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

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NODE POSITIONING FOR INCREASED DEPENDABILITY OF WIRELESS SENSOR NETWORKS
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clustered network architectures where multiple base stations operate independently. Section 9.4 highlights the challenges of coordinated repositioning of multiple nodes and describe few attempts to tackle these challenges. Finally, Section 9.5 concludes the chapter.
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9.2 STATIC POSITIONING OF NODES Node placement schemes prior to network startup usually base their choice of the particular nodes positions on metrics that are independent of the network state or assume a xed network operation pattern that stays unchanged throughout the lifetime of the network. Examples of such static metrics are area coverage, internode distance, and so on. Static network operation models often assume periodic data collection over preset routes. In this section we give an overview of the different categories of static node positioning and discuss sample work from the literature. We cover both sensor and base-station placement in distinct subsections. 9.2.1 Sensor Node Placement As mentioned before, the position of nodes have dramatic impact on the effectiveness of the WSN and the ef ciency of its operation. In this section we discuss contemporary sensor placement strategies in the literature. We classify them according to the deployment schemes, the optimization objective of the placement, and how they relate to the network topology. Deployment Schemes. Sensors can generally be placed in an area of interest either deterministically or randomly. The choice of the deployment scheme depends highly on the type of sensors, application, and the environment in which the sensors will operate. Controlled node deployment is viable and often necessary when sensors are expensive or when their operation is signi cantly affected by their position. Examples of such scenarios include when populating an area with highly precise seismic nodes and when placing imaging and video sensors. On the other hand, in some applications, random deployment schemes are the only feasible option. This is particularly true for harsh environments such as a battle eld or a disaster region. Depending on the node distribution and the level of redundancy, random node deployment can achieve the required performance goals. Deterministic deployment is usually pursued for indoor applications of WSNs. Examples of indoor networks include the Active Sensor Network (ASN) project at the University of Sydney in Australia [15], the Multiple Sensor Indoor Surveillance (MSIS) project at Accenture Technology Labs in Chicago [16], and the Sensor Network Technology projects at Intel [17]. The ASN and MSIS projects gear for serving surveillance applications such as secure installations and enterprise asset management. At Intel, the main focus is on applications in manufacturing plants and engineering facilities for example, preventative equipment maintenance (Figure 9.2).
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STATIC POSITIONING OF NODES
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Figure 9.2. Sensors are mounted to analyze the vibration and assess the health of equipment at a semiconductor fabrication plant. (Photographs are from 17.)
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Hand-placed sensors are also used to monitor the health of large buildings in order to detect corrosions and overstressed beams that can endanger the structure integrity [18, 19]. Another notable effort is the Sandia Water Initiative at Sandia National Lab which addresses the problem of placing sensors in order to detect and identify the source of contamination in the air or water supplies [20, 21]. Deterministic placement is also very popular in applications of range nders, imaging, and video sensors. In general, these sensors are involved in three-dimensional (3-D) application scenarios, which is much more dif cult to analyze compared to two-dimensional deployment regions. Poduri et al. [10] investigated the applicability of contemporary coverage analysis and placement strategies pursued for 2-D space to 3-D setups. They concluded that many of the popular formulation such as art-gallery and sphere-packing problems, which are optimally solvable in 2-D, become NP-hard in 3-D. Most placement approaches for these types of sensor strive to enhance the quality of visual images and/or accuracy of the assessment of the detected objects. For example, Gonzalez-Banos and Latombe [22] studied the problem of nding the minimum number of range nders, which estimate the proximity of a target, or video sensors and their location in order to cover an area. Unlike other sensors, such
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