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Figure 2.12. Coverage for grid points.
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The uniform coverage algorithm is extended to provide differentiated coverage. To provide a coverage degree c of a certain grid point, the high-end node correspondingly adjusts the time awake for each low-end sensor in the coverage area. For a grid point covered by k sensor nodes, T/k slots in each round T are assigned to provide coverage of degree 1. For coverage of degree c, the number of slots must be c T/k. The complete algorithm for differentiated coverage is provided in reference 35. While the focus of Mhatre et al. [15] is on maximizing the lifetime of the network, one of the constraints relates to coverage. Two types of node are deployed over a sensor eld for the purpose of surveillance. One type of node does the sensing while the other type acts as cluster heads. An aircraft visits the area periodically and gathers data about the activity in the eld from the sensor nodes. The problem is treated assuming that the base station (aircraft) receives updates from every cluster. However, if the base station is interested in receiving updates from only a few clusters (an extrasensitive region), then the analysis can be modi ed to accommodate this requirement. More nodes are deployed over the regions of frequent updates, and these nodes are taken into account in the overall network cost. The redundant nodes stay inactive while the battery energy of other nodes lasts; they join the cluster when the other nodes start to expire. 2.4.2 Stochastic Coverage in Heterogeneous WSNs Lazos and Poovendran [31] study the following stochastic coverage problem in heterogeneous WSNs: Given a planar sensor eld and n sensor nodes deployed according to a known distribution, compute the fraction of the sensor eld that is covered by at least k sensor nodes, k 1. This may also be viewed as a problem in k-coverage [33]. The problem is formulated as a set intersection problem arising in integral geometry. Analytical expressions for stochastic coverage as then derived. The formulation does not require the sensor nodes to have identical sensing capability, and it does not restrict the distribution according to which the sensors are deployed. In addition, the formulation is applicable to scenarios where the sensing area of each sensor node has arbitrary shape. The validity of the derived expressions are veri ed by simulation.
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2.5 MANAGEMENT OF HETEROGENEOUS WSNs By de nition, heterogeneous WSNs have more than one type of sensor, making their management increasingly important. Management includes: r r r r r r Coordinating and scheduling tasks for sensors. Optimizing the use of capabilities and resources. Managing the sensor data aggregation and correlation. Assessing the situation. Adapting the sensor network. Reducing human involvement.
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Figure 2.13. Position of management in the system model.
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Vaidya et al. [36] propose a framework for sensor con guration and management to take the responsibility of making decisions in order to coordinate the assignment and scheduling of sensor nodes best suited for the application. The application considered is tracking and movement of objects in a moderately occupied con ned space. Figure 2.13 shows how the management component is positioned in the uni ed sensing system model. A manager is designed to operate over a heterogeneous WSN that provides sensory data from multiple types of sensor. The goal of the management system is to minimize the energy consumption and the required bandwidth while preserving the quality of tracking. When tracking the movement of one object, the system uses a set of three sensor nodes to determine the current location of the object and to predict the next set of sensor nodes to use according to its velocity and direction. This allows the rest of the sensor nodes in the network to go to sleep for the next detection round. For multiple targets that are far away from each other, tracking is similar to a single object moving. When multiple objects move very close to each other, there is ambiguity in the data acquired from the sonar sensors. In this case, visual sensors come to the aid. Figure 2.14 shows the complete ow chart for the sensor management system. With the help of management system, a signi cant energy reduction is achieved compared to a randomized activation scheme. The challenge of correlating the data gathered by several sensor nodes listening to live traf c is studied by Andersson et al. [37]. Correlating data from different types of sensor brings a number of bene ts. The rst is a reduction of the number of alerts that a user must address. The correlation engine should recognize when reports from multiple sensors refer to the same incident. Correlation can enhance the detection capability as a second bene t. In addition, correlation can exploit the complementary coverage from several sensors. Reports from several sensors employing diverse
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