Pareto Optimal Decision Making in a Distributed Opportunistic Sensing Problem

Joseph Fitzgerald, Christopher Griffin

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


We extend prior results on a single decision maker opportunistic sensing problem to a distributed, multidecision maker setting. The original formulation of the problem considers how to opportunistically use "in-flight" sensors to maximize target coverage. In that paper, the authors show that this problem is NP-hard with a strong polynomial heuristic for a single decision maker. This paper extends this by considering a distributed decision making scenario in which multiple independent parties attempt to simultaneously engage in opportunistic sensor assignment while managing interassignment conflict. Specifically, we develop an algorithm that: 1) produces a Pareto optimal opportunistic sensor allocation; 2) requires fewer bits of communicated information than a completely centralized deconfliction approach; and 3) runs in distributed polynomial time once the individual decision makers identify their preferred (optimal) sensor allocations. We validate these claims using appropriate simulations.

Original languageEnglish (US)
Article number8098606
Pages (from-to)719-725
Number of pages7
JournalIEEE Transactions on Cybernetics
Issue number2
StatePublished - Feb 2019

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Information Systems
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering


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