Position and Movement Detection of Wireless Sensor Network Devices Relative to a Landmark Graph

We present a novel probabilistic framework for reliable indoor positioning of mobile sensor network devices. Compared to existing approaches, ours adopts complex computations in exchange for high localization accuracy while needing low hardware investment and moderate set-up cost. To that end, we use full distributional information on signal measurements at a set of discrete locations, termed landmarks. Positioning of a mobile device is done relative to the resulting landmark graph and the device can be found near a landmark or in the area between two landmarks. Key elements of our approach include profiling the signal measurement distributions over the coverage area using a special interpolation technique; a two-tier statistical positioning scheme that improves efficiency by adding movement detection; and joint clusterhead placement optimization for both localization and movement detection. The proposed system is practical and has been implemented using standard wireless sensor network hardware. Experimentally, our system achieved an accuracy equivalent to less than 5 meters with 95 percent success probability and less than 3 meters with an 87 percent success probability. This performance is superior to well-known contemporary systems that use similar low-cost hardware.