Research And Implementation Of Data Compression And Clustering Algorithm In Wireless Sensor Networks

Wireless sensor networks have been the active area of research in the recent due to its broad application prospects in military and civil field. Differentiating from traditional networks, WSN are very resource constrained, so its primary design goal is efficient use of energy. It is a hot issue to save communication energy meanwhile ensures the sampled data reliable. Data compression algorithm removes redundant data using the data correlation, and is dramatically significant for reducing energy consumption. Meanwhile, network topology control has a significant impact on the data compression algorithm in the wireless sensor network, which is large-scale, distributed and self-organizing. This paper mainly opens out the research based on the data compression algorithm and the clustering algorithm, with the follow production:Firstly, this paper designs and implements a robust wavelet-based compression algorithm, based on the ordinary wavelet compression algorithm. In allusion to packet loss sensitive issues from the ordinary wavelet compression algorithm, we made a three-point improvement, including packet scheduling, binary encoding of the wavelet coefficients and logic inference mechanism of data restoration. Benefited from the above three points, the data compression efficiency and the robustness have been improved by fusing the address and the serial number of the packet and restoring some lost data. Experimental results show that, the robust wavelet-based compression algorithm obtains better performance in compression ratio, reduction rate and mean square error.Secondly, this paper analyzes the effects on data compression from the clustering algorithm in wireless sensor network. We present a non-linear integer programming model for the overall energy consumption of the network optimization problem, and then propose a low complex and near optimal heuristic cluster head election algorithm. A dynamic clustering algorithm is proposed based on aggregation gains, which can elect the cluster head in a distributed way. Theoretical analysis and experimental results show that the proposed dynamic clustering algorithm can resolve the load balance problem, improve the network energy efficiency, and prolong the network lifetime.Thirdly, a simulation system and a performance statistics subsystem are designed and developed based on open source OMNeT++ platform and Mobility Framework, to assessment the performance of the data compression algorithm and the clustering algorithm. The simulation system builds three-tier structure, consisted of application layer, network layer and mac layer, and six performance statistics models, including energy model, delay model, etc. The performance statistics subsystem can monitor the real-time experimental data. The simulation system can meet the application requirements of-wireless sensor networks.