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

共 389 条记录，以下是11-20 订阅

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Research on distributed Photovoltaic Monitoring System Based on internet of Things and Cloud computing 3

Research on Distributed Photovoltaic Monitoring System Based...

引用

3rd international conference on Energy, Power and Electrical technology, ICEPET 2024

作者： Qin, Huiling Li, Shuang He, Chengyu Rao, Zhi Gan, Di China Southern Power Grid Company Limited Guangxi Power Grid Corporation Guangxi China China Southern Power Grid Company Limited Energy Development Research Institute Guangdong China

ISBN: (纸本)9798350352658

Due to the depletion of fossil fuels globally and the rapid advancement of solar power generating technologies, solar power generation has increasingly emerged as a viable method for energy development. The concept of a distributed Photovoltaic Power System (DPPS) promotes the generation, connection to the grid, conversion, and utilisation of power in close proximity. This approach has the potential to significantly decrease the consumption of fossil fuels. Managing photovoltaic power stations is becoming challenging due to the proliferation of distributed solar power stations. This study proposes a distributed PV monitoring system that utilises the internet of things (IoT) and cloud computing. The system aims to achieve centralised management of several solar power stations. It offers cloud-based storage, data processing, and retrieval, and delivers a wide range of information to the client. A comprehensive explanation of the operational principles and execution procedure of the suggested plan is provided. This paper discusses a case study focused on achieving remote monitoring and control. In addition, the text explores the interconnected cycle of data generation, transmission, processing, and presentation. © 2024 IEEE.

关键词： distributed cloud

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

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

引用

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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Integrated PON-VLC System for IoT Applications 3

Integrated PON-VLC System for IoT Applications

引用

3rd IEEE international conference on distributed computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Kumari, Meet Sharma, Neha Chauhan, Rahul Joshi, Kireet Kumar, Amit Uie Chandigarh University Dept. Of Ece Mohali Gharuan India Chitkara University Institute Of Engineering And Technology Chitkara University Punjab Rajpura India Graphic Era Hill University Computer Science & Engineering Uttarakhand Dehradun India Graphic Era Deemed To Be University Computer Science & Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350318609

Healthcare is one of the vital and well-known application fields of internet of things (IoT) technology, which is being extensively accepted worldwide. In modern healthcare systems, the IoT -eHealth is playing a prominent role in healthcare fields by spreading the devices and proficient people interaction. This system can minimise the span of hospital accommodation as well as healthcare costs, upgrade results and precludes re-admission of patients to the hospitals. The performance of these systems, especially transmission distance, security and information rate of medical data, could be affected highly by high power consumption and cost with the increase in number of patients. This motivates towards passive optical network and visible light communication (PON-VLC) system using sensors for IoT -eHealth services. The results show that the proposed system utilizing multi-diagonal code offers faithful 50km fiber and 10m VLC distance at 130Gbps per channel transmission rate for simultaneously 53 patients. © 2024 IEEE.

关键词： internet of things

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Pearson Correlation Coefficient based Improved Least Square - Support Vector Machine for Cyber-Attack Detection in internet of Things 3

Pearson Correlation Coefficient based Improved Least Square ...

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3rd IEEE international conference on distributed computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Senthilkumar, A. Joshika, S. Santhi, L. Shashidhara, K.S. Charanarur, Panem School Of Science And Information Technology Skyline University Nigeria Kano State Nigeria Srm Institute Of Science And Technology Department Of Cse With Cyber Security Tamilnadu Trichy India Psgr Krishnammal College For Women Tamilnadu Coimbatore India Nitte Meenakshi Institute Of Technology Dept. Of Ece Bengaluru India Tripura India

ISBN: (纸本)9798350318609

The wide utilization of smart devices and enormous weakness of security in networks has maximized a count of cyber-attacks in internet of Things (IoT). The detection and classification of malicious traffic is essential for ensuring a security of system in IoT. For detecting and classifying the vulnerable threats in IoT, proposed a Pearson Correlation Coefficient (PCC) based Improved Least Square - Support Vector Machine (ILS-SVM). The proposed algorithm provided high classification accuracy and detection rate with NSL-KDD dataset. The data are pre-processed by checking the missing values and normalization of data. Then the features are selected by using PCC and the selected features are classified by ILS-SVM method with high detection rate. The metrics taken for evaluating the proposed algorithm are accuracy, detection rate, precision and f1-score. The proposed PCC algorithm achieved 99.71% accuracy, 99.03% detection rate, 99.26% precision and 99.37% f1-score that is performed well than previous methods like Hybrid Feature Reduced method and Multi-class Support Vector Machine (SVM). © 2024 IEEE.

关键词： internet of things

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Implementing Blockchain and Smart Encryption for Immutable Purchase and Generates Digital Ownership Certificates 3

Implementing Blockchain and Smart Encryption for Immutable P...

引用

3rd international conference on Artificial Intelligence For internet of Things, AIIoT 2024

作者： Sodhi, Arshdeep Singh Das, Sanjana Loganathan, Sathyapriya SRM Institute of Science and Technology Kattankulathur School of Computing Department of Computing Technologies Chennai603203 India

ISBN: (纸本)9798350372120

Blockchain functions as a decentralized and distributed ledger technology, ensuring the secure recording and verification of transactions across a network of computers. It tackles trust, transparency, and resistance to tampering in conventional centralized systems by creating a clear and unalterable transaction record, eliminating the necessity for a central authority. Addressing trust-related challenges in transactions, Blockchain eliminates intermediaries, diminishes fraud risks through cryptographic security, and boosts transparency by establishing an immutable shared transaction history. The proposed system, built on a blockchain foundation, aims to securely and efficiently store purchase details by utilizing a duplicated and distributed digital ledger of transaction records across all participant computers in the network. Key features such as immutability, enhanced security through smart contracts and encryption, digital ownership certificates for authenticity verification, and a user-friendly search function contribute to the secure storage of purchase details. This innovative system extends the transformative impact of blockchain to the insurance industry, providing a decentralized alternative to counter centralization, inefficiency, and high costs. The result obtained enabling direct transactions between users and eliminating intermediaries, the system reduces frictional costs and enhances trust and transparency in insurance practices. This groundbreaking approach signifies a shift towards a more secure, efficient, and transparent future for both purchase record management and insurance processes, leveraging the full potential of blockchain technology. © 2024 IEEE.

关键词： Smart contract

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Research on distributed Storage Algorithm for Multi-Source Survey Data in Power Grid Engineering 3

Research on Distributed Storage Algorithm for Multi-Source S...

引用

3rd international conference on Artificial Intelligence, internet of Things and Cloud computing technology, AIoTC 2024

作者： Zhang, Jiyong Yu, Gao Liu, Donghui Sun, Xiaohu Huang, Jiangqian He, Bangzheng State Grid Economic and Technical Research Institute Co. State Grid Corporation of China Beijing China Economic and Technical Research Institute State Grid Zhejiang Electric Power Co. State Grid Corporation of China Zhejiang China School of Geomatics and Urban Spatial Information Beijing University of Civil Engineering and Architecture Beijing China

ISBN: (纸本)9798331530280

With the rapid development of power grid engineering, especially the rapid development of extra-high voltage engineering, the multi-source heterogeneous investigation data is on the increase, and the traditional investigation data storage methods can no longer meet the basic needs. Based on this situation, this paper adopts the consistent hash algorithm to achieve horizontal elasticity expansion of the storage system, and combines the virtual nodes of consistent hash with the weighted polling algorithm to improve the storage strategy of distributed file system (HDFS) under the Hadoop platform, to achieve balanced effect of distributed data storage. Experimental results show that the distributed storage algorithm for heterogeneous survey data from multiple sources proposed in this paper reduces the cluster load difference to different degrees under different conditions, and shows better load balance performance compared to HDFS and ordinary consistent hashing algorithms, which proves that the algorithm can effectively reduce the load difference between cluster nodes and satisfy the needs of distributed storage of power grid engineering survey data. © 2024 IEEE.

关键词： consistent hash algorithm distributed storage Survey data weighted polling algorithm

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Anomaly-Based Intrusion Detection System Using Bidirectional Long Short-Term Memory for internet of Things 3

Anomaly-Based Intrusion Detection System Using Bidirectional...

引用

3rd IEEE international conference on distributed computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Jagdish Kumar, P. Neduncheliyan, S. Adnan, Myasar Mundher Sudhakar, K. Sudhakar, A.V.V. Bharath Institute Of Higher Education And Research Department Of Cse Tamil Nadu Chennai India School Of Computing Bharath Institute Of Higher Education And Research Tamil Nadu Chennai India College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute Of Technology Dept. Of Ai & Ds Bengaluru India Sr University Department Of Electrical And Electronics Engineering Warangal India

ISBN: (纸本)9798350318609

The developing use of internet of Things (IoT) applications in several aspects created a large number of data which is need the occurrence of existing techniques like fog and cloud computing. The Intrusion Detection System (IDS) is recognized several cybersecurity problems and its resources. In this paper, the Bidirectional Long Short-Term Memory (BiLSTM) is proposed for anomaly-based IDS in IoT network. The BoT-IoT and ToN-IoT datasets are used for identifying IDS in IoT networks. The normalization is used as preprocessing and Gain Ratio is used for selecting optimal features and the BiLSTM is used for classification of IDS in network which enables the model to scale large dataset and enhance the accuracy. The performance of BiLSTM is estimated by accuracy, f1 score, recall and precision. The BiLSTM achieves better accuracy of 98.76% and 96.84% for BoT-IoT and ToN-IoT dataset respectively which is better when compared to existing techniques like Group Theory Binary Spring Search-Hybrid Deep Neural Network (GTBSS-HDNN) and DNN. © 2024 IEEE.

关键词： internet of things

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Mayfly Optimization Algorithm with Bidirectional Long-Short Term Memory for Intrusion Detection System in internet of Things 3

Mayfly Optimization Algorithm with Bidirectional Long-Short ...

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3rd IEEE international conference on distributed computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Pillai, Sanjaikanth E. Vadakkethil Somanathan Polimetla, Kiran Alsalami, Zaid Pareek, Piyush Kumar Kumar, Deepak School Of Electrical Engineering And Computer Science University Of North Dakota Grand Forks United States Adobe Inc. Dept. Of Information Technology AustinTX United States College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute Of Technology Department Of Aiml And Ipr Cell Bengaluru India University Of The Cumberlands Department Of Information Technology WilliamsburgKY United States

ISBN: (纸本)9798350318609

The rapid development of number of connected devices exchange sensitive and personal significant data through internet of Things (IoT)-assisted global network, attacks which are targeting security services are also enhancing every day. This paper proposed a Mayfly Optimization Algorithm (MOA) with Bidirectional Long-Short Term Memory (BiLSTM) for Intrusion Detection System (IDS) in IoT. The MOA is used for feature selection which select relevant features through balancing exploration and exploitation. The BiLSTM is used for classifying network into attack and normal which process the data in both forward and backward directions, allowing them to capture dependencies between events that might be far apart in sequence. The min-max normalization is used to normalize the continuous features from NSL-KDD, ToN-IoT and UNSW-NB15 datasets. The recall, accuracy, f1-score and precision are considered to evaluate MOA-BiLSTM performance. The MOA-BiLSTM realizes accuracy of 99.25%, 89.61% and 99.35% accuracy for NSL-KDD, ToN-IoT and UNSW-NB15 datasets when compared to existing techniques such as LSTM and Deep Neural Network (DNN). © 2024 IEEE.

关键词： internet of things

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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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IoT Security Detection and Evaluation for Smart Cyber Infrastructures Using LSTMs with Attention Mechanism 3

IoT Security Detection and Evaluation for Smart Cyber Infras...

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3rd IEEE international conference on distributed computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Pillai, Sanjaikanth E Vadakkethil Somanathan Polimetla, Kiran Prakash, Chidurala Sai Pareek, Piyush Kumar Pawar, Priyanka Pramod School Of Electrical Engineering And Computer Science University Of North Dakota Grand Forks United States Adobe Inc. Dept. Of Information Technology AustinTX United States Sr University Department Of Electrical And Electronics Engineering Warangal India Nitte Meenakshi Institute Of Technology Department Of Aiml And Ipr Cell Bengaluru India University Of The Cumberlands Department Of Information Technology WilliamsburgKY United States

ISBN: (纸本)9798350318609

A computer network may be impacted by malware, computer viruses, and other hostile attacks. One of the most important elements of network security as an active defence technology is Intrusion Detection System (IDS). Issues with traditional intrusion detection systems include low detection effectiveness, inaccurate results, and an inability to handle new types of intrusions. This paper implemented a deep LSTMs with attention mechanism approach for IDS. The proposed technique is determined on Bot-IoT dataset, and z-score normalization is performed to normalize data. Information Gain (IG) is utilized to choose the features. A deep LSTMs with attention mechanism is used to effectively identify and categorize IDS effectively. The IG-deep LSTMs with attention mechanism achieves better accuracy using of 99.70% and 99.60% in NSL-KDD and CIC-IDS2017 datasets respectively. © 2024 IEEE.

关键词： internet of things

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