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In this paper, we study two problems of providing reliable data transmission and developing aggregation techniques for correlated data in wireless sensor networks. A system with forward error correction (FEC) can provide an objective reliability while using less transmission power than a system without FEC. Because of the additional parity bits and encoding/decoding energy consumptions, we study the effect of FEC on energy efficiency. We propose to use LDPC codes for FEC. We show that wireless sensor networks using LDPC codes are almost 45% more energy efficient than those that use BCH codes, which were shown to be 15% more energy efficient than the best performing convolutional codes. Then we study the problem of providing aggregation for two and three correlated nodes in wireless sensor networks. We propose to use LDPC codes in wireless sensor networks for source and channel coding to obtain a two-fold energy savings. For two correlated nodes, we study both distributed source coding and joint source-channel coding. While distributed source coding using LDPC codes was studied before, joint source-channel coding using LDPC codes is introduced for the first time. The difference between our work in distributed source coding using LDPC codes and the previous work lies in the LDPC code design procedure. The simulation results show that our proposed design criteria improves the performance of the source coding. The convergence of the non-uniform LDPC code of our design technique is almost 60% closer to the Slepian-Wolf limit. For three correlated nodes, we study distributed source coding using LDPC codes. We simplified the problem of design procedure to randomly punctured LDPC codes. This is a new approach for designing LDPC codes and the simulation results for code of length 1000 shows that the convergence of the LDPC code is achieved at compression rate 0.3174 which is only 0.08 away from the Slepian-Wolf limit.