3d Extention of the Vor Algorithm to Determine and Optimize the Coverage of Geosensor Networks

Abstract

KEY WORDS: Geosensor networks deployment, Network coverage problem, Voronoi based optimization algorithm ABSTRACT:Recent advances in electrical, mechanical and communication systems have led to development of efficient low-cost and multifunction geosensor networks. The efficiency of a geosensor network is significantly based on network coverage, which is the result of network deployment. Several optimization methods have been proposed to enhance the deployment efficiency and hence increase the coverage, but most of them considered the problem in the 2D environment models, which is usually far from the real situation. This paper extends a Voronoi-based deployment algorithm to 3D environment, which takes the 3D features into account. The proposed approach is applied on two case studies whose results are evaluated and discussed.

On Coverage of 3D Terrains by Wireless Sensor Networks

“…To take into account the topography impact on C(p i , s j ), most formulations proceed to verify the "visibility" between p i and s j , by using the Line of Sight (LoS) [15], [18], [19] method (Fig. 3).…”

“…, and compares the altitude z i of each point q i with the RoI height E(x i , y i ), provided by a terrain model E. Thus, p i and s j are considered inter-visible, if each point q i is above the terrain. Once the visibility between p i and s j verified, its impact on C(p i , s j ) takes two forms: (i) Deterministic impact [20], [15], [18], [19], by considering that p i can be covered by s j , only if p i and s j are inter-visible. In the opposite case, [14] by considering that C(p i , s j ) deteriorates (does not cancel out) according to the number of obstacles separating p i and s j .…”

“…[26], [20], [18], [19], [4], [ be discontinuous, such as the matrix model or continuous, such as the mathematical and the TIN (Triangulated Irregular Network) models. In the matrix model, Cov(A, N ) represents the rate of points of E covered by the N SNs [11], [12], [15], [18], considering that these points have the same level [11], [12], [13], [15], [18] or different levels [10] of importance. Thus, Cov(A, N ) is given by Eq.…”

Terrain Partitioning Based Approach for Realistic Deployment of Wireless Sensor Networks

“…The main difference between the existing approaches for the problem at hand lies in the terrain representation. As such, most approaches [2][3][4][7][8][9][10][11][12][13][14][15] adopt the matrix model, in which the terrain is represented by a matrix, where the value of each cell corresponds to the elevation of the place that it represents on the terrain. Another digital model, used by other approaches [5,6,[16][17][18], consists in representing the terrain by triangles network form.…”

“…The problem of WSNs deployment on a 3D terrains, proved NP-hard [5,6], has been the subject of several researches [2][3][4][5][6][7][8][9][10][11][12][13][14][15] that have proposed, under different hypotheses and formulations, approximates solutions based on heuristics or meta-heuristics, aiming to find the best nodes positions, that meet the network objectives. However, these works treat the entire RoI in the same way, without taking into account the differences in topographic complexity between its different sub-regions.…”