2,294 research outputs found
Optimal Sensor Collaboration for Parameter Tracking Using Energy Harvesting Sensors
In this paper, we design an optimal sensor collaboration strategy among
neighboring nodes while tracking a time-varying parameter using wireless sensor
networks in 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 environment. In order to minimize the
mean square estimation error of parameter tracking, we propose an online sensor
collaboration policy subject to real-time energy harvesting constraints. The
proposed energy allocation strategy is computationally light and only relies on
the second-order statistics of the system parameters. For this, we first
consider an offline non-convex optimization problem, which is solved exactly
using semidefinite programming. Based on the offline solution, we design an
online power allocation policy that requires minimal online computation and
satisfies the dynamics of energy flow at each sensor. We prove that the
proposed online policy is asymptotically equivalent to the optimal offline
solution and show its convergence rate and robustness. We empirically show that
the estimation performance of the proposed online scheme is better than that of
the online scheme when channel state information about the dynamical system is
available in the low SNR regime. Numerical results are conducted to demonstrate
the effectiveness of our approach
Distributed Learning in Wireless Sensor Networks
The problem of distributed or decentralized detection and estimation in
applications such as wireless sensor networks has often been considered in the
framework of parametric models, in which strong assumptions are made about a
statistical description of nature. In certain applications, such assumptions
are warranted and systems designed from these models show promise. However, in
other scenarios, prior knowledge is at best vague and translating such
knowledge into a statistical model is undesirable. Applications such as these
pave the way for a nonparametric study of distributed detection and estimation.
In this paper, we review recent work of the authors in which some elementary
models for distributed learning are considered. These models are in the spirit
of classical work in nonparametric statistics and are applicable to wireless
sensor networks.Comment: Published in the Proceedings of the 42nd Annual Allerton Conference
on Communication, Control and Computing, University of Illinois, 200
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