TY - JOUR
T1 - RSS-based localization in wireless sensor networks using convex relaxation
T2 - Noncooperative and cooperative schemes
AU - Tomic, Slavisa
AU - Beko, Marko
AU - Dinis, Rui
N1 - Sem PDF conforme despacho.
Fundacao para a Ciencia e a Tecnologia under Projects PEst-OE/EEI/UI0066/2014, EXPL/EEI-TEL/0969/2013-MANY2COMWIN and EXPL/EEI-TEL/1582/2013-GLANC, PEst-OE/EEI/LA0008/2013 (IT pluriannual founding and HETNET), PEst-OE/EEI/UI0066/2011 (UNINOVA pluriannual founding), EnAcoMIMOCo EXPL/EEI-TEL/2408/2013, and ADIN PTDC/EEI-TEL/2990/2012, and Grant SFRH/BD/91126/2012 and the Ciencia 2008 Post-Doctoral Research grant. This paper was presented in part at the IEEE Workshop on Signal Processing Advances in Wireless Communications (SPAWC 2013) Darmstadt, Germany, June 16-19, 2013.
PY - 2015/5/1
Y1 - 2015/5/1
N2 - In this paper, we propose new approaches based on convex optimization to address the received signal strength (RSS)-based noncooperative and cooperative localization problems in wireless sensor networks (WSNs). By using an array of passive anchor nodes, we collect the noisy RSS measurements from radiating source nodes in WSNs, which we use to estimate the source positions. We derive the maximum likelihood (ML) estimator, since the ML-based solutions have particular importance due to their asymptotically optimal performance. However, the ML estimator requires the minimization of a nonconvex objective function that may have multiple local optima, thus making the search for the globally optimal solution hard. To overcome this difficulty, we derive a new nonconvex estimator, which tightly approximates the ML estimator for small noise. Then, the new estimator is relaxed by applying efficient convex relaxations that are based on second-order cone programming and semidefinite programming in the case of noncooperative and cooperative localization, respectively, for both cases of known and unknown source transmit power. We also show that our approaches work well in the case when the source transmit power and the path loss exponent are simultaneously unknown at the anchor nodes. Moreover, we show that the generalization of the new approaches for the localization problem in indoor environments is straightforward. Simulation results show that the proposed approaches significantly improve the localization accuracy, reducing the estimation error between 15% and 20% on average, compared with the existing approaches.
AB - In this paper, we propose new approaches based on convex optimization to address the received signal strength (RSS)-based noncooperative and cooperative localization problems in wireless sensor networks (WSNs). By using an array of passive anchor nodes, we collect the noisy RSS measurements from radiating source nodes in WSNs, which we use to estimate the source positions. We derive the maximum likelihood (ML) estimator, since the ML-based solutions have particular importance due to their asymptotically optimal performance. However, the ML estimator requires the minimization of a nonconvex objective function that may have multiple local optima, thus making the search for the globally optimal solution hard. To overcome this difficulty, we derive a new nonconvex estimator, which tightly approximates the ML estimator for small noise. Then, the new estimator is relaxed by applying efficient convex relaxations that are based on second-order cone programming and semidefinite programming in the case of noncooperative and cooperative localization, respectively, for both cases of known and unknown source transmit power. We also show that our approaches work well in the case when the source transmit power and the path loss exponent are simultaneously unknown at the anchor nodes. Moreover, we show that the generalization of the new approaches for the localization problem in indoor environments is straightforward. Simulation results show that the proposed approaches significantly improve the localization accuracy, reducing the estimation error between 15% and 20% on average, compared with the existing approaches.
KW - Centralized localization
KW - cooperative localization
KW - noncooperative localization
KW - received signal strength (RSS)
KW - second-order cone programming (SOCP) problem
KW - semidefinite programming (SDP) problem
KW - wireless localization
KW - wireless sensor network (WSN)
UR - http://www.scopus.com/inward/record.url?scp=84929376596&partnerID=8YFLogxK
U2 - 10.1109/TVT.2014.2334397
DO - 10.1109/TVT.2014.2334397
M3 - Article
AN - SCOPUS:84929376596
VL - 64
SP - 2037
EP - 2050
JO - IEEE Transactions on Vehicular Technology
JF - IEEE Transactions on Vehicular Technology
SN - 0018-9545
IS - 5
M1 - 6847233
ER -