ADiDA: adaptive differential data aggregation for cluster based wireless sensor networks

It has been observed in large scale dynamic cluster based wireless sensor networks that the size of clusters vary significantly in terms of number of nodes. In these networks, data aggregation at cluster heads do not adapt adequately to such variances in cluster sizes. In this paper, we propose a novel and an adaptive differential data aggregation (ADiDA) method that can minimise the complexity of aggregating large amount of data into small sized data packets. ADiDA: in addition to reducing the cost of redundant data transfer in the network, also optimally utilises the available space in data packets at each cluster head. We have analysed ADiDA on different types of sensing environments. The results have shown that ADiDA can reduce the payload size requirement to almost one-fourth of the non-compressed payload and the distortion percentage in aggregated data decreases by 16-41%, compared to the summary-based aggregated data.