalability, and application performance.

关键词： Computational modeling Optimization Servers Adaptation models Solid modeling Surveillance Edge computing Computation sharing convolutional neural networks (CNNs) edge computing model pruning resource optimization

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Hierarchical Federated Learning With Social Context Clustering-Based Participant Selection for Internet of Medical Things Applications

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2023年第4期10卷 1742-1751页

作者： Zhou, Xiaokang Ye, Xiaozhou Wang, Kevin I-Kai Liang, Wei Nair, Nirmal Kumar C. Shimizu, Shohei Yan, Zheng Jin, Qun Shiga Univ Fac Data Sci Hikone 5228522 Japan RIKEN RIKEN Ctr Adv Intelligence Project Tokyo 1030027 Japan Univ Auckland Dept Elect Comp & Software Engn Auckland 1142 New Zealand Hunan Univ Technol & Business Changsha Social Lab Artificial Intelligence Changsha 410205 Peoples R China Cent South Univ Business Sch Changsha 410017 Peoples R China Xidian Univ State Key Lab Integrated Serv Networks Xian 710026 Peoples R China Xidian Univ Sch Cyber Engn Xian 710026 Peoples R China Aalto Univ Dept Commun & Networking Espoo 02150 Finland Waseda Univ Fac Human Sci Tokorozawa 3591192 Japan

The proliferation in embedded and communication technologies made the concept of the Internet of Medical Things (IoMT) a reality. Individuals' physical and physiological status can be constantly monitored, and numerous data can be collected through wearable and mobile devices. However, the silo of individual data brings limitations to existing machine learning approaches to correctly identify a user's health status. Distributed machine learning paradigms, such as federated learning, offer a potential solution for privacy-preserving knowledge sharing without sending raw personal data. However, federated learning is vulnerable to harmful participants that can degrade the overall model quality by sharing low-quality data. Therefore, it is critical to select suitable participants to ensure the accuracy and efficiency of federated learning. In this article, a unique clustering-based approach is proposed to use social context data for participant selection. Different edge participant groups will be established, and group-specific federated learning will be performed. The models of various edge groups will be further aggregated to strengthen the robustness of the global model. The experimental results demonstrated that through participant selection, clustering-based hierarchical federated learning can achieve better results with less participants in two different IoMT applications for ECG and human motion monitoring. This shows the efficacy of the proposed method in improving federated learning performance and efficiency in various IoMT applications.

关键词： Federated learning data models Computational modeling Biomedical monitoring Distributed databases Context modeling Monitoring Clustering federated learning Internet of Medical Things (IoMT) participant selection

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Automatic Detection and Characterization of Human Veins Using Infra-Red Image Processing

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Journal of Computer and Communications 2024年第9期12卷 141-159页

作者： Jean Ndoumbe Brice Ekobo Akoa Gaelle Patricia Talotsing Frederic Franck Kounga Samuel Kaissassou Bertin Chouanmo Njo Laboratory of Computer Engineering Data Science and Artificial Intelligence Department of Computer & Telecommunications Engineering National Higher Polytechnic School Douala Cameroon Laboratory of Electrical Engineering Mechatronics and Signal Processing Department of Electrical & Telecommunications Engineering National Advanced School of Engineering Yaound Cameroon Mobile Computing and Networking Research Laboratory Department of Computer and Software Engineering Polytechnique Montreal Montreal Canada DigiPlus SARL Bonamoussadi Douala Cameroon

The detection and characterization of human veins using infrared (IR) image processing have gained significant attention due to its potential applications in biometric identification, medical diagnostics, and vein-based authentication systems. This paper presents a low-cost approach for automatic detection and characterization of human veins from IR images. The proposed method uses image processing techniques including segmentation, feature extraction, and, pattern recognition algorithms. Initially, the IR images are preprocessed to enhance vein structures and reduce noise. Subsequently, a CLAHE algorithm is employed to extract vein regions based on their unique IR absorption properties. Features such as vein thickness, orientation, and branching patterns are extracted using mathematical morphology and directional filters. Finally, a classification framework is implemented to categorize veins and distinguish them from surrounding tissues or artifacts. A setup based on Raspberry Pi was used. Experimental results of IR images demonstrate the effectiveness and robustness of the proposed approach in accurately detecting and characterizing human. The developed system shows promising for integration into applications requiring reliable and secure identification based on vein patterns. Our work provides an effective and low-cost solution for nursing staff in low and middle-income countries to perform a safe and accurate venipuncture.

关键词： Vein Detection Blood Radiation Infrared Image CLAHE Algorithm Raspberry Pi

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A Physics-based and data-driven Approach for Localized Statistical Channel Modeling

arXiv

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arXiv 2023年

作者： Zhang, Shutao Ning, Xinzhi Zheng, Xi Shi, Qingjiang Chang, Tsung-Hui Luo, Zhi-Quan School of Science and Engineering The Chinese University of Hong Kong Shenzhen China Shenzhen Research Institute of Big Data Shenzhen China School of Software Engineering Tongji University Shanghai China Pengcheng Lab Shenzhen China Networking and User Experience Lab Huawei Technologies Shenzhen China

Localized channel modeling is crucial for offline performance optimization of 5G cellular networks, but the existing channel models are for general scenarios and do not capture local geographical structures. In this paper, we propose a novel physics-based and data-driven localized statistical channel modeling (LSCM), which is capable of sensing the physical geographical structures of the targeted cellular environment. The proposed channel modeling solely relies on the reference signal receiving power (RSRP) of the user equipment, unlike the traditional methods which use full channel impulse response matrices. The key is to build the relationship between the RSRP and the channel’s angular power spectrum. Based on it, we formulate the task of channel modeling as a sparse recovery problem where the non-zero entries of the sparse vector indicate the channel paths’ powers and angles of departure. A computationally efficient weighted non-negative orthogonal matching pursuit (WNOMP) algorithm is devised for solving the formulated problem. Finally, experiments based on synthetic and real RSRP measurements are presented to examine the performance of the proposed method. © 2023, CC BY.

关键词： Statistical Physics

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