Localization refinement for wireless sensor networks

International Journal of Distributed Sensor Networks

Zhong

Huang

et al. 2014

Self Cite

For large-scale underwater wireless sensor networks (UWSNs) with a minority of anchor nodes, multihop localization is a popular scheme for determining the geographical positions of normal nodes. However, existing multihop localization studies have considered the anchor positions to be free of errors, which is not a valid assumption in practice. In this paper, the problems existing in nonlinear least square-based node self-localization schemes are analyzed, and the biased distribution characteristic of multihop distance estimation errors is pointed out. Then, the orthogonal regression method is employed for the localization of normal nodes in the presence of anchor position errors. In particular, the influences of errors in independent variables and biases in dependent variables on node coordinate estimation are taken into account simultaneously. Extensive simulation results illustrate the robustness and effectiveness of our method.

Section: Impact Of Network Connectivitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Orthogonal Regression Based Multihop Localization Algorithm for Large-Scale Underwater Wireless Sensor Networks

International Journal of Distributed Sensor Networks

Zhong

Huang

et al. 2014

Self Cite

“…And then, the distance fitting is used to fit correct distance measurements, which makes the final estimated position out of the effect by the layout of anchor nodes. The authors in [5] proposed three multihop localization schemes based on least squares and multilateration, namely, Taylor-LS, weighted Taylor based least squares and constrained total least squares, respectively. Additionally, a generalized Cramér-Rao lower bound is developed to analyze the performance of multihop localization approaches.…”

Section: Related Workmentioning

confidence: 99%

“…For the large-scale UWSNs, this situation will no doubt result in a greater extra cost. Fortunately, multihop localization methods can elegantly handle this situation without any additional requirements [3][4][5][6][7]. With the multihop approach, the ordinary nodes not only can directly measure the distance by short-range communications, but canalso infer the distances to nonneighboring anchors through 2 International Journal of Distributed Sensor Networks approximating the length of the shortest path to the Euclidean distance.…”

Section: Introductionmentioning

confidence: 99%

Set-Membership Adaptive Localization Algorithm with Time-Varying Error Bounds for Underwater Wireless Sensor Networks

International Journal of Distributed Sensor Networks

Wang

et al. 2013

Self Cite

This paper presents a set-membership adaptive localization algorithm with time-varying error bounds for underwater wireless sensor networks (UWSNs). In large-scale UWSNs, the nonstationary underwater environments, the insufficient prior information of hybrid noise, the small sample size of available distance measurements, and the node mobility all pose severe challenges for localization, and most current schemes are not applicable. Unlike most of the existing approaches, we tackle the multihop localization problem in a set-membership framework based on the consideration that the distance measurement uncertainty can be cast into an unknown but bounded (UBB) context. The principle of our scheme is firstly to use the bootstrap method to build confidence intervals and error bounds from a small sample set of distance measurements and then to determine the positions by a low-complexity interval analysis method as well as an adaptive localization update specification with time-varying error bounds. Simulation results show that our proposed scheme is an effective and efficient localization approach in large-scale UWSNs.

“…To solve this problem, three types of localization schemes are proposed for large-scale localization, namely, mobile anchor assisted localization algorithms [4][5][6], recursive algorithms [7][8][9], and multihop algorithms [10][11][12], respectively. Hereinto, mobile anchor assisted localization methods commonly employ some expensive mobile equipment such as Autonomous Underwater Vehicle (AUV) which can roam across the monitoring region to assist localization.…”

Section: Introductionmentioning

confidence: 99%

Nonparametric Bootstrap-Based Multihop Localization Algorithm for Large-Scale Wireless Sensor Networks in Complex Environments

International Journal of Distributed Sensor Networks

Xiao

et al. 2013

Self Cite

This paper presents a nonparametric bootstrap multihop localization algorithm for large-scale wireless sensor networks (WSNs) in complex environments. Unlike most of the existing schemes, this work is based on the consideration that it is not feasible to obtain a lot of available distance measurements sample for estimation and to get exact noise distributions or enough prior information for conventional statistical methods, which is a situation commonly encountered in complex environments practically. For the first time, we introduce a nonparametric bootstrap method into multihop localization to build confidence intervals for multihop distance estimation, which can eliminate the risk of small sample size and unknown distribution. On this basis, we integrate the interval analysis method with bootstrap approach for ordinary nodes localization. To reduce the computational complexity, boxes approach is utilized to approximate the irregular intersections. Simulation results show that our proposed scheme is less affected by the variation of unknown distributions and indicate that our method can achieve high localization coverage with relatively small average localization error in large-scale WSNs, especially in sparse and complex network with smaller connectivity and anchor percentage.