editorial localization in wireless sensor...

Hindawi Publishing CorporationInternational Journal of Distributed Sensor NetworksVolume 2013, Article ID 457874, 3 pageshttp://dx.doi.org/10.1155/2013/457874

EditorialLocalization in Wireless Sensor Network

Long Cheng,1 Carsten Maple,2 Chengdong Wu,1 and Wei Meng3

1 College of Information Science and Engineering, Northeastern University, Shenyang 110819, China2 Computer Science and Technology Department, University of Bedfordshire, Luton LU1 3JU, UK3 School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798

Correspondence should be addressed to Long Cheng; [email protected]

Received 3 September 2013; Accepted 3 September 2013

Copyright © 2013 Long Cheng et al. This is an open access article distributed under the Creative Commons Attribution License,which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Wireless sensor networks that consist of thousands of low-cost sensor nodes have been used in many promising appli-cations. Localization is one of the most important subjectsbecause the location information is typically useful for cov-erage, deployment, routing, location service, target tracking,and rescue. Hence, location estimation is a significant tech-nical challenge for the researchers. Obviously, there are somechallenges for locating sensor nodes needed to be solved.Thefirst challenge is the energy consumption and localizationaccuracy problem.The second challenge is the NLOS rangingerror problem. The third challenge is localization in lowbeacon density.

Themain objective of this special issue is to explore inno-vative, exciting, and fresh ideas for node location estimationalgorithms and localization systems. Out of 30 submissions,12 exceptional contributions were finally selected after severalrounds of review by the invited reviewers and the guesteditors.

The paper by J.-R. Jiang et al. proposes the AoA localiza-tion with RSSI differences (ALRDs)method to estimate angleof arrival (AoA) by comparing the received signal strengthindicator (RSSI) values of beacon signals received from twoperpendicularly oriented directional antennas installed atthe same place. The experimental results showed that asensor node can estimate its location by using only fourbeacon signals within 0.1 s with an average localization errorof 124 cm. Hence, ALRD conserves the time and energyspent on localization. They further propose two methods,namely, maximum-point minimum-diameter and maximum-point minimum-rectangle, to reduce ALRD localization errorsby gatheringmore beacon signals within 1 s for finding the setof estimated locations of maximum density. Such estimated

locations are then averaged to obtain the final locationestimation. Experimental results obtained demonstrate thatthe two methods can reduce the average localization errorby a factor of about 29% to 89 cm. Hence, ALRD is suitablefor mobile sensing and actuating applications, as it allows asensor node to quickly localize itself with lower localizationerrors.

In the paper “A survey of localization in wireless sen-sor network,” the authors classify the localization methodsinto target/source localization and node-self localization. Intarget localization, they mainly introduce the energy-basedmethod. Then they investigate the node-self localizationmethods. Since the widespread adoption of the wirelesssensor network, the localization methods are different invarious applications. So there are several challenges in somespecial scenarios. They present a comprehensive surveyof these challenges: localization in non-line-of-sight, nodeselection criteria for localization in energy-constrained net-work, scheduling the sensor node to optimize the tradeoffbetween localization performance and energy consumption,cooperative node localization, and localization algorithm inheterogeneous network. Finally, they introduce the evalua-tion criteria for localization in wireless sensor network.

The paper by P. Gao et al. proposes a path-planning, alocation predicting method (PPLP) for indoor mobile targetlocalization. They firstly establish the path-planning modelto constrain the movement trajectory of the mobile targetin indoor environment according to indoor architecturalpattern. Then, they use MLE approach to get one certainlocation result of the target. After that, based on the path-planning model and some previous localization results of thetarget, the best possible position of the target in the next

2 International Journal of Distributed Sensor Networks

time interval can be predicted with the proposed predictingapproach. Finally, the MLE result and prediction result areweighted to obtain the final position.

In the paper “Localization with single stationary anchorfor mobile node in wireless sensor networks,” the authorspropose a localization algorithm named LSARSSI for mobilenode based on received signal strength indicator (RSSI)between locating sensor node with inertia module built-inand the single anchor. In order to avoid errors from directlymapping absolute RSSI values to distances, they obtainthe geometrical relationship of sensors by contrasting themeasured RSSI values. They then design a novel localizationscheme, LSARSSI, which has a better accuracy and lowoverhead. The simulation results show that the proposedschemes perform high accuracy and feasibility, even in large-scale environment.

In the paper “Range-free localization scheme in wirelesssensor networks based on bilateration,” the authors proposea low-cost yet effective localization scheme for wirelesssensor networks (WSNs). The proposed scheme uses onlytwo anchor nodes and uses bilateration to estimate thecoordinates of unknown nodes. In this scheme, two anchornodes are installed at the bottom-left corner (Sink X) and thebottom-right corner (Sink Y) of a square monitored regionof the WSN. Sensors are identified with the same minimumhop counts pair to Sink X and Sink Y to form a zone, andthe estimated location of each unknown sensor is adjustedaccording to its relative position in the zone. Simulationresults show that the proposed scheme outperforms the DV-Hop method in localization accuracy, communication cost,and computational complexity.

The paper by Y. Wang et al. proposes a prior knowledge-based correction strategy (PKCS) to locate the robot. Theyfirstly investigate the RSS-based NLOS identificationmethodusing the recorded measurements. Then the ratio of NLOSpresent in the record of measurements and the expectationof the NLOS errors are used to mitigate the NLOS errors.Kalman filter is employed to improve the estimated range.Finally, they use the residual weighting algorithm to estimatethe location of the robot. Simulation results show that thePKCS has much better performance than those methodswithout the correctionmethod and significantly improves thelocalization accuracy.

The paper by S. Zhang et al. proposes a human motiontracking approach for daily life surveillance in a distributedwireless sensor network using ultrasonic range sensors. Ituses cheap range sensor nodes in wireless sensor networks byjointly selecting the next tasking sensor and determining thesampling interval based on predicted tracking accuracy andtracking cost under the UKF frame. Simulation results showthat the new scheme can achieve significant energy efficiencywithout degrading the tracking accuracy.

In the paper “On the joint time synchronization andsource localization using toa measurements,” the authorsconsider the problem of estimating the clock bias and theposition of an unknown source using time of arrival (TOA)measurements obtained at a sensor array to achieve timesynchronization and source localization. The mean squareerror (MSE) analysis is firstly performed for the case where

the source is localized via TOA positioning when assumingthe source clock bias does not exist, but in fact it is non-zero. Comparing the obtained source localization MSE withthat from joint estimating the source position and clock bias,they derive a condition under which ignoring the sourceclock biasmay provide a smaller localizationMSE. Computersimulations are conducted to corroborate the theoreticaldevelopment and illustrate the good performance of theproposed algorithm.

The paper by D. Arbula and K. Lenac presents Pymote,the library that provides support for simulation and analysisof distributed algorithms built on top of comprehensivePython environment. Pymote is designed to allow rapidinteractive testing of new algorithms, their analysis, andvisualization while minimizing developer’s time. It supportsboth interactive algorithm simulation and automation ofexperiments and provides visualization tools for both. It hasbeen deliberately kept simple, easy to use, and extensible.

The paper by E. Navarro-Alvarez et al. presents a newadaptive method to calculate the path loss exponent (PLE)for microcell outdoor dynamic environments in the 2.4GHzindustrial, scientific, and medical (ISM) frequency band.Themain contribution of this method is the formulation of aparametric mathematical model which improves the PLEaccuracy by using the equivalent isotropic radiated power(EIRP) and effective antenna aperture (EAA) parameterscalculated before obtaining the PLE. A second contributionis the combination of GPS data and RSSI readings in order toidentify the RSSI long term behavior.

In the paper “A novel lightness localization algorithmbased on anchor nodes equilateral triangle layout in WSNs,”the authors present a novel equilateral triangle localizationalgorithm (LETLA) that is a lightweight approximate local-ization algorithm and could provide better precision with lesspower consumption. The LETLA is an approximate localiza-tion based on the concept of substituting the approximatecoordinates for the real coordinates, which could result inless accuracy and save more energy. In order to avoid theranging ambiguities arising from the interference of noise,the LETLA adopts the order of ranging results to representthe location relationship of unknown node and anchors.Simulations show that the LETLA performs better than otherstate-of-the-art approaches in terms of energy consumptionwith the same localization precision.

The paper by X. Qu et al. presents minimax estima-tion fusion method in distributed multisensor systems. Thismethod aims to minimize the worst-case squared estimationerror when the cross-covariances between local sensors areunknown and the normalized estimation errors of local sen-sors are norm bounded. The simulation results illustrate thatthe proposed fusion method is a robust fusion in localizationand tracking andmore accurate than the previous covarianceintersection method.

Acknowledgment

The guest editors would like to acknowledge the contri-butions of the many experts who submitted their works.

International Journal of Distributed Sensor Networks 3

We thank all the reviewers for their helpful feedback andsuggestions to improve the quality of each paper.

Long ChengCarsten MapleChengdong Wu

Wei Meng

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