Energy-Saving Algorithms For Wireless Sensor Networks Based On Network Coding And Compressed Sensing

Limited energy of the node becomes a major bottleneck to limit the performance of the network in wireless sensor networks. The research of how to make full use of the limited network resources, reduce the energy consumption of the network node and prolong the network lifetime have become a key part of wireless sensor networks. Network coding can improve the network data delivery ratio, network throughput and energy efficiency by allowing the network node to encode the data before forwarding it. According to compressed sensing theory, the data is projected onto the sparse domain. Based on a portion of the compressed data, the sink node can reconstruct approximation of the original data with high probability, which can reduce the amount of data transmitted over the network and save the network resources. With the goal of reducing the network energy consumption, the work starts with reducing the redundancy of data, then studies deeply the energy-saving methods of sensor nodes with combining the network coding and compressed sensing. The main contents and innovations of this paper are as follows:1. An energy-saving multi-path algorithm based on random network coding in WSN(RNC-ESMP) is proposed. Firstly, the algorithm thinks about the probability of path selection by the residual energy and communication energy consumption of the nodes in each path, and then introduces a conditional of the transfer value rate. Secondly, the algorithm builds a feedback mechanism from the sink node to the source node. Thirdly, A encoding options of the relay node is proposed in the algorithm. The results show that the algorithm can reduce energy consumption, transmission delay and the number of encoding nodes of the network. Comparing with other multi-path and network coding algorithm, RNC-ESMP can reduce the average network energy consumption by about15%-50%and reduce transmission delay by about12%-33%, so as to improve the performance of the network.2. An energy-saving algorithm for wireless sensor networks based on network coding and duty-cycle (NCDES) is proposed. The algorithm depends on the ID information which embedded in the data to determine the working status and avoid receiving a duplicate data. Combining network coding and duty-cycle in wireless sensor networks, it reduces the transmission coding coefficients and retransmissions. The energy efficiency of the network increases as more volume of data will be transmitted to the sink with the same number of transmissions. Though the theoretical calculation, the paper analyzed the maximum of the network energy consumption for NCDES and proved the optimal value of the network energy consumption for the multi-hop network. The simulation results show that NCDES has a great improvement on data delivery ratio and energy efficiency. Comparing with random duty-cycle algorithm based on network coding and the improved adaptive network coding algorithm, NCDES prolongs the network lifetime about4.02%and8.51%, the algorithm also improves the packet delivery rate about14.83%and4.65%.3. An energy-saving algorithm for wireless sensor networks based on network coding and compressed sensing(CS-NCES) is proposed, the algorithm not only makes full use of the data spatial and temporal correlations, but also uses the similarities between the encoding matrix of network coding and the measurement matrix of compressed sensing, the source node encodes the data, and then compresses the data by compressed sensing over finite fields, it’s namely integrating encoding and compression to realize the data Encoding-Compression-Encoding, the relay node only needs to transmit a small amounts of the data and then reduces the energy consumption, the sink improves the data delivery rate by the way of Decoding-Reconstruction-Decoding. At the same time, the algorithm has constructed an improved polynomial deterministic matrix and verified the feasibility of the matrix by studying compressed sensing and network coding on the real and finite fields respectively. The simulation results show that, comparing with network coding, CS-NCES can reduce the network energy consumption by25.3%-34.5%and improve the efficiency of data reconstruction by1.56%-5.98%. The algorithm not only enhances the usability of network coding in WSN,but also improves the performance of the network.