Round 0 in .NET

Generate QR-Code in .NET Round 0
Round 0
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Round 1
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Figure 9.13. Sensors pursue relocation iteratively until no improvement in coverage can be achieved. (Taken from reference 51.)
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NODE POSITIONING FOR INCREASED DEPENDABILITY OF WIRELESS SENSOR NETWORKS
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(b )
Figure 9.14. Steps for SMART: (a) Initial 2D mesh. (b) Row scan. (c) Column scan.
of energy, such increase can be justi ed. Nonetheless, this process still can be very slow and hence prolong the deployment time. With the objective of reducing the overall deployment time, Wu and Yang [53] proposed another solution to the same problem based on two-dimensional scan of a clustered network, called SMART. The approach adopts a popular scheme for balancing load among nodes in parallel processing architectures by assigning an equal number of tasks to each processor. This idea is applied to a multicluster WSN where each cluster is represented with a square cell forming a 2D mesh, as seen in Figure 9.14, which is redrawn from reference 53. The number of sensors annotated on every cell represents the load of that cluster. Each cluster head knows only its location within the mesh (i.e., row and column indices) and the number of sensors in its cluster. It is assumed that a cluster head can only communicate with its counterparts in neighboring cells. Achieving uniform coverage is then mapped to the load-balancing problem with a goal of evening the distribution of sensors among the clusters. To achieve this goal, each cluster head performs both a row-based and a column-based scan to exchange the load information. In a row-based scan, the leftmost cluster head forwards its load (i.e., number of sensors) to its right neighbor. Each neighbor on the row adds the received load to its own load and forwards it until the rightmost cluster head is reached. This cluster head computes the average load for its row and sends a message back until the leftmost cluster head gets such average (Figure 9.14b). After the scan process, the sensors are relocated to match the desired node count per cluster. That is, the overloaded clusters give sensors while the underloaded clusters take sensors. The same procedure is applied for each column (Figure 9.14c). The approach also handles possible holes in the network when there are clusters with no sensors. The simulation results compared VOR (discussed above) and SMART with respect to the number of moves made by the sensors and the number of rounds until termination. SMART was shown to provide the minimum number of moves. Although it was also shown that SMART converges in a fewer number of rounds for densely populated WSNs, VOR was found to be superior for sparsely populated networks. Another similar post-deployment relocation work for improving the initial coverage and providing uniform distribution of sensors is presented in reference 56. Although the idea is similar to the VEC mechanism reported in reference 48, this time it is inspired by the equilibrium of particles in Physics. The particles follow
DYNAMIC REPOSITIONING OF NODES
the law of Coulomb and push themselves to reach equilibrium in an environment. Therefore, the authors de ne forces for each sensor node in the network based on the internode distances and the local density of nodes. The partial force on a node i from node j at time t is expressed as follows: ft