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Critical Density for K-Coverage Under Border Effects in Camera Sensor Networks With Irregular Obstacles Existence | IEEE Journals & Magazine | IEEE Xplore

Critical Density for K-Coverage Under Border Effects in Camera Sensor Networks With Irregular Obstacles Existence


Abstract:

From the perspective of saving energy, it is important to maintain a desired coverage ratio with a minimum sensor density in camera sensor networks (CSNs). In some actual...Show More

Abstract:

From the perspective of saving energy, it is important to maintain a desired coverage ratio with a minimum sensor density in camera sensor networks (CSNs). In some actual applications, the Field of Interest (FoI) is often an irregular bounded area with obstacles existence. The existence of obstacles would have an adverse effect on quality of coverage in CSNs. Besides, due to the camera sensor may be located in the boundary of the FoI, it also has influence on the coverage contribution of the camera sensor. In this article, we assume that heterogeneous camera sensors are randomly deployed in a convex polygon FoI with irregular obstacles existence and consider the border effects to derive the critical density (CD) of camera sensors for a desired coverage ratio in CSNs. We propose the concept of occlusion K -coverage, and present the expected effective possible sensing region to evaluate the coverage contribution of the camera sensor. Next, a series of simulation experiments are conducted to demonstrate the impact of border effects and parameters of camera sensor on the occlusion K -coverage ratio. The results show that our method can effectively estimate the CD for a desired occlusion K -coverage ratio.
Published in: IEEE Internet of Things Journal ( Volume: 11, Issue: 4, 15 February 2024)
Page(s): 6426 - 6437
Date of Publication: 04 September 2023

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I. Introduction

Currently, with the advancement of CMOS sensor and wireless communication technologies, camera sensors networks (CSNs) have witnessed a vigorous development. As a derivative of wireless sensor networks (WSNs) and an important part of Internet of Things, a typical camera sensor networks consists of a great number of camera sensors with limited sensing region and adjustable sensing orientations. It can capture rich information from the environment in the form of images or videos. Moreover, due to the widespread applications of CSNs, including smart cities, industrial monitoring, target recognition, intrusion monitoring, smart agriculture, and surveillance security [1], [2], [3], [4], [5], [6], [7], [8], it has attracted extensive attention from scholars.

References

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