TY - GEN
T1 - Bio-inspired node localization in wireless sensor networks
AU - Kulkarni, Raghavendra V.
AU - Venayagamoorthy, Ganesh K.
AU - Cheng, Maggie X.
PY - 2009
Y1 - 2009
N2 - Many applications of wireless sensor networks (WSNs) require location information of the randomly deployed nodes. A common solution to the localization problem is to deploy a few special beacon nodes having location awareness, which help the ordinary nodes to localize. In this approach, non-beacon nodes estimate their locations using noisy distance measurements from three or more non-collinear beacons they can receive signals from. In this paper, the ranging-based localization task is formulated as a multidimensional optimization problem, and addressed using bio-inspired algorithms, exploiting their quick convergence to quality solutions. An investigation on distributed iterative localization is presented in this paper. Here, the nodes that get localized in an iteration act as references for remaining nodes to localize. The problem has been addressed using particle swarm optimization (PSO) and bacterial foraging algorithm (BFA). A comparison of the performances of PSO and BFA in terms of the number of nodes localized, localization accuracy and computation time is presented.
AB - Many applications of wireless sensor networks (WSNs) require location information of the randomly deployed nodes. A common solution to the localization problem is to deploy a few special beacon nodes having location awareness, which help the ordinary nodes to localize. In this approach, non-beacon nodes estimate their locations using noisy distance measurements from three or more non-collinear beacons they can receive signals from. In this paper, the ranging-based localization task is formulated as a multidimensional optimization problem, and addressed using bio-inspired algorithms, exploiting their quick convergence to quality solutions. An investigation on distributed iterative localization is presented in this paper. Here, the nodes that get localized in an iteration act as references for remaining nodes to localize. The problem has been addressed using particle swarm optimization (PSO) and bacterial foraging algorithm (BFA). A comparison of the performances of PSO and BFA in terms of the number of nodes localized, localization accuracy and computation time is presented.
KW - Bacterial foraging algorithm
KW - Localization
KW - Particle swarm optimization
KW - Wireless sensor networks
UR - http://www.scopus.com/inward/record.url?scp=74849092270&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=74849092270&partnerID=8YFLogxK
U2 - 10.1109/ICSMC.2009.5346107
DO - 10.1109/ICSMC.2009.5346107
M3 - Conference contribution
AN - SCOPUS:74849092270
SN - 9781424427949
T3 - Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
SP - 205
EP - 210
BT - Proceedings 2009 IEEE International Conference on Systems, Man and Cybernetics, SMC 2009
T2 - 2009 IEEE International Conference on Systems, Man and Cybernetics, SMC 2009
Y2 - 11 October 2009 through 14 October 2009
ER -