Geography-informed energy conservation for Ad Hoc routing
Proceedings of the 7th annual international conference on Mobile computing and networking
Exposure in wireless sensor networks: theory and practical solutions
Wireless Networks
An inhomogeneous spatial node distribution and its stochastic properties
Proceedings of the 10th ACM Symposium on Modeling, analysis, and simulation of wireless and mobile systems
Power conservation for strongly connected topology control in wireless sensor network
Proceedings of the 1st ACM international workshop on Foundations of wireless ad hoc and sensor networking and computing
NPART - node placement algorithm for realistic topologies in wireless multihop network simulation
Proceedings of the 2nd International Conference on Simulation Tools and Techniques
Energy scaling laws for distributed inference in random fusion networks
IEEE Journal on Selected Areas in Communications - Special issue on stochastic geometry and random graphs for the analysis and designof wireless networks
Lifetime benefits through load balancing in homogeneous sensor networks
WCNC'09 Proceedings of the 2009 IEEE conference on Wireless Communications & Networking Conference
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Uniformly random or Poisson distributions are widely accepted models for the location of the nodes in wireless sensor networks if nodes are deployed in large quantities and there is little control over where they are dropped. On the other hand, by placing nodes in regular topologies, we expect benefits both in coverage and efficiency of communication. We describe and analyze a basic localized algorithm and three modifications for topology control that provide a tradeoff between performance and deployment cost. The objective is to regularize the topology for improved energy efficiency. The basic algorithm produces quasiregular networks, which only use nodes as sentries and relays that are approximately evenly spaced, thereby emulating a regular grid topology. It is shown that quasiregular networks have a significant energy and lifetime advantage compared with purely random networks. We consider two specific types of quasiregular networks: the ones that are based on a Gaussian deviation about an ideal grid point (type A), and the ones that consist of a subset of nodes taken from a Poisson point process (type B). We show that the two types are equivalent for a certain density of the Poisson point process and, in particular, that in both cases the deviation from the ideal regular grid follows a Rayleigh distribution, whereas the distance between nearest neighbors is Ricean.