A Compensated Multi-Anchors TOF-Based Localization Algorithm for Asynchronous Wireless Sensor Networks

Abstract

Tag localization for asynchronous wireless sensor networks requires the development of a scheme for clock synchronization. This remains a difficult and open problem since the performance of tag localization can be adversely affected by complications such as reply time and relative clock skew. Joint clock synchronization and a tag localization algorithm that implements a multi-anchor compensated timeof-flight (TOF) to the asynchronous wireless sensor network is a possible and viable solution. Although previous methods that leverage TOF measurements are effective and easily conducted, their performance is not always superior due to the relative clock skew. In this paper, we propose to extend the joint clock/tag synchronization/localization algorithm by introducing a compensation factor that can cancel relative clock skews from multi-tag anchor pairs. We apply a least squares estimation (LSE) algorithm to both the time of emission (TOE) and time of arrival (TOA) for the clock synchronization step. Under the assumption of a Gaussian measurement noise model, the tag localization problem is approximately solved by maximum likelihood estimation (MLE). To assess the performance of our algorithm, we derive the mean square error (MSE) of both relative clock skew and tag location and numerically evaluate the Cramér-Rao lower bound (CRLB) as a benchmark. The simulation results show that the accuracy of the relative clock skew-based estimation and tag localization are significantly improved over traditional algorithms when the appropriate reply time is selected. This is what our proposed algorithm focuses on: it is robust to tag mobility to some extent. We test the performance of proposed algorithm using a well-designed experiment. Based on the experiment results, the localization algorithm can achieve high accuracy without an additional restriction on the reply time and the clock skew.INDEX TERMS Source localization, wireless sensor network (WSN), time-of-flight (TOF), symmetric double-sided two-way ranging (SDS-TWR), relative clock skew.

A Hybrid Localization Algorithm Based on TOF and TDOA for Asynchronous Wireless Sensor Networks

“…We consider four anchors in the 2D scene of a 100 m×100 m area. The coordinates of the anchors are (0,0) m, (0,100) m, (100,0) m, and (100,100) m, and the tag is placed at (70,40) m. The deployment of the localization system in this paper is similar to that in [27]. First, a simulation is conducted to test the impact of measurement noise.…”

“…where α T ,A = α T − α A is the relative clock skew between the tag and anchor. To obtain a more accurate ranging result, we need to estimate the relative clock skews [27]. Subtract (2) from (3), we have t roundT − t roundA = t replyA − t replyT (10) Considering the impact of the clock skew, (10) is reshaped based on the clock model and Fig.…”

“…This paper pays attention to the range-based methods because they tend to provide better performances. Four metrics can be used to implement range-based localization algorithms: angle-of-arrival (AOA) [19]- [21], received-signal-strength (RSS) [22]- [24], time-of-arrival (TOA) [25]- [27], and timedifference-of-arrival (TDOA) [28]- [30]. AOA requires antenna arrays equipped with sensor nodes, which limits the development of the scalability of wireless sensor networks.…”

“…The authors of [35] fuse signal strength and angle of arrival measurements and introduce a novel non-convex estimator by the least-squares (LS) method. The authors in [27] propose an accurate TOF-based localization algorithm, but there are many messages transmitted between tag-anchor pairs. Power efficiency is an important criterion for evaluating the performance of localization algorithms.…”

Finding Maximal Localizable Region in Wireless Sensor Networks by Merging Rigid Clusters

“…Wireless sensor networks are a collection of sensor nodes deployed in various applications including environmental monitoring, search and rescue missions, autonomous driving, target tracking, healthcare monitoring, forest fire detection etc. [13][7] [16] [17]. Awareness of the exact location of the sensors is cruicial to the success of these applications.…”