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It can detect ambient conditions and convey data for processing purposes; In recent years, the wireless sensor network has surpassed all other networks in popularity. The implementation of WSNs is fraught with several significant obstacles, such as concerns over energy usage and safety. WSNs are potentially vulnerable to a wide variety of assaults, any one of which might compromise their capacity for reliable communication or lead to the loss of sensitive data. Consequently, as the network deployment gets more extensive and intricate, there is a massive increase in the requirement for intrusion detection-based energy-efficient approaches. The effectiveness of the networks may be evaluated via the use of Qualnet simulation. Using an artificial neural network and a MATLAB Simulink model, this study intends to improve the effectiveness of a power-based intrusion detection strategy. The results reveal that WSN’s detection of intrusions is enhanced by using an ideal technique inspired by biological nervous systems. Not only that, but the unguarded nodes are having a negative effect on network performance and producing disturbances in the network’s behavior. Both methods use regression analysis to distinguish between fully protected and partially protected nodes. Therefore, the packet delivery ratio and the power consumption of the network may be used for accurate node identification in an artificial neural network.
Caleb et al. (Thu,) studied this question.