In heterogeneous wireless sensor networks, the data collection method based on compressed sensing technology is susceptible to packet loss and noise, which leads to a decrease in data reconstruction accuracy in unreliable links. Combining compressed sensing and matrix completion, we propose a clustering optimization algorithm based on structured noise matrix completion, in which the cluster head transmits the compressed sampling data and compression strategy to the base station. The algorithm we proposed can reduce the energy consumption of the node in the process of data collection, redundant data and transmission delay. The rank-1 matrix completion algorithm constructs an extremely sparse observation matrix, which is adopted by the sink node to complete the reconstruction of the whole network data. Simulation experiments show that the proposed algorithm reduces network transmission data, balances node energy consumption, improves data transmission efficiency and reconstruction accuracy, and extends the network life cycle.
A unified framework for k-coverage and data collection in heterogeneous wireless sensor networks