http://nova.newcastle.edu.au/vital/access/services/Feed ${session.getAttribute("locale")} 5 http://nova.newcastle.edu.au/vital/access/manager/Repository/uon:11253 We study a networked control architecture where wireless sensors are used to measure and transmit plant outputs to a remote controller. Packet loss probabilities depend upon the time-varying communication channel gains and the transmission powers of the sensors. Within this context, we develop a centralized stochastic nonlinear model predictive controller. It determines the sensor power levels by trading energy expenditure for expected plant state variance. To further preserve sensor energies, the power controller sends coarsely quantized power increment commands only when necessary. Simulations on measured channel data illustrate the performance achieved by the proposed controller. 2013-03-24T05:24:12.138Z ]]> http://nova.newcastle.edu.au/vital/access/manager/Repository/uon:6140 We investigate state estimation via wireless sensor networks over fading channels causing random packet loss. Packet loss probabilities depend upon the time-varying channel gains and transmission power levels used by the sensors. We develop a predictive controller which trades off sensor energy expenditure versus state estimation accuracy. The latter is measured by the expected value of the future covariance matrices provided by the associated time-varying Kalman Filter. To further conserve energy at the sensors, the controller is located at the gateway and sends coarsely quantized power increment commands, only whenever necessary. Simulations based on real channel measurements show that the proposed approach gives excellent results. 2013-03-24T05:22:01.598Z ]]> http://nova.newcastle.edu.au/vital/access/manager/Repository/uon:8780 We study a state estimation architecture for sensor networks, where several sensors transmit quantized innovations to a central estimator. Transmission is via a wireless channel, which is prone to fading leading to random packet loss. State estimation is carried out at the gateway via a time-varying Kalman filter which accounts for packet loss and quantization effects. To form the innovations at the sensors, the estimator transmits information regarding its current state estimate to the sensors. This information could be dedicated to each sensor or broadcast to all sensors. In addition, the gateway also decides upon power levels and quantization step-sizes to be used by each sensor node. Here, we adopt elements of predictive control to trade off estimation performance versus energy use. 2013-03-24T05:17:29.033Z ]]> http://nova.newcastle.edu.au/vital/access/manager/Repository/uon:8716 We study state estimation via wireless sensor networks over fading channels affected by random packet loss. In the configuration examined, the sensors send their measurements to a single gateway, which decides upon the source coding scheme and the sensor transmitter power levels. The decision process is carried out on-line and adapts to changing channel conditions to achieve an optimal trade-off between estimation quality and sensor energy expenditure. In particular, if some channel conditions are poor, then the gateway commands the corresponding sensors to increase power levels and use multiple-description coding. Simulations based on measured channel data illustrate that the proposed scheme gives excellent results. 2013-03-24T05:16:23.126Z ]]> http://nova.newcastle.edu.au/vital/access/manager/Repository/uon:11260 We present a predictive power controller for state estimation of a stationary ARMA process over a wireless sensor network (WSN), consisting of sensor nodes, relays, and a single gateway (GW). The state estimate is formed centrally at the GW by using packets received from sensors and relays. The latter perform network coding of sensor measurements. Communication from sensors and relays to the GW is over a fading channel. Packet loss probabilities depend upon the timevarying channel gains and the transmission powers used. To achieve an optimal trade-off between state estimation quality and energy expenditure, in our approach the GW decides upon the in general time-varying transmission powers of sensors and relays. This decision process is carried out on-line and adapts to changing channel conditions by using elements of stochastic model predictive control. Simulations on measured channel data illustrate the performance achieved by the proposed controller. 2013-03-24T05:10:17.851Z ]]> http://nova.newcastle.edu.au/vital/access/manager/Repository/uon:9214 We study stochastic stability for Kalman filtering over fading wireless channels where variable channel gains are counteracted by the use of power control to alleviate the effects of packet drops. The Kalman filter and the controller are located at a single gateway which acquires data from the wireless sensors. We establish sufficient conditions which ensure that the Kalman filter covariance matrix is exponentially bounded in norm. The conditions obtained are then used to formulate stabilizing optimal power allocation laws which minimize the total sensor power budget. In deriving the optimal power allocation laws, both statistical channel information and full channel information are considered. The effect of system instability on the power budget is also investigated for both these cases. 2013-03-24T04:54:08.185Z ]]> http://nova.newcastle.edu.au/vital/access/manager/Repository/uon:9216 Stochastic stability for centralized Kalman filtering over a wireless sensor network with correlated fading channels is studied. On their route to the gateway, sensor packets, possibly aggregated with measurements from several nodes, may be dropped because of fading links. By assuming the network states to be Markovian, we establish sufficient conditions that ensure the Kalman filter to be exponentially bounded in norm. In the one sensor case, this new stability condition is shown to include previous results obtained in the literature as special cases. The results also hold when applying power control, where the transmission power of each node is a nonlinear mapping of the network state and the channel gains. 2013-03-24T04:53:44.284Z ]]> http://nova.newcastle.edu.au/vital/access/manager/Repository/uon:6761 We study state estimation via wireless sensors over fading channels. Packet loss probabilities depend upon time-varying channel gains, packet lengths and transmission power levels of the sensors. Measurements are coded into packets by using either independent coding or distributed zero-error coding. At the gateway, a time-varying Kalman filter uses the received packets to provide the state estimates. To trade sensor energy expenditure for state estimation accuracy, we develop a predictive control algorithm which, in an online fashion, determines the transmission power levels and codebooks to be used by the sensors. To further conserve sensor energy, the controller is located at the gateway and sends coarsely quantized power increment commands, only whenever deemed necessary. Simulations based on real channel measurements illustrate that the proposed method gives excellent results. 2013-03-17T23:27:15.455Z ]]> http://nova.newcastle.edu.au/vital/access/manager/Repository/uon:6742 We study a networked control architecture where wireless sensors are used to measure and transmit plant outputs to a remote controller. Packet loss probabilities depend upon the time-varying communication channel gains and the transmission powers of the sensors. Within this context, we develop a centralized stochastic nonlinear model predictive controller. It determines the sensor power levels by trading energy expenditure for expected plant state variance. To further preserve sensor energies, the power controller sends coarsely quantized power increment commands only when necessary. Simulations on measured channel data illustrate the performance achieved by the proposed controller. 2013-03-11T00:48:10.321Z ]]> http://nova.newcastle.edu.au/vital/access/manager/Repository/uon:11732 We study stochastic stability of centralized Kalmanfiltering for linear time-varying systems equipped with wireless sensors. Transmission is over fading channels where variable channel gains are counteracted by power control to alleviate the effects of packet drops. We establish sufficient conditions for the expected value of the Kalman filter covariance matrix to be exponentially bounded in norm. The conditions obtained are then used to formulate stabilizing power control policies which minimize the total sensor power budget. In deriving the optimal power control laws, both statistical channel information and full channel information are considered. The effect of system instability on the power budget is also investigated for both these cases. 2013-03-07T23:39:08.085Z ]]>