An Autonomous Fault-Awareness model adapted for upgrade performance in clusters of homogeneous wireless sensor networks
| Autor: | Salah M. El-Sayed, Walaa M. Elsayed, Hazem M. El-Bakry |
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| Rok vydání: | 2020 |
| Předmět: |
Computer Networks and Communications
Computer science business.industry Real-time computing 020302 automobile design & engineering 020206 networking & telecommunications 02 engineering and technology Feedback loop Fault (power engineering) Adaptive filter Upgrade 0203 mechanical engineering Filter (video) Robustness (computer science) 0202 electrical engineering electronic engineering information engineering Wireless Electrical and Electronic Engineering business Wireless sensor network Information Systems |
| Zdroj: | Wireless Networks. 26:5085-5100 |
| ISSN: | 1572-8196 1022-0038 |
| Popis: | Wireless sensor networks (WSNs) have conquered comprehensive survey progressions in the regular control and management fields. Although WSN allows the spatial monitoring of real-world events, the mobility action depletes a huge part of a sensor’s energy cost in wireless communication. WSN sensors are often prone to various faults as frequent crashes and temporary or permanent failures. This is because it propagates them in very complex and harsh environments. So, we tend to design a Self-Adaptive based Autonomous Fault-Awareness (SAAFA) model, to limit the impact of such failures and filter them. In this paper, we incorporate the two of adaptive-filters FIR with RLS through three adaptive two-stages performed at the level of cluster head, for independent fault-correction during the propagation platform. The proposed model (SAAFA) included two stages, the first stage comprised self-detection the failure and self-aware for the lost scales, in which relied on responses of delay port and prior-knowledge of absent sensor-signals throughout monitoring, through adjusting the filter weights in the adaptive feedback loop for awarding convergent signals for the lost ones. The second stage is adaptive filtering the registered signals from the above stage for gaining pure measures and free of interferences. Compared to the state-of-the-art methods, the scheduled model attained a speed in diagnosing faults and awareness the missing readings with a rate of accuracy reached 98.8% improving the robustness of performance. Evaluation criteria revealed the progress of SAAFA in reducing the radio communication to ~ 97.47% that kept about 93.7% of battery-energy throughout the picked dataset sample. Hence, it expanded the whole network lifetime. |
| Databáze: | OpenAIRE |
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