Distributed DCT based data compression in clustered wireless sensor networks

In this paper, an integration between Discrete Cosine Transform (DCT) matrix and clustering in wireless sensor networks (WSNs) is exploited. Since sensor readings in WSNs are highly correlated and are suitable to be transformed in DCT domain, in each cluster in the network the sensory data is transformed and only a small number of large DCT coefficients are sent from the cluster-head (CH) to the base-station (BS) directly or in multi-hop routing. All data from the network can be recovered based on the transformed large coefficients at the BS. Based on stochastic problems, we analyze and formulate the communication cost as the power consumption for transmitting data in such networks. Some common clustering algorithms are applied and compared to analysis results. Both noise and noiseless environments for this method are considered.