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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Published on: September 8, 2023

Distributed inference in wireless sensor networks.

Venugopal V Veeravalli1, Pramod K Varshney

  • 1ECE Department and Coordinated Science Laboratory, University of Illinois at Urbana-Champaign, Urbana-Champaign, IL 61801-2918, USA. vvv@illinois.edu

Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences
|November 30, 2011
PubMed
Summary
This summary is machine-generated.

Distributed inference in wireless sensor networks (WSNs) presents unique challenges. This paper reviews recent developments in distributed detection, estimation, and tracking, considering network and resource constraints.

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Last Updated: May 27, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

Area of Science:

  • Computer Science
  • Electrical Engineering
  • Signal Processing

Background:

  • Statistical inference is a well-established field.
  • Modern wireless sensor networks (WSNs) introduce novel distributed inference challenges.
  • Recent years have seen a resurgence of research in this domain.

Purpose of the Study:

  • To introduce readers to the unique features of distributed inference in WSNs.
  • To summarize recent research advancements in this area.
  • To highlight solutions that account for network and resource limitations.

Main Methods:

  • Review of existing literature on distributed inference in WSNs.
  • Focus on distributed detection, parameter estimation, and tracking algorithms.
  • Analysis of solutions considering communication networks and sensor resource constraints.

Main Results:

  • Novel features of distributed inference in WSNs identified.
  • Recent developments in distributed detection, estimation, and tracking summarized.
  • Emphasis on practical solutions for resource-constrained WSNs.

Conclusions:

  • Distributed inference in WSNs is a dynamic research area.
  • Effective solutions must address network topology and sensor limitations.
  • Further research is needed to optimize inference in complex WSNs.