Process Estimation from Randomly Deployed Wireless Sensors with Position Uncertainty

Wireless sensor networks (WSNs) have a large number of applications, number of applications. Among them, the estimation of spatial processes from sparse sensing nodes is important for environmental monitoring. In this work we analyze the process estimation accuracy for a randomly deployed self-organizing WSN taking into account crucial aspects such as random sampling, nodes' connectivity, communications protocols, interpolation techniques, and uncertainty of nodes' positions. These aspects are typically not considered into a common framework. The joint analysis of all these aspects enables a WSN designer to understand the role of interpolation techniques and nodes' position uncertainty on process estimation to reach a given target estimation accuracy.