N-queens-based algorithm for moving object detection in distributed wireless sensor networks

TL;DR

This paper presents an N-queens-based local image processing algorithm for wireless sensor networks that significantly reduces energy consumption in moving object detection tasks, enabling more efficient intruder detection.

Contribution

The paper introduces a novel N-queens-inspired algorithm for in-network image processing that improves energy efficiency over traditional methods in WSNs.

Findings

Energy consumption reduced by a factor of N/2 compared to traditional techniques.

Algorithm is highly suitable for intruder detection applications.

Experimental results validate the energy efficiency of the proposed method.

Abstract

The main constraint of wireless sensor networks (WSN) in enabling wireless image communication is the high energy requirement, which may exceed even the future capabilities of battery technologies. In this paper we have shown that this bottleneck can be overcome by developing local in-network image processing algorithm that offers optimal energy consumption. Our algorithm is very suitable for intruder detection applications. Each node is responsible for processing the image captured by the video sensor, which consists of NxN blocks. If an intruder is detected in the monitoring region, the node will transmit the image for further processing. Otherwise, the node takes no action. Results provided from our experiments show that our algorithm is better than the traditional moving object detection techniques by a factor of (N/2) in terms of energy savings.