Skip to main content
We gratefully acknowledge support from the Simons Foundation, member institutions, and all contributors. Donate
arXiv logo
Cornell University Logo

Computer Science > Information Theory

arXiv:cs/0701059 (cs)
[Submitted on 9 Jan 2007 (v1), last revised 25 Apr 2007 (this version, v3)]

Title:Enhancing Sensor Network Lifetime Using Interactive Communication

Authors:Samar Agnihotri, Pavan Nuggehalli
View PDF
Abstract: We are concerned with maximizing the lifetime of a data-gathering wireless sensor network consisting of set of nodes directly communicating with a base-station. We model this scenario as the m-message interactive communication between multiple correlated informants (sensor nodes) and a recipient (base-station). With this framework, we show that m-message interactive communication can indeed enhance network lifetime. Both worst-case and average-case performances are considered.
Comments: 5 pages. To appear in ISIT-2007 (Final version). Major revision wrt version 2
Subjects: Information Theory (cs.IT)
ACM classes: C.2; E.4; H.1.1
Cite as: arXiv:cs/0701059 [cs.IT]
  (or arXiv:cs/0701059v3 [cs.IT] for this version)
  https://doi.org/10.48550/arXiv.cs/0701059

Submission history

From: Samar Agnihotri [view email]
[v1] Tue, 9 Jan 2007 03:31:38 UTC (13 KB)
[v2] Wed, 10 Jan 2007 08:54:43 UTC (13 KB)
[v3] Wed, 25 Apr 2007 09:26:23 UTC (63 KB)
Full-text links:

Access Paper:

  • View PDF
  • TeX Source
  • Other Formats
view license
Current browse context:
cs.IT
< prev   |   next >
new | recent | 2007-01

References & Citations

DBLP - CS Bibliography

listing | bibtex
Samar Agnihotri
Pavan Nuggehalli
export BibTeX citation

Bookmark

BibSonomy logo Reddit logo

Bibliographic and Citation Tools

Bibliographic Explorer (What is the Explorer?)
Connected Papers (What is Connected Papers?)
Litmaps (What is Litmaps?)
scite Smart Citations (What are Smart Citations?)

Code, Data and Media Associated with this Article

alphaXiv (What is alphaXiv?)
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Hugging Face (What is Huggingface?)
Papers with Code (What is Papers with Code?)
ScienceCast (What is ScienceCast?)

Demos

Replicate (What is Replicate?)
Hugging Face Spaces (What is Spaces?)
TXYZ.AI (What is TXYZ.AI?)

Recommenders and Search Tools

Influence Flower (What are Influence Flowers?)
CORE Recommender (What is CORE?)

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)