NODE POSITIONING FOR INCREASED DEPENDABILITY OF WIRELESS SENSOR NETWORKS in Visual Studio .NET

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NODE POSITIONING FOR INCREASED DEPENDABILITY OF WIRELESS SENSOR NETWORKS
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relocation is exploited: (1) post-deployment and (2) on-demand relocation. We discuss these two categories of relocation in details in the following subsections. Post-Deployment Sensor Relocation. This type of relocation is pursued at the conclusion of the sensor deployment phase when the sensor nodes are being positioned in the area. As we discussed earlier, in most of the WSN applications, sensor deployment is performed randomly due to the inaccessibility of the monitored areas. However, this random con guration usually does not provide an adequate coverage of the area without deploying an excessive number of nodes. Alternatively, the coverage quality can be improved by moving the sensor nodes if they are able to do so. In that case, the sensor nodes can be relocated to the regions that do not have the desired level of coverage or even are not covered at all. Given the energy cost of mechanical movement and the communication messages involved in directing the motion, the relocation process should be lightweight and should conclude in a reasonable time. Wang et al. [48] utilizes sensor s ability to move to distribute the sensor nodes as evenly as possible in the region. The goal is to maximize the area coverage within the least time duration and with minimal overhead in terms of travel distances and inter-sensor message traf c. The main idea is that each sensor assesses the coverage in its vicinity after deployment and decides on whether it should move to boost the coverage. To assess the coverage, a sensor node creates a Voronoi polygon with respect to neighboring sensors, as illustrated in Figure 9.12. Every point in a Voronoi polygon is closer to the sensor of that polygon (i.e., Si in Figure 9.12) than any other sensor. The intersection of the disk that de nes the sensing range and the Voronoi polygon would identify any uncovered area, which would motivate a sensor to move. In order to decide where to reposition a sensor, three methods were proposed: vector-based (VEC), Voronoi-based (VOR), and minimax. The main idea of the VEC method is borrowed from electromagnetics where close particles are subject to an
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Voronoi Polygon for Si Si
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Figure 9.12. Every sensor Si forms Voronoi polygon with respect to the position of its neighboring sensor. The part of the polygon that lies outside the sensing range is not covered by Si .
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DYNAMIC REPOSITIONING OF NODES
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expelling force to keep them apart. In the context of WSNs, virtual forces are applied to a sensor node by its neighbors and by the boundaries of its Voronoi polygon in order to change its location. While in VEC the nodes are pushed away from the densely populated areas, VOR pulls the sensors to the sparsely populated areas. In VOR, the sensor node is pulled toward the farthest Voronoi vertex to x the coverage hole in the polygon, point A in Figure 9.12. However, the sensor will be allowed to travel only a distance that equals half of its communication range, point B in Figure 9.12, in order to avoid stepping into the area handled by another sensor that was out of reach prior to the move (i.e., is not a current neighbor of Si ,), which can lead to an unnecessary move backward later on. In the minimax method, a sensor also gets closer to its farthest Voronoi vertex. However, unlike VOR, the minimax approach strives to keep most of the other vertices of the Voronoi polygon within the sensing range. It thus relocates the sensor to a point inside the Voronoi polygon whose distance to the farthest Voronoi vertex is minimized. The minimax scheme is more conservative in the sense that it avoids creating coverage holes by going far from the closest vertices, leading to more regularly shaped Voronoi polygon. The conserved departure from current sensor location leads to a gradual relocation, round by round as shown in Figure 9.13. This usually causes zigzag movement of each sensor rather than directly going to the nal destination. In order to shorten the total travel distance, a proxy-based approach is proposed in reference 54. In this approach, the sensor nodes do not move physically unless their nal destination is computed. The authors consider a network with stationary and mobile sensors. Mobile sensors are used to ll coverage holes identi ed in a distributed way by stationary nodes. Thus, mobile sensors only move logically and designate the stationary sensor nodes as their proxies. With this approach, signi cant improvements can be made to the total and average distance traveled by mobile nodes while at the same time achieving exactly the same level of coverage reported in reference 48. The approach only increases the message complexity. However, given that movement is more costly in terms
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