文档介绍：The Cluster Density of a Distributed Clustering
Algorithm in Ad works
Christian Bettstetter
o Euro-Labs, working Lab, Munich, Germany
Technische Universitat¨ Munchen,¨ Institute works, Munich, Germany (previously)
Abstract— Given is a wireless work whose nodes new cluster (e a clusterhead) or affiliate with an existing
are randomly distributed according to a homogeneous Poisson cluster (e an ordinary node). Each node has a weight w
ρ
point process of density (in nodes per unit area). work that determines its chance to e a clusterhead; the larger
employs Basagni’s distributed mobility–adaptive clustering (DMAC)
algorithm to achieve a work structure. We the weight of a node, the better it is suited to be a clusterhead.
show that the cluster density, ., the expected number of cluster- These weights may be assigned randomly or according to cer-
ρ= ρµ
heads per unit area, is c 1+µ/2 ,where denotes the expected tain characteristics of the node (., its IP address, transmission
number of neighbors of a node. Consequently, a clusterhead is ex- power). We assume that each node has a unique weight, at least
pected to incorporate half of its neighboring nodes into its cluster. among all nodes within a distance of two hops.
This result also holds in a scenario with mobile nodes and serves
as a bound for inhomogeneous spatial node distributions.
Index Terms— Ad