Optimal sensors placement scheme for targets coverage with minimized interference using BBO

Naik, Chandra; Shetty, D. Pushparaj

doi:10.1007/s12065-021-00624-8

Cited by 5 publications

(2 citation statements)

References 32 publications

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“…Adjacency matrix, Judgment matrix. [25,26,[108][109][110][111][112][113][114][115] Network lifetime Life cycle of a WSN-based SHM system.…”

Section: Network Connectivitymentioning

confidence: 99%

Wireless Sensor Placement Optimization for Bridge Health Monitoring: A Critical Review

Chen,

Shi,

et al. 2024

Buildings

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In recent years, wireless sensors have progressively supplanted conventional limited sensors owing to their attributes of small size, low cost, and high accuracy. Consequently, there has been a growing interest in leveraging wireless sensor networks for bridge structural health monitoring applications. By employing wireless sensor nodes to gather data from various segments of the bridge, information is relayed to a signal-receiving base station. Subsequently, the health status of the bridge is inferred through specific data processing and analysis, aiding monitoring personnel in making informed decisions. Nonetheless, there are limitations in this research, particularly pertaining to power consumption and efficiency issues in data acquisition and transmission, as well as in determining the appropriate wireless sensor types and deployment locations for different bridge configurations. This study aims to comprehensively examine research on the utilization of wireless sensor networks in the realm of bridge structural health monitoring. Employing a systematic evaluation methodology, more than one hundred relevant papers were assessed, leading to the identification of prevalent sensing techniques, data methodologies, and modal evaluation protocols in current use within the field. The findings indicate a heightened focus among contemporary scholars on challenges arising during the data acquisition and transmission processes, along with the development of optimal deployment strategies for wireless sensor networks. In continuing, the corresponding technical challenges are provided to address these concerns.

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“…Adjacency matrix, Judgment matrix. [25,26,[108][109][110][111][112][113][114][115] Network lifetime Life cycle of a WSN-based SHM system.…”

Section: Network Connectivitymentioning

confidence: 99%

Wireless Sensor Placement Optimization for Bridge Health Monitoring: A Critical Review

Chen,

Shi,

et al. 2024

Buildings

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“…Several meta-heuristics were used in the literature to solve WSN coverage and connectivity issues [16]. The work in [12] focused on the optimization of the deployment of SNs using a Biogeography Based Optimization where the objectives are the minimization of the sensing interference and the number of deployed SNs, and the maximization of the target coverage, under the the connectivity constraint. Authors in [7], proposed a hybrid meta-heuristic based on the tunicate swarm optimizer and the salp swarm optimizer, with the main aim of determining a minimum number of potential positions selected to place SNs and maximize the target coverage and network connectivity.…”

Section: Related Workmentioning

confidence: 99%

Optimization of the Deployment of Wireless Sensor Networks Dedicated to Fire Detection in Smart Car Parks using Chaos Whale Optimization Algorithm

Benghelima

Ould‐Khaoua

Benzerbadj

et al. 2022

ICC 2022 - IEEE International Conference on Communications

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Smart Car Parks (SCPs) based on Wireless Sensor Networks (WSNs) are one of the most interesting IoT applications. The present study addresses the deployment optimization problem of two-tiered WSNs dedicated to fire monitoring in smart parking lots. Networks deployed inside the SCP consist of three types of nodes: Sensor Nodes (SNs) which cover the spots within the parking area, Relay Nodes (RNs) which forward alert messages generated by SNs, and the Sink node which is connected to the outside world (e.g, firefighters), through a high bandwidth connection. This paper proposes an algorithm based on chaos theory and Whale Optimization Algorithm (WOA), which minimizes simultaneously the deployed number of SNs, RNs, and the maximum distance from SNs to the sink node (network diameter) while ensuring coverage and connectivity. To evaluate the effectiveness of our proposal, we have conducted extensive tests. The results show that the Chaos WOA (CWOA) outperforms the original WOA in terms of solution quality and computation time and by comparison with the exact method, CWOA finds results very close to the optimal in terms of fitness value and is efficient in terms of computational time when the problem becomes more complex.

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FLAG: fuzzy logic augmented game theoretic hybrid hierarchical clustering algorithm for wireless sensor networks

Naik

Shetty

2022

Telecommun Syst

Self Cite

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Optimal sensors placement scheme for targets coverage with minimized interference using BBO

Cited by 5 publications

References 32 publications

Wireless Sensor Placement Optimization for Bridge Health Monitoring: A Critical Review

Wireless Sensor Placement Optimization for Bridge Health Monitoring: A Critical Review

Optimization of the Deployment of Wireless Sensor Networks Dedicated to Fire Detection in Smart Car Parks using Chaos Whale Optimization Algorithm

FLAG: fuzzy logic augmented game theoretic hybrid hierarchical clustering algorithm for wireless sensor networks

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