A study on boundary detection in wireless sensor networks

Kundu, Srabani; Das, Nabanita

doi:10.1007/s11334-022-00488-w

Cited by 2 publications

(1 citation statement)

References 46 publications

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“…Several studies have been conducted on improving the performance and security of WSNs through ML algorithms. Kundu et al [6] proposed a gravitational search algorithm (GSA)-based algorithm for optimizing WSN deployment to ensure full coverage of the target area while minimizing the number of sensors required. Their approach outperforms other state-of-the-art algorithms in terms of coverage rate, connectivity rate, and the number of sensors required.…”

Section: Related Workmentioning

confidence: 99%

Optimized Performance of Attacks Detection in WSN based on Machine Learning Algorithms

Farid

Khalil

Shawky

et al. 2023

2023 International Telecommunications Conference (ITC-Egypt)

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Wireless sensor networks (WSN) are a type of wireless network composed of numerous sensors that collaborate to sense, collect, process, and transmit information about the physical environment within the network's geographical area. The information is ultimately received by the network owner. However, typical attacks such as Blackhole, Grayhole, Flooding, and Scheduling can pose a significant threat to the WSN, potentially causing significant damage to the system in a short period. Detection methods, such as snooping, have demonstrated low detection and high false alarm rates, and require significant computational resources. Additionally, they tend to produce redundant network data. To address these limitations, we propose a novel intervention approach called "Ensemble Bagged Trees," which employs a squared backward sequence selection (SBS) algorithm to reduce data dimensionality and computational overhead in the feature space of native traffic data. The Ensemble Bagged Trees algorithm is then utilized to detect various network attacks. Experimental results using the WSN-DS dataset demonstrate that the proposed method outperforms typical machine learning detection algorithms, with a detection rate of 99.1% for the normal black hole, gray hole, flood, and tabulation attacks.

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Section: Related Workmentioning

confidence: 99%

Optimized Performance of Attacks Detection in WSN based on Machine Learning Algorithms

Farid

Khalil

Shawky

et al. 2023

2023 International Telecommunications Conference (ITC-Egypt)

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Angle Based Critical Nodes Detection (ABCND) for Reliable Industrial Wireless Sensor Networks

Shukla

2023

Wireless Pers Commun

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Node failure in the Wireless Sensor Networks (WSN) topology may lead to economic loss, endanger people, and cause environmental damage. Node reliability can be achieved by adequately managing network topology using structural approaches, where the critical nodes are precisely detected and protected. This paper addresses the problem of critical node detection and presents two-phase algorithms (ABCND). Phase-I, a 2 D Critical Node ( C - N ) detection algorithm, is proposed, which uses only the neighbor’s Received Signal Strength Indicator ( RSSI ) information. In Phase II, a correlation-based reliable RSSI approach is proposed to increase the node resilience against the adversary. The proposed algorithms ( ABCND ) require time for convergence and for Critical Node detection, N represents the number of IoT devices, and is the cost required to forward the message. We compare our algorithm (ABCND) with the current state-of-the-art on C - N detection algorithms. The simulation result shows that the proposed ABCND algorithm consumes 50% less energy to detect C - N with 90% to 95% accurate Critical Nodes ( C - N ).

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A study on boundary detection in wireless sensor networks

Cited by 2 publications

References 46 publications

Optimized Performance of Attacks Detection in WSN based on Machine Learning Algorithms

Optimized Performance of Attacks Detection in WSN based on Machine Learning Algorithms

Angle Based Critical Nodes Detection (ABCND) for Reliable Industrial Wireless Sensor Networks

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