Open AccessFault autonomous model handling through integrated adaptive‐filters for eliminating deployment faults in wireless sensor networks
Author(s) -
Elsayed Walaa M.,
ElBakry Hazem M.,
ElSayed Salah M.
Publication year - 2020
Publication title -
iet wireless sensor systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.433
H-Index - 27
ISSN - 2043-6394
DOI - 10.1049/iet-wss.2020.0023
Subject(s) - computer science , software deployment , wireless sensor network , real time computing , fault (geology) , process (computing) , filter (signal processing) , fault detection and isolation , signal (programming language) , wireless , interference (communication) , distributed computing , computer network , actuator , artificial intelligence , telecommunications , channel (broadcasting) , seismology , computer vision , programming language , geology , operating system
Wireless Sensor Networks (WSNs) are exposed to various data‐deployment faults during the communication action. These faults may impact the behaviour of the sensors that degrade its performance and cuts its life. Therefore, we tend to implement the integration of two independent trends are self‐awareness and self‐adaptation capabilities along with two integrated adaptive filters, FIR and RLS. The proposed Autonomous Fault‐Awareness and Adaptive (AFAA) model composed of three adaptive two‐stage executed self‐awareness approach to limit the impact of such faults during the propagation process. In this paper, we introduce the operational mechanism of AFAA that manages to identify the failure and aware of the lost signal values autonomously, then filter the perceptive‐signals for eliminating the accompanied interference and gaining convergent values. It executed the incorporated autonomous model at the level of Cluster Head (CH) for independent fault‐correction using an adaptive feedback model. Compared to the state‐of‐the‐art methods, the proposed model achieved speed in fault diagnosis; also high‐accuracy rate in the prediction of the lost signal values as much as 98.63%, thus improving the percentage of performance efficiency to 3:1 times along of duty cycle. Hence, it enhanced the overall network lifetime.