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several targets and classifying them according to their motion pattern. Schmitt et al. [69] propose a collaborative algorithm to nd the location of mobile robots in a known environment and track moving objects. 1.2.5 Data Fusion Data fusion algorithms [70] are orthogonal to the above-mentioned problems, in the sense that these algorithms can be applied to any solution that needs to make inferences or improve estimates. Classical data fusion techniques have been used to assist solving many problems. For instance, the Least-Squares method has been used to predict sensor data [71] and nd nodes locations [8, 12]; the moving average lter has been used to estimate link connectivity statistics [72], estimate data traf c [73] and the number of events [74], and track targets [75]; the Kalman lter has been applied to re ne location and distance estimates [6, 76], track different targets [77], predict the best frame size for MAC protocols [26], and predict sensor data to reduce communication [78]. As discussed in the following, data fusion can have an important role when we design an integrated solution for a wireless sensor network.
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1.3 CHALLENGE: SYNTHESIS PROCESS One of the most important challenges in the design of wireless sensor networks is to deal with the dynamics of such networks. The physical world where the sensors are embedded is dynamic. Over time, the operating conditions and the associate tasks to be performed by the sensors can change. Some of the causes that might trigger these changes are the events occurring in the network, amount of resources available at nodes (particularly energy), and recon guration of nodes. Furthermore, it is important that sensors adapt themselves to the environment since manual con guration may be unfeasible or even impossible. In summary, the kind of distributed system we are dealing with calls for an entire new class of algorithms for large-scale, highly dynamic, and unattend WSN. The complete design of a wireless sensor network, considering a particular application, should take into account many different aspects such as application goals, traf c pattern, sensor node capability and availability, expected network lifetime, access to the monitoring area, node replacement, environment characteristics, and cost. Given a particular monitoring application, the network designer should clearly identify its main goals and the corresponding QoS parameters. For instance, given a re detection application for a rain forest, we would like to guarantee that the network will operate for the expected lifetime. However, as soon as a re spot is detected, this information should reach the sink node as fast and reliable as possible, probably not worrying about the energy expenditure of the nodes involved in this communication. Power-ef cient communication paradigms for a given application should consider both routing and media access algorithms. The routing algorithms must be tailored for ef cient network communication while maintaining connectivity when required to source or relay packets. In this case, the research challenge of the routing problem
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is to nd a power-ef cient method for scheduling the nodes such that a multihop path may be used to relay the data. But, when we consider the particular aspects of the monitoring application, we could apply, for instance, information fusion and density control algorithms to reduce the amount of data packets to be relayed and sensor nodes that need to be active, respectively. As the sensor network starts to operate, it may be necessary to adjust the functionality of individual nodes. This re nement can take several different forms. Scalar parameters, like duty cycle or sampling rates, may be adjusted using self-con guration and self-organization algorithms. This process may occur in different ways along the operation of the network lifetime. Ideally, a WSN designer should come up with both the hardware and software necessary to accomplish the aspects mentioned above. Unfortunately, it seems that we are far from this scenario. We are still giving the rst steps in the design process of a wireless sensor network as we move toward to a more disciplined development. Most of the studies found in the literature study particular problems for a WSN. That is possibly the way we should go since we need to have more experience before we can design a complete solution in a more systematic and automated way. Figure 1.3 depicts a possible monitoring application for a rain forest. In this case, we might be interested in detecting different events such as the presence of a rare bird, a re spot, and different environmental variables. The operation of the sensor
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