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Proactive Caching With distributed Deep Reinforcement Learning in 6G Cloud-edge Collaboration computing

IEEE TRANSACTIONS ON PARALLEL AND distributed SYSTEMS 2024年第8期35卷 1387-1399页

作者： Wu, Changmao Xu, Zhengwei He, Xiaoming Lou, Qi Xia, Yuanyuan Huang, Shuman Chinese Acad Sci Inst Soft ware Beijing 100190 Peoples R China Henan Normal Univ Coll Comp & Informat Engn Xinxiang 453007 Peoples R China Nanjing Univ Posts & Telecommun Coll Internet Things Nanjing 210049 Peoples R China

Proactive caching in 6G cloud-edge collaboration scenarios, intelligently and periodically updating the cached contents, can either alleviate the traffic congestion of backhaul link and edge cooperative link or bring multimedia services to mobile users. To further improve the network performance of 6G cloud-edge, we consider the issue of multi-objective joint optimization, i.e., maximizing edge hit ratio while minimizing content access latency and traffic cost. To solve this complex problem, we focus on the distributed deep reinforcement learning (DRL)-based method for proactive caching, including content prediction and content decision-making. Specifically, since the prior information of user requests is seldom available practically in the current time period, a novel method named temporal convolution sequence network (TCSN) based on the temporal convolution network (TCN) and attention model is used to improve the accuracy of content prediction. Furthermore, according to the value of content prediction, the distributional deep Q network (DDQN) seeks to build a distribution model on returns to optimize the policy of content decision-making. The generative adversarial network (GAN) is adapted in a distributed fashion, emphasizing learning the data distribution and generating compelling data across multiple nodes. In addition, the prioritized experience replay (PER) is helpful to learn from the most effective sample. So we propose a multivariate fusion algorithm called PG-DDQN. Finally, faced with such a complex scenario, a distributed learning architecture, i.e., multi-agent learning architecture is efficiently used to learn DRL-based methods in a manner of centralized training and distributed inference. The experiments prove that our proposal achieves satisfactory performance in terms of edge hit ratio, traffic cost and content access latency.

关键词： Costs Training 6G mobile communication Servers Predictive models Generative adversarial networks Optimization 6G distributed edge computing proactive caching deep reinforcement learning multi-agent learning architecture

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GNN at the edge: Cost-Efficient Graph Neural Network Processing Over distributed edge Servers

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IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 2023年第3期41卷 720-739页

作者： Zeng, Liekang Yang, Chongyu Huang, Peng Zhou, Zhi Yu, Shuai Chen, Xu Sun Yat sen Univ Sch Comp Sci & Engn Guangzhou 510006 Peoples R China

edge intelligence has arisen as a promising computing paradigm for supporting miscellaneous smart applications that rely on machine learning techniques. While the community has extensively investigated multi-tier edge deployment for traditional deep learning models (e.g. CNNs, RNNs), the emerging Graph Neural Networks (GNNs) are still under exploration, presenting a stark disparity to its broad edge adoptions such as traffic flow forecasting and location-based social recommendation. To bridge this gap, this paper formally studies the cost optimization for distributed GNN processing over a multi-tier heterogeneous edge network. We build a comprehensive modeling framework that can capture a variety of different cost factors, based on which we formulate a cost-efficient graph layout optimization problem that is proved to be NP-hard. Instead of trivially applying traditional data placement wisdom, we theoretically reveal the structural property of quadratic submodularity implicated in GNN's unique computing pattern, which motivates our design of an efficient iterative solution exploiting graph cuts. Rigorous analysis shows that it provides parameterized constant approximation ratio, guaranteed convergence, and exact feasibility. To tackle potential graph topological evolution in GNN processing, we further devise an incremental update strategy and an adaptive scheduling algorithm for lightweight dynamic layout optimization. Evaluations with real-world datasets and various GNN benchmarks demonstrate that our approach achieves superior performance over de facto baselines with more than 95.8% cost reduction in a fast convergence speed.

关键词： edge intelligence graph neural networks cost optimization distributed edge computing

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Explainable edge AI Framework for IoD-Assisted Aerial Surveillance in Extreme Scenarios

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IEEE INTERNET OF THINGS JOURNAL 2025年第5期12卷 4570-4578页

作者： Zhu, Hailong Demirbaga, Umit Aujla, Gagangeet Singh Shi, Lei Zhang, Peiying Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Bartin Univ Dept Comp Engn TR-74110 Bartin Turkiye Univ Durham Dept Comp Sci Durham DH1 3LE England Commun Univ China State Key Lab Media Convergence & Commun Beijing 100024 Peoples R China Minzu Univ China Key Lab Ethn Language Intelligent Anal & Secur Gov Beijing 100081 Peoples R China China Univ Petr East China Qingdao Inst Software Coll Comp Sci & Technol Qingdao 266580 Peoples R China Qilu Univ Technol Shandong Acad Sci Key Lab Comp Power Network & Informat Secur Minist Educ Jinan 250013 Peoples R China

Drones are sophisticated machines that can hover over extreme locations, conduct aerial surveillance, collect surveillance data, and disseminate it to the distributed edge for processing and analysis. The distributed edge deploys advanced artificial intelligence (AI) models to detect any unwarranted activity or object based on surveillance data. However, these lightweight and low-power unmanned aerial vehicles (UAVs) may experience faults due to unprecedented workload when deployed in extreme surveillance domains. In this article, we have designed an AI framework to detect any safety concerns with drones deployed for aerial surveillance in extreme locations based on real-time drone critical parameters. We also propose a MapReduce-based object recognition and classification module to process large-scale images captured by drones efficiently. However, conventional AI systems behave like black box systems, leading to a lack of trust and transparency. Thus, we convert the traditional framework of AI into an explainable edge AI framework using Shapley additive explanations (SHAPs) that opens Pandora's black box. The experimental results show the effectiveness of the proposed framework in detecting drone safety concerns through explainable health status tracking alongside ensuring an effective object detection mechanism.

关键词： Drones Artificial intelligence Surveillance Decision trees Safety Measurement Internet of Things Aerial surveillance distributed edge computing explainable AI (XAI) unmanned aerial vehicles (UAVs)

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Task-Decoding Assisted Cooperative Transmission for Coded edge computing

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2024年第8期23卷 9044-9058页

作者： Li, Tianheng He, Xiaofan Jin, Richeng Dai, Huaiyu Wuhan Univ Elect Informat Sch Wuhan 430072 Peoples R China Zhejiang Univ Dept Informat & Commun Engn Hangzhou 310027 Peoples R China Zhejiang Prov Key Lab Informat Proc Commun & Netwo Hangzhou 310027 Peoples R China North Carolina State Univ Dept Elect & Comp Engn Raleigh NC 27695 USA

distributed edge computing has been advocated as a key enabling technology to tackle large-scale intelligence applications, which is however hampered by the straggling effect. To overcome straggling, coded edge computing emerges as a promising solution by creating judiciously designed redundant computations using coding theory. Nonetheless, existing transmission schemes for coded edge computing that make edge nodes (ENs) transmit independently are often sub-optimal, as the computation results are correlated due to coding redundancy. This entails a pressing need for more effective transmission for coded edge computing. With this consideration, a novel task-decoding assisted cooperative transmission scheme is proposed in this work to facilitate cooperative transmission in general coded edge computing settings. Specifically, by exploiting the structural relation among the encoded sub-tasks, a task-decoding mechanism is developed to enable ENs to reconstruct computation results of all other ENs, so that they can cooperatively transmit with any other EN by forming a virtual multi-antenna system. To characterize the delay performance of the proposed scheme, an analytic bound with closed-form expression is derived first, followed by a more accurate algorithmic bound for scenarios with a relatively small recovery threshold. Simulations are conducted to validate the effectiveness of the proposed scheme.

关键词： Task analysis Encoding edge computing Delays Wireless communication Mobile handsets Downlink distributed edge computing coded computing task-decoding cooperative transmission

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Dynamic Power Control for Delay-Optimal Coded edge computing

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2024年第4期23卷 3283-3297页

作者： Geng, Dongqing He, Xiaofan Jin, Richeng Dai, Huaiyu Wuhan Univ Elect Informat Sch Wuhan 430072 Peoples R China Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310058 Peoples R China North Carolina State Univ Dept Elect & Comp Engn Raleigh NC 27695 USA

Coded edge computing is envisioned as a promising solution to cope with the ever-increasing large-scale and computation-intensive mobile applications. Besides alleviating the computation straggling issue, task encoding in coded edge computing is also beneficial to the transmission of computation results. Nonetheless, existing pioneering works in this direction mainly take an information-theoretical perspective and assume the ideal scenarios of high signal-to-noise ratio. To the best of our knowledge, the issue of power control still remains largely unexplored for coded edge computing. In this work, two novel power control schemes are developed for coded edge computing in dynamic wireless environments, which apply to the repetition encoded task computing and the general linearly encoded task computing, respectively. However, the corresponding optimization problems turn out to be non-convex and highly non-trivial. To this end, by exploiting the underlying structural property, a novel partition-based iterative optimization method is developed to obtain the closed-form expression of the optimal dynamic power control strategy for repetition encoded task computing. For the case of more general linearly encoded task computing, the corresponding problem is transformed into a sum-of-ratio problem and then solved iteratively. Simulations are conducted to corroborate the effectiveness of the proposed schemes.

关键词： Task analysis edge computing Power control Mobile handsets Wireless communication Encoding Signal to noise ratio Coded computing distributed edge computing dynamic power control lyapunov optimization

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Facial Expression Recognition System on a distributed edge-Cloud Infrastructure

Facial Expression Recognition System on a Distributed Edge-C...

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IEEE Cloud Summit

作者： Cui, Kai Zhang, Guoting Zhang, Fan Khan, Samee U. Nanjing Tech Univ Nanjing Peoples R China Zhejiang Univ Hangzhou Zhejiang Peoples R China Mississippi State Univ Mississippi State MS 39762 USA

ISBN: (数字)9781665451130

ISBN: (纸本)9781665451130

Time-sensitive AI applications usually pre-process the raw data on edge devices without having to offload them all to the cloud. However, deploying the AI applications on a distributed edge-cloud infrastructure is still an open issue since separating the roles between the edge and the cloud has no existing rule to follow. In this paper, we implemented a Facial Expression Recognition (FER) system, as a case study AI application, on an edge-cloud infrastructure to bridge the gap. FER system is distributed, fault tolerant, performant and completely edge-cloud separated. FER performs light-weight algorithms such as extracting facial feature points on the edge, while it performs heavy-weight algorithms such as deep neural network inference on the cloud. We performed experiments on different cloud providers, and we have seen that we reduced the network overhead significantly and improved the performance by 25% compared with deploying it solely on the cloud, with only the feature

关键词： facial expression recognition system facial action unit distributed edge computing edge device Docker container

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A Multi-Agent Federated DRL Model for Vehicular Task Offloading in WPT-Aided eROAD Environment

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2025年

作者： Paul, Anal Singh, Keshav Li, Chih-Peng Natl Sun Yat Sen Univ Inst Commun Engn Kaohsiung 80424 Taiwan

This paper introduces a novel multi-agent federated deep reinforcement learning (MA-FDRL) framework designed to minimize vehicular task offloading latency in electrified road (eROAD) environments. The solution integrates inductive coil-based wireless power transfer (WPT) systems with full duplex multiple input and multiple output (MIMO) vehicular networks, enabling continuous charging and reducing computational delays for electric vehicles (EVs) on eROADs. The MA-FDRL framework optimizes the offloading of vehicular tasks, with support from base stations (BS), unmanned aerial vehicles (UAVs), and satellites, while ensuring data privacy through differential privacy techniques. By intelligently distributing resources across these supporting entities, the framework enhances the efficiency of task processing. Key challenges such as dynamic wireless charging, intermittent BS coverage, and privacy-preserving task offloading are addressed using a comprehensive WPT framework, a mobility model, and a differential privacy-enhanced MA-FDRL algorithm. The proposed MA-FDRL solution effectively reduces the latency of vehicle task offloading by 17.05% over proximal policy optimization (PPO) algorithm, ensures balanced task distribution between edge servers, and offers a scalable and privacy-preserving approach for future autonomous electric vehicles and connected wireless environments.

关键词： Privacy Vehicle dynamics Roads Servers Real-time systems Coils 6G mobile communication Resource management Mobility models Delays Intelligent transport systems vehicular task offloading electrified roads wireless power transfer terrestrial and non-terrestrial networks distributed edge computing federated learning privacy-preserving deep reinforcement learning

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Microservice-Based Vehicular Network for Seamless and Ultra-Reliable Communications of Connected Vehicles

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FUTURE INTERNET 2024年第7期16卷

作者： Zarie, Mira M. Ateya, Abdelhamied A. Sayed, Mohammed S. Elaffendi, Mohammed Abdellatif, Mohammad Mahmoud Zagazig Univ Dept Elect & Commun Engn Zagazig 44519 Egypt British Univ Egypt Fac Engn Dept Elect Engn Cairo 11837 Egypt Prince Sultan Univ Coll Comp & Informat Sci EIAS Data Sci Lab Riyadh 11586 Saudi Arabia Egypt Japan Univ Sci & Technol Dept Elect & Commun Engn Alexandria 21934 Egypt

The fifth-generation (5G) cellular infrastructure is expected to bring about the widespread use of connected vehicles. This technological progress marks the beginning of a new era in vehicular networks, which includes a range of different types and services of self-driving cars and the smooth sharing of information between vehicles. Connected vehicles have also been announced as a main use case of the sixth-generation (6G) cellular, with ultimate requirements beyond the 5G (B5G) and 6G eras. These networks require full coverage, extremely high reliability and availability, very low latency, and significant system adaptability. The significant specifications set for vehicular networks pose considerable design and development challenges. The goals of establishing a latency of 1 millisecond, effectively handling large amounts of data traffic, and facilitating high-speed mobility are of utmost importance. To address these difficulties and meet the demands of upcoming networks, e.g., 6G, it is necessary to improve the performance of vehicle networks by incorporating innovative technology into existing network structures. This work presents significant enhancements to vehicular networks to fulfill the demanding specifications by utilizing state-of-the-art technologies, including distributed edge computing, e.g., mobile edge computing (MEC) and fog computing, software-defined networking (SDN), and microservice. The work provides a novel vehicular network structure based on micro-services architecture that meets the requirements of 6G networks. The required offloading scheme is introduced, and a handover algorithm is presented to provide seamless communication over the network. Moreover, a migration scheme for migrating data between edge servers was developed. The work was evaluated in terms of latency, availability, and reliability. The results outperformed existing traditional approaches, demonstrating the potential of our approach to meet the demanding requirements of ne

关键词： distributed edge computing software-defined networks VANETs 5G fog computing microservices

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edge data integrity verification scheme supporting data dynamics and batch auditing

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JOURNAL OF SYSTEMS ARCHITECTURE 2022年 128卷

作者： Ding, Yan Li, Yanping Yang, Wenjie Zhang, Kai Shaanxi Normal Univ Sch Math & Stat Xian 710119 Shaanxi Peoples R China Guangxi Key Lab Cryptog & Informat Secur Guilin 541004 Peoples R China Shaanxi Univ Sci & Technol Sch Math & Data Sci Xian 710021 Peoples R China

With the rapid development of 5G technology and wireless networks, new applications such as VR/AR-based immersive online games have developed rapidly. These applications are very sensitive to network delays. To address the time delay of data transfer, the vendors of these delay-sensitive applications often deploy data replicas on edge servers to provide users with a low-latency application experience. However, the edge computing environment is highly distributed and dynamic, which makes the data replicas stored on edge servers often face intentional or unintentional damage. In addition, the computing resources of edge servers are very limited compared to cloud servers. Therefore, how to efficiently audit the integrity of data replicas stored on a large number of edge servers becomes an urgent problem to be solved. This paper first designs a data integrity verification scheme called EDI-DA in edge environment to help application vendors to check the integrity of data replicas stored on distributed edge servers. Then, we extend EDI-DA to allow application vendors to verify the integrity of different edge data replicas simultaneously, i.e., our EDI-DA supports batch auditing of multiple original data. Next, we propose an improved data structure I-SLT based on which EDI-DA supports full data dynamics, including insertion, deletion and modification. Finally, security and performance analyses indicate the security and practicability of our EDI-DA.

关键词： distributed edge computing edge data integrity Corruption localization Data dynamics Privacy protection

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edge-Centric Security Framework for Electric Vehicle Connectivity: A Deep Learning Approach 28

Edge-Centric Security Framework for Electric Vehicle Connect...

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28th Asia-Pacific Conference on Communications (APCC)

作者： Mondal, Koustav Kumar Mahendia, Divya Das, Debasis Kalra, Sumit IIT Jodhpur IDRP IoT & Applicat Jodhpur Rajasthan India IIT Jodhpur Dept CSE Jodhpur Rajasthan India IIT Jodhpur IDRP IoT Jodhpur Rajasthan India

ISBN: (纸本)9798350382617

As connectivity in electric vehicles (EVs) expands, so does their vulnerability to cyber threats within the Internet of Vehicles (IoV). This study introduces a potent Intrusion Detection System (IDS) that leverages distributed edge computing, Convolutional Neural Networks (CNNs), and ensemble techniques to address this concern in EV systems. Performance evaluations on reputable IoV security datasets have demonstrated this IDS's efficacy, delivering detection rates and F1-scores over 100%. These results affirm the system's potential to significantly enhance the cybersecurity of both internal and external vehicular networks in the context of connected EVs.

关键词： distributed edge computing Convolutional Neural Networks (CNNs) Intrusion Detection System (IDS) Electric Vehicles (EVs) Internet of Vehicles (IoV) Vehicular Networks

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