Abstract
Localization is an essential service in most of the wireless sensor network (WSN) applications. This paper focuses on utilizing the moving passive event to achieve the network localizability in an initially nonlocalizable network. The hybrid TDOA and distance measurement is firstly used in the network localizability. We propose the sufficient conditions in terms of node localizability and network localizability, respectively. Besides, a more promising TDOA estimation approach, which is approximately synchronization-free, is proposed to well support our localization approach. We design a corresponding algorithm in distributed manner for the implementation of our approach. Compared with the current works, our approach requires no extra hardware cost and any adjustment of the current topology of the network and hence is more feasible in practice. We evaluate the performance of our approach via extensive simulations on a one-thousand-node network. Simulation results show that our TDOA estimation approach achieves the accuracy of 0.1 milliseconds. More than 90% nodes can be localized within 0.04 r, where r denotes the ranging radius of a sensor.
Highlights
Localization [1] is one of the most significant research issues in the low-power wireless sensor networks (WSN)
We propose the sufficient condition in terms of node localizability and network localizability when a passive event is passing through the sensor field
As shown in the simulation results, our time difference of arrival (TDOA) estimation approach achieves the accuracy of 0.1 milliseconds or better, irrespective of the distance or signal to noise ratios (SNR)
Summary
Localization [1] is one of the most significant research issues in the low-power wireless sensor networks (WSN). The current efforts to localize the nonlocalizable part of the WSN fall into one of the three categories: (1) the probabilistic based approaches [4, 9, 10] use different probabilistic models to infer the location of each node via extensive observations, but lack of accuracy and incur large computational overhead; (2) mobile assisted approaches [11,12,13] use some controllable mobile nodes to provide thorough information for localization, but require extra expensive hardware (e.g., a mobile robot) and incur much more adjustment delay and controlling overheads; (3) network adjustment approaches [14,15,16] increase the ranging capability of some sensor nodes to provide additional distance constraints and achieve the network localizability, but require to adequately augment numerous sensors adaptively and incurs more energy consumption.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
