Random access sensor networks: Field reconstruction from incomplete data

We address efficient data gathering from a network of distributed sensors deployed in a challenging field environment with limited power and bandwidth. Utilizing the low-rank property of the sensing field, we leverage results from the matrix completion theory to integrate the sensing procedure with a simple and robust communication scheme based on random channel access. Results show that the space-time map of the sensing field can be recovered efficiently, using only a small subset of sensor measurements, collected over a fading random access channel.