Spatio-temporal sampling rates and energy efficiency in wireless sensor networks

Bandyopadhyay, Subhajyoti; Coyle, Eugene

doi:10.1109/infcom.2004.1354584

Cited by 38 publications

(42 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There has been some research efforts to study the correlation in WSN [2]- [7]. In [3], the problem of correlated data gathering with the objective of energy minimization is studied using different coding models.…”

Section: Introductionmentioning

confidence: 99%

Spatio-temporal Characteristics of Point and Field Sources in Wireless Sensor Networks

Vuran

Akan

2006

2006 IEEE International Conference on Communications

View full text Add to dashboard Cite

Abstract-Wireless Sensor Networks (WSN) are comprised of densely deployed sensor nodes collaboratively observing and communicating extracted information about a physical phenomenon. Dense deployment of sensor nodes makes the sensor observations highly correlated in the space domain. In addition, consecutive samples obtained by a sensor node are also temporally correlated for the applications involving the observation of the variation of a physical phenomenon. Based on the physical characteristics and dispersion pattern over the area, the phenomenon to be observed can be modeled as point source or field source. Clearly, understanding the spatio-temporal correlation characteristics of the point and field sources brings potential advantages to be exploited in the design of efficient communication protocols. In this paper, a theoretical analysis of spatio-temporal correlation in WSN is carried out. The objective of this analysis is to capture the spatio-temporal characteristics of point and field sources in WSN. First, the model for point and field sources are developed and their spatio-temporal characteristics are analytically derived along with the distortion functions. Based on the theoretical analysis, numerical simulations are performed. This analytical work provides tools for finding the feasible operating region in terms of spatial and temporal resolution for a specific distortion constraint considering spatio-temporal correlation, signal properties, and network variables in WSN.

show abstract

Section: Introductionmentioning

confidence: 99%

Spatio-temporal Characteristics of Point and Field Sources in Wireless Sensor Networks

Vuran

Akan

2006

2006 IEEE International Conference on Communications

View full text Add to dashboard Cite

show abstract

“…In [12], the spatio-temporal sampling rate tradeoffs of a sensor network for minimum energy usage is studied. Authors in [13] provide a mathematical model for the spatio-temporal correlation of the signals observed by the sensor nodes.…”

Section: B Related Workmentioning

confidence: 99%

Sampling and distortion tradeoffs for indirect source retrieval

Mohammadi

Fallah

Marvasti

2016

2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP)

View full text Add to dashboard Cite

Consider a continuous signal that cannot be observed directly. Instead, one has access to multiple corrupted versions of the signal. The available corrupted signals are correlated because they carry information about the common remote signal. The goal is to reconstruct the original signal from the data collected from its corrupted versions. Known as the indirect or remote reconstruction problem, it has been mainly studied in the literature from an information theoretic perspective. A variant of this problem for a class of Gaussian signals, known as the "Gaussian CEO problem", has received particular attention; for example, it has been shown that the problem of recovering the remote signal is equivalent with the problem of recovering the set of corrupted signals (separation principle).The information theoretic formulation of the remote reconstruction problem assumes that the corrupted signals are uniformly sampled and the focus is on optimal compression of the samples. On the other hand, in this paper we revisit this problem from a sampling perspective. More specifically, assuming restrictions on the sampling rate from each corrupted signal, we look at the problem of finding the best sampling locations for each signal to minimize the total reconstruction distortion of the remote signal. In finding the sampling locations, one can take advantage of the correlation among the corrupted signals. The statistical model of the original signal and its corrupted versions adopted in this paper is similar to the one considered for the Gaussian CEO problem; i.e., we restrict to a class of Gaussian signals.Our main contribution is a fundamental lower bound on the reconstruction distortion for any arbitrary nonuniform sampling strategy. This lower bound is valid for any sampling rate. Furthermore, it is tight and matches the optimal reconstruction distortion in low and high sampling rates. Moreover, it is shown that in the low sampling rate region, it is optimal to use a certain nonuniform sampling scheme on all the signals. On the other hand, in the high sampling rate region, it is optimal to uniformly sample all the signals. We also consider the problem of finding the optimal sampling locations to recover the set of corrupted signals, rather than the remote signal. Unlike the information theoretic formulation of the problem in which these two problems were equivalent, we show that they are not equivalent in our setting.

show abstract

“…In [15], the use of a proxy node was added to the approach of [12], whereas in [14] the additional use of a main cluster head was implemented, with the main cluster head relaying the data from cluster heads to the BS. The work of [12] was extended in [4] to include multihop communications in addition to clustering. In addition, an approach to determine the optimal number of cluster heads is proposed.…”

Section: Related Workmentioning

confidence: 99%