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Figure 1.1. Position estimation methods: (a) triangulation, (b) trilateration, and (c) multilateration. (Adapted from reference 10.)
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nodes is estimated based on different methods, such as Received Signal Strength Indicator (RSSI), Time of Arrival (ToA), and Time Difference of Arrival (TDoA) [4]. Once the distance is estimated, at least three methods can be used to compute the node location: triangulation, trilateration, and multilateration [9], as depicted in Figure 1.1. Another method to estimate the node location is called the Angle of Arrival (AoA), which uses the angle in which the received signal arrives and the distance between the sender and receiver. Solutions for nding the nodes location are often based on localized algorithms in the sense that every node is usually able to estimate its position. For instance, Sichitiu and Ramadurai [11] use the Bayesian inference to process information from a mobile beacon and determine the most likely geographical location (and region) of each node, instead of nding a unique point for each node location. The Directed Position Estimation (DPE) [8] is a recursive localization algorithm in which a node uses only two references to estimate its location. This approach leads to a localization system that can work in a low-density sensor network. Besides, the controlled way in which the recursion occurs leads to a system with smaller and predictable errors. Liu et al. [12] propose a robust and interactive Least-Squares method for node localization in which, at each iteration, nodes are localized by using a least-squares-based algorithm that explicitly considers noisy measurements. Node Placement. In some applications, instead of throwing the sensor nodes on the environment (e.g., by airplane), they can be strategically placed in the sensor eld according to a priori planning. In this approach, there is no need to discover the nodes location. However, good planning depends on the knowledge of the terrain and the environmental particularities that might interfere in the operation of the sensor nodes and the quality of the gathered data. The node placement problem has been addressed using different approaches [13 15]. However, current solutions are basically concerned with assuring spatial coverage while minimizing the energy cost. The SPRING algorithm is a node placement algorithm that also performs information fusion. In SPRING it is possible to migrate the fusion role. Besides spatial coverage [13, 15], other aspects should be considered in a node placement algorithm, such as node diversity [14] and the fusion performance. When
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Figure 1.2. An example of node scheduling: Gray nodes are asleep and black nodes are awake.
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nodes perform data fusion, an improper node placement may lead to the degradation of information fusion as illustrated by Hegazy and Vachtsevanos [16]. Density Control. The main node scheduling objective is to save energy using a density control algorithm [17 20]. Such algorithms manage the network density by determining when each node will be operable (awake) and when it will be inoperable (asleep). Figure 1.2 depicts an example of the result of a node scheduling algorithm in which gray nodes are asleep because their sensing areas are already covered by awaken nodes (in black). Density control is an inherently localized algorithm where each node assesses its vicinity to decide whether or not it will be turned on. Some of the node scheduling algorithms, such as GAF [17], SPAN [19], and STEM [18], consider only the communication range to choose whether or not a node will be awake. Therefore, it is possible that some regions remain uncovered, and the application may not detect an event. Other solutions, such as PEAS [20], try to preserve the coverage. However, none of the current node scheduling algorithms consider the information fusion accuracy. As a result, nodes that are important to information fusion might be turned off. A key issue regarding density control algorithms is the integration with other functions such as data routing. Siqueira et al. [21] propose two ways of integrating density control and data routing: synchronizing both algorithms or redesigning an integrated algorithm. 1.2.2 Data Communication In wireless sensor networks, the problem of data communication is mainly related to medium access control, routing, and transport protocols. MAC Protocols. The link or medium access control (MAC) layer controls the node access to the communication medium by means of techniques such as contention [22, 23] and time division [24, 25]. Basically, the MAC layer must manage the communication channels available for the node, thereby avoiding collisions and errors in the communication.
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