Optimal surface deployment problem in wireless sensor networks

Coverage and Connectivity in 3D Wireless Sensor Networks

“…However, this 2D model loses its relevance for most submerged and airborne deployments. In fact it is shown in [40,43,75] that even terrestrial networks deployed on complex 3D surfaces need to be analyzed as 3D WSNs as opposed to 2D WSNs since the 3D model-based node placement strategies yield better coverage and reduced number of sensor nodes. Although as important and crucial as it may sound, 3D WSNs is still relatively unexplored niche in the WSN domain, and it has only been since recently that concentrated research efforts have been undertaken in this domain.…”

“…Jin et al [40] addresses the surface deployment problem in terms of sensing quality by introducing a general function to measure the unreliability of monitored data in the entire sensor network. Sensors do not always make perfect measurements, but exhibit unreliability that in general depends on the distance between the sensor and target to be sensed.…”

“…In the simulation study [40] sensors are deployed on the testing surface models. The first row of Fig.…”

“…22 The first row shows random sensing partition. The second row shows Voronoi-based sensing partition, and the third row shows generalized centroidal voronoi-based sensing partition [40] The scheme uses conformal mapping to estimate the 3D depth of the region and then partitions the field accordingly for optimal sensor deployment resulting in minimum sensing unreliability of the Field of Interest.…”

“…For regular terrains, general expression of the expected coverage ratio for the 3D surface function z = f(x,y) is derived. For irregular terrains, Digital Elevation Model (DEM) is used to derive analytical [40] expression of the expected coverage ratios. By partitioning the Field of Interest in areas of small triangles which have very have very small dimensions compared to the sensing radius of the sensor, the coverage ratio of the surface is estimated.…”