TY - GEN
T1 - Change sensor topology when needed
T2 - 46th IEEE Conference on Decision and Control 2007, CDC
AU - Shi, Ling
AU - Johansson, Karl Henrik
AU - Murray, Richard M.
PY - 2007
Y1 - 2007
N2 - New control paradigms are needed for large networks of wireless sensors and actuators in order to efficiently utilize system resources. In this paper we consider when feedback control loops are formed locally to detect, monitor, and counteract disturbances that hit a plant at random instances in time and space. A sensor node that detects a disturbance dynamically forms a local multi-hop tree of sensors and fuse the data into a state estimate. It is shown that the optimal estimator over a sensor tree is given by a Kalman filter of certain structure. The tree is optimized such that the overall transmission energy is minimized but guarantees a specified level of estimation accuracy. A sensor network reconfiguration algorithm is presented that leads to a suboptimal solution and has low computational complexity. A linear control law based on the state estimate is applied and it is argued that it leads to a closed-loop control system that minimizes a quadratic cost function. The sensor network reconfiguration and the feedback control law are illustrated on an example.
AB - New control paradigms are needed for large networks of wireless sensors and actuators in order to efficiently utilize system resources. In this paper we consider when feedback control loops are formed locally to detect, monitor, and counteract disturbances that hit a plant at random instances in time and space. A sensor node that detects a disturbance dynamically forms a local multi-hop tree of sensors and fuse the data into a state estimate. It is shown that the optimal estimator over a sensor tree is given by a Kalman filter of certain structure. The tree is optimized such that the overall transmission energy is minimized but guarantees a specified level of estimation accuracy. A sensor network reconfiguration algorithm is presented that leads to a suboptimal solution and has low computational complexity. A linear control law based on the state estimate is applied and it is argued that it leads to a closed-loop control system that minimizes a quadratic cost function. The sensor network reconfiguration and the feedback control law are illustrated on an example.
UR - https://openalex.org/W2142076932
UR - https://www.scopus.com/pages/publications/62749172370
U2 - 10.1109/CDC.2007.4434979
DO - 10.1109/CDC.2007.4434979
M3 - Conference Paper published in a book
SN - 1424414989
SN - 9781424414987
T3 - Proceedings of the IEEE Conference on Decision and Control
SP - 5478
EP - 5485
BT - Proceedings of the 46th IEEE Conference on Decision and Control 2007, CDC
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 12 December 2007 through 14 December 2007
ER -