TY - GEN

T1 - Worst-case performance of a mobile sensor network under individual sensor failure

AU - Park, Hyongju

AU - Hutchinson, Seth

PY - 2013/11/14

Y1 - 2013/11/14

N2 - In this paper, we consider the problem of worst-case performance by a mobile sensor network (MSN) when some of the nodes in the network fail. We formulate the problem as a game in which some subset of the nodes act in an adversarial manner, choosing their motion strategies to maximally degrade overall performance of the network as a whole. We restrict our attention in the present paper to a target detection problem in which the goal is to minimize the probability of missed detection. We use a partitioned cost function that is minimized when each sensor executes a motion strategy given by Lloyd's algorithm (i.e., each agent moves toward the centroid of its Voronoi partition at each time instant), and when the probability of missed detection for each functioning sensor increases with the distance between sensor and target for correctly functioning sensors; adversarial nodes in the network are unable to detect the target, and move to maximally increase the probability of missed detection by the properly functioning sensors. We pose the problem as a multi-stage decision process, and use forward dynamic programming over a finite horizon to numerically compute optimal strategies for the adversaries. We compare the resulting strategies to a greedy algorithm, providing both system trajectories and evolution of the probability of missed detection during execution.

AB - In this paper, we consider the problem of worst-case performance by a mobile sensor network (MSN) when some of the nodes in the network fail. We formulate the problem as a game in which some subset of the nodes act in an adversarial manner, choosing their motion strategies to maximally degrade overall performance of the network as a whole. We restrict our attention in the present paper to a target detection problem in which the goal is to minimize the probability of missed detection. We use a partitioned cost function that is minimized when each sensor executes a motion strategy given by Lloyd's algorithm (i.e., each agent moves toward the centroid of its Voronoi partition at each time instant), and when the probability of missed detection for each functioning sensor increases with the distance between sensor and target for correctly functioning sensors; adversarial nodes in the network are unable to detect the target, and move to maximally increase the probability of missed detection by the properly functioning sensors. We pose the problem as a multi-stage decision process, and use forward dynamic programming over a finite horizon to numerically compute optimal strategies for the adversaries. We compare the resulting strategies to a greedy algorithm, providing both system trajectories and evolution of the probability of missed detection during execution.

UR - http://www.scopus.com/inward/record.url?scp=84887303079&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84887303079&partnerID=8YFLogxK

U2 - 10.1109/ICRA.2013.6630679

DO - 10.1109/ICRA.2013.6630679

M3 - Conference contribution

AN - SCOPUS:84887303079

SN - 9781467356411

T3 - Proceedings - IEEE International Conference on Robotics and Automation

SP - 895

EP - 900

BT - 2013 IEEE International Conference on Robotics and Automation, ICRA 2013

T2 - 2013 IEEE International Conference on Robotics and Automation, ICRA 2013

Y2 - 6 May 2013 through 10 May 2013

ER -