Pareto Optimal Decision Making in a Distributed Opportunistic Sensing Problem

Joseph Fitzgerald, Christopher Griffin

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

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
Volume49
Issue number2
DOIs
StatePublished - Feb 1 2019

Fingerprint

Decision making
Sensors
Polynomials
Computational complexity

All Science Journal Classification (ASJC) codes

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

Cite this

@article{5df2359459904a92a957cd0e2c254a7c,
title = "Pareto Optimal Decision Making in a Distributed Opportunistic Sensing Problem",
abstract = "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.",
author = "Joseph Fitzgerald and Christopher Griffin",
year = "2019",
month = "2",
day = "1",
doi = "10.1109/TCYB.2017.2766451Y",
language = "English (US)",
volume = "49",
pages = "719--725",
journal = "IEEE Transactions on Cybernetics",
issn = "2168-2267",
publisher = "IEEE Advancing Technology for Humanity",
number = "2",

}

Pareto Optimal Decision Making in a Distributed Opportunistic Sensing Problem. / Fitzgerald, Joseph; Griffin, Christopher.

In: IEEE Transactions on Cybernetics, Vol. 49, No. 2, 8098606, 01.02.2019, p. 719-725.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Pareto Optimal Decision Making in a Distributed Opportunistic Sensing Problem

AU - Fitzgerald, Joseph

AU - Griffin, Christopher

PY - 2019/2/1

Y1 - 2019/2/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85033664085&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85033664085&partnerID=8YFLogxK

U2 - 10.1109/TCYB.2017.2766451Y

DO - 10.1109/TCYB.2017.2766451Y

M3 - Article

VL - 49

SP - 719

EP - 725

JO - IEEE Transactions on Cybernetics

JF - IEEE Transactions on Cybernetics

SN - 2168-2267

IS - 2

M1 - 8098606

ER -