  Since a spatial distribution of communication requests is inhomogeneous and
related to a population, in constructing a network, it is crucial for
delivering packets on short paths through the links between proximity nodes and
for distributing the load of nodes how to locate the nodes as base-stations on
a realistic wireless environment. In this paper, from viewpoints of complex
network science and biological foraging, we propose a scalably self-organized
geographical network, in which the proper positions of nodes and the network
topology are simultaneously determined according to the population, by
iterative divisions of rectangles for load balancing of nodes in the adaptive
change of their territories. In particular, we consider a decentralized routing
by using only local information,and show that, for searching targets around
high population areas, the routing on the naturally embedded fractal-like
structure by population has higher efficiency than the conventionally optimal
strategy on a square lattice.
