Coverage optimization for IoT-based network using Monarch Butterfly Optimization with greedy strategy and self-adaptive crossover operator

In the dynamic development of technology, the Internet of Things (IoT) stands as a driving force of innovation and opening new frontiers. Data collection and processing in IoT systems provide services to decision making in various fields including home automation, disaster management, healthcare, an...

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Vydáno v:International journal of parallel, emergent and distributed systems Ročník 39; číslo 6; s. 696 - 711
Hlavní autoři: Pavithra, R., Arivudainambi, D., Afizudeen, S., Poorani, T.S.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Abingdon Taylor & Francis 01.11.2024
Taylor & Francis Ltd
Témata:
Adaptive algorithms
Crossovers
Greedy algorithms
Heuristic methods
Internet of Things
Meta-heuristic algorithm
Optimal sensor placement
Optimization
Quality of coverage
Statistical analysis
Variance analysis
Wireless sensor network
ISSN:1744-5760, 1744-5779
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Shrnutí:In the dynamic development of technology, the Internet of Things (IoT) stands as a driving force of innovation and opening new frontiers. Data collection and processing in IoT systems provide services to decision making in various fields including home automation, disaster management, healthcare, and so on. In such services, the IoT has certain limitations to perform the required tasks and it can cause extreme consequences in the network. One of the main challenge in IoT is to ensure maximum coverage by optimally placing the sensors. To address the coverage problem, a meta-heuristic algorithm Monarch Butterfly Optimization with greedy strategy and self-adaptive crossover operator (GCMBO) is proposed for Optimal Sensor Placement (OSP) in 3D monitoring region. The performance of proposed algorithm is evaluated by a series of simulation and it provide efficient results when compared to other similar existing optimization algorithms. Additionally, a statistical analysis (ANOVA and LSD post-hoc) is exhibited from the comparative results.
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ISSN:1744-5760
1744-5779
DOI:10.1080/17445760.2024.2417870