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computing Resource Allocation for Hybrid applications of Blockchain and Mobile Edge computing 19th

Computing Resource Allocation for Hybrid Applications of B...

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19th EAI international conference on collaborative computing: networking, applications and worksharing, CollaborateCom 2023

作者： Fan, Yuqi Zhang, Jun Ding, Xu Jin, Zhifeng Shi, Lei School of Computer Science and Information Engineering Anhui Province Key Laboratory of Industry Safety and Emergency Technology Hefei University of Technology Anhui Hefei230601 China Institute of Industry and Equipment Technology Hefei University of Technology Anhui Hefei230009 China

ISBN: (纸本)9783031545207

In mobile edge computing (MEC), each user chooses and then offloads the task to an edge server, whereas data security is a concern in MEC due to the lack of trust between users and edge servers. Blockchain is introduced to provide a reliable environment for MEC. In blockchain-based MEC, edge servers are used as the nodes in both MEC and blockchain. After processing the users’ tasks, the edge servers upload the results and other task-related information to the blockchain. the edge servers simultaneously execute two kind of tasks, i.e., the tasks offloaded by the users and the blockchain tasks. therefore, the user offloading decision affects the processing latency of MEC tasks, and there is a trade-off between the resource allocation for MEC and blockchain tasks. However, most existing studies optimize the resource allocation for blockchain and MEC individually, which leads to the suboptimal performance of blockchain-based MEC. In this paper, we study the problem of user offloading decision and the computing resource allocation of edge servers for MEC and blockchain tasks, with the objective to minimize the total processing delay of MEC and blockchain tasks. We propose an algorithm for joint computing resource allocation for MEC and blockchain (JMB). theoretical analysis proves that JMB is a 3.16-approximation algorithm. Simulation results show that JMB can effectively reduce the delay in blockchain-based MEC. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Blockchain

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FederatedMesh: collaborative Federated Learning for Medical Data Sharing in Mesh Networks 19th

FederatedMesh: Collaborative Federated Learning for Medical...

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19th EAI international conference on collaborative computing: networking, applications and worksharing, CollaborateCom 2023

作者： Shkurti, Lamir Selimi, Mennan Besimi, Adrian Faculty of Contemporary Sciences and Technologies South East European University Tetovo Macedonia Max van der Stoel Institute South East European University Tetovo Macedonia

ISBN: (纸本)9783031545306

Edge computing is a paradigm that involves performing local processing on lightweight devices at the edge of networks to improve response times and reduce bandwidth consumption. While machine learning (ML) models can run on smaller computing devices at the edge, training ML models presents challenges for low-capacity devices. this paper aimed to evaluate the performance of Federated Learning (FL) - a distributed ML framework, when training a medical dataset using Raspberry Pi devices as client nodes. the testing accuracy, CPU usage, RAM memory usage and network performance were measured for different number of clients and epochs. the results showed that increasing the number of devices generally improved the testing accuracy, with the greatest improvement observed in the earlier epochs. However, increasing the number of devices also increased the CPU usage, with a significant increase observed in the later epochs. Additionally, the RAM memory usage increased slightly as the number of clients and epochs increased. the findings suggest that FL can be an effective way to train medical models using distributed devices, but careful consideration must be given to the trade-off between accuracy and computational resources. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Edge computing

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Dynamic Trust-Based Resource Allocation Mechanism for Secure Edge computing 18th

Dynamic Trust-Based Resource Allocation Mechanism for Secure...

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18th European-Alliance-for-Innovation (EAI) international conference on collaborative computing - networking, applications and worksharing (CollaborateCom)

作者： Yu, Huiqun Tang, Qifeng Shao, Zhiqing Yue, Yiming Fan, Guisheng Chen, Liqiong East China Univ Sci & Technol Dept Comp Sci & Engn Shanghai Peoples R China Shanghai Key Lab Comp Software Evaluating & Testi Shanghai Peoples R China Shanghai Inst Technol Shanghai 201418 Peoples R China

ISBN: (纸本)9783031243851;9783031243868

Edge computing plays an important role in processing and storing data. By offloading tasks to the edge server, mobile users can access necessary computing resources on demand. However, security of edge computing service is still a major concern. this paper proposes an edge computing resource allocation mechanism based on dynamic trust. First, security problems due to lack of reliability in the resource allocation process are solved based on the trust mechanism. this mechanism considers the resource allocation process between the mobile user node and the edge server as a transaction, according to the trading behavior in the transaction process of server to give its corresponding trust. Second, a trust mechanism is used for dynamic credit granting. Mobile users with similar behaviors form a group, where a representative is elected to trade resources and bundle information into a block and attach it on the chain. At the same time, the delay problem is added as a constraint to the trust calculation. Finally, the simulation experiment shows that the mechanism improves security of edge computing.

关键词： Edge computing Security Trust Blockchain Resource allocation

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System Completion Time Minimization with Edge Server Onboard Unmanned Vehicle 18th

System Completion Time Minimization with Edge Server Onboard...

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18th European-Alliance-for-Innovation (EAI) international conference on collaborative computing - networking, applications and worksharing (CollaborateCom)

作者： Peng, Wen Wu, Hongyue Chen, Shizhan Dong, Lei Zhao, Zhuofeng Feng, Zhiyong Tianjin Univ Coll Intelligence & Comp Tianjin Peoples R China North China Univ Technol Beijing Key Lab Integrat & Anal Large Scale Strea Beijing Peoples R China

ISBN: (纸本)9783031243820;9783031243837

With the advantages of flexibility and powerful computing resources, edge servers mounted on unmanned vehicles (V-edge) have attracted significant interest in mobile edge computing (MEC). In this paper, we design an offloading scheme for vehicle-mounted edge rescue systems with the consideration of road limitations. In these systems, edge servers can receive data and process them while on the move. the objective is to minimize the completion time of tasks within the system under both time-varying communication and computation resource constraints. the formulated problem is decomposed into two subproblems, i.e., task completion time minimization within communities and V-edge travel time minimization between communities. For task completion time minimization, we propose an SQP-based iterative algorithm, which can generate feasible stopping points by using quadratic programming. V-edge travel time minimization problem can be converted to a TSP problem and thus can be solved by some existing methods. Finally, experiments are conducted to verify the usability of the proposed approach.

关键词： Edge computing Computation offloading Mobility server Unmanned vehicle

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EBA: An Adaptive Large Neighborhood Search-Based Approach for Edge Bandwidth Allocation 18th

EBA: An Adaptive Large Neighborhood Search-Based Approach fo...

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18th European-Alliance-for-Innovation (EAI) international conference on collaborative computing - networking, applications and worksharing (CollaborateCom)

作者： Hu, Qinghong Peng, Qinglan Shang, Jiaxing Li, Yong He, Junjie Chongqing Univ Coll Comp Sci Chongqing Peoples R China Minist Educ Key Lab Dependable Serv Comp Cyber Phys Soc Chongqing Peoples R China Henan Univ Sch Artificial Intelligence Zhengzhou Peoples R China Guizhou Minzu Univ Informat & Data Ctr Guiyang Peoples R China

ISBN: (纸本)9783031243820;9783031243837

As a promising computing paradigm, edge computing aims at delivering high-response and low-latency computing, storage, and bandwidth resources to end-users at the edge of the network. However, those edge-based novel applications (e.g., live broadcast, edge cloud game, real-time AR/VR rendering, etc.) are usually bandwidth-consuming, which has made a considerable contribution to the operating costs of edge application providers. Meanwhile, the bandwidth pricing modes of edge infrastructure providers are also complicated. therefore, how to allocate the bandwidth demands of edge users to suitable edge servers to minimize the monetary cost of edge application providers becomes an important issue. In this paper, we consider the widely adopted 95th-percentile bandwidth billing mode and Quality-of-Service constrained edge bandwidth allocation problem, and propose a neighborhood search-based approach, shorts for EBA. It firstly employs a network flow-based heuristic to find a feasible initial solution quickly. then, an adaptive large neighborhood search-based method is utilized to perform iterative optimization, which contains hill climbing and simulated annealing mechanisms. therefore, the proposed EBA approach can expand the searching space, accelerate the optimization convergence speed, and avoid falling into local optimal. Experiments based on a real-world edge bandwidth consumption dataset illustrate the effectiveness of the proposed approach.

关键词： Edge computing Edge bandwidth allocation 95th-percentile billing

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NPGraph: An Efficient Graph computing Model in NUMA-Based Persistent Memory Systems 19th

NPGraph: An Efficient Graph Computing Model in NUMA-Based P...

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19th EAI international conference on collaborative computing: networking, applications and worksharing, CollaborateCom 2023

作者： Li, Baoke Cao, Cong Yuan, Fangfang Yang, Yuling Su, Majing Liu, Yanbing Fu, Jianhui Institute of Information Engineering Chinese Academy of Sciences Beijing100085 China School of Cyber Security University of Chinese Academy of Sciences Beijing100085 China The 6th Research Institute of China Electronic Corporations Beijing China Shandong Institutes of Industrial Technology Jinan China

ISBN: (纸本)9783031545276

the massive volume and the inherent imbalance of graphs are inevitable challenges for efficient graph computing, primarily due to the limited capacity of main memory (DRAM). Fortunately, a promising solution has emerged in the form of hybrid memory systems (HMS) which combine DRAM and persistent memory (PMEM) to enable data-centric graph computing. However, directly transitioning existing DRAM-based models to HMS can lead to inefficiency issues, especially when crossing Non-Uniform Memory Access (NUMA) nodes. In this paper, we present NPGraph, a novel approach that fully exploits the advantages of HMS for in-memory graph computing models. the main contributions of NPGraph lie in three aspects. Firstly, a dual-block graph representation strategy is devised to accelerate the process of subgraph construction. By utilizing data layering, it fully utilizes the storage architecture of HMS and optimizes the data access process. Secondly, an adaptive push-pull update strategy is proposed to optimize the message-updating process. With data-driven algorithms, it dynamically migrates subgraphs which are used in future iterations. thirdly, the effectiveness of NPGraph is evaluated on five public graph data sets. Our model can improve the temporal locality and the spatial locality of graph computing concurrently. Extensive evaluation results show that NPGraph outperforms state-of-the-art graph computing models by 21.67%–32.03%. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Dynamic random access storage

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Research on Intelligent Dynamic Obstacle Avoidance Strategy for Space collaborative Robots 17th

Research on Intelligent Dynamic Obstacle Avoidance Strategy ...

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17th international conference on Intelligent Robotics and applications

作者： Wang, Yaobing Xie, Fanglin Zhang, Yahang Wang, Lingxin China Acad Space Technol Beijing 100094 Peoples R China Harbin Inst Technol Harbin 150001 Peoples R China

ISBN: (纸本)9789819607822;9789819607839

To achieve the great safety requirements for space collaborative robots and astronauts working in remote, unknown and complex space environment, the robots should be clever enough to avoid moving human or obstacles in a quite short time to keep both safe. In this paper, Deep Reinforcement Learning (DRL) techniques were applied to robot manipulators with a grasping workspace invaded by unpredictable moving obstacles. By introducing a hierarchical curriculum learning framework, a multidimensional agent was trained in a high-precision virtual environment. the dynamic obstacle avoidance success rate of strategy trained by Soft Actor-Critic (SAC) achieves 94.6%, higher than 68.4% by Deep Deterministic Policy Gradient (DDPG). then the Actor network's weight and bias matrices were copied and successfully implanted in a real RM63-6F anthropomorphic robot manipulator. the robot manipulator's end clamp can reach about 0.02m near the target site, which verifies that using DRL to obtain the motion planning strategy of space robot manipulator with limited computing resources is feasible.

关键词： space collaborative robots deep reinforcement learning motion planning dynamic obstacle avoidance strategy sim-to-real

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A collaborative Graph Convolutional Networks and Learning Styles Model for Courses Recommendation 18th

A Collaborative Graph Convolutional Networks and Learning St...

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18th European-Alliance-for-Innovation (EAI) international conference on collaborative computing - networking, applications and worksharing (CollaborateCom)

作者： Zhu, Junyi Wang, Liping Liu, Yanxiu Chen, Ping-Kuo Zhang, Guodao Zhejiang Univ Technol Coll Comp Sci & Technol Hangzhou 310023 Peoples R China Shandong Womens Univ Sch Data & Comp Sci Jinan 250300 Peoples R China Great Bar Univ Dongguan 523000 Peoples R China Hangzhou Dianzi Univ Sch Media & Design Hangzhou 310018 Peoples R China

ISBN: (纸本)9783031243820;9783031243837

With the rise of Massive Open Online Courses (MOOCs) and the deepening of lifelong learning, there is a growing demand for learners to learn on online learning platforms. the vast amount of course resources provides learners with massive and easy access while posing challenges in terms of personalized and precise selection. Traditional recommendation models have room for improvement in performance and interpretability in massive open online course scenarios while under-utilizing the potential interaction signals in user-course interactions and ignoring the impact of the user's learning style as a learner. In order to solve the above problems, this paper proposes a collaborative graph convolutional networks and learning styles model for courses recommendation (CGCNLS). First, the course prediction rating is obtained by propagating the learner-course interaction information recursively through the graph convolutional networks;further, a course and learning styles matching scale is created to calculate the course learning styles similarity score;finally, the course prediction rating is combined with the course learning styles similarity score to make personalized course recommendations. the experimental results show that the model proposed in this paper can effectively recommend courses for learners and outperforms the baseline approach in terms of Precision, Recall, and NDCG performance metrics.

关键词： Graph neural networks Learning styles Course recommendation collaborative models

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An Efficient Authentication and Key Agreement Scheme for CAV Internal applications 19th

An Efficient Authentication and Key Agreement Scheme for C...

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19th EAI international conference on collaborative computing: networking, applications and worksharing, CollaborateCom 2023

作者： Li, Yang Zhang, Qingyang Cao, Wenwen Cui, Jie Zhong, Hong Anhui Province Key Laboratory of Cyberspace Security Situation Awareness and Evaluation Hefei230037 China School of Computer Science and Technology Anhui University Hefei230039 China Anhui Engineering Laboratory of IoT Security Technologies Anhui University Hefei230039 China Institute of Physical Science and Information Technology Anhui University Hefei230039 China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei230039 China

ISBN: (纸本)9783031545276

the data of applications in connected and autonomous vehicles are important, which is usually collected by service providers to improve their services, such as object detection model. But, wireless communication is susceptible to various kinds of attacks. thus, the data of the application module needs to be securely shared to the corresponding service provider. However, current schemes are with limited performance while a service provider collects multiple application data at the same time. By adopting signcryption and chaotic map, an efficient authentication and key agreement scheme is proposed, while batch authentication is achieved for efficient message authentication of multiple applications, and the efficient revocation is realized based on Chinese remainder theorem under the assistance of trusted execution environment supported vehicle computing/communication unit. the formal security proof shows that the scheme is secure under the random oracle model, and the experiment results shows that the scheme is more efficient than related schemes and can meet the requirements of CAV. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Authentication

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Research on the Update Method of CP-ABE Access Control Strategy Based on Smart Contract 18th

Research on the Update Method of CP-ABE Access Control Strat...

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18th European-Alliance-for-Innovation (EAI) international conference on collaborative computing - networking, applications and worksharing (CollaborateCom)

作者： Hao, Yu Cui, Bo Li, Ru Song, Tingting Hou, Wenhan Inner Mongolia Univ Coll Comp Sci Inner Mongolia Key Lab Wireless Networking & Mobi Hohhot Peoples R China

ISBN: (纸本)9783031243820;9783031243837

the CP-ABE access control method based on blockchain realizes fine-grained access control of data and ensures the storage of ciphertext security. However, there are some problems in updating the access control policy. For example, the blockchain cannot be used as ample data storage. the ciphertext size increases with the number of attributes and the complexity of the access control policy, and the CP-ABE based on bilinear mapping is expensive to calculate. therefore, this paper proposes dividing the ciphertext into data-related ciphertext and policy-related ciphertext, which are stored in blockchain and IPFS. It is worth noting that this paper uses the RSA-based CP-ABE encryption method, which can effectively reduce the computational cost of encryption and decryption and achieve a constant ciphertext size. In addition, we also systematically analyze and compare the advantages and costs generated by CP-ABE based on bilinear mapping and CP-ABE based on RSA. through experimental analysis, security analysis, and formal analysis, compared with the existing access control policy update methods, the scheme proposed in this paper shows better performance when frequently updating the access control policy.

关键词： Blockchain CP-ABE IPFS Access Control Policy update

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