Localised sensor direction adjustments with geometric structures of Voronoi diagram and Delaunay triangulation for directional sensor networks

Abstract

A directional sensor network (DSN) consists of directional sensors instead of the omnidirectional ones in the conventional wireless sensor networks. The sensing coverage of a DSN depends on the directionality and size-specific sensing angle of the sensors. The conditions are dissimilar from those of omnidirectional sensor networks for researches, especially on the sensing coverage. For coverage problem in DSNs, the geometric structures of Voronoi diagram and Delaunay triangulation have not drawn the attention of researchers. This study utilised Voronoi diagram and Delaunay triangulation and proposed four basic distributed and localised sensor direction adjustment algorithms with the characteristics of these two geometric structures to explore the field coverage improvement in DSNs. The simulation results and comparisons of coverage performance of the proposed four basic algorithms are provided. They can give the clarity of the performance results and have a reference value for future advanced studies on various coverage issues in DSNs.

An Area Coverage and Energy Consumption Optimization Approach Based on Improved Adaptive Particle Swarm Optimization for Directional Sensor Networks

“…With the increasing application of surveillance camera and video sensing network, some approaches for directional sensor networks have been proposed. Some consider both the rotation and movement of sensor nodes [13,14,15,16,17,18], and the others only consider the rotation of static sensor nodes [19,20,21,22].…”

“…Different from above two approaches, Ref. [18] presents several coverage increment algorithms namely vertex-based adjustment with Voronoi diagram (V-VD), edge-based adjustment with Voronoi diagram (E-VD), edge-based adjustment with Delaunay triangulation (E-DT) and angle-based adjustment with Delaunay triangulation (A-DT).…”

Local Coverage Optimization Strategy Based on Voronoi for Directional Sensor Networks

“…Wen et al [8,15] adopted Voronoi and Delaunay triangulation theory to design a distributed self-redeployment coverage enhancement algorithm. In [8], they proposed DVSA based on the idea that calculates corresponding field angles and side lengths of each vertex side in each polygon, and then compares AoV and $R_s$ with field angles and side lengths.…”

“…If there is no difference between the results, then a sensor moves to that vertex and takes the longer side as one boundary of the sensing sector. In [15], they presented several coverage increment algorithms, vertex-based adjustment with Voronoi diagram (V-VD), edge-based adjustment with Voronoi diagram (E-VD), edge-based adjustment with Delaunay triangulation (E-DT), and angle-based adjustment with Delaunay triangulation (A-DT). In E-VD, the sensor chooses the midpoint of the farthest edge of its own cell as a working direction.…”

Priority‐based target coverage in directional sensor networks

“…In addition, redundant sensors which cover overlapping areas are deactivated permanently or in a regular periodic manner to extend network lifetime. Greedy algorithms [8][9][10][11][12][13][14], clustering techniques [15,16], artificial intelligence techniques [4,11,[17][18][19], and geometrical solutions [21][22][23][24] are also used for solving the target coverage problem.…”