A Novel Positioning System Based on Coverage Area Pruning in Wireless Sensor Networks

Abstract

Wireless sensor networks are commonly applied in environmental monitoring applications. The crucial factor in such applications is to accurately retrieve the location of a monitoring event. Although many technologies have been proposed for target positioning, the devices used in such methods require better computational abilities or special hardware that is unsuitable for sensor networks with limited ability. Therefore, a range-free positioning algorithm, named coverage area pruning positioning system (CAPPS), is proposed in this study. First, the proposed CAPPS approach determines the area that includes the target approximately by using sensor nodes that can detect the target. Next, CAPPS uses sensor nodes that cannot detect the target to prune the area to improve positioning accuracy. The radio coverage variation is evaluated in a practical scenario, and a heuristic mechanism is proposed to reduce false positioning probability. Simulation results show that the size of the positioning area computed by CAPPS is smaller than that computed using distance vector hop, angle of arrival, and received signal strength indicator by approximately 98%, 97%, and 93%, respectively. In the radio variation scenario, the probability of determining an area excluding the target can be reduced from 50%–95% to 10%–30% by applying the proposed centroid point mechanism.

Mobile Anchor Route Scheduling with an Iterative Sensor Positioning Algorithm in Wireless Sensor Networks

“…The positioning results obtained by using triangulation to compute the location of unknown nodes become a region, instead of a point. The ISP algorithm is used to apply the range-free positioning algorithm coverage area pruning positioning system (CAPPS) [40] to compute the positioning area instead of using triangulation on the basis of three reference points. All neighboring-position-aware nodes of an unknown node will serve as reference points in CAPPS.…”

“…Figure 4 illustrates the main idea of the CAPPS. The example and the explanation were extracted from the content provided in [40]. Let Ω be the sensor nodes that detect the target, Ω c be the set of vertices comprising the convex hull of the set Ω, and T g be the centroid of the coordinates of the sensor nodes in the set Ω.…”

“…The deployment area is considered to be a 600 m × 600 m square, with no obstacles or sources of radio interference. The numbers of unknown nodes evaluated are {20, 25,30,35,40,45,50,55, 60, 65}. The evaluated communication ranges are R = {50 m, 75 m, 100 m, 125 m, 150 m}.…”

SPICE-ML Algorithm for Direction-of-Arrival Estimation

“…Moreover, wireless sensor networks (WSNs) are an emerging paradigm in wireless communications [4]. In WSNs one of the most significant challenges is localization in which DOA is a major parameter to estimate [5,6]. The research on DOA estimation is mainly divided into two aspects: (1) array geometry, such as the recently proposed nested array [7] and coprime array [8,9].…”