Joint localization and time synchronization in wireless sensor networks with anchor uncertainties

Although localization and synchronization share many aspects in common, they are traditionally treated separately. In this paper, we present a unified framework to jointly solve these two problems at the same time. The joint approach is attractive because it can solve both localization and synchronization using the same set of message exchanges. This is extremely important for energy saving, especially for the energy constrained wireless sensor networks. Furthermore, since the accuracy of localization and synchronization is very sensitive to the accuracy of anchor locations and timings, the joint localization and synchronization problem with inaccurate anchor is considered in this paper. A novel generalized total least squares (GTLS) based method is proposed and the Cramer-Rao lower bound (CRLB) for the joint localization and time synchronization is derived. Simulation results show that the mean square error performances of the proposed estimator can attain the CRLB. © 2009 IEEE.published_or_final_versionThe IEEE Conference on Wireless Communications and Networking (WCNC 2009), Budapest, Hungary, 5-8 April 2009. In Proceedings of IEEE WCNC, 2009, p. 1-

Passive source localization using power spectral analysis and decision fusion in wireless distributed sensor networks

Source localization is a challenging issue for multisensor multitarget detection, tracking and estimation problems in wireless distributed sensor networks. In this paper, a novel source localization method, called passive source localization using power spectral analysis and decision fusion in wireless distributed sensor networks is presented. This includes an energy decay model for acoustic signals. The new method is computationally efficient and requires less bandwidth compared with current methods by making localization decisions at individual nodes and performing decision fusion at the manager node. This eliminates the requirement of sophisticated synchronization. A simulation of the proposed method is performed using different numbers of sources and sensor nodes. Simulation results confirmed the improved performance of this method under ideal and noisy conditions

D-SLATS: Distributed Simultaneous Localization and Time Synchronization

Through the last decade, we have witnessed a surge of Internet of Things (IoT) devices, and with that a greater need to choreograph their actions across both time and space. Although these two problems, namely time synchronization and localization, share many aspects in common, they are traditionally treated separately or combined on centralized approaches that results in an ineffcient use of resources, or in solutions that are not scalable in terms of the number of IoT devices. Therefore, we propose D-SLATS, a framework comprised of three different and independent algorithms to jointly solve time synchronization and localization problems in a distributed fashion. The First two algorithms are based mainly on the distributed Extended Kalman Filter (EKF) whereas the third one uses optimization techniques. No fusion center is required, and the devices only communicate with their neighbors. The proposed methods are evaluated on custom Ultra-Wideband communication Testbed and a quadrotor, representing a network of both static and mobile nodes. Our algorithms achieve up to three microseconds time synchronization accuracy and 30 cm localization error

Cooperative Simultaneous Localization and Synchronization in Mobile Agent Networks

Cooperative localization in agent networks based on interagent time-of-flight measurements is closely related to synchronization. To leverage this relation, we propose a Bayesian factor graph framework for cooperative simultaneous localization and synchronization (CoSLAS). This framework is suited to mobile agents and time-varying local clock parameters. Building on the CoSLAS factor graph, we develop a distributed (decentralized) belief propagation algorithm for CoSLAS in the practically important case of an affine clock model and asymmetric time stamping. Our algorithm allows for real-time operation and is suitable for a time-varying network connectivity. To achieve high accuracy at reduced complexity and communication cost, the algorithm combines particle implementations with parametric message representations and takes advantage of a conditional independence property. Simulation results demonstrate the good performance of the proposed algorithm in a challenging scenario with time-varying network connectivity.Comment: 13 pages, 6 figures, 3 tables; manuscript submitted to IEEE Transaction on Signal Processin