PROJECT TITLE :

Optimal Sensor Collaboration for Parameter Tracking Using Energy Harvesting Sensors - 2018

ABSTRACT:

During this Project, we style an optimal sensor collaboration strategy among neighboring nodes whereas tracking a time-varying parameter using wireless sensor networks within the presence of imperfect Communication channels. The sensor network is assumed to be self-powered, where sensors are equipped with energy harvesters that replenish energy from the setting. So as to reduce the mean square estimation error of parameter tracking, we tend to propose an online sensor collaboration policy subject to real-time energy harvesting constraints. The proposed energy allocation strategy is computationally light and solely depends on the second-order statistics of the system parameters. For this, we initial take into account an offline nonconvex optimization drawback, that is solved specifically when using semidefinite programming. Based mostly on the offline answer, we style an on-line power allocation policy that requires minimal online computation and satisfies the dynamics of energy flow at every sensor. We tend to prove that the proposed online policy is asymptotically resembling the optimal offline resolution and show its convergence rate and robustness. We empirically show that the estimation performance of the proposed on-line scheme is better than that of the.Net scheme when channel state information concerning the dynamical system is on the market in the low SNR regime. Numerical results demonstrate the effectiveness of our approach.


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