Delaunay Triangulation as a New Coverage Measurement Method in Wireless Sensor Network

Abstract

Sensing and communication coverage are among the most important trade-offs in Wireless Sensor Network (WSN) design. A minimum bound of sensing coverage is vital in scheduling, target tracking and redeployment phases, as well as providing communication coverage. Some methods measure the coverage as a percentage value, but detailed information has been missing. Two scenarios with equal coverage percentage may not have the same Quality of Coverage (QoC). In this paper, we propose a new coverage measurement method using Delaunay Triangulation (DT). This can provide the value for all coverage measurement tools. Moreover, it categorizes sensors as ‘fat’, ‘healthy’ or ‘thin’ to show the dense, optimal and scattered areas. It can also yield the largest empty area of sensors in the field. Simulation results show that the proposed DT method can achieve accurate coverage information, and provides many tools to compare QoC between different scenarios.

Construction of optimal wire sensor network for the area of complex shape

“…If a template is completely covered with zones of sensors, located in its apexes, the entire area can be covered by tessellation of such polygons. The more advanced methods of coverage, based on the arrangement of sensors in determined units, include the arrangement of sensors on the basis of using the Voronoi diagrams [5] and the Delaunay [6] triangulation.…”

“…The degree of uncertainty is determined by the level of knowledge on the state and behavior of a system under study and the environment in which the system operates. It is essential to take this uncertainty into account while solving problems of assessing and predicting the states of systems in engineering [1,2], military affairs [3,4], medicine [5], economy [6,7], as well as problems of structural and parametric optimization [8][9][10].…”

Coverage Enhancement Algorithms for Distributed Mobile Sensors Deployment in Wireless Sensor Networks

“…The sensing model describes the possibility of event detection by sensor in monitoring field [30]. Every sensor type has its own unique sensing model which is characterized by its sensing area, accuracy, and resolution.…”

“…For each Edge in polygon (10) Find midpoint of Edge (11) Append midpoint to new polygon (12) End For each Edge (13) Until vertices are very close to each other (14) Record the statistical average of new candidate location into newpositions (15) End For each polygon (16) Checkforbettercoverage (VD, positions, newpositions) // Function for if NCL > CL (17) Newcov = calculatecoverage (VD, newpositions) (18) Newcovpercent = (newcov/MFL 2 ) * 100 (19) IF coverage enhanced then (20) IF Round ≤ MR (21) calcEnergy (positions, newposition, permeterEnergy) (22) position = newpositions (23) totalcov = newtotalcov (24) totalcovpercent = newtotalcovpercent (25) else (26) exit Loop (27) End IF Round (28) Else (29) Exit Loop (30) End IF coverage (31) Next Round (32) calculatecoverage average for all rounds (33) calculateEnergy consumption average for all rounds (34) calculateconvergence average for all rounds Algorithm 3: Edge Based Centroid (EBC) of th Voronoi polygon ( ).…”

“…Every sensor type has its own unique sensing model which is characterized by its sensing area, accuracy, and resolution. The sensing area relies on numerous factors such as the signal strength generated from the source, distance between the sensor node and source, the rate of attenuation in propagation, and the required self-confidence sensing level [30]. In [31], a network of acoustic sensors is deployed to detect mobile vehicles.…”