Sparse power-efficient topologies for wireless ad hoc sensor networks

dc.creatorBagchi, Amitabha
dc.date2008-05-27
dc.date2008-07-18
dc.date.accessioned2026-07-07T09:50:50Z
dc.date.available2026-07-07T09:50:50Z
dc.descriptionWe study the problem of power-efficient routing for multihop wireless ad hoc sensor networks. The guiding insight of our work is that unlike an ad hoc wireless network, a wireless ad hoc sensor network does not require full connectivity among the nodes. As long as the sensing region is well covered by connected nodes, the network can perform its task. We consider two kinds of geometric random graphs as base interconnection structures: unit disk graphs $\UDG(2,λ)$ and $k$-nearest-neighbor graphs $\NN(2,k)$ built on points generated by a Poisson point process of density $λ$ in $\RR^2$. We provide subgraph constructions for these two models $\US(2,λ)$ and $\NS(2,k)$ and show that there are values $λ_s$ and $k_s$ above which these constructions have the following good properties: (i) they are sparse; (ii) they are power-efficient in the sense that the graph distance is no more than a constant times the Euclidean distance between any pair of points; (iii) they cover the space well; (iv) the subgraphs can be set up easily using local information at each node. We also describe a simple local algorithm for routing packets on these subgraphs. Our constructions also give new upper bounds for the critical values of the parameters $λ$ and $k$ for the models $\UDG(2,λ)$ and $\NN(2,k)$.
dc.descriptionA few of the results (dealing with nearest-neighbor graphs) have appeared earlier in arXiv:0804.3784v1. A brief announcement of those results will appear in PODC 2008
dc.identifierhttps://arxiv.org/abs/0805.4060
dc.identifierhttp://arxiv.org/abs/0805.4060
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/165083
dc.subjectNetworking and Internet Architecture
dc.subjectProbability
dc.subjectC.2.1; G.3
dc.titleSparse power-efficient topologies for wireless ad hoc sensor networks
dc.typetext

Files

Collections