Abstract
The optimum sampling in the one- and two-dimensional (1-D and 2-D) wireless sensor networks (WSNs) with spatial-temporally correlated data is studied in this article. The impacts of the node density in the space domain, the sampling rate in the time domain, and the space-time data correlation on the network performance are investigated asymptotically by considering a large network with infinite area but finite node density and finite temporal sampling rate, under the constraint of fixed power per unit area. The impact of space-time sampling on network performances is investigated in two cases. The first case studies the estimations of the space-time samples collected by the sensors, and the samples are discrete in both the space and time domains. The second case estimates an arbitrary data point on the space-time hyperplane by interpolating the discrete samples collected by the sensors. Optimum space-time sampling is obtained by minimizing the mean square error distortion at the network fusion center. The interactions among the various network parameters, such as spatial node density, temporal sampling rate, measurement noise, channel fading, and their impacts on the system performance are quantitatively identified with analytical and numerical studies.
Highlights
Data collected by a wireless sensor network (WSN) often contain redundancy due to the spatial and temporal correlation inherent in the monitored object(s)
It is assumed that sensors deliver the measured data to the fusion center (FC) through an orthogonal media access control (MAC) scheme, such as the deterministic frequency division multiple access (FDMA), or the random exponentially-interval MAC (EI-MAC) [16], such that collision-free communication is achieved at the FC
The impacts of the spatial node density and the temporal sampling rate on the network performance were investigated through asymptotic analysis and numerical studies
Summary
Data collected by a wireless sensor network (WSN) often contain redundancy due to the spatial and temporal correlation inherent in the monitored object(s). The FC attempts to reconstruct the time-varying and spatially continuous data field from the discrete sensor samples by exploiting the data correlation in both the space and time domains with the minimum mean square error (MMSE) receiver. The impacts of various practical factors, such as measurement noise, channel fading, and random network topology, on the performance of networks with spatial-temporally correlated data are studied through numerical analysis and simulations. In these two sections, the optimum spatialtemporal samplings in various networks are identified with asymptotic analysis and simulations.
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