Figure 12.9. Algorithm for self-localization using angle estimation used at the sensor nodes. in Visual Studio .NET

Generation QR Code JIS X 0510 in Visual Studio .NET Figure 12.9. Algorithm for self-localization using angle estimation used at the sensor nodes.
Figure 12.9. Algorithm for self-localization using angle estimation used at the sensor nodes.
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LOCATION DISCOVERY IN SENSOR NETWORKS
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Figure 12.10. Experimentally obtained locations of sensors that are placed in 8 8 grid at separations of 1 ft. The angular speed of rotation of beacons was xed at 9.5 rpm.
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Experimental evaluation of the performance of the system shows that when used in a 10-ft 10-ft area, errors in localization is less than 4 inches and the maximum time required for any sensor to localize is 45 seconds. Localization results of nodes located 1 ft apart in the area are shown in Figure 12.10. Although this scheme needs special considerations for designing the beacon generators, the attractive feature is that the sensors do not need any special hardware except for being able to detect the beacon signal (i.e., photodetector), which is usually present in most sensor nodes. The primary drawback of this scheme is its requirement for line of sight. Within indoor environments, this can be mostly achieved for most locations by mounting the beacons at the ceiling. For those nodes that do not have line of sight from all three beacons, additional methods may be used, such as atomic or iterative multilateration, to be performed after a suf cient number of nodes in the network have self-localized.
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12.6 OTHER LOCALIZATION TECHNIQUES While the previous sections described the key approaches to localization in sensor networks, the list of new ideas keep growing and it is dif cult to capture all the existing work in this rapidly developing topic. Here, samples of some other wellknown approaches are presented.
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OTHER LOCALIZATION TECHNIQUES
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12.6.1 Range-Free Position Estimation Range-free techniques are those where location estimates are obtained without using concrete distance or angle measurements from speci c nodes. The principle here is to determine an unknown location from the proximity to beacon nodes or nodes with known locations. This avoids problems arising from errors in range or angle estimation, and it typically requires less costly hardware and simpler calculations. However, they generally have lower precision of localization than those using rangebased methods. A popular example of range-free localization is the centroid method proposed in reference 6. Here a node counts the number of beacon signals received from a set of pre-positioned beacon nodes and achieves localization by obtaining the centroid of the received beacon generators. Precision is dependent on the density and locations of the beacon generators. The DV-HOP solution uses locations of anchor nodes (which are special nodes that broadcast control packets to enable localization), the hop counts from anchors, and the average distance per hop for localization. It uses a mechanism that is similar to classical distance vector routing. The anchor nodes broadcast beacon packets that are ooded throughout the network. The beacon packets carry hop counts and the locations of the corresponding anchor. Each receiving node maintains the minimum counter value from each anchor, thereby allowing them to determine the shortest hop distance to each anchor. The location is estimated using average hop distance from anchors. In reference 22, the authors present a Point-in-Triangulation (PIT) test, where nodes use a set of signal strength measurements from neighboring beacon nodes to determine the closest set of three nodes forming a triangle within which it is located. This is repeated with different anchor combinations until all combinations from nodes that are within range are exhausted. Localization is then performed using the center of gravity of the intersection of all the triangles within which the node is located. A cluster-based distributed localization scheme is presented in reference 23. This method avoids using distance or AOA measurements and long-range beacons by utilizing the regularity of clusters in the network. The localization algorithm starts with the development of regular-shaped clusters of nodes, each with a cluster-head node. Such regular clusters can be formed using an algorithm like ACE [24]. Initially, some of the cluster heads are location-aware anchor nodes (assuming that these nodes have GPS or are manually deployed). The locations of other location-unaware cluster heads are determined by a process of self-calibration, which utilizes the uniformity of cluster shapes and the average edge length of clusters. The regularity of cluster shapes is also utilized to re ne early location estimates of cluster heads at a second stage of the algorithm. When all cluster heads are calibrated, other follower nodes can calibrate using the same range-free principle. A localization scheme that uses RF connectivity and centralized computations is presented in reference 25. The authors show that given a set of convex proximity constraints and connectivity information under the constraints, fairly accurate location
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