Distributed Voronoi-Based Self-Redeployment for Coverage Enhancement in a Mobile Directional Sensor Network

Abstract

A directional sensor network is different from conventional wireless sensor networks. It uses directional sensors instead of omnidirectional ones in the network for different applications, and the effective sensing range is characterized by directionality and size-specific sensing angle. Therefore, conditions of directional sensor networks are dissimilar to those of generic wireless sensor networks for researches, especially on the sensing coverage. This study proposed a distributed approach to enhance the overall field coverage by utilizing mobile and direction-rotatable sensors in a directional sensor network. The algorithm makes sensors self-redeploy to the new location and new direction without global information by utilizing the features of geometrical Voronoi cells. Simulations were used to evaluate and prove the effectiveness of the proposed algorithm. The results show that the approach contributes to significant field coverage improvement in directional sensor networks.

Rotational Wireless Video Sensor Networks with Obstacle Avoidance Capability for Improving Disaster Area Coverage

“…In order to address the coverage problem and to study the effect of the presence of obstacles in the area coverage, we have compared the proposed approach with the already existing model used in [17] (denoted WVSN), since it is based on the cover set construction approach. Some performance comparisons with another distributed algorithm [19] (denoted DVSA [mentioned in Subsection 3.1]) are also presented, so as to verify that the proposed approach can better enhance area coverage.…”

“…Finally, the video sensor switches to the best and occlusion-free orientation in order to ensure high area coverage in the sensing field. The obtained results show that the proposed approach outperforms the already existing model [17] and another distributed coverage algorithm presented by [19] in terms of coverage by minimizing the effects of occlusion. This is mainly due to obstacles and takes into account fault tolerance in the network since more cover sets have been found.…”

“…Huang et al [33] focused on the multimedia image sensor networks and proposed a virtual potential field-based method with the considerations of sensor direction and movement for the coverage enhancement. The authors in [19] proposed a distributed Voronoi-based self-redeployment algorithm (DVSA) to deal with the enhancement of the overall coverage ratio in the sensing field of a directional sensor network, which consists of mobile and rotational directional sensors. The authors used a Voronoi diagram to determine the sensing directions of sensors.…”

Review Over Hole Healing Techniques in Wireless Sensor Networks

“…Sensing model of directional sensor. [2] Here, s(x s ,y s ) is the sensor, F s (Field of View, FoV) is the area covered by the sensor, α is called angle of view of the sensor, θ is the working direction of the sensor and it is measured relative to positive x-axis, r is the sensing radius, ^w is the unit vector for working direction of the sensor, p(x,y) is a point covered by the sensor 's'. δ is the angle between the vector sp -> and unit vector ^w .…”

“…The vertex whose angle is greater than angle of view of the sensor and whose edges are greater than sensing radius of the sensor is selected as new location for sensor movement. The angle β of the vertex v is calculated by using the information about its left adjacent vertex v L and right adjacent vertex v R as shown in fig 18. The coordinates of these adjacent vertices are (x L ,y L ) and (x R ,x R ) respectively and angle β is calculated as [2]: Figure 18. Calculation of the angle β.…”