Formation Detection with Wireless Sensor Networks

We consider the problem of detecting the formation of a set of wireless sensor nodes based on the pairwise measurements of signal strength corresponding to all transmitter/receiver pairs. We assume that formations take values in a discrete set and develop a composite hypothesis testing approach which uses a Generalized Likelihood Test (GLT) as the decision rule. The GLT distinguishes between a set of probability density function (pdf) families constructed using a custom pdf interpolation technique. The GLT is compared with the simple Likelihood Test (LT). We also adapt one prevalent supervised learning approach, Multiple Support Vector Machines (MSVMs), and compare it with our probabilistic methods. Due to the highly variant measurements from the wireless sensor nodes, and these methods' different adaptability to multiple observations, our analysis and experimental results suggest that GLT is more accurate and suitable for formation detection. The formation detection problem has interesting applications in posture detection with Wireless Body Area Networks (WBANs), which is extremely useful in health monitoring and rehabilitation. Another valuable application we explore concerns autonomous robot systems.