Figure 2.10. Chessboard clustering scheme. in VS .NET

Creating QR Code JIS X 0510 in VS .NET Figure 2.10. Chessboard clustering scheme.
Figure 2.10. Chessboard clustering scheme.
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Figure 2.11. A black (left) and white (right) clustering of the heterogeneous WSN.
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forwarding than do other sensor nodes, switching the color of the clustering balances the energy consumption among sensors, and prolongs the network lifetime. Figure 2.11 shows an example of clustering based on black and on white cells, respectively. In the black clustering, sensor node 3 is a critical node forwarding packets on behalf of nodes 1 and 2. In the white clustering, nodes 1 and 2 become critical forwarding packets on behalf of other nodes in the cluster; in particular, node 2 now forwards the packets of node 3. In order to form a black (white) clustering, each black (white) high-end sensor node broadcasts a hello packet, containing its identi er and its location. Low-end sensor nodes may receive hello packets from multiple black (white) high-end nodes. In a two-dimensional sensor eld, each low-end sensor node selects the closest high-end sensor node as the cluster head; this leads to the formation of Voronoi cells where the cluster heads correspond to the nuclei of the cells [16]. The decision to switch the color of the clustering is based on the energy levels of the high-end nodes. Suppose the current clustering is black. Periodically, each black high-end sensor node exchanges packets with its neighboring white high-end nodes. The packets contain the energy remaining in the node. If the remaining energy of the black high-end node drops below a threshold, its neighboring white high-end nodes become active and initiate cluster formation. As the network runs, the black highend nodes drain their energy and become unavailable. Gradually, the white high-end nodes become active. Both intra- and intercluster routing protocols are proposed [13, 16]. Routing within a cluster is achieved via a greedy geographic routing protocol. Each low-end sensor node simply forwards a packet to the neighbor closest to the cluster head. In order to support intercluster routing, after the clusters are formed, each cluster head sends its location to the sink. The sink then broadcasts the locations of all clusters heads. For a cluster head to communicate with the sink, it draws a line between itself and the sink. The line intersects some number of Voronoi cells. The packet is forwarded from the source cluster head to the sink through the cluster heads in these relay cells. The chessboard routing protocol achieves a higher delivery ratio, lower total energy consumption, smaller end-to-end delay, and better throughput than two routing protocols for homogeneous WSNs. The details of the chessboard clustering
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and routing protocols, as well as their performance evaluation in simulation, can be found in references 13 and 16. 2.3.3 Analyses of System Lifetime in Heterogeneous WSNs Mhatre and Rosenberg [24] present a cost-based comparative study of (a) homogeneous WSNs and (b) heterogeneous WSNs with two types of nodes. Their model takes into account the cost of manufacturing the hardware as well as the battery energy of the sensor nodes. First, a single-hop clustered architecture is considered, with LEACH [16] selected as the representative in a single-hop homogeneous WSN. For the multihop homogeneous clustered architecture a multihop variant, called M-LEACH, is proposed and analyzed. In comparing (a) the cost of the multihop homogeneous clustered architecture with M-LEACH and (b) a multihop heterogeneous clustered architecture, the homogeneous WSN can outperform the heterogeneous one if the nonuniform energy drainage problem is not addressed. Mhatre et al. continue their study of heterogeneous WSNs in reference 15. They consider a WSN with nodes of two types distributed over a sensor eld using twodimensional homogeneous Poisson point processes: (a) type 0 nodes with intensity (average number per unit area) 0 and battery energy E0 and (b) type 1 nodes with intensity 1 and battery energy E1 . The type 0 nodes do the sensing while the type 1 nodes act as the cluster heads. Nodes use multihop paths to communicate with their closest cluster head. The optimum node intensities ( 0 , 1 ) and node energies (E0 , E1 ) that guarantee a lifetime of at least T units, while ensuring both connectivity and coverage of the surveillance area with high probability, are determined. The overall cost of the network is minimized under these constraints. Here, the network lifetime is de ned as the number of successful data gathering trips (or cycles) that are possible until connectivity and/or coverage are lost. Conditions for a sharp cutoff are taken into account. This means that it is ensured that almost all the nodes run out of energy at about the same time so that there is very little energy lost due to residual energy. The results comparing a random deployment of nodes with a deployment in which nodes are placed deterministically along grid points show that 1 scales approximately as 0 . The results can be extended to take into account unreliable nodes. Duarte-Melo and Liu [25] examine the performance and the energy consumption of a heterogeneous WSN providing periodic data from a sensor eld to a remote receiver. A at homogeneous WSN is compared to one in which an overlay of fewer more powerful sensor nodes is added. The energy consumption is formulated and the estimated lifetime based on a clustering mechanism with varying parameters related to the sensor eld, such as size and distance, is studied. The optimal number of clusters is quanti ed based on the model. Also, an allocation of energy between the two levels of the architecture is discussed. Li and Mohapatra [26] develop an analytical model for the problem of nonuniform energy drainage. It is found that density does not affect the energy consumption rate of a node. This con rms the fact that simply deploying more nodes in a network cannot prolong its lifetime. Using the model, they investigate the effectiveness of some existing approaches toward mitigating the nonuniform energy drainage problem in a
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