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检索条件"任意字段=3rd International Conference on Distributed Computing and Internet Technology, ICDCIT 2006"

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3rd international conference on distributed computing and internet technology, icdcit 2006

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3rd international conference on distributed computing and internet technology, icdcit 2006

ISBN: (纸本)9783540683797

The proceedings contain 40 papers. The special focus in this conference is on distributed computing and internet technology. The topics include: Ticket-based binding update protocol for mobile IPV6*;distributed node-based transmission power control for wireless ad hoc networks;indexing and retrieval of document images by spatial reasoning;energy-aware improved directed diffusion algorithm for area event monitoring in wireless sensor network;multimedia data hiding in spatial and transformed domain;Extension of r-tree for SPATIO-temporal Olap operations*;lossless data hiding for high embedding capacity;mining sequential support affinity patterns with weight constraints;mining images of material nanostructure data;Dynamic primary copy with piggy-backing mechanism for replicated UDDI registry;an empirical study on a web server queueing system and traffic generation by simulation;research into verifying semistructured data;materialized view tuning mechanism and usability enhancement;efficient remote user authentication and key establishment for multi-server environment;optimisation problems based on the maximal breach path measure for wireless sensor network coverage;Modified Raymond's algorithm for priority (MRA-P) based mutual exclusion in distributed systems;requirements-driven modeling of the web service execution and adaptation lifecycle;an improved e-commerce protocol for fair exchange;multicontext-aware recommendation for ubiquitous commerce;Allocating QOS-constrained applications in AWEB service-oriented grid;an automatic approach to displaying web applications as Portlets;improving the deployability of existing windows-based client/server business information systems using Activex;a generalized linear programming based approach to optimal divisible load scheduling;application-level checkpointing techniques for parallel programs;multimedia traffic distribution using capacitated multicast tree;net-centric computing: The future of computers and networking.

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Mitigating Security Challenges in Edge computing: Attacks, Defence Strategies, and Algorithms 3

Mitigating Security Challenges in Edge Computing: Attacks, D...

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3rd international conference on Intelligent Data Communication Technologies and internet of Things, IDCIoT 2025

作者： Matta, Venkata Durga Rao Maheswara Rao, V.V.R. Battula, Venkata Sai Jayasurya Addagatla, Mahesh Babu Computer Science and Engineering Department Vishnu Institute of Technology Andhra Pradesh Bhimavaram India Computer Science and Engineering Department Shri Vishnu Engineering College for Women Andhra Pradesh Bhimavaram India

ISBN: (纸本)9798331527549

Edge computing has emerged as a crucial paradigm to fulfill the increasing demand for rapid data processing, low latency, and efficient resource utilization, particularly in applications such as the internet of Things (IoT), smart cities, autonomous vehicles, and automated industrial systems. By processing data closer to its source, edge computing minimizes reliance on centralized cloud infrastructures, thereby improving response times and optimizing bandwidth. However, this transition also presents significant security challenges, including heightened risks of distributed Denial of Service (DDoS) attacks, data breaches, and unauthorized physical access to edge nodes. This paper conducts a comprehensive analysis of these security vulnerabilities, presenting a framework that integrates advanced defence mechanisms and algorithms tailored to enhance data protection and resource management in edge environments. Our findings indicate that the proposed strategies effectively mitigate identified threats while maintaining system performance. Furthermore, we outline ongoing challenges related to scalability, interoperability, and data governance, providing insights into future research avenues aimed at fortifying edge computing systems against evolving security threats. This study contributes to a deeper understanding of the critical role edge computing plays in the domain of next-generation computing technologies. © 2025 IEEE.

关键词： Cloud security

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Detection of DDoS Attacks in Fog computing using Interpretable Quantum-based 2Q Learning Approach 3

Detection of DDoS Attacks in Fog Computing using Interpretab...

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3rd international conference on Intelligent and Innovative Technologies in computing, Electrical and Electronics, IITCEE 2025

作者： Sohan Jayram Reddy, K. Bhargavi, K. Siddaganga Institute of Technology Department of Computer Science and Engineering Karnataka Tumakuru India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515911

It is seen that there is a rapid increase in internet of Things (IoT) devices, and fog computing has emerged as an efficient and viable solution to reduce latency by performing computations closer to the edge. However, this nature of fog computing makes it vulnerable to distributed Denial of Service (DDoS) attacks, which can disrupt services. DDoS causes major impact over the fog performance which include resource saturation, increased latency, reduced resource availability, decrease in processing capacity of the fog nodes, and many more. There have been various works proposed to detect DDoS attacks in fog computing, but they lack scalability and have high response times. In this paper, a novel Quantum-based-2QLearning (Q2QL) framework is designed to efficiently detect and mitigate DDoS attacks in fog computing. This framework using quantum-enhanced reinforcement learning reduces overestimation bias and improve speed of convergence. Q2QL framework also achieves high detection accuracy and reduces response time. The results show that the proposed framework performs well across critical metrics, including latency, attack detection time, and overall system performance. © 2025 IEEE.

关键词： Fog computing

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Transforming Agriculture with Cloud computing: Data Processing and Analysis Innovations 3

Transforming Agriculture with Cloud Computing: Data Processi...

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3rd international conference on Intelligent and Innovative Technologies in computing, Electrical and Electronics, IITCEE 2025

作者： Mohammed, Shehu Singh, Arun Kaur, Harpreet Lovely Professional University Department of Computer Application Phagwara India Lovely Professional University Department of Computer Science and Engineering Phagwara India Lovely Professional University School of Computer Science and Engineering Phagwara India

ISBN: (纸本)9798331515911

Cloud computing is a game-changer in modern agriculture, providing new forms to access enormous data analysis. In this paper, we investigate how cloud computing solutions are being integrated with agricultural practices to improve the efficiency, scalability and availability in managing data pertaining to agriculture. Farming operations generate massive amounts of data from a multitude of sources like sensors, drones, and satellite imagery., the performance bottleneck becomes the traditional methods with which the data are being processed. So the common factor in all these problems lies with Cloud computing itself, it has a scalable infrastructure that provides reliable analysis and Real-time processing of large data sets throwing light on an array of questions people have about various aspects. In this paper, we explore the major innovations in cloud computing that power precise agriculture and examine how machine learning algorithms, data fusion methods, and distributed processing platforms are utilized. It also discusses cloud-based solutions, which can reduce costs of the use of resources, crop yield prediction, and risk management to weather/climate and pests. Cloud computing opens up a world of monitoring, analysis, and optimization to help provide higher yields more efficiently while maintaining a commitment to sustainable agriculture. Challenges and considerations in deploying cloud computing in agriculture, e.g., data security risks, privacy concerns, and the need for stable internet connection for rural environments are also articulated in the paper. The results of this project underscore the vital importance of cloud technology in redefining the agri-future and providing a robust platform for smarter and more sustainable agriculture. © 2025 IEEE.

关键词： agricultural data processing Cloud computing data analysis machine learning precision agriculture sustainable farming

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Deploy a Secure Chat Application for Log Monitoring in Kubernetes 3

Deploy a Secure Chat Application for Log Monitoring in Kuber...

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3rd international conference on Intelligent Data Communication Technologies and internet of Things, IDCIoT 2025

作者： Dinesh, S. Nivedha, R. Mahalakshmi, S. Balasaigayathri, B. Department Of CSE Cyber Security Sathyabama Institute of Science and Technology Chennai India School of computing Sathyabama Institute of Science and Technology Chennai India

ISBN: (纸本)9798331527549

This paper presents the development and deployment of a secure chat application in a Kubernetes environment for real-time log monitoring and communication. The research addresses the challenges of managing and aggregating logs in a distributed, containerized environment, specifically within Kubernetes clusters. Traditional logging solutions often struggle with providing real-time collaborative troubleshooting, centralized log aggregation, and robust security. The proposed system integrates Kubernetes-native features, along with Fluentd and the ELK stack (Elasticsearch, Logstash, Kibana), to centralize log management and provide a unified interface for real-time communication through WebSockets. The application is designed to ensure high scalability, fault tolerance, and security by implementing TLS encryption and Role-Based Access Control (RBAC). The outcome is a scalable, secure, and efficient log monitoring solution that improves operational efficiency, enhances system reliability, and fosters collaborative debugging, making it a valuable tool for developers and administrators in Kubernetes-based environments. © 2025 IEEE.

关键词： Fault tolerance

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Delay-Optimized Task Scheduling Based on Deep Reinforcement Learning in computing Power Networks 3rd

Delay-Optimized Task Scheduling Based on Deep Reinforcement ...

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3rd international conference on Machine Learning, Cloud computing and Intelligent Mining, MLCCIM 2024

作者： Tang, Qinqin Xu, Deyun Xie, Renchao Feng, Li Chen, Tianjiao Huang, Tao Beijing University of Posts and Telecommunications Beijing China China Mobile Research Institute Beijing China

ISBN: (纸本)9789819624676

With the rapid advancement of information and communication technology, there arises an increasing demand for heightened computing capabilities to facilitate a myriad of intelligent services. The computing power network (CPN) amalgamates an array of resources including cloud and edge devices to enable collaborative sensing, routing, and scheduling. This integration significantly amplifies the utilization of computing network resources while concurrently augmenting service quality. In the realm of multi-node distributed joint computing within CPNs conventional scheduling methodologies frequently neglect the interplay between computing and the network, resulting in suboptimal task scheduling. To address this issue, this study proposes a delay-optimized task scheduling mechanism based on deep reinforcement learning. Simulation results demonstrate that this framework achieves faster convergence speed, particularly under dual-depth networks, outperforming other algorithms in reducing system delays, energy and enhancing scheduling efficiency. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： computing power

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distributed Deep Reinforcement Learning Based Deterministic Task Offloading in End-Edge-Cloud Collaborative computing Networks 3rd

Distributed Deep Reinforcement Learning Based Deterministic ...

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3rd international conference on Cyberspace Simulation and Evaluation, CSE 2024

作者： Yao, Yingfei Yao, Shunchun Zhou, Nan Liang, Xiaojun Yang, Chunhua Gui, Weihua South China University of Technology Guangzhou 510640 China Pengcheng Laboratory Shenzhen 518000 China Central South University Changsha 410083 China

ISBN: (纸本)9789819645022

In the industrial field, the requirements for deterministic and reliable communication cannot be satisfied by current end-edge-cloud collaborative computing (EECC) networks. Time-sensitive networking (TSN) technology not only provides bounded latency guarantees but also gives support to integrate different types of traffic on a single network. However, the development of TSN-based EECC networks remains in its early stage. In light of this, we propose an intelligent end-edge-cloud collaborative deterministic task offloading framework that combines EECC and TSN networks. Based on distributed Deep Reinforcement Learning (DDRL), a deterministic task offloading algorithm named DDRL-E2CDTO is introduced to address the joint optimization problem of TSN traffic scheduling and end-edge-cloud task offloading. This algorithm employs TSN traffic scheduling (TTS) networks and deterministic task offloading (DTO) networks to train distributed agents to make instant and efficient deterministic decisions of task offloading. Simulation experiments demonstrate that our proposed approaches can obtain significant performance achievements in acceleration of convergence speed and computation rate. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deterministic task offloading distributed deep reinforcement learning End-edge-cloud collaboration computing Industrial internet of things Time-sensitive network

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An Efficient and Secure Authentication Protocol for distributed internet of Vehicle in Smart City 3rd

An Efficient and Secure Authentication Protocol for Distribu...

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3rd international conference on Cyberspace Simulation and Evaluation, CSE 2024

作者： Yu, Xujin Deng, Tao Cai, Haoyu Wang, Ziqi Wang, Dinghao Zhang, Yuchen Jia, Jingjing Lu, Ruichao Jiang Xi Traffic Monitoring Command Center Nanchang China Southern University of Science and Technology (SUSTech) Shenzhen China Beijing University of Posts and Telecommunications Beijing 100000 China

ISBN: (纸本)9789819645084

Currently, the distributed internet of vehicle (DIoV) of smart city leverages the transmitted messages form the distributed network servers and vehicles, and so effectively enables the prompt DIoV’s service, e.g., vehicle travel and accident rescue. However, in the open communication channel of DIoV, those important or invaluable transmitted messages in DIoV are vulnerable to illegal access or even manipulation. Therefore, a reliable and secure message authentication protocol is a necessary condition to ensure the services of distributed vehicular networks. Moreover, most of the existing authentication schemes cannot effectively resist various network attacks, which greatly reduces the security of vehicular communication. In this work, we propose a secure and efficient distributed authentication and key agreement protocol for securing the communication in DIoV. The vehicles can authenticate each other and establish session keys through pre-shared keys. In addition, our scheme can realize the function of vehicle joining and leaving the network without frequently updating the network key. Through heuristic analysis and BAN logic analysis, the security of the scheme is proved. Compared with the existing schemes, the proposed scheme satisfies the security requirements and has obvious advantages in computing and communication overhead. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Authentication distributed internet of Vehicles (DIoV) Key agreement Pre-shared keys Smart City

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Application and Evaluation of Real-Time Analysis Algorithms in Dynamic Data Processing Systems

Application and Evaluation of Real-Time Analysis Algorithms ...

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Integrated Circuits and Communication Systems (ICICACS), IEEE international conference on

作者： Si Chen College of Digital Business Liaoning Vocational University of Technology Jinzhou Liaoning China

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Dynamic data processing systems are key technologies in modern data-driven applications, widely used in fields such as the internet of Things, finance and healthcare. This study explores the application and evaluation of real-time analytical algorithms for dynamic data processing. An efficient algorithmic framework combining adaptive windowing, incremental learning and distributed computing is proposed to achieve low-latency and high-accuracy decision-making capabilities. Through experimental validation in a real data streaming environment, the results show that the proposed algorithm significantly outperforms traditional algorithms with about 35% reduction in latency and 20% improvement in prediction accuracy. The research results in this paper show that the real-time analysis algorithm has a promising application in dealing with dynamic heterogeneous data streams. Meanwhile, the article also discusses the limitations and future optimisation directions of the method, which provides a reference for further research in related fields.

关键词： Performance evaluation Incremental learning Accuracy Heuristic algorithms Data processing Prediction algorithms Real-time systems Low latency communication distributed computing Streams

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Deploy a Secure Chat Application for Log Monitoring in Kubernetes

Deploy a Secure Chat Application for Log Monitoring in Kuber...

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Intelligent Data Communication Technologies and internet of Things (IDCIoT), international conference on

作者： Dinesh S R Nivedha Mahalakshmi S B Balasaigayathri Department Of CSE Cyber Security Sathyabama Institute of Science and Technology Chennai India School of computing Sathyabama Institute of Science and Technology Chennai India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

关键词： Access control Sockets Scalability Collaboration Serverless computing Real-time systems User experience Encryption Monitoring Standards

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