The fault node detection and recovery is essential in WSN for improving the network lifetime and node connectivity. Ensuring the tradeoff between energy consumption and node recovery is challenged due to redundant nod...
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The fault node detection and recovery is essential in WSN for improving the network lifetime and node connectivity. Ensuring the tradeoff between energy consumption and node recovery is challenged due to redundant nodes. Hence, a novel approach is developed based on the objective of automatic recovery from failure, redundant node elimination, faultnode replacement, and minimalizing energy consumption. In this approach, the fuzzy boosted sooty tern optimization (FBSTO) technique is proposed for fault node detection and replacement. Based on the node density and distance, the nodes are clustered. Then, the cluster head selection process can be accomplished with a fuzzy logic approach based on distance calculation, energy consumption, and Quality of service (QoS) nodes. The node replacement is carried out through cascaded movement, which minimizes energy utilization. The efficiency of the FBSTO approach is estimated with packet delivery ratio, packet loss ratio, end-to-end delay, and energy consumption. The proposed approach reduces the end-to-end delay to 10 ms for random deployment of 100 nodes. Also, the packet delivery ratio performance of the proposed approach is 143 for 100 SNs. For existing Multi-objective Cluster Head Based Energy-aware Optimized Routing (MCH-EOR), Ant Lion Optimization (ALO), Particle swarm Optimization (PSO), Gray Wolf Optimization (GWO), and Genetic approach (GA), the packet delivery ratio is reduced to 130, 60, 50, and 100. Compared with the existing approaches, the proposed approach provides better performance.
Ensuring data integrity in wireless sensor networks (WSNs) is crucial for accurate monitoring, yet missing data due to sensor faults present a significant challenge. This research introduces an innovative approach tha...
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Ensuring data integrity in wireless sensor networks (WSNs) is crucial for accurate monitoring, yet missing data due to sensor faults present a significant challenge. This research introduces an innovative approach that integrates advanced data recovery techniques with leading-edge methods to address this issue. The system begins by identifying and isolating faultnodes using a specialized algorithm that analyzes network behavior. By applying fuzzy density-based spatial clustering of applications with noise (FDBSCAN), potential faultnodes are precisely located based on deviations from expected patterns. Subsequently, an intelligent missing data recovery mechanism powered by bidirectional long short-term memory (Bi-LSTM) networks takes action. The Bi-LSTM model is trained on existing sensor data to capture intricate patterns and dependencies, enabling accurate prediction and reconstruction of missing values caused by identified faults. The synergy between Bi-LSTM for missing data recovery and FDBSCAN for fault node detection comprehensively addresses the missing data problem in WSNs. In missing data recovery, it demonstrates low mean absolute deviation (MAD) ranging from 0.021 to 0.13 and mean squared deviation (MSD) ranging from 0.0025 to 0.05 across various missing data ratios. Data reliability remains consistently high at 96% to 98%, even with up to 80% missing data. For fault node detection, the approach achieves precision of 95.7%, recall of 96.3%, F1-score of 96.1%, and accuracy of 97.4%, outperforming existing techniques. The computational cost during training is noted at 5.79 h, presenting a limitation compared to other methods. This research highlights the importance of integrating fault node detection into missing data recovery mechanisms, presenting an innovative solution for the advancement of WSNs. Our proposed system integrates advanced data recovery techniques, employing bi-directional long short-term memory (Bi-LSTM) networks with an efficient fault n
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