Figure 7.1. Graph representation of a WSN. in Visual Studio .NET

Create QR Code ISO/IEC18004 in Visual Studio .NET Figure 7.1. Graph representation of a WSN.
Figure 7.1. Graph representation of a WSN.
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7.2 FUNDAMENTAL CORRESPONDING BETWEEN WIRELESS SENSOR NETWORKS AND GRAPH THEORY A wireless sensor network is a set of sensors deployed in a sensor eld to monitor speci c characteristics of the environment, to measure those characteristics, and to collect the data related to those phenomena. The sensors are small devices with limited resources: limited battery power, low memory, little computing capability, very low data rates, low bandwidth processing, variable link quality, and so on. However, despite their constraints, when the sensors are deployed in large numbers, they can provide us with a very real picture of the eld being sensed. WSN can provide an area coverage that was not possible with other wired and wireless networks. They can be deployed in different environments and can be permanently attended or can be left unattended once they have been deployed in the eld. The use of the WSN potential will provide ef cient and cost-effective solutions for many problems. However, it is necessary to implement mechanisms or procedures to deal with the sensor constraints. The use of clustering techniques has been proposed to help solve some of those constraints, by allowing the organization of the sensors in a hierarchical manner, grouping them into clusters and assigning a speci c task to the sensor in the clusters, before moving the information to higher levels. The concept of clustering is very useful in different contexts of WSN. Clustering is a fundamental mechanism to design scalable sensor network protocols. In general terms, clustering is the classi cation of similar objects into different groups or subsets. The formed subsets in some sense belong together, because they share one or more similar characteristics or behaviors. Examples of such common characteristics could be: proximity according to some de ned distance measure, similar behaviors, common data patterns, and so on. In the most general problem the number of clusters or groups is unknown, as are the properties that make them similar. Clustering techniques have been proposed in wireless networks in order to achieve high energy ef ciency and assure long network lifetime, for bandwidth reuse, for data gathering [1] and target tracking [2], one-to-many, many-to-one, one-to-any, or one-to-all communications, routing [3 6], and so on. Clustering is particularly useful for applications that require scalability to hundreds or thousands of nodes. Scalability in this context implies the need for load balancing, ef cient resource utilization, and data aggregation [7]. Also, many routing protocols can use clustering to create a hierarchical structure and minimize the path cost when communicating with the base station. In many sensors network applications where data collection and processing can be done in situ, this hierarchical approach is a promising method for ef ciently organizing the network. Also, many signal processing algorithms used for extraction of nal information from the data gathered by the sensors are well-suited for local processing of data within the clusters. Graph Theory concepts can be used to describe, analyze and represent a WSN in a very clear way. Several of these concepts refer to structures and algorithms that had been previously used to address other aspects like topology management, localization techniques and routing, not only in WSN but also in other types of wireless and wired networks [8 11]. For example, the OSPF (Open Shorted Path First) routing algorithm
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used for routing in wired LANs implements the SPF (Shorted Path First) algorithm or Dijkstra s algorithm, which solves the single-source shortest path problem for a directed graph with nonnegative edge weights. Graph data structures and algorithms can easily represent the network with stationary wireless sensor. The construction of basic graph structures like trees, cliques or dominating sets, and the use of graph algorithms like Breadth First Search (BFS) or Depth First Search (DFS) will help in the construction of clusters to improve the communication in the WSN. However, in mobile WSN, the dynamic change of the network topology due to the sensor movement creates additional challenges when forming the clusters. In this situation it is necessary to implement additional mechanisms to control the changes in the cluster graph. In this section, we give the basic concepts and de nitions that provide a detailed overview of the corresponding relation between clustering in WSN and graph theory, focusing on concepts and de nitions that are important for the understanding of the material to follow. 7.2.1 Wireless Sensor Networks and Graph Theory Concepts A cluster in WNS consists of three main different elements: sensor nodes (SNs), base station (BS), and cluster heads (CH); see Figure 7.2. The SNs are the set of sensors present in the network, arranged to sense the environment and collect the data. The main task of an SN in a sensor eld is to detect events, perform quick local data processing, and then transmit the data. But the greatest constraint it has is the power consumption, which usually is caused when the sensor is observing its surroundings,
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