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

Generator Quick Response Code in Visual Studio .NET NODE POSITIONING FOR INCREASED DEPENDABILITY OF WIRELESS SENSOR NETWORKS
NODE POSITIONING FOR INCREASED DEPENDABILITY OF WIRELESS SENSOR NETWORKS
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placement can theoretically meet all primary and secondary objectives, the quest for minimizing the required network resources keeps the problem very hard. Area Coverage is the deployment objective that has received the most attention in the literature. Assessing the coverage varies based on the underlying model of sensor s eld of view and the metric used to measure the collective coverage of deployed sensors. The bulk of the published work (e.g., reference 28) assumed a disk coverage zone centered at the sensor with a radius that equals its sensing range. However, some recent work has started to employ more practical sensor s eld of view in the form of irregular polygons [29]. Some of the published papers, especially early ones, use the ratio of the covered area to the overall deployment region as a metric for the quality of coverage [28]. Recent work, however, has focused on the worst-case coverage, usually referred to least exposure, measuring the probability that a target would travel across an area or an event would happen without being detected [30]. The advantage of exposure-based coverage assessment is the inclusion of a practical object detection probability that is based on signal processing formulations (e.g., signal distortion) as applicable to speci c sensor types. As mentioned earlier, optimized sensor placement is not an easy problem, even for deterministic deployment scenarios. Complexity is often introduced by the quest for employing the least number of sensors for meeting the application requirements and by the uncertainty in a sensor s ability to detect an object due to distortion that may be caused by terrain or the sensor s presence in a harsh environment. Dhillon and Chakrabarty [13] considered the placement of sensors on a grid approximation of the deployment region. They formulated a sensing model that factors in the effect of terrain in the sensor s surroundings and inaccuracy in the sensed data (Figure 9.5). The model is then used to identify the grid points on which sensors are to be placed, so that an application-speci c minimum con dence level on object detection is met. They proposed a greedy heuristic that strives to achieve the coverage goal through the least number of sensors. The algorithm is iterative. In each iteration, one sensor
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Figure 9.5. Knowing the coordinate of obstacles, the sensor s eld of view is adjusted. In the shown example, redrawn from reference 13, the sensor on grid point 14 is not suf cient to cover the area in direction to point 11. The same applies for (2,7), (6,3), and (10,15).
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STATIC POSITIONING OF NODES
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Figure 9.6. The dark circle marks the location of sensors. The weights on the individual line segments are based on the probability that a target is detected by all sensors (combined exposure). The least exposure path, marked in dark line, is found by applying Dijkstra s algorithm.
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is placed at the grid point with the least coverage. The algorithm terminates when the coverage goal is met or a bound on the sensor count is reached. Clouqueur et al. [14] also studied the problem of populating an area of interest with the least number of sensors so that targets can be detected with the highest probability. Unlike reference 13, random deployment is assumed in this work. The authors propose a metric called path exposure to assess the quality of sensor coverage. The idea is to model the sensing range of deployed nodes and establish a collective coverage map of all sensors based on a preset probability of false alarm (detection error). The map is then checked in order to identify the least exposure path on which a target may slip by, with the highest probability of being undetected. Figure 9.6, taken from reference 14, illustrates the idea on a grid structure. Employing such a metric, the authors further introduced a heuristic for incremental node deployment so that every target can be detected with a desired con dence level using the fewest sensors count. The idea is to randomly deploy a subset of the available sensors. Assuming that the sensors can determine and report their positions, the least exposure path is identi ed and the probability of detection is calculated. If the probability is below a threshold, additional nodes are deployed in order to ll holes in the coverage along the least exposure path. This procedure would be repeated until the required coverage is reached. The paper also tried to answer the question of how many additional nodes are deployed per iteration. On the one hand, it is desirable to use the least number of sensors. On the other hand, the means for sensor deployment may be expensive or risky (e.g., sending a helicopter). The authors derive a formulation that accounts for the cost of deploying nodes and the expected coverage as a function of sensors count. The formulation can be used to guide the designer for the most effective way to populate the area.
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