TY - GEN
T1 - Game theoretic cooperative control of PTZ visual sensor networks for environmental change monitoring
AU - Hatanaka, Takeshi
AU - Wasa, Yasuaki
AU - Fujita, Masayuki
PY - 2013
Y1 - 2013
N2 - In this paper, we investigate cooperative environmental monitoring for Pan-Tilt-Zoom(PTZ) visual sensor networks based on game theoretic cooperative control. In particular, we focus on one of the key goals of the monitoring task, i.e. monitoring environmental changes from a normal state. For this purpose, this paper first presents a novel formulation of the optimal environmental monitoring problem reflecting the above objective and characteristics of vision sensors. Then, the optimization problem is reduced to a potential game with potential function equal to the formulated objective function through an existing utility design technique, where the designed utility is shown to be computable through local computation and communication. We finally present a payoff-based learning algorithm, which refines [18] so that the sensors eventually take the potential function maximizes with high probability and local action constraints are dealt with. Finally, we run experiments on a testbed in order to demonstrate the effectiveness of the presented approach.
AB - In this paper, we investigate cooperative environmental monitoring for Pan-Tilt-Zoom(PTZ) visual sensor networks based on game theoretic cooperative control. In particular, we focus on one of the key goals of the monitoring task, i.e. monitoring environmental changes from a normal state. For this purpose, this paper first presents a novel formulation of the optimal environmental monitoring problem reflecting the above objective and characteristics of vision sensors. Then, the optimization problem is reduced to a potential game with potential function equal to the formulated objective function through an existing utility design technique, where the designed utility is shown to be computable through local computation and communication. We finally present a payoff-based learning algorithm, which refines [18] so that the sensors eventually take the potential function maximizes with high probability and local action constraints are dealt with. Finally, we run experiments on a testbed in order to demonstrate the effectiveness of the presented approach.
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U2 - 10.1109/CDC.2013.6761101
DO - 10.1109/CDC.2013.6761101
M3 - Conference contribution
AN - SCOPUS:84902349493
SN - 9781467357173
T3 - Proceedings of the IEEE Conference on Decision and Control
SP - 7634
EP - 7640
BT - 2013 IEEE 52nd Annual Conference on Decision and Control, CDC 2013
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 52nd IEEE Conference on Decision and Control, CDC 2013
Y2 - 10 December 2013 through 13 December 2013
ER -