A minimum cost heterogeneous sensor network with a lifetime constraint

  • Vivek P. Mhatre
  • , Catherine Rosenberg
  • , Daniel Kofman
  • , Ravi Mazumdar
  • , Ness Shroff

Research output: Contribution to journalArticlepeer-review

Abstract

We consider a heterogeneous sensor network in which nodes are to be deployed over a unit area for the purpose of surveillance. An aircraft visits the area periodically and gathers data about the activity in the area from the sensor nodes. There are two types of nodes that are distributed over the area using two-dimensional homogeneous Poisson point processes; type 0 nodes with intensity (average number per unit area) λ 0 and battery energy E 0; and type 1 nodes with intensity λ 1, and battery energy E 1. Type 0 nodes do the sensing while type 1 nodes act as the cluster heads besides doing the sensing. Nodes use multihopping to communicate with their closest cluster heads. We determine the optimum node intensities (λ 0, λ 1,) and node energies (E 0, E 1) that guarantee a lifetime of at least T units, while ensuring connectivity and coverage of the surveillance area with a high probability. We minimize the overall cost of the network under these constraints. Lifetime is defined as the number of successful data gathering trips (or cycles) that are possible until connectivity and/or coverage are lost. Conditions for a sharp cutoff are also taken into account, i.e., we ensure that almost all the nodes run out of energy at about the same time so that there is very little energy waste due to residual energy. We compare the results for random deployment with those of a grid deployment in which nodes are placed deterministically along grid points. We observe that in both cases λ 1 scales approximately as λ 0. Our results can be directly extended to take into account unreliable nodes.

Original languageEnglish
Pages (from-to)4-14
Number of pages11
JournalIEEE Transactions on Mobile Computing
Volume4
Issue number1
DOIs
Publication statusPublished - 1 Jan 2005

Keywords

  • Energy
  • Lifetime
  • Sensor networks
  • Stochastic geometry
  • Voronoi cells

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