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Proceedings Paper

A market-based optimization approach to sensor and resource management
Author(s): Dan Schrage; Christopher Farnham; Paul G. Gonsalves
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Paper Abstract

Dynamic resource allocation for sensor management is a problem that demands solutions beyond traditional approaches to optimization. Market-based optimization applies solutions from economic theory, particularly game theory, to the resource allocation problem by creating an artificial market for sensor information and computational resources. Intelligent agents are the buyers and sellers in this market, and they represent all the elements of the sensor network, from sensors to sensor platforms to computational resources. These agents interact based on a negotiation mechanism that determines their bidding strategies. This negotiation mechanism and the agents' bidding strategies are based on game theory, and they are designed so that the aggregate result of the multi-agent negotiation process is a market in competitive equilibrium, which guarantees an optimal allocation of resources throughout the sensor network. This paper makes two contributions to the field of market-based optimization: First, we develop a market protocol to handle heterogeneous goods in a dynamic setting. Second, we develop arbitrage agents to improve the efficiency in the market in light of its dynamic nature.

Paper Details

Date Published: 9 May 2006
PDF: 9 pages
Proc. SPIE 6229, Intelligent Computing: Theory and Applications IV, 62290I (9 May 2006); doi: 10.1117/12.665153
Show Author Affiliations
Dan Schrage, Charles River Analytics, Inc. (United States)
Christopher Farnham, Charles River Analytics, Inc. (United States)
Paul G. Gonsalves, Charles River Analytics, Inc. (United States)

Published in SPIE Proceedings Vol. 6229:
Intelligent Computing: Theory and Applications IV
Kevin L. Priddy; Emre Ertin, Editor(s)

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