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Distributed Fusion of Sensor Data in a Constrained Wireless Network.

Charikleia Papatsimpa1, Jean-Paul Linnartz2,3

  • 1SPS group, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands. C.Papatsimpa@tue.nl.

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Summary

This study introduces a distributed sensing approach for smart buildings using hidden Markov models (HMMs). It enables accurate presence detection by fusing data from multiple sensors, reducing communication needs in Internet of Things (IoT) networks.

Keywords:
Internet of Things (IoT)efficient transmissionsensor fusionsmart buildingwireless sensor networks

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Ubiquitous Computing

Background:

  • Smart buildings increasingly utilize Internet of Things (IoT) devices for functions like connected lighting and sensing.
  • Exponential growth in IoT devices generates massive, often redundant data, necessitating local data pre-processing and fusion.
  • Constrained communication environments in distributed systems require efficient data merging strategies.

Purpose of the Study:

  • To address the challenge of local data fusion in distributed sensor networks with communication limitations.
  • To propose a novel distributed sensing of a hidden Markov model (DS-HMM) for presence detection in smart buildings.
  • To leverage a posteriori probabilities or likelihood ratios as an interface for heterogeneous sensors.

Main Methods:

  • Developed a DS-HMM framework for presence detection, treating it as a distributed sensing problem.
  • Utilized a posteriori probabilities/likelihood ratios as a standardized interface between heterogeneous sensors.
  • Proposed an efficient transmission policy and a fusion algorithm for merging data from independently running HMMs on sensor nodes.

Main Results:

  • A proof-of-concept prototype demonstrated the feasibility of the DS-HMM concept in an office environment.
  • The proposed scheme achieved high accuracy in presence detection.
  • Significant reduction in communication requirements was observed compared to traditional methods.

Conclusions:

  • The DS-HMM concept effectively enables distributed information fusion in wireless sensor networks.
  • A posteriori probabilities serve as a robust interface for merging data from diverse sensors with varying error profiles.
  • The approach is adaptable for various distributed sensing and data fusion applications beyond presence detection.