An energy fault and consumption optimization strategy in wireless sensor networks with edge computing

Li, Guozhi; Yan, Tao; Zhang, Ge; Zeng, Yue

doi:10.1016/j.jksuci.2022.12.005

Cited by 3 publications

(1 citation statement)

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“…In Reference 25, an Edge Computing (EC) model was presented to investigate the energy fault analysis and assessment in IWSNs. The major focus of the anticipated model is to mitigate the energy hole problem by detecting the energy fault in the network.…”

Section: Related Workmentioning

confidence: 99%

An energy‐aware software fault detection system based on hierarchical rule approach for enhancing quality of service in internet of things‐enabled wireless sensor network

Balraj,

Prasanth

2024

Trans Emerging Tel Tech

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Of late, the Internet of Things (IoT) has progressed in its pervasiveness across the globe for diverse applications. Wireless Sensor Network (WSN) is one of the prominent technologies employed in IoT environments where multiple tiny sensor nodes are distributed to sense real‐time observations about unforeseeable areas for control and managerial purposes. Owing to the presence of sensors in inaccessible regions and their battery restrictions, different types of software faults occur in IoT‐enabled WSNs (IWSNs). These faults create uncertainty in data reading which causes serious damage to the sensor network. Hence, the IWSN necessitates an effective fault‐detection methodology to continue optimal activity despite the existence of software faults. This work proposes a novel Energy‐Aware Hierarchical Rule‐based Software Fault Detection (HRSFD) model to identify various software faults with minimum energy depletion in the IWSN environment. Primarily, the proposed model extracts antecedent attributes from the characteristics of the sensed data. Its abnormal values can be identified based on the obtained antecedent attributes. Subsequently, the category of the software fault is determined by applying a hierarchical rule strategy. Finally, from the simulation results, it is apparent that the fault detection accuracy rate of the proposed HRSFD model attains 99.12% for dense networks. The lifetime of the network is also prolonged by 18% as compared to the existing state‐of‐the‐art models.

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Section: Related Workmentioning

confidence: 99%