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For example, the security system is part of the auto system, antitheft system X is a kind of security system, and product A is an instance of antitheft system X First, the substitution procedure will check all other instances in the class antitheft system X in which product A belongs Since no record is found, the search moves to the next class (ie, antitheft system Y) to nd a suitable substitution In this class, products B and C are found Both of them will be checked to ascertain whether they are of high performance and can be installed in Toyota Sedan 07 After checking, it is found that product B satis es the constraint, and therefore it is used in the substitution As a result, a new solution is derived based on a constraint-based substitution.

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Note that other components, such as the price of the new model, may need readjustment Following are the steps in constraint-based substitution Step Step Step Step 1 2 3 4 Retrieve the most similar case from the case base Determine any constraint violation Substitute the components violated using the prede ned semantic knowledge Perform the substitution and make other adjustments, if necessary..

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presented in [72] produces clustering of a variable diameter In this case, the diameter depends on the degree of mobility of the nodes: the more the nodes move, the smaller the clusters (easier to maintain), and vice versa More recent work for clustering and backbone set up is described for networks quite different from the general ad hoc model considered in [72] and in the other works already mentioned In [86], some nodes are assumed to have backbone capabilities such as the enhanced radio interface that makes it possible to communicate with other backbone nodes at much longer distances than those covered by generic sensor nodes Thus, in this case, the network becomes heterogenous, since it comprehends different types of nodes The solution proposed in [87] constructs a CDS relying on all nodes having a common clock (time is slotted and nodes are synchronized to the slot) 346.

XEROX PARC AND ETHERNET Xerox PARC was the center of development not only for the world s rst personal computer the Alto but also for many of the ancillary concepts of computing including the graphical user interface, the high-resolution display, the word processor, the mouse, and the technology for connecting computers networking called Ethernet. Today, Ethernet is the dominant networking technology. It provides the backbone of corporate networks and is an important feature in wireless WiFi networks as well. The idea for Ethernet was the result of the work of a brilliant

2 Clustering for WSNs We now review clustering and backbone formation protocols that have been proposed explicitly for WSNs The main problem here is that of devising energy ef cient techniques to transport data from the sensors to the sink The overall goal is to increase the network lifetime Hierarchical solutions like those provided by clustering and backbones appear to be viable for accomplishing this task, as demonstrated in [88, 89, 90] Rather than belonging to one of the general classes of protocols for clustering and backbone formation described above, papers on clustering for WSNs are often speci c for a given scenario of application, and, as already mentioned, are designed to achieve given desirable goals, such as prolonged network lifetime, improved tracking, etc Among the protocols that use clustering for increasing network longevity, one of the rst is the LEACH protocol presented in [91].

12000 10000 Number of Paths 8000 6000 4000 2000 0 0.0 0.5 1.0 1.5 2.0 2.5 3.0 Measured insertion Loss (dB) (b) 3.5 4.0

LEACH uses randomized rotation of the cluster heads to evenly distribute the energy load among the sensors in the network In addition, when possible, data are compressed at the cluster head to reduce the number of transmissions A limitation of this scheme is that it requires all current cluster heads to be able to transmit directly to the sink (single-hop topologies) Improvements to the basic LEACH algorithms have been proposed by [92, 93] where multi-layer LEACH-based clustering is proposed and the optimal number of cluster heads is analytically derived to minimize the energy consumption throughout the network An alternative method for selecting CHs for a LEACH-like model is presented in [94] Particle swarm optimization (PSO) is used for dividing the network into clusters A CH is then selected in each cluster based on its mean distance from all the other nodes in the cluster.

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