Random Field Estimation with Delay-Constrained and Delay-Tolerant Wireless Sensor Networks
- PDF / 689,463 Bytes
- 14 Pages / 600.05 x 792 pts Page_size
- 10 Downloads / 177 Views
Research Article Random Field Estimation with Delay-Constrained and Delay-Tolerant Wireless Sensor Networks ´ Javier Matamoros and Carles Anton-Haro Centre Tecnol`ogic de Telecomunicacions de Catalunya (CTTC), Parc Mediterrani de la Tecnologia, Av. Carl Friedrich Gauss 7, 08860-Castelldefels, Barcelona, Spain Correspondence should be addressed to Javier Matamoros, [email protected] Received 23 February 2010; Accepted 3 May 2010 Academic Editor: Davide Dardari ´ Copyright © 2010 J. Matamoros and C. Anton-Haro. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. In this paper, we study the problem of random field estimation with wireless sensor networks. We consider two encoding strategies, namely, Compress-and-Estimate (C&E) and Quantize-and-Estimate (Q&E), which operate with and without side information at the decoder, respectively. We focus our attention on two scenarios of interest: delay-constrained networks, in which the observations collected in a particular timeslot must be immediately encoded and conveyed to the Fusion Center (FC); delay-tolerant (DT) networks, where the time horizon is enlarged to a number of consecutive timeslots. For both scenarios and encoding strategies, we extensively analyze the distortion in the reconstructed random field. In DT scenarios, we find closed-form expressions of the optimal number of samples to be encoded in each timeslot (Q&E and C&E cases). Besides, we identify buffer stability conditions and a number of interesting distortion versus buffer occupancy tradeoffs. Latency issues in the reconstruction of the random field are addressed, as well. Computer simulation and numerical results are given in terms of distortion versus number of sensor nodes or SNR, latency versus network size, or buffer occupancy.
1. Introduction In recent years, research Wireless Sensor Networks (WSNs) has attracted considerable attention. This is in part motivated by the large number of applications in which WSNs are called to play a pivotal role, such as parameter estimation (i.e., moisture, temperature), event detection (leakage of pollutants, earthquakes, fires), or localization and tracking (e.g., border control, inventory tracking), to name a few [1]. Typically, a WSN consists of one Fusion Center (FC) and a potentially large number of sensor nodes capable of collecting and transmitting data to the FC over wireless links. In many cases, the underlying phenomenon being monitored can be modeled as a spatial random field. In these circumstances, the set of sensor observations are correlated, with such correlation being typically a function of their spatial locations (see, e.g., [2]). By effectively handling correlation in the data encoding process, substantial energy savings can be achieved.
In a source coding context, the work in [3] constitutes a generalization to sensor trees of Wyner-Ziv’s pioneering studies [4]. The authors pro
Data Loading...
