A data fusion based data aggregation and sensing technique for fault detection in wireless sensor networks

Wireless Sensor Networks (WSNs) are networks formed using a large number of low-cost sensor nodes that have limited energy sources, limited processing capability, low storage capacity, and generate a large amount of sensed data with high temporal coherency. Due to high node density in sensor networks, the same data is sensed by many nodes, which results in data redundancy. The problem becomes worse if the redundant transmission contains both normal and faulty data. This creates the issue of differentiating between normal and faulty behavior. This redundancy can be eliminated by using data fusion based techniques. Data aggregation based data fusion is considered an important technique that can reduce the repetitive transmission of the sensed data and can improve the network lifetime. Hence for maintaining the reliability and longevity of the sensor network, in this article, we propose a novel combination of data aggregation based data fusion with effective fault detection by utilizing the properties of Grey Model (GM) and Kernel-based Extreme Learning Machine (KELM). Here, GM is utilized as a data fusion scheme that records the single datum pattern by rejecting the repetitive data received from the different sensor nodes. Trained KELM is utilized for effective detection of fault thus maintaining high confidentiality of the network. The proposed technique is trained and tested using the standard WSN datasets recorded from different laboratories. The simulation results show that the proposed technique can effectively reduce the repetitive transmission and can efficiently detect the fault in the network. The solved problems result in extending the lifetime of the network by taking the low computational time and fast speed.