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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| Published in: | Electronics (Basel) Vol. 14; no. 21; p. 4198 |
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| Language: | English |
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01.11.2025
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| ISSN: | 2079-9292, 2079-9292 |
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| Abstract | 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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| AbstractList | 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. |
| Audience | Academic |
| Author | Al-Nader, Issam Raheem, Rand Lasebae, Aboubaker |
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| Cites_doi | 10.3390/en15155743 10.1007/978-981-99-8612-5_28 10.1002/047011276X 10.20944/preprints202411.2335.v1 10.1161/01.CIR.24.2.434 10.1007/s11063-022-10851-4 10.3390/electronics12234766 10.1007/978-3-031-29056-5_40 10.3390/electronics13010019 10.1007/s11277-023-10434-z 10.3390/j8020019 10.1109/IC3P52835.2022.00024 10.1016/j.jnca.2010.03.002 10.3390/s20072081 10.1007/978-3-319-21410-8_10 10.1186/s13638-019-1511-4 |
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| DOI | 10.3390/electronics14214198 |
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| References_xml | – ident: ref_5 doi: 10.3390/en15155743 – volume: Volume 379 start-page: 347 year: 2024 ident: ref_12 article-title: A New Perceptron-Based Neural-Network Algorithm to Enhance the Scheduling Performance of Safety–Critical WSNs of Increased Dependability publication-title: Information Systems for Intelligent Systems doi: 10.1007/978-981-99-8612-5_28 – ident: ref_3 doi: 10.1002/047011276X – ident: ref_4 doi: 10.20944/preprints202411.2335.v1 – volume: 24 start-page: 434 year: 1961 ident: ref_13 article-title: Hibernation in mammals publication-title: Circulation doi: 10.1161/01.CIR.24.2.434 – volume: 55 start-page: 2587 year: 2023 ident: ref_6 article-title: A Review on Multi-objective Optimization in Wireless Sensor Networks Using Nature Inspired Meta-heuristic Algorithms publication-title: Neural Process. Lett. doi: 10.1007/s11063-022-10851-4 – ident: ref_7 doi: 10.3390/electronics12234766 – ident: ref_9 doi: 10.1007/978-3-031-29056-5_40 – ident: ref_11 doi: 10.3390/electronics13010019 – ident: ref_15 – volume: 131 start-page: 371 year: 2023 ident: ref_16 article-title: E-FEERP: Enhanced Fuzzy Based Energy Efficient Routing Protocol for Wireless Sensor Network publication-title: Wirel. Pers. Commun. doi: 10.1007/s11277-023-10434-z – ident: ref_10 doi: 10.3390/j8020019 – ident: ref_1 doi: 10.1109/IC3P52835.2022.00024 – volume: 33 start-page: 383 year: 2010 ident: ref_8 article-title: A clique base node scheduling method for wireless sensor networks publication-title: J. Netw. Comput. Appl. doi: 10.1016/j.jnca.2010.03.002 – ident: ref_14 – ident: ref_2 doi: 10.3390/s20072081 – volume: Volume 9158 start-page: 128 year: 2015 ident: ref_17 article-title: Clustering Wireless Sensor Networks Based on Bird Flocking Behavior publication-title: Computational Science and Its Applications—ICCSA 2015 doi: 10.1007/978-3-319-21410-8_10 – volume: 2019 start-page: 203 year: 2019 ident: ref_18 article-title: Data prediction model in wireless sensor networks based on bidirectional LSTM publication-title: EURASIP J. Wirel. Commun. Netw. doi: 10.1186/s13638-019-1511-4 – volume: 13 start-page: 835 year: 2022 ident: ref_19 article-title: Cnn-lstm based approach for dos attacks detection in wireless sensor networks publication-title: Int. J. Adv. Comput. Sci. Appl. |
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| SubjectTerms | 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 |
| Title | A Novel Adaptive AI-Based Framework for Node Scheduling Algorithm Selection in Safety-Critical Wireless Sensor Networks |
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