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Integrated Circuits and Communication systems (ICICACS), ieee international conference on

作者： M. Nirmal Kumar T. Vijayan B. Karthik Department of Electronics and Communication Engineering Bharath Institute of Higher Education and Research Chennai India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

This paper presents an efficient Intrusion Detection System specified to the requirements of WSNs, targeting blackholes, floods, and scheduling-related intrusion attacks. The increasing vulnerability of WSNs to evolving attack patterns and their constrained resources make traditional security solutions, such as cryptography and trust-based systems, inadequate. The most widely used security technologies employ cryptography and trust-based systems have built-in shortcomings at protecting WSNs against evolving attack patterns and constricted resources. The proposed IDS will circumvent these challenges because it uses ensemble classifiers, namely REP Tree, Random Forest, and JRip, that demonstrate significant performance in the heavily constrained environment and unbalanced datasets. The results show that the system outperforms traditional IDS methods, with reduced resource consumption and a low false positive rate. Using the WSN-DS benchmark dataset, the system managed an average accuracy of 98.3% and a precision of 97.1%. This system is optimal for use in actual WSN installations because of its better performance than traditional IDS methods and small consumption of the resources.

关键词： Measurement Integrated circuits Wireless sensor networks Accuracy Scalability Intrusion detection Real-time systems Cryptography Floods Random forests

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Optimization of Node Deployment in Underwater sensor Networks Using Maximum Coverage and Connectivity Self-Deployment Algorithm Based on Particle Swarm Optimization

Optimization of Node Deployment in Underwater Sensor Network...

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Integrated Circuits and Communication systems (ICICACS), ieee international conference on

作者： G. Renuka V. Gurumurthy Raed Hameed P. K. Chidambaram Sudhakar Chinnakathiyan Department of Electronics and Communication Engineering Anurag University Hyderabad India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Mechanical Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai India Department of Electronics and Communication Engineering Dhanalakshmi Srinivasan University Trichy India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

In recent years, Underwater Wireless sensor Networks (UWSNs) monitor underwater environment such as aquatic life. Moreover, to enhance network efficiently, sensor nodes are strategically placed in a 3D underwater space. However, underwater networks failed to find the optimal depth for each sensor in a 3D underwater network and failed in maintaining the connectivity to the surface station. Further, this research proposes a Maximum Coverage and Connectivity self-deployment algorithm based on Particle Swarm Optimization namely (MCC-PSO) for maximum coverage of water surface. Initially, the system models such as Network Scenario and Coverage Model are designed to optimize node deployment in UWSNs by simulating realistic environments. Then the problem identified that the optimal depth for N sensors deployed on a water surface to maximize network coverage and ensure connectivity to the surface station. To address this, a MCC and Connectivity Scheme is designed, Further, the MCC-PSO enhanced for optimizing the node development and PSO optimizes moving particles in search space by dynamic adaptation to environmental factors, such as node distribution and communication range. From the results, proposed MCC-PSO achieves a 50% higher maximum coverage rate compared to the existing Growth Ring Style Uneven Node Depth-Adjustment Self-Deployment Optimization Algorithm (GRSUNDSOA).

关键词： Integrated circuits Wireless sensor networks Three-dimensional displays Heuristic algorithms Environmental factors Real-time systems Reliability Particle swarm optimization Optimization Monitoring

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Enhancing Coverage and Connectivity with Hybrid Harris Hawk and Arithmetic Optimization in Clustering and Routing

Enhancing Coverage and Connectivity with Hybrid Harris Hawk ...

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Integrated Circuits and Communication systems (ICICACS), ieee international conference on

作者： Sreelakshmi Tadigotla Jayanthi K Murthy Department of Electronics and Communication Engineering B.M.S. College of Engineering Bangalore India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Wireless sensor Networks (WSN) focus on designing energy efficient mechanisms by selecting an optimal Cluster Head (CH) and implementing secure routing protocols. The WSN aims to reduce energy consumption while dynamically maximizing network efficiency. However, secure routing and Cluster Head (CH) selection in dynamic networks is optimal but challenging in WSNs. To overcome this problem, a hybrid optimization-based algorithm, Harris Hawks Optimization with Arithmetic Optimization Algorithm HHAOA is proposed to choose the optimal CHs in a dynamic network. The proposed HHAOA is utilized for solving coverage and connectivity issues in the routing algorithm. The proposed HHAOA system minimizes energy consumption for superior network performance. Additionally, multi-hop transmission and distance-based fitness function are employed for efficient CH and route path selection to ensure that coverage and connectivity parameters are taken into consideration for model proficiency with minimal energy consumption. The fitness function is used to measure the distance between CH and Base Station (BS), node degree, location factor, energy and distance for optimal CH and routing path selection. The proposed HHAOA consumes 63 (J) energy for 100 nodes with a throughput of 98 (kbps) for each100 rounds.

关键词： Wireless sensor networks Energy consumption Base stations Heuristic algorithms Clustering algorithms Spread spectrum communication Routing Throughput Energy efficiency Optimization

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Design and Implementation of a Personalized Recommendation System Based on Deep Learning distributed Collaborative Filtering Algorithm on Social Media Platforms

Design and Implementation of a Personalized Recommendation S...

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Integrated Circuits and Communication systems (ICICACS), ieee international conference on

作者： Junyu Chen Alexa Music Amazon Seattle WA United States

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Social media platforms mainly provide personalized recommendation services for users, and usually adopt recommendation methods related to user or project similarity based on the characteristics of different platforms. However, current recommendation techniques mostly rely on simple similarity calculations, which can significantly reduce accuracy when dealing with large amounts of data. To address this issue, this paper proposes a collaborative filtering recommendation method based on social media gene maps. Specifically, this article draws on the concept of genes in biology, collects social media user behavior data, extracts biological genes, social genes, and network gene fragments, calculates the core gene matching degree of each gene fragment, and uses entropy weight method to assign different weights to gene fragments, thereby constructing a complete social media gene map and optimizing the recommendation mechanism. The experimental results show that this method outperforms traditional recommendation algorithms in terms of recommendation performance. Subsequently, this article proposes to combine social media gene maps with project similarity to further improve recommendation algorithms. When measuring project similarity, a fusion of scoring similarity and semantic similarity was used, combined with user gene matching degree, to optimize the weighted Slope One algorithm and achieve more accurate recommendation services. The results indicate that the algorithm proposed in this paper has significantly improved in evaluation metrics such as MAE and MRSE.

关键词： Weight measurement Accuracy Social networking (online) Collaborative filtering Diversity reception Semantics Biology User experience Real-time systems Recommender systems

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DR3DH: A DoS resistant extended triple Diffie–Hellman for mobile edge networks

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Computer Networks 2025年 265卷

作者： Altaweel, Ala Bouridane, Ahmed Department of Computer Engineering College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates

Mobile Edge Networks (MENs) are wireless networks that establish "close-to-end-users-clouds" to provide storage, computation, and communication services. MENs employ the Extended Triple Diffie–Hellman (X3DH) protocol to establish secret keys that provide end-to-end encryption among their nodes. X3DH relies on a cloud server that stores the public keys of the nodes to address the asynchronous scenarios when they operate offline. The cloud server in X3DH protocol represents a single point of failure (SPoF), as the exchange of secure keys is disrupted if the server fails or is compromised. Moreover, a malicious attacker, Eve, can launch various DoS attacks against the MEN nodes (e.g., packet dropping or packet modification) and wireless links (e.g., jamming) to disrupt the execution of X3DH during secure key exchange. This paper proposes the DoS-Resistant-X3DH (DR3DH) protocol to resolve the SPoF of the cloud server in X3DH and to address packet dropping, packet modification, and jamming DoS attacks. DR3DH employs Reed–Solomon Erasure Coding to encode the X3DH public keys into fragments that are distributed into a set of storage nodes. These storage nodes, which are determined by an Integer Linear Programming (ILP) solver, are optimum. That is, they have lowest probabilities to launch packet dropping/modification attacks and lowest probabilities that the wireless links toward them are jammed. The authors evaluated DR3DH through trace-driven simulations in MATLAB using two mobility traces and a proof-of-concept implementation in Java. The authors also compared DR3DH with two approaches that employ Random and Greedy policies when selecting the storage nodes for keys fragments. The results demonstrate the resistance of DR3DH to DoS attacks, achieving a high success rate that outperforms the Random and Greedy approaches by between 13.6% and 83.0%. Additionally, the results confirm the feasibility of DR3DH under different mobility models and node speeds, as demonstrated by

关键词： Packet loss

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8th international conference on Autonomic computing, ICAC 2011 Co-located Workshops - Proceedings of the 3rd Workshop on Biologically Inspired Algorithms for distributed systems, BADS 2011: Foreword

8th International Conference on Autonomic Computing, ICAC 20...

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8th international conference on Autonomic computing, ICAC 2011 Co-located Workshops - Proceedings of the 3rd Workshop on Biologically Inspired Algorithms for distributed systems, BADS 2011 2011年 iii-iv页

作者： Folino, Gianluigi Mastroianni, Carlo Mostaghim, Sanaz Suzuki, Junichi Italy Germany University of Massachusetts Boston MA United States

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NeuroSpectrum Deep Learning Architecture for Automated Spectrum Analysis Resource Optimization

NeuroSpectrum Deep Learning Architecture for Automated Spect...

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Integrated Circuits and Communication systems (ICICACS), ieee international conference on

作者： K. Tamilselvan T. Senthil B. Buvaneswari Santhiya S Mukesh Soni Department of Information Technology AVS Engineering College Salem India Department of Computer Science and Engineering Sreenivasa Institute of Technology and Management Studies chittoor India Department of Information Technology Panimalar Engineering College Chennai India Department of Computer science and Engineering Excel Engineering College Namakkal India Department of Division of Research and Development Lovely Professional University Phagwara India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Reducing the availability of spectrum, as well as the nature of allocating channels in the present wireless telecommunication networks, is one of the critical issues facing modern wireless technology. Such spectrum sensing methods may not be highly flexible or efficient in environments where the radio spectrum undergoes frequent changes. This work proposes an improved deep learning spectrum sensing model based on CNN with reinforcement learning. The proposed system design calls for a pioneering dual mechanism, incorporating both spectrum analysis and a resource allocation learning model. The framework achieved remarkable performance metrics: 96 The spectrum sensing method achieved a 3% occurrence rate, a 94.8% precision rate for resource allocation, and a 92.7% efficiency in dynamic spectrum use. In the present investigation, we have employed it for different topologies and got 45% improvement in spectrum efficiency and 87% less interference than the conventional techniques. The work challenges deep learning-based spectrum sensing as well as management and indicates that they are prepared for the following generation of the wireless communication network.

关键词： Deep learning Wireless sensor networks Wireless networks Transfer learning Training data sensors Resource management Optimization Spectral analysis Radio spectrum management

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Vehicle-Mounted Millimeter-Wave Radar Vital Signs Detection and Early Warning Control System

Vehicle-Mounted Millimeter-Wave Radar Vital Signs Detection ...

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Integrated Circuits and Communication systems (ICICACS), ieee international conference on

作者： Wenshan Li Hunan Institute of Traffic Engineering Hengyang Hunan China

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Traditional methods of monitoring vital signs in vehicles often fall short when it comes to response time, especially in fast-moving situations. To tackle this, our paper introduces a groundbreaking solution: leveraging millimeter-wave radar technology to monitor vital signs in real time within the vehicle, providing timely alerts when necessary. This approach not only enhances the accuracy of readings but also ensures a swift response that is crucial during vehicle operation. By tapping into the unique abilities of millimeter-wave radar to detect subtle human signs, we propose a novel method that processes radar signals to monitor vital signs like heart rate and respiratory rate. This technique is highly effective in meeting the stringent demands of real-time accuracy and speed, especially while driving. Our system includes three main components: radar sensors, a signal processing unit, and an early-warning control module, working together seamlessly to ensure optimal monitoring. The radar sensor continuously transmits and receives millimeter-wave signals, allowing it to track vital signs without interruption. A specially designed feature extraction algorithm filters out noise and extracts critical information, including heart and respiratory rates. Then, advanced algorithms analyze this data in real time, spotting any abnormalities and triggering alerts through the control module. What sets this system apart is its rigorous testing under various vehicle speeds and environmental conditions, confirming its stability, reliability, and fast response times. In real-world applications, the system responds to a high heart rate in 3.7 to 4.0 seconds and to a rapid breathing rate in 4.1 to 4.3 seconds. This millimeter-wave radar-based system offers a fresh approach to vehicle safety, combining practicality with promising future potential.

关键词： Heart rate Accuracy Signal processing algorithms Millimeter wave radar Real-time systems Stability analysis sensor systems sensors Time factors Monitoring

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Predictive Maintenance for IoT-Enabled Wireless Devices Using AdaBoost with Synthetic Minority Oversampling Technique

Predictive Maintenance for IoT-Enabled Wireless Devices Usin...

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Integrated Circuits and Communication systems (ICICACS), ieee international conference on

作者： Montater MuhsnHasan Meghana A Panjagari Kavitha T. Aditya Sai Srinivas P. K. Chidambaram Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Al Diwaniyah Iraq Department of Electrical and Electronics Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Electronics and Communication Engineering CVR College of Engineering Hyderabad India Department of Artificial Intelligence and Machine Learning Jayaprakash Narayan College of Engineering Mahbubnagar India Department of Mechanical Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Nowadays, the rapid growth of predictive maintenance methods has become crucial for Internet of Things (IoT) enabled wireless devices. The existing Random Forest (RF) utilized to predict device failures although, the complexity of IoT device data led to scalability issues. Hence, this research proposes an Adaptive Boosting with Synthetic Minority Oversampling Technique (AdaBoost-SMOTE) for predicting device failures. The proposed AdaBoost-SMOTE overcome the scalability issues by handling imbalanced data and reducing dimensionality for accurate prediction. Initially, the input data is collected from IoT-enabled sensors and preprocessed with z-score normalization to scale the features. Then, Permutation Importance (PI) is employed to select the optimal features by ranking the feature score based on its importance. After that, SMOTE is used to handle the imbalance data and then, AdaBoost is employed to predict the machine failures by assigning the higher weights to misclassified samples in an iterative manner. Finally, hyperparameter tuning is done by using Grid Search Cross-Validation (GSCV) to optimize the parameters by selecting best CV score as a best set of hyperparameters. From the results, the proposed AdaBoost-SMOTE achieved best results in terms of accuracy (97%), precision (95%), recall (93%), and F1-score (94%) when compared to existing AdaBoost.

关键词： Wireless communication Wireless sensor networks Accuracy Scalability Data preprocessing Boosting Internet of Things Integrated circuit modeling Tuning Predictive maintenance

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IoT-Integrated Convolutional Neural Network for Accurate Vehicle Acoustic Signal Analysis

IoT-Integrated Convolutional Neural Network for Accurate Veh...

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Integrated Circuits and Communication systems (ICICACS), ieee international conference on

作者： P. Sasikala K Subha Sharmini Venkatesan Sorakka Ponnappan K. R. Kavitha K. Gunasekaran C. Srinivasan Department of Mathematics Vinayaka Mission's Kirupananda Variyar Engineering College Vinayaka Mission's Research Foundation (Deemed to be University) Salem India Department of Electrical and Electronics Engineering SRM Institute of Science and Technology Kattankulathur Chennai India Department of Mechanical Engineering Sathyabama Institute of Science and Technology Chennai India Department of Electronics and Communication Engineering Sona College of Technology Salem India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

This paper examines the use of IoT-integrated Convolutional Neural Networks (CNNs) for precise analysis of vehicle audio signals. The context highlights the growing intricacy of vehicle systems and the need for advanced monitoring methods. Acoustic signals generated by different vehicle components may provide significant insights about vehicle health; nevertheless, traditional analysis techniques are often ineffective. The Internet of Things (IoT) technology allows real-time data collection from sensors installed on vehicles, which is then analyzed using CNNs to identify certain patterns and abnormalities in auditory signals. The approaches used include using IoT sensors to record various vehicle noises and utilizing CNNs for feature extraction and classification, providing great defect detection precision. The results indicate the system's capacity to detect engine faults, braking deficiencies, and other mechanical concerns early, enabling quick treatments. It presents a novel methodology for vehicle monitoring, enhancing predictive maintenance, safety, and performance, with prospects for future developments in autonomous diagnostics. The proposed system improved vehicle acoustic signal processing using IoT and CNN models, achieving high accuracy of 99% in anomaly detection, preventive maintenance, and real-time traffic monitoring.

关键词： Training Accuracy Noise Real-time systems Acoustics sensor systems sensors Convolutional neural networks Internet of Things Intelligent sensors

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