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collaborative edge computing for Social Internet of Things: Applications, Solutions, and Challenges

IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2022年第1期9卷 291-301页

作者： Dong, Peiran Ge, Jingyi Wang, Xiaojie Guo, Song Chongqing Univ Posts & Telecommun Sch Commun & Informat Engn Chongqing 400065 Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China Dalian Univ Technol Sch Software Dalian 116024 Peoples R China

The paradigm of Internet of Things (IoT) has attracted much attention in both academia and industry areas in the past decades. Recently, the integration of IoT and social networks is advocated to promote further development. This article focuses on the applications, solutions, and challenges of Social Internet of Things (SIoT) over collaborative edge computing, which exploits the advantages of both mobile edge computing and social relationships among SIoT users. First, applications of collaborative offloading, caching, and streaming data processing are illustrated, followed by representative social-aware solutions, including auction, coalition game, and federated learning. Finally, several research challenges are described. The main contributions of this article are as follows: 1) we specify the role of social ties in traditional applications of IoT and its impacts on individual's selection;2) we elaborate the reasons why the presented three approaches can be applied in SIoT;and 3) the discussed challenges can contribute to the future development of secure, robust, and intelligent SIoT frameworks.

关键词： Collaboration Task analysis edge computing Servers Internet of Things Device-to-device communication Computer architecture collaborative edge computing Social Internet of Things (SIoT)

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collaborative edge computing With FPGA-Based CNN Accelerators for Energy-Efficient and Time-Aware Face Tracking System

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2022年第1期9卷 252-266页

作者： Liu, Xing Yang, Jianfeng Zou, Chengming Chen, Qimei Yan, Xin Chen, Yuao Cai, Chenran Wuhan Univ Technol Hubei Key Lab Transport Internet Things Wuhan 430070 Peoples R China Yangtze Univ Western Res Inst Jingzhou Peoples R China Wuhan Univ Sch Elect Informat Wuhan 430072 Peoples R China Wuhan Univ Technol Sch Comp Sci & Technol Wuhan 430070 Peoples R China

Convolutional neural networks (CNNs) have become the critical technology to realize face detection and face recognition in the face tracking (FT) system. However, traditional CNNs usually have nontrivial computational time and high energy consumption, making them inappropriate to be deployed in the large-scale time-sensitive FT system. To address this challenge, we design an artificial intelligence and Internet of Things (AIoT) empowered edge-cloud collaborative computing (ECCC) system based on the energy-efficient field-programmable gate array (FPGA)-based CNN accelerators for the purpose of realizing a low-latency and low-power FT. First, we present the AIoT-empowered ECCC system architecture, which consists of an intelligent computing subsystem, an Internet-of-Things (IoT) subsystem, an edge-cloud collaborative subsystem, and an application subsystem. In what follows, we investigate the enabling technologies for these subsystems. Thereafter, we develop an FPGA-based hardware accelerator dedicated to the compact MobileNet CNN by using the hardware design techniques, such as systolic array, matrix tiling, fixed-point precision, and parallelism. Furthermore, we integrate the FPGA accelerators with CPUs and GPUs to build a context-aware CPU/GPU/FPGA heterogeneous computing system. Finally, we implement a delay-aware energy-efficient scheduling algorithm dedicated to this heterogeneous system. With the above hardware and software codesign mechanism, the energy cost and execution time of CNNs can be decreased significantly. The real-world experiments on the CPU/GPU/FPGA-based ECCC system proved the effectiveness of the proposed schemes in reducing the latency and improving the power efficiency of the FT system.

关键词： Face recognition Face detection Acceleration Task analysis Servers Image edge detection Hardware Accelerator collaborative edge computing convolutional neural network (CNN) face tracking (FT)

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collaborative edge computing Advances in Children's Behavior Observation for Social Internet of Things Systems in Preschool Education

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INTERNATIONAL JOURNAL OF DISTRIBUTED SYSTEMS AND TECHNOLOGIES 2022年第7期13卷 1-21页

作者： Liu, Danqing Liu, Luqun Jiujiang Univ Dept Educ Jiujiang Peoples R China

Teachers who observe and analyze children's conduct can learn more about children and the reasons for their behavior. Promoting the fusion of adult and preschool vision through discussion is a basic competency of preschool teachers. Social internet of things (SIoT) uses recent advances in collaborative and social relationships among SIoT users to take advantage of the network's strengths while posing new challenges. With the help of the theory of development, behavioral cues, and individual circumstances, a thorough and holistic interpretation can be made of children's behavior. There is a brief introduction to the concepts of collaborative edge computing and data storage in addition to examples of various socially-minded solutions;at long last, a list of research obstacles is provided. When measured numerically, the ECBO has a positive impact on student accomplishment (97.9%), developmental progress (95.3%), interpersonal interaction (94.6%), parental participation (98.5%), and attention ratio (98%).

关键词： Behavior Observation Children Classrooms Cognitive collaborative edge computing Preschool Recent Advances SIoT Teacher

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Dynamic Parallel Multi-Server Selection and Allocation in collaborative edge computing

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IEEE TRANSACTIONS ON MOBILE computing 2024年第11期23卷 10523-10537页

作者： Xu, Changfu Guo, Jianxiong Li, Yupeng Zou, Haodong Jia, Weijia Wang, Tian BNU HKBU United Int Coll Guangdong Prov Key Lab IRADS Zhuhai 519087 Peoples R China Hong Kong Baptist Univ Hong Kong 999077 Peoples R China Beijing Normal Univ Inst Artificial Intelligence & Future Networks Zhuhai 519087 Peoples R China BNU HKBU United Int Coll Guangdong Key Lab AI & Multimodal Data Proc Zhuhai 519087 Peoples R China

collaborative Mobile edge computing (MEC) has emerged as a promising approach to provide low service latency for computation-intensive Internet of Things applications, facilitated by the cooperation of edge-edge and edge-cloud resources. However, existing collaborative MEC methods typically restrict the collaborative processing between any two edge Servers (ESs) or one ES and the cloud server for a task request, limiting the exploitation of available resources on other ESs. Moreover, these conventional methods rely on offline task partitioning, potentially leading to extended make-span, especially when ES computing capacities exhibit heterogeneity. In this paper, we propose an innovative method named SMCoedge. This method performs dynamic parallel multi-ES selection and workload allocation in heterogeneous collaborative MEC environments, thus simultaneously enabling multiple ESs' idle resources to accelerate task processing. We formulate our problem into an online linear programming problem, with the objective of minimizing task computing and transmission make-spans. To enhance computational efficiency, we decompose the problem into two stages: multi-ES selection and workload allocation. Then, we propose an online Deep Reinforcement Learning based Simultaneous Multi-ES Offloading (DRL-SMO) algorithm along with a top-$k$k deep Q-learning network model to effectively solve our problem, where an efficient algorithm is proposed to achieve the optimal solution for the workload allocation stage. Furthermore, we provide a theoretical performance analysis, demonstrating that the DRL-SMO algorithm achieves a near-optimal solution for our problem within an approximate linear time complexity. Finally, our extensive experimental results demonstrate the substantial advantages of our method. It consistently reduces the average make-span by 19.63% and keeps a lower offloading failure rate, when compared to state-of-the-art methods. These findings underline the efficacy of our meth

关键词： Task analysis Collaboration Resource management Delays Computational modeling Quality of service Internet of Things collaborative edge computing dynamic parallel multi-server selection and allocation edge-edge collaboration make-span optimization

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Reward-Oriented Task Offloading in Energy Harvesting collaborative edge computing Systems

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IEEE TRANSACTIONS ON MOBILE computing 2024年第12期23卷 14414-14426页

作者： Ni, Zhichen Chen, Honglong Gao, Birong Lin, Kai Wu, Liantao Yu, Jiguo China Univ Petr Coll Control Sci & Engn Qingdao 266580 Peoples R China Qingdao Univ Sci & Technol Sch Informat Sci & Technol Qingdao 266061 Peoples R China East China Normal Univ Software Engn Inst Shanghai Key Lab Trustworthy Comp Shanghai 200062 Peoples R China Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 611731 Peoples R China Qilu Univ Technol Big Data Inst Jinan 250353 Peoples R China

The widespread deployment of Internet of Things (IoT) devices brings more and more computation intensive or delay sensitive tasks, causing a series of challenges to efficient services. collaborative edge computing is an effective way to solve them, where the tasks will be processed in the devices, edge servers, and cloud server in parallel. However, the above collaborative paradigm requires dense deployment of base stations (BSs) and consumes lots of energy. To address this problem, in this paper, we introduce energy harvesting technology and construct a collaborative edge computing system powered by hybrid energy. Considering the highly variable task execution delay caused by the resource contention and the unstable energy state, we further introduce the Holt Linear Exponential Smoothing Prediction to predict the delay and then propose an Online Server Control schedule called OSC based on Lyapunov optimization to obtain the optimized offloading decision without the knowledge of the future system state. The extensive simulations illustrate that the proposed OSC outperforms other benchmark ones.

关键词： Task analysis Servers edge computing Collaboration Delays Energy harvesting Optimization collaborative edge computing energy harvesting lyapunov optimization task offloading

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Joint Task Allocation and Resource Optimization for Blockchain Enabled collaborative edge computing

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China Communications 2024年第12期21卷 218-229页

作者： Xu Wenjing Wang Wei Li Zuguang Wu Qihui Wang Xianbin College of Electronic and Information Engineering Nanjing University of Aeronautics and AstronauticsNanjing 211106China Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Ministry of Industry and Information TechnologyNanjing 211106China Department of Electrical and Computer Engineering Western UniversityCanada

collaborative edge computing is a promising direction to handle the computation intensive tasks in B5G wireless ***,edge computing servers(ECSs)from different operators may not trust each other,and thus the incentives for collaboration cannot be *** this paper,we propose a consortium blockchain enabled collaborative edge computing framework,where users can offload computing tasks to ECSs from different *** minimize the total delay of users,we formulate a joint task offloading and resource optimization problem,under the constraint of the computing capability of each *** apply the Tammer decomposition method and heuristic optimization algorithms to obtain the optimal ***,we propose a reputation based node selection approach to facilitate the consensus process,and also consider a completion time based primary node selection to avoid monopolization of certain edge node and enhance the security of the *** results validate the effectiveness of the proposed algorithm,and the total delay can be reduced by up to 40%compared with the non-cooperative case.

关键词： blockchain collaborative edge computing resource optimization task allocation

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Multi-Hop Task Routing in Vehicle-Assisted collaborative edge computing

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2024年第2期73卷 2444-2455页

作者： Deng, Yiqin Zhang, Haixia Chen, Xianhao Fang, Yuguang Shandong Univ Sch Control Sci & Engn Jinan 250061 Shandong Peoples R China Shandong Key Lab Wireless Commun Technol Jinan 250061 Shandong Peoples R China Univ Hong Kong Dept Elect & Elect Engn Hong Kong Peoples R China Univ Hong Kong HKU Musketeers Fdn Inst Data Sci Pok Fu Lam Hong Kong Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China

collaborative edge computing has emerged as a novel paradigm that allows edge servers (ESs) to share data and computing resources, effectively mitigating network congestion in traditional multi-access edge computing (MEC) scenarios. However, existing research in collaborative edge computing often limits offloading to only one hop, which may lead to suboptimal computing resource sharing due to challenges such as poor channel conditions or high computing workload at ESs located just one hop away. To address this limitation and enable more efficient computing resource utilization, we propose a multi-hop MEC approach that leverages omnipresent vehicles in urban areas to create a data transportation network for task delivery. Here, we propose a general multi-hop task offloading framework for vehicle-assisted collaborative edge computing where tasks from users can be offloaded to powerful ESs via potentially multi-hop transmissions. Under the proposed framework, we formulate an aggregated service throughput maximization problem by designing the task routing path subject to end-to-end latency requirements, spectrum, and computing resources. To efficiently address the curse of dimensionality problem due to vehicular mobility and channel variability, we develop a deep reinforcement learning, i.e., multi-agent deep deterministic policy gradient, based multi-hop task routing approach. Numerical results demonstrate that the proposed algorithm outperforms existing benchmark schemes.

关键词： Task analysis Routing Spread spectrum communication Relays Resource management Optimization Servers collaborative edge computing vehicular networks computation offloading multi-hop routing deep reinforcement learning (DRL)

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V2V Energy Trading Scheme Based on collaborative edge computing and Lightning Network

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第11期25卷 18252-18263页

作者： Jiang, Wenxian Guan, Zhenglei Huaqiao Univ Coll Comp Sci & Technol Xiamen 361000 Peoples R China

The development of new energy vehicles is still in its nascent stages, and the scarcity of computing resources in new energy transactions has become a pressing concern. In order to address this challenge, we propose a Vehicle-to-Vehicle (V2V) energy trading scheme based on collaborative edge computing (CEC) and Lightning Network (LN) technology. Firstly, collaborative vehicles are employed to perform edge computing in a more reasonable manner, effectively mitigating the task resource overload problem of fixed edge computing servers. Secondly, reliable and efficient transaction processing is achieved through the use of blockchain LN technology in transaction communication between vehicles. Thirdly, edgeCloudSim is used to simulate the workload, and PCNsim is used to implement the LN energy trading scheme. The experimental results demonstrate that the edge computing scheme with collaborative mobile devices can reduce the task failure rate by 80%. Furthermore, it can also reduce network latency and significantly enhance the overall performance of the system in high contention and high workload environments.

关键词： edge computing Collaboration Task analysis Servers Blockchains Security Scalability collaborative edge computing lightning network Internet of Vehicles energy trading blockchain

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HAT: Task Offloading and Resource Allocation in RIS-Assisted collaborative edge computing

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2024年第5期11卷 4665-4678页

作者： Tan, Lin Guo, Songtao Zhou, Pengzhan Kuang, Zhufang Jiao, Xianlong Chongqing Univ Coll Comp Sci Key Lab Dependable Serv Comp Cyber Phys Soc Minist Educ Chongqing 400044 Peoples R China Cent South Univ Forestry & Technol Sch Comp & Informat Engn Changsha 410004 Peoples R China

The problem of joint offloading decisions, resource allocation, and Reconfigurable Intelligent Surface (RIS) beamforming matrices for RIS-Assisted edge computing is a challenging issue. In this paper, user tasks can be either executed locally, or offloaded to a collaborative device or edge server with the assistance of the RIS, where RIS elements are grouped and assigned to all users to enable parallel services. The objective is formulated as a mixed integer nonlinear programming (MINLP) problem, where collaborative offloading decisions, RIS beamforming matrices, transmission power allocation, and computation resource allocation are jointly optimized to minimize the energy consumption. To address this problem, we propose a discrete-continuous Hybrid Action adapted Twin Delayed Deep Deterministic policy gradient (TD3) algorithm based on Deep Reinforcement Learning, named HAT. HAT constructs a latent representation space for the original discrete-continuous hybrid actions, fully considering the relations among highly coupled hybrid optimization variables. Experimental results demonstrate that HAT achieves significant performance gains over existing work (e.g., MELO, DDPG, PADDPG) and other benchmark schemes.

关键词： Array signal processing Collaboration Reconfigurable intelligent surfaces Performance gain Benchmark testing Hybrid power systems Resource management collaborative edge computing deep reinforcement learning reconfigurable intelligent surface resource allocation task offloading

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Online-learning task scheduling with GNN-RL scheduler in collaborative edge computing

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CLUSTER computing-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2024年第1期27卷 589-605页

作者： Jian, Chengfeng Pan, Zhuoyang Bao, Lukun Zhang, Meiyu Zhejiang Univ Technol Comp Sci & Technol Coll Hangzhou 310023 Peoples R China

With the development of collaborative edge computing (CEC), the manufacturing market is gradually moving toward large-scale, multi-scenario, and dynamic directions. The existing scheduling strategies based on machine learning or deep learning are only applicable to specific scenarios, which is difficult to meet the requirements of dynamic real-time scheduling in multiple scenarios. The proposed digital twin technology provides a new solution for real-time scheduling of multiple scenarios. In this paper, a digital twin-oriented multi-scene real-time scheduler (GNN-RL) is proposed. This scheduler converts task sequences into node trees and sets up two learning layers. The first layer is an online learning representation layer, which uses GNN to learn node features of embedded structures in real time to boost large instances without additional training. The second layer is the online learning policy layer, which introduces imitation learning mappings into optimal scheduling behavior policies adapted to multiple scenarios. Finally, our approach is validated in several scenarios in 3D digital twin factories, such as computationally intensive, delay-sensitive, and task-urgent scenarios. Since the scheduler proposed in this paper learns general features of the embedding graph rather than instance-specific features, it has good generality and scalability, with good generalization and scalability, outperforming other scheduling rules and schedulers on various benchmarks.

关键词： Task scheduling Graph neural network collaborative edge computing Digital twin

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