Mobile ad hoc networks (MANETs) are wireless networks that may be rapidly built and self-organize. They are ideal for military operations, disaster relief, outdoor events, and communications in areas without radio inf...
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Mobile ad hoc networks (MANETs) are wireless networks that may be rapidly built and self-organize. They are ideal for military operations, disaster relief, outdoor events, and communications in areas without radio infrastructure. In order to find more security flaws, it is advised to employ intrusion detection, which controls the system. For further security against unauthorized access and prevention, intrusion monitoring is essential. Depending on how long the system lasts, a mobile node's capacity to forward packets may be impacted by the loss of its power supply. This research proposes the use of hybrid stochastic bandgap optimization (SBO) and mixstyle neural networks (MNNs) to optimize the cluster head for multipath routing in mobile ad hoc networks. The proposed method combines both SBO and MNNs. The SBO method is used to choose the optimum pathways, and the MNN method is used to select the multipath routing in MANET. The MATLAB platform is used to build the suggested solution, which is then assessed based on several performance metrics, including detection rate, energy consumption, delay, and throughput. The suggested method outperformed other approaches like deep convolutional neural networks (DCNNs) and bacteria for aging optimization algorithm (BFOA) with a maximum detection rate of 96% and a low energy consumption of 0.12 mJ.
Mobile Ad-hoc Networks (MANETs) face significant challenges related to security threats and energy efficiency due to their dynamic nature. Traditional approaches have struggled with optimizing energy consumption while...
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Mobile Ad-hoc Networks (MANETs) face significant challenges related to security threats and energy efficiency due to their dynamic nature. Traditional approaches have struggled with optimizing energy consumption while ensuring robust security. This paper introduces a novel multipath routing protocol for MANETs that integrates simple contrastive graph clustering (SCGC) with Deep Operator Neural Networks (DONN) to enhance intrusion detection for various attack types including DoS, R2L, U2R, and Probe. The protocol utilizes Artificial Rabbits Optimization (ARO) to prioritize secure and energy-efficient nodes during multipath routing, selecting optimal paths based on metrics such as throughput, energy efficiency, trust, and network connectivity. The proposed method, implemented in Python, is evaluated using metrics such as packet receiving percentage ratio (PRPR), detection rate, throughput, energy consumption, precision, link failure rate, and network lifetime. Comparative analysis demonstrates that the proposed MRCP-DNID-MANET-DONN approach significantly outperforms existing methods, with improvements in throughput by 25.26 %, 16.22 %, and 26.27 %, and detection rate by 18.29 %, 24.31 %, and 23.26 % when compared to AE-IDS-MANET, MSAGCN-MIS-MANET, and DBF-MID-MANET, *** proposed MRCP-DNID-MANET-DONN approach significantly improves throughput and detection rate outperforming existing methods in both security and energy efficiency in MANETs.
The rapid development of the Internet of Things (IoT), particularly in relation to constrained Wireless Sensor Networks (WSNs), has garnered significant attention and advanced rapidly in terms of protocol structure. T...
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The rapid development of the Internet of Things (IoT), particularly in relation to constrained Wireless Sensor Networks (WSNs), has garnered significant attention and advanced rapidly in terms of protocol structure. The aim of these networks is to attain effective resource utilization and improved service distribution. In this manuscript, a Design and Development of Communication Protocol using a Gated Fusion Adaptive graph Neural Network in a Wireless Sensor Network (DCP-GAGNN-WSN) is proposed. Initially, the input data are collected from the sink node. Then the input nodes are fed to the cluster formation process using simple contrastive graph clustering (SCGC). The cluster head (CH) selection process is used to improve the efficiency of data transmission under the Snow Ablation Optimizer (SAO) approach for selecting criteria such as range (Ra), latency (Lat), Reducing Congestion (RC), and resource (Re). The Gated Fusion Adaptive graph Neural Network (GAGNN) method is employed for designing the communication protocol in WSN. The proposed DCP-GAGNN-WSN method is implemented and its efficiency is evaluated with the help of some performance metrics, such as Throughput, Energy Consumption, Packet Delivery Ratio (PDR), and Network Lifetime. Finally, the proposed DCP-GAGNN-WSN attains 25.49%, 32.77%, and 28.93% higher throughput, 34.73%, 32.96%, and 31.74% higher network lifetime than existing techniques, such as Collaborative energy-efficient routing protocol for sustainable communication in 5G/6G wireless sensor networks (SC-CEERP-WSN), spectrum sensing utilizing deep learning for effectual data transmission in WSN for wireless communication (SS-CNN-WSN), and an intelligent routing approach for energy prediction of 6G-powered WSN (IRA-PSOA-WSN), respectively.
Video surveillance continues to have difficulties with identifying the anomalies such as illegal activities and crimes despite the development of interactive multimedia anomaly detection systems. To address this issue...
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Video surveillance continues to have difficulties with identifying the anomalies such as illegal activities and crimes despite the development of interactive multimedia anomaly detection systems. To address this issue, an Optimized Interpretable Generalized Additive Neural Networks based Malicious Activity Detection with Video Surveillance (IGANN-MAD-VS-EOSSOA) is proposed in this paper. Initially, the input videos are collected from UCF-Crime and ShanghaiTech dataset. The collected video is fed to pre-processing for improving the quality of video, removing the noise and enhancing the clarity of image using Multiple Local Particle Filtering (MLPF). The pre-processed video is fed to the segmentation process. Here, the input videos are segmented into image using Maximum Entropy Scaled Super-pixels Segmentation (MESPS). Then the feature extraction is done by Synchro-Transient-Extracting Transform (STET) to extract the features, like color, texture, size, shape, and orientation. The extracted features are provided to the Interpretable Generalized Additive Neural Networks (IGANN) for classifying malicious activity, like Normal, Assault, Fighting, Shooting, Vandalism, Abuse and Accident. In general, IGANN does not adapt any optimization techniques for determining the optimal parameters to assure appropriate categorization. Hence, Elite opposite Sparrow Search Optimization Algorithm (EOSSOA) is proposed to enhance the weight parameter of IGANN for the detection of malicious activity with video surveillance. The proposed IGANN-MAD-VS-EOSSOA method is implemented in Python. The proposed technique attains 26.36%, 20.69% and 30.29% higher accuracy, 19.12%, 28.32%, and 27.84% higher precision when compared with the existing methods: Video anomaly detection scheme with deep convolutional and recurrent techniques (AD-CNN-VS), Toward trustworthy human suspicious activity detection from surveillance videos with deep learning (HSAD-SV-RNN), Deep learning-based real-world object dete
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