Optimal sensor allocation by integrating causal models and set-covering algorithms

Jing Li, Jionghua Jin

Research output: Contribution to journalArticlepeer-review

27 Scopus citations


Massive amounts of data are generated in Distributed Sensor Networks (DSNs), posing challenges to effective and efficient detection of system abnormality through data analysis. This article proposes a new method for optimal sensor allocation in a DSN with the objective of timely detection of the abnormalities in a underlying physical system. This method involves two steps: first, a Bayesian Network (BN) is built to represent the causal relationships among the physical variables in the system; second, an integrated algorithm by combining the BN and a set-covering algorithm is developed to determine which physical variables should be sensed, in order to minimize the total sensing cost as well as satisfy a prescribed detectability requirement. Case studies are performed on a hot forming process and a large-scale cap alignment process, showing that the developed algorithm satisfies both the cost and detectability requirements.

Original languageEnglish (US)
Pages (from-to)564-576
Number of pages13
JournalIIE Transactions (Institute of Industrial Engineers)
Issue number8
StatePublished - Aug 2010


  • Bayesian networks
  • Causal models
  • Sensor allocation
  • Set-covering algorithm

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering


Dive into the research topics of 'Optimal sensor allocation by integrating causal models and set-covering algorithms'. Together they form a unique fingerprint.

Cite this