Identifying small world network properties in ad-hoc networks
Small World Network is a densely connected network with low degree of separation. These types of networks have high clustering property like regular network as well as have low average path length like random network. Thus, Small World Networks are both Locally and Globally ecient as compared to other networks. The above prop- erties can be described using parameters like characteristic path length (L), clustering coefficient (C), local efficiency (Eloc) and global efficiency (Eglob). Through experiments, done in the past, it has been found that many real world networks exihibit the properties of small world network. This has given rise To finding models for real world networks so that the models reflect small world ness of the real world network. This thesis uses the model of converting a square grid into a small world model by introducing some special nodes. The equations to compute the small world parameters for this model with one and or two special nodes derived in this thesis.
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