Geo-Spatial Location Estimation for Internet of Things (IoT) Networks With One-Way Time-of-Arrival via Stochastic Censoring

We develop new algorithms for geo-spatial location estimation for Internet of Things (IoT) networks by utilizing a one way time of arrival (OW-TOA) approach. We first demonstrate the limitations of current OW-TOA location estimation algorithms for IoT networks when some of the anchor nodes (ANs) are outside the communication range with the source, which results in ambiguous level-sets of the likelihood function, causing inaccurate source localization. We then show that in IoT networks, in addition to using the conventional noisy ranging measurements from nodes which are in the communication range, we can make use of the audibility information (which indicates whether a node is able/unable to communicate with the target) that each node has. By leveraging on this available information, we considerably improve the localization performance, by mitigating the well known ambiguity problem which arises when a few AN are inaudible. The first algorithm we develop is the joint source location and time offset maximum likelihood estimation (MLE) and the second is the differential source location MLE. Our algorithms require no additional hardware and the novelty lies in using the available audibility information to resolve the likelihood ambiguity. Finally, we develop the Cramér-Rao bounds of the source location estimate for both algorithms. Extensive simulations show the significant benefits of our algorithms compared to the conventional MLE algorithms.