Aspect of ESB With Wireless Sensor Network

Mittal, Mohit; Bhadoria, Robin Singh

doi:10.4018/978-1-5225-2157-0.ch010

Cited by 4 publications

(1 citation statement)

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“…The sensor nodes that are situated in each quadrant 1 hop distant to BS are not required to send data first to manager node; nodes can directly send data to the BS [23]. If sensor nodes are further than 1 hop, then sensor nodes must transmit their aggregated data to the neighboring manager nodes, and finally, manager nodes transfer the data to BS [24][25][26]. If sensor nodes are situated in the 2nd sector but they are distant from the 2nd sector manager node, then the sensor node has no need to send data first to the 2nd position manager node, it can send data to a neighboring sensor node, which acts as an intermediate node, and then that node transmit the data to the neighboring manager node [27,28].…”

Section: Eesr Routing Protocolmentioning

confidence: 99%

Machine Learning Techniques for Energy Efficiency and Anomaly Detection in Hybrid Wireless Sensor Networks

et al. 2021

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Wireless sensor networks (WSNs) are among the most popular wireless technologies for sensor communication purposes nowadays. Usually, WSNs are developed for specific applications, either monitoring purposes or tracking purposes, for indoor or outdoor environments, where limited battery power is a main challenge. To overcome this problem, many routing protocols have been proposed through the last few years. Nevertheless, the extension of the network lifetime in consideration of the sensors capacities remains an open issue. In this paper, to achieve more efficient and reliable protocols according to current application scenarios, two well-known energy efficient protocols, i.e., Low-Energy Adaptive Clustering hierarchy (LEACH) and Energy–Efficient Sensor Routing (EESR), are redesigned considering neural networks. Specifically, to improve results in terms of energy efficiency, a Levenberg–Marquardt neural network (LMNN) is integrated. Furthermore, in order to improve the performance, a sub-cluster LEACH-derived protocol is also proposed. Simulation results show that the Sub-LEACH with LMNN outperformed its competitors in energy efficiency. In addition, the end-to-end delay was evaluated, and Sub-LEACH protocol proved to be the best among existing strategies. Moreover, an intrusion detection system (IDS) has been proposed for anomaly detection based on the support vector machine (SVM) approach for optimal feature selection. Results showed a 96.15% accuracy—again outperforming existing IDS models. Therefore, satisfactory results in terms of energy efficiency, end-to-end delay and anomaly detection analysis were attained.

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