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. 2024 Aug 15;24(16):5299.
doi: 10.3390/s24165299.

Distributed Estimation of Fields Using a Sensor Network with Quantized Measurements

Chethaka Jayasekaramudeli  1 , Alex S Leong  2 , Alexei T Skvortsov  2 , David J Nielsen  2 , Omar Ilaya  2
Affiliations

Distributed Estimation of Fields Using a Sensor Network with Quantized Measurements

Chethaka Jayasekaramudeli et al. Sensors (Basel). .

Abstract

In this paper, the problem of estimating a scalar field (e.g., the spatial distribution of contaminants in an area) using a sensor network is considered. The sensors are assumed to have quantized measurements. We consider distributed estimation algorithms where each sensor forms its own estimate of the field, with sensors able to share information locally with its neighbours. Two schemes are proposed, called, respectively, measurement diffusion and estimate diffusion. In the measurement diffusion scheme, each sensor broadcasts to its neighbours the latest received measurements of every sensor in the network, while in the estimate diffusion scheme, each sensor will broadcast local estimates and Hessians to its neighbours. Information received from its neighbours will then be iteratively combined at each sensor to form the field estimates. Time-varying scalar fields can also be estimated using both the measurement diffusion and estimate diffusion schemes. Numerical studies illustrate the performance of the proposed algorithms, in particular demonstrating steady state performance close to that of centralized estimation.

Keywords: distributed estimation; field estimation; quantized measurements; sensor networks; time-varying systems.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Sensor network example.
Figure 2
Figure 2
Example 1: true field.
Figure 3
Figure 3
Example 1: estimated fields with varying numbers of sensors for the centralized scheme. The light blue dots represent the sensor locations.
Figure 4
Figure 4
Example 2: time-varying fields.
Figure 5
Figure 5
Example 2: sensor network.
Figure 6
Figure 6
Example 2: centralized.
Figure 7
Figure 7
Example 2: measurement diffusion.
Figure 8
Figure 8
Example 2: estimate diffusion using covariance intersection.
Figure 9
Figure 9
Example 2: estimate diffusion using inverse covariance intersection.
Figure 10
Figure 10
Example 3: average concentration data for PM10 air pollutants in Europe during the year 2021.
Figure 11
Figure 11
Example 3: close-up of the area bounded by the green box in Figure 10.
Figure 12
Figure 12
Example 3: sensor network.
Figure 13
Figure 13
Example 3: performance comparison.
Figure 14
Figure 14
Example 3: estimated fields after 50 time steps.
See this image and copyright information in PMC

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