On semantic clustering and adaptive robust regression based energy-aware communication with true outliers detection in WSN

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To conserve energy and enhance the lifetime of the wireless sensor network (WSN), reducing the amount of data communication by exploiting temporal and spatial correlation of sensed data is well suitable technique. So, instead of sending every data to the destination, it can be worthy of introducing a prediction method to reduce redundant data transmission by exploiting the temporal correlation of sensed data. We show that the prediction accuracy of source data depends not only on the method applied but also on the correctness of the sample data provided by the source nodes. Erroneous sample data (outliers) leads to the wrong prediction. In this paper, we propose an energy efficient SEMantic CLustering (SEMCL) model to mitigate high energy consumption problem in a clustered WSN. Our model produces energy efficient clusters by strong intra-cluster data similarity to exploit spatial correlation of data. We adopt the Robust and Efficient Weighted Least Square method (REWLS) to provide accurate data prediction with negligible errors. Because REWLS method lacks to differentiate true and false outliers and thus to improve further the Quality of Service (QoS) on data accuracy, we propose a separate algorithm, named, True Outlier Detection (TOD). Moreover, to improve the QoS in communications, a reliable backbone network based on the link quality of the data forwarding path has been implemented. Our proposed model has been simulated using real data and compared with the existing techniques to show its efficacy and superiority in terms of QoS on data accuracy, energy consumption, and network lifetime.