Efficient Data Gathering in Wireless Sensor Networks Based on Low Rank Approximation

As the energy of nodes in Wireless Sensor Networks (WSNs) is generally constrained, it is urgent to develop an efficient data gathering algorithm. Recently, Low Rank approximation is deeply studied and successfully applied in WSNs data recovery, in which a subset of nodes is randomly selected for sensing the environmental data, such as temperature and humidity. But this random sampling solution generally results in uneven energy consumption of nodes and shortens the life time of networks. In this paper, we propose an efficient data gathering method based on the low rank approximation, in which the spatial distribution of the selected nodes and the networks energy consumption are considered simultaneously. The sensing nodes selection is modeled as a multi-objective 0-1 programming problem which is resolved by the Particle Swarm Optimization (PSO) with high efficiency. In the optimization, the Gini index is adopted to measure the spatial distribution of the selected nodes and energy consumption of the networks. By the presented temporal constraint low rank approximation, the complete sensing data is recovered from the sensing data of the selected node at the sink node. We perform our method both on the real and simulated wireless networks. The experimental results demonstrate our model is practical and efficient.