Routing and data compression in sensor networks: stochastic models for sensor data that guarantee scalability

This paper discusses the compression of correlated data samples in multihop sensor network by means of distributed source coding techniques under plausible stochastic models. This paper shows that there are cases where high density sensor networks are not only possible but increased density can potentially be used to increase the precision of the measurements or decrease the transmission error. The routing algorithms and reencoding of the sensor data is proposed in this paper to provide a perfect feasible sensor networks.