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collaborative Decision-Making Processes Analysis of Service Ecosystem: A Case Study of Academic Ecosystem Involution 19th

Collaborative Decision-Making Processes Analysis of Service ...

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

作者： Yan, Xiangpei Xue, Xiao Peng, Chao Liu, Donghua Feng, Zhiyong Xiao, Wang School of Computer Software College of Intelligence and Computing Tianjin University Tianjin China China Waterborne Transport Research Institute Tianjin China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783031545306

With the collaboration of several intelligent services, a crowd intelligence service network has been formed, and a service ecosystem has gradually emerged. As a novel service organization model, the Service Ecosystem (SE) can provide more sophisticated, precise, and thorough services and has attracted widespread attention. However, it also brings negative effects such as involution, and information cocoon room. thus, how to analyze the collaborative decision-making mechanism between the SE regulation algorithm and the crowd intelligence group, exploring the reasons behind the negative effects, and finding effective intervention strategies have become problems in this field. To solve the challenges, we propose a Computational Experiments-based method Decision-making processes Analysis model in SE, namely CEDA. the proposed CEDA model consists of three modules: the autonomous evolution mechanism module, the learning evolution mechanism module, and the collaborative decision-making analysis module. Among them, the computational experiments can provide a customized test environment for the analysis of collaborative decision-making processes and find out the appropriate intervention strategy. Finally, the validity of the CEDA model is verified through the case of academic ecosystem involution. the results show that computational experiments can provide new ideas and paths for collaborative decision-making processes analysis. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Ecosystems

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A Code Search Method Incorporating Code Annotations 19th

A Code Search Method Incorporating Code Annotations

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

作者： Li, Qi Liu, Jianxun Zhang, Xiangping School of Computer Science and Engineering Hunan University of Science and Technology Hunan Xiangtan411201 China Hunan Key Lab for Services Computing and Novel Software Technology Hunan University of Science and Technology Hunan Xiangtan411201 China

ISBN: (纸本)9783031545207

Code search is a technique for users to retrieve code snippets from the Code base using natural language, which is dedicated to retrieve the target code accurately and quickly to improve the efficiency of software development. the deep learning based code search technique greatly improves the accuracy of search by learning the relationship between code and query statements. Since it relies on the extracted code features, acquiring more code features is the key to quickly improve the search performance. However, most of the previous works have not taken code annotations into consideration. In this paper, we take code annotations as code features and apply them to code search, which is named ICA-CS (Code Search that Incorporates Code Annotations). In the method, firstly, the code features are embedded to get the corresponding vector representation. It is then processed by bidirectional LSTM (Long Short-Term Memory) network or multi-head attention respectively, followed by features fusion. And finally, the model is trained by joint embedding and using the minimised ranking loss function. As the experimental results show, on the evaluation metric MRR (mean reciprocal rank) compared to the state-of-the-art models DeepCS, SAN-CS, CARLCS-CNN and SelfAtt, the proposed model improves 48.96%, 17.11%, 41.01% and 13.07%, respectively. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Long short-term memory

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Secure Traffic Data Sharing in UAV-Assisted VANETs 19th

Secure Traffic Data Sharing in UAV-Assisted VANETs

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

作者： Liu, Yilin Wang, Yujue Yi, Chen Ding, Yong Yang, Changsong Wang, Huiyong School of Mathematics and Computing Science Guilin University of Electronic Technology Guilin541004 China Hangzhou Innovation Institute of Beihang University Hangzhou310000 China Laboratory of Cryptography and Information Security School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China Institute of Cyberspace Technology HKCT Institute for Higher Education 999077 Hong Kong

ISBN: (纸本)9783031545276

Aiming at the issues of low comprehensiveness and timeliness of data, difficulty in balancing data anonymity and traceability, and challenges of securely storing massive data in traditional traffic data sharing systems, this paper proposes a UAV-VANET integrated system (UVIS) based on consortium blockchain. the UAV integrated into the VANET can promptly provide drivers and traffic managers with comprehensive traffic information and images for traffic planning, thus enhancing transportation efficiency and safety. To achieve traceability of anonymous data sharing, we introduce a proxy re-encryption mechanism to realize precise data access control, which can not only protect data and identity privacy but also trace the true identity of malicious users. Additionally, it effectively prevents the collusion between proxies and data requesters from stealing unauthorized confidential information. To alleviate the pressure of traffic data storage, we adopt a storage method that combines blockchain and IPFS, ensuring secure storage of massive data. Security analysis shows that the UVIS has achieved secure sharing of traffic data. We analyze its efficiency theoretically, and demonstrate the practicality through experiments. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Metadata

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Edge Intelligence empowered Social Image Recognition using Microservices Architecture 17

Edge Intelligence empowered Social Image Recognition using M...

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17th IEEE international conference on Social computing and networking, SocialCom 2024

作者： Tao, Ming Chen, Zhongnan Liao, Lingling Xie, Renping Su, Lingfeng Dongguan University of Technology School of Computer Science and Technology Dongguan China

ISBN: (纸本)9798331521097

With the emergence of diversified mobile applications in the social field, e.g., image recognition, the massive growth in mobile data has imposing more service requests with strict real-time requirement on servers, therefore, the edge computing as an effective paradigm has been used to provide low-latency intelligence services. To improve the efficiency of concurrent processing on the edge server, the microservices architecture also has been adopted to offer a promising data processing solution. In this paper, through combining the stated advantages for social image recognition, an edge intelligence empowered scheme using microservices architecture is proposed. To enhance the efficiency of model training for image recognition, an optimized training model within the native framework of convolutional neural network (CNN) is firstly proposed. And then, to achieve the effective and low-latency social image recognition, a two-layer edge-end architecture is designed, within which, the image classification model is divided into feature extractor and image classifier, which are respectively deployed to the edge server and mobile terminals. Using the partial offloading, the mobile terminals perform the image preprocessing and classification, while the image feature extraction is executed on the edge server. In the experiments, the efficiency of the optimized training model is verified through comparing the training loss and accuracy, and the efficiency of the designed edge-end architecture is demonstrated with the concurrent testing. © 2024 IEEE.

关键词： Mobile edge computing

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Dynamic Offloading Based on Meta Deep Reinforcement Learning and Load Prediction in Smart Home Edge computing 19th

Dynamic Offloading Based on Meta Deep Reinforcement Learnin...

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

作者： Li, Mingchu Li, Shuai Qi, Wanying School of Software Technology Dalian University of Technology Dalian116620 China

ISBN: (纸本)9783031545207

In the edge computing enabled smart home scenario. Various smart home devices generate a large number of computing tasks, and users can offload these tasks to servers or perform them locally. Offloading to the server will result in lower delay, but it will also require paying the corresponding offloading cost. therefore, users need to consider the low delay and additional costs caused by offloading. Different users have different trade-offs between latency and offload costs at different times. If the trade-off is set as a fixed hyperparameter, it will give users a poor experience. In the case of dynamic trade-offs, the model may have difficulty adapting to arrive at an optimal offloading decision. By jointly optimizing the task delay and offloading cost, We model it as a long-term cost minimization problem under dynamic trade-off (DT-LCMP). To solve the problem, we propose an offloading algorithm based on multi-agent meta deep reinforcement learning and load prediction (MAMRL-L). Combined with the idea of meta-learning, the DDQN method is used to train the network. By training the sampling data in different environments, the agent can adapt to the dynamic environment quickly. In order to improve the performance of the model, LSTNet is used to predict the load level of the next slot server in real time. the simulation results show that our algorithm has higher performance than the existing algorithms and benchmark algorithms. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Reinforcement learning

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NOMA-Based Task Offloading and Allocation in Vehicular Edge computing Networks 18th

NOMA-Based Task Offloading and Allocation in Vehicular Edge ...

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

作者： Zhao, Shuangliang Shi, Lei Shi, Yi Zhao, Fei Fan, Yuqi Hefei Univ Technol Sch Comp Sci & Informat Engn Hefei 230009 Peoples R China Virginia Tech Dept ECE Blacksburg VA 24061 USA Minist Educ Engn Res Ctr Safety Crit Ind Measurement & Contro Hefei 230009 Peoples R China

ISBN: (纸本)9783031243820;9783031243837

Vehicular Edge computing (VEC) is envisioned as a promising approach to process explosive vehicle tasks. In the VEC system, vehicles can choose to upload tasks to nearby edge nodes for processing. this approach requires an efficient communication method, and Non-Orthogonal Multiple Access (NOMA) can improve channel spectrum efficiency and capacity. However, in the VEC system, the channel condition is complex due to the fast mobility of vehicles, and the arrival time of each task is stochastic. these characteristics greatly affect the latency of tasks. In this paper, we adopt a NOMA-based task offloading and allocation scheme to improve the VEC system. To cope with complex channel conditions, we use NOMA to upload tasks in batches. We first establish the mathematical model, and divide the offloading and allocation of tasks into two processes: transmission and computation. then we determine appropriate edge nodes for transmission and computation according to the position and speed of vehicles. We define the optimization objective as maximizing the number of tasks completed, and find that it is an integer nonlinear problem. Since there are more integer variables, this optimization problem is difficult to solve directly. through further analysis, we design Asymptotic Inference Greedy Strategy (AIGS) algorithm based on heuristics. Simulation results demonstrate that our algorithm has great advantages.

关键词： VEC NOMA Task offloading Task allocation collaborative processing

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Blockchain-Based EMR Enhancement: Introducing PMI-Chain for Improved Medical Data Security and Privacy 19th

Blockchain-Based EMR Enhancement: Introducing PMI-Chain for...

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

作者： Cui, Bo Mei, Tianyu Liu, Xu Inner Mongolia University Inner Mongolia Key Laboratory of Wireless Networking and Mobile Computing Hohhot010021 China

ISBN: (纸本)9783031545207

We have created a solution called PMI-Chain to address the challenges faced by Electronic Medical Records (EMR) management systems. these challenges include data security, patient privacy, and access regulation, which are becoming increasingly complex. PMI-Chain is a blockchain-based system that aims to tackle these issues. Traditional EMR systems rely on centralized medical data storage, exposing them to risks such as cyberattacks and data breaches and reducing patient autonomy over their medical records. PMI-Chain is a secure and confidential data-sharing platform for patients, medical institutions, and insurance companies. It uses token-based smart contracts and dual ElGamal homomorphic encryption to protect vast amounts of medical data while allowing for reliable sharing among institutions. Patients have control over their encrypted medical records, and an advanced encryption scheme keeps their information private during insurance claim processing. PMI-Chain has undergone security analysis and testing, showcasing its robust security features and exceptional stability. Additionally, compared to other available solutions, it boasts a 44% and 67% reduction in encryption and decryption time overhead. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Access control

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Secure and Private Coding for Edge computing Against Cooperative Attack with Low Communication Cost and Computational Load 18th

Secure and Private Coding for Edge Computing Against Coopera...

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

作者： Zou, Xiaotian Wang, Jin Liu, Can Li, Lingzhi Gu, Fei Li, Guojing Soochow Univ Dept Comp Sci & Technol Suzhou Peoples R China

ISBN: (纸本)9783031243820;9783031243837

Edge computing is an efficient computing paradigm, which can utilize computing devices at the edge of network to provide real-time proximity service. Since edge devices lack centralized management, they are more vulnerable to being attacked. therefore, the issues of data security and user privacy in edge computing are particularly important. A large number of existing literature focus on the data security and user privacy with independent attackers. However, cooperative attacks, in which multiple attackers can collaborate to obtain the data content and user privacy, have not been fully investigated. In particular, we take the matrix-vector multiplication which is a basic component of most machine learning algorithms as the basic task. therefore, in this paper, we focus on the Secure and Privacy Matrix-vector Multiplication (SPMM) issue for edge computing against cooperative attack and design a general coded computation scheme to achieve lowest system resource consumption, i.e. communication cost and computational load. Specifically, we propose two coding schemes: Secure and Private Coding with lower communication Cost (SPCC) and Secure and Private Coding with lower computational Load (SPCL). We also conduct solid theoretical analyses and extensive experiments to demonstrate that both two proposed coding schemes can achieve lower communication cost and computational load than existing work. Finally, we perform extensive analyses to the superiority of the proposed schemes.

关键词： Edge computing Security Privacy Communication cost Computational load

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Control Plane TCP Attacks Detection and Prevention in SDN Networks using Deep Learning Model 10

Control Plane TCP Attacks Detection and Prevention in SDN Ne...

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17th INDIACom;2023 10th international conference on computing for Sustainable Global Development, INDIACom 2023

作者： Anand Kumar, M. Ghanshala, Anshul Purohit, Kamelsh Chandra Singh, Anuj Ezhilarasi, M. Department of Computer Applications Dehradun India The Chinese University of Hong Kong Department of Computer Science Hong Kong Dehradun India Sri Ramakrishna Engineering College Department of Eee Coimbatore India

ISBN: (纸本)9789380544472

the separation of the data and control planes and an integrated software-based administration strategy grounded on a central controller are two considerations put forth by the software-defined networking (SDN) paradigm to make it easier to deploy new applications and services. those architectural concepts set the stage for a software-controlled network that is more flexible, efficient, and dynamic. With SDN networks, the network can be managed by software that uses minimal network hardware components and enables easy interconnection and installation. It provides the easiest mechanism for network administrators to deploy modern applications and security services. As a result, most network services will be more adaptable, programmable, and unrestricted by platforms. However, there are several difficulties and security-related problems with these centralized and programmable approaches. Security problems in the high- speed network are growing as network traffic grows as well. However, there are a variety of security weaknesses in the SDN architecture's components that might be used by intruders to execute malicious deeds and disrupt the infrastructure and its operations. this research proposes a deep learning-based attack classification method to classify the malicious and legitimate hosts and to block unwanted traffic in the SDN networks. this work mainly focuses on the detection and prevention of TCP-based assaults of the control plane in the SDN networks. the experimental results show better classification accuracy with minimum overhead in terms of efficiency. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Controllers

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A Pareto-Efficient Task-Allocation Framework Based on Deep Reinforcement Learning Algorithm in MEC 18th

A Pareto-Efficient Task-Allocation Framework Based on Deep R...

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

作者： Liu, Wenwen Zhao, Sinong Yu, Zhaoyang Wang, Gang Liu, Xiaoguang Nankai Univ Coll Comp Sci TJ Key Lab NDST Tianjin Peoples R China

ISBN: (纸本)9783031243851;9783031243868

Mobile-edge computing (MEC) has emerged as a promising paradigm that moves tasks running in the cloud to edge servers. In MEC systems, there are various individual requirements, such as less user-perceived time and lower energy consumption. In this case, substantial efforts have been paid to task allocation, aiming at enabling lower latency and higher resource utilization. However existing studies on multiple-objectives task allocation algorithms rarely consider the Pareto efficient problem, where no objective could be further improved without vitiating the other objectives optimization. In this paper, we propose a Pareto-efficient task-allocation framework based on a deep reinforcement learning algorithm. We give the formal formulations for objectives and construct a multi-objectives' optimization model for task allocation. then a Pareto efficient algorithm is proposed to solve the problem of conflicting among multi-objectives. By coordinating multi-objectives parameters get from Pareto efficient algorithm, the deep reinforcement learning model takes a Pareto-efficient task allocation to improve real-time and resource utilization performance. We evaluate the proposed framework over various real-world tasks and compare it with existing allocating tasks models in edge computing networks. By using the proposed framework, we can get an accuracy that not be lower than 90% under the 0.6 s latency requirement. the simulation results also show that the proposed framework achieves lower latency and higher resource utilization compared to other task allocation methods.

关键词： Mobile edge computing Pareto-efficient Task-allocation framework Deep reinforcement learning algorithm

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