Area Coverage Under Low Sensor Density

TL;DR

This paper introduces a neural network-based approach to estimate coverage in wireless sensor networks with low sensor density, addressing sensor failure issues without relying on mobile robots.

Contribution

It proposes a supervised neural network method with a hybrid backpropagation technique to predict coverage in sensor networks with insufficient sensors.

Findings

Effective coverage estimation using neural networks

Accelerated learning convergence with hybrid backpropagation

Validated on real-world meteorological data

Abstract

This paper presents a solution to the problem of monitoring a region of interest (RoI) using a set of nodes that is not sufficient to achieve the required degree of monitoring coverage. In particular, sensing coverage of wireless sensor networks (WSNs) is a crucial issue in projects due to failure of sensors. The lack of sensor equipment resources hinders the traditional method of using mobile robots to move around the RoI to collect readings. Instead, our solution employs supervised neural networks to produce the values of the uncovered locations by extracting the non-linear relation among randomly deployed sensor nodes throughout the area. Moreover, we apply a hybrid backpropagation method to accelerate the learning convergence speed to a local minimum solution. We use a real-world data set from meteorological deployment for experimental validation and analysis.