A knapsack approach to sensor-mission assignment with uncertain demands

Diego Pizzocaro, Matthew P. Johnson, Hosam Rowaihy, Stuart Chalmers, Alun Preece, Amotz Bar-Noy, Thomas La Porta

Research output: Contribution to journalConference article

2 Scopus citations

Abstract

A sensor network in the field is usually required to support multiple sensing tasks or missions to be accomplished simultaneously. Since missions might compete for the exclusive usage of the same sensing resource we need to assign individual sensors to missions. Missions are usually characterized by an uncertain demand for sensing resource capabilities. In this paper we model this assignment problem by introducing the Sensor Utility Maximization (SUM) model, where each sensor-mission pair is associated with a utility offer. Moreover each mission is associated with a priority and with an uncertain utility demand. We also define the benefit or profit that a sensor can bring to a mission as the fraction of mission's demand that the sensor is able to satisfy, scaled by the priority of the mission. The goal is to find a sensor assignment that maximizes the total profit, while ensuring that the total utility cumulated by each mission does not exceed its uncertain demand. SUM is NP-Complete and is a special case of the well known Generalized Assignment Problem (GAP), which groups many knapsack-style problems. We compare four algorithms: two previous algorithms for problems related to SUM, an improved implementation of a state-of-the-art pre-existing approximation algorithm for GAP, and a new greedy algorithm. Simulation results show that our greedy algorithm appears to offer the best trade-off between quality of solution and computation cost.

Original languageEnglish (US)
Article number711205
JournalProceedings of SPIE - The International Society for Optical Engineering
Volume7112
DOIs
Publication statusPublished - Dec 1 2008
EventUnmanned/Unattended Sensors and Sensor Networks V - Cardiff, Wales, United Kingdom
Duration: Sep 16 2008Sep 18 2008

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All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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