A Novel Adaptive AI-Based Framework for Node Scheduling Algorithm Selection in Safety-Critical Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are vital to a wide range of applications, spanning from environmental monitoring to safety-critical systems. Ensuring dependable operation in these networks critically depends on selecting an optimal node scheduling algorithm; however, this remains a major challenge...

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Vydané v:Electronics (Basel) Ročník 14; číslo 21; s. 4198
Hlavní autori: Al-Nader, Issam, Raheem, Rand, Lasebae, Aboubaker
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Basel MDPI AG 01.11.2025
Predmet:
Adaptability
Algorithms
Artificial intelligence
Connectivity
Decision making
Disaster recovery
Efficiency
Energy consumption
Environmental monitoring
Fault tolerance
Forest fire detection
Load
Markov chains
Markov processes
Military aspects
Military deployment
Military operations
Neural networks
Nodes
Optimization
Optimization techniques
Safety critical
Scheduling
Self organizing maps
Sensors
Wireless sensor networks
ISSN:2079-9292, 2079-9292
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Shrnutí:Wireless Sensor Networks (WSNs) are vital to a wide range of applications, spanning from environmental monitoring to safety-critical systems. Ensuring dependable operation in these networks critically depends on selecting an optimal node scheduling algorithm; however, this remains a major challenge since no single approach performs best under all conditions. To address this issue, this paper proposes an AI-driven framework that evaluates scenario-specific functional requirements—such as coverage, connectivity, and network lifetime—to identify the optimal node scheduling algorithm from a pool that includes Hidden Markov Models (HMMs), BAT, Bird Flocking, Self-Organizing Maps (SOFMs), and Long Short-Term Memory (LSTM) networks. The framework was evaluated using a neural network trained on simulated data and tested across five real-world scenarios: healthcare monitoring, military operations, industrial IoT, forest fire detection, and disaster recovery. The results clearly demonstrate the effectiveness of the proposed framework in identifying the most suitable algorithm for each scenario. Notably, the LSTM algorithm frequently achieved near-optimal performance, excelling in critical objectives such as network lifetime, connectivity, and coverage. The framework also revealed the complementary strengths of other algorithms—HMM proved superior for maintaining connectivity, while Bird Flocking excelled in extending network lifetime. Consequently, this work validates that a scenario-aware selection strategy is essential for maximizing WSN dependability, as it leverages the unique advantages of diverse algorithms.
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ISSN:2079-9292
2079-9292
DOI:10.3390/electronics14214198