Performance evaluation of real-time multivariate data reduction models for adaptive-threshold in wireless sensor networks

This paper presents a new metric to assess the performance of different multivariate data reduction models in wireless sensor networks (WSNs). The proposed metric is called Updating Frequency Metric (UFM) which is defined as the frequency of updating the model reference parameters during data collection. A method for estimating the error threshold value during the training phase is also suggested. The proposed threshold of error is used to update the model reference parameters when it is necessary. Numerical analysis and simulation results show that the proposed metric validates its effectiveness in the performance of multivariate data reduction models in terms of the sensor node energy consumption. Furthermore, the proposed adaptive threshold enhances the model's performance more than the non-adaptive threshold in decreasing the frequency of updating the model reference parameters which positively prolongs the lifetime of the node. The adaptive threshold improves the frequency of updating the parameters by 80% and 52% in comparison to the non-adaptive threshold for multivariate data reduction models of MLR-B and PCA-B respectively

Enhanced Payload Data Reduction Approach for Cluster Head (CH) Nodes

In this paper, we suggested two approaches to minimizing the CH packet size by considering the accuracy of prediction of sensed data at the base station. The proposed coding schemes based relative difference (CS-RD) and based the factor of precision (CS-FP) instead of the absolute change method that has been used in recent work. The aim is to enhance the accuracy of prediction data at the base station. Therefore, the performance metric was evaluated in term of the accuracy of prediction data at the base station. Simulation results showed that the proposed approaches performed better in term of the accuracy of prediction data at the base station. Specifically, the distortion percentage and average Absolut error in the CS-RD and CS-FP method decreased by 50% and 88% better than the current new aggregation method (ADATDC). However, our proposed CS-FP showed a low reduction ratio for some states