Reinforcement-Learning-Enabled Partial Confident Information Coverage for IoT-Based Bridge Structural Health Monitoring
| Autor: | Xianjun Deng, Lingzhi Yi, Minghua Wang, Hengshan Wu, Yi Situ, Laurence T. Yang |
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| Rok vydání: | 2021 |
| Předmět: |
Scheme (programming language)
Learning automata Computer Networks and Communications Computer science Distributed computing 020208 electrical & electronic engineering 020206 networking & telecommunications 02 engineering and technology Bridge (nautical) Computer Science Applications Scheduling (computing) Hardware and Architecture Signal Processing 0202 electrical engineering electronic engineering information engineering Reinforcement learning Structural health monitoring computer Information Systems computer.programming_language Efficient energy use |
| Zdroj: | IEEE Internet of Things Journal. 8:3108-3119 |
| ISSN: | 2372-2541 |
| DOI: | 10.1109/jiot.2020.3028325 |
| Popis: | Internet-of-Things (IoT)-based bridge structural health monitoring (BSHM) has recently attracted considerable attention from both academic and industrial communities of civil engineering and computer science. In conjunction with researchers from civil engineering and computer science, this article studied a fundamental problem motivated from practical IoT-based BSHM: how to effectively prolong network lifetime while guaranteeing desired coverage. Integrating a promising reinforcement learning model named learning automata (LA) with confident information coverage (CIC) model, this article presented an energy-efficient sensor scheduling strategy for partial CIC coverage in IoT-based BSHM system to guarantee network coverage and prolong network lifetime. The proposed scheme fully exploits cooperation among deployed nodes and alternatively schedules the wake/sleep status of nodes while satisfying network connectivity and partial coverage ratio. Especially, the proposed scheme takes full advantage of the LA model to adaptively learn the optimal sensor scheduling strategy and significantly extend network lifetime. A series of comparison simulations using real data sets collected by a practical BSHM system strongly verify the effectiveness and energy efficiency of the proposed algorithm. To the best of our knowledge, this is the first study on how to combine the reinforcement learning mechanism with partial coverage for maximizing the network lifetime of the IoT-based BSHM. |
| Databáze: | OpenAIRE |
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