Ad-hoc networks : fundamental properties and network by Ramin Hekmat

By Ramin Hekmat

This booklet presents an unique graph theoretical method of the elemental homes of instant cellular ad-hoc networks. This strategy is mixed with a pragmatic radio version for actual hyperlinks among nodes to supply new perception into community features like connectivity, measure distribution, hopcount, interference and capability. The e-book establishes directives for designing ad-hoc networks and sensor networks. it is going to curiosity the educational neighborhood, and engineers who roll out ad-hoc and sensor networks.

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1. Comparison of network models. link probability ad-hoc networks random graph regular lattice graph scale-free graph pathloss geometric random graph lognormal geometric random graph local correlation small-world property depends on the distance yes, grows and fading weaker as fading increases distance independent, no same for any two nodes distance dependent, no same for any two adjacent nodes distance independent, (2) higher for links to ”hubs” distance dependent, yes same for any two nodes at the same distance distance dependent, a yes, grows probabilistic function of weaker as ξ distance and ξ (4) increases (1) yes no yes (strongly) (3) depends on ξ (5) Notes: (1) If the increase in number of nodes is combined with an increase in the size of the service area, network diameter increases and small-world property is not present.

This result has been proved in both [50] and [44]. 2 Regular lattice graph model A regular lattice graph is constructed with nodes (vertices) placed on a regular grid structure. Adjacent nodes on the grid are all equidistant (although this distance can be defined to be non-metric). The probability that two adjacent nodes on the grid are connected is p. Non-adjacent nodes cannot be linked directly. Links (edges) are then created independently and are all equiprobable. 5 shows an example of a 2-dimensional lattice graph on a square grid of size 10 × 20 for two different values of p.

The clustering coefficient of node i is defined as: ci = number of opposite edges of i . di (di − 1) /2 The clustering coefficient is thus the ratio between the actual number of links between the neighbors of node i and the maximum possible number of links between these neighbors. 2). The clustering coefficient of G, denoted by CG , is the average of ci for all nodes with di ≥ 2. Local correlation Let node i be connected to node j. If the probability of node i being connected to the neighbors of node j is higher than the probability of node i being connected to other nodes in the network (all nodes except node i’s one-hop and two-hop neighbors), we say that edges are locally correlated.

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