Incorporating Data from Multiple Sensors for Localizing Nodes in Mobile Ad Hoc Networks

The ad hoc network localization problem deals with estimating the geographical location of all nodes in an ad hoc network, focusing on those nodes that do not have a direct way (for example, GPS) to determine their own location. Proposed solutions to the ad hoc localization problem (AHLP) assume that nodes are capable of measuring received signal strength indication (RSSI) and/or are able to do coarse (sectoring) or fine signal angle-of-arrival (AoA) measurements. Existing algorithms exploit different aspects of such sensory data to provide either better localization accuracy or higher localization coverage. However, there is a need for a framework that could benefit from the interactions of nodes with mixed types of sensors. In this paper, we study the behavior of RSSI and AoA sensory data in the context of AHLP by using both geometric analysis and computer simulations. We show which type of sensor is better suited for which type of network scenario. We study how nodes using either, both, or none of these sensors could coexist in the same localization framework. We then provide a general particle-filtering framework, the first of its kind, that allows heterogeneity in the types of sensory data to solve the localization problem. We show that, when compared to localization scenarios where only one type of sensor is used, our framework provides significantly better localization results. Furthermore, our framework provides not only a location estimate for each nonanchor, but also an implicit confidence measure as to how accurate this estimate is. This confidence measure enables nodes to further improve on their location estimates using a local, iterative one-hop simple message exchange without having to rely on synchronized multiphase operations like in traditional multilateration methods.