A market-based optimization approach to sensor and resource management

Dan Schrage; Christopher Farnham; Paul G. Gonsalves

doi:10.1117/12.665153

9 May 2006 A market-based optimization approach to sensor and resource management

Dan Schrage, Christopher Farnham, Paul G. Gonsalves

Proceedings Volume 6229, Intelligent Computing: Theory and Applications IV; 62290I (2006) https://doi.org/10.1117/12.665153
Event: Defense and Security Symposium, 2006, Orlando (Kissimmee), Florida, United States

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.

Citation Download Citation

Dan Schrage, Christopher Farnham, and Paul G. Gonsalves "A market-based optimization approach to sensor and resource management", Proc. SPIE 6229, Intelligent Computing: Theory and Applications IV, 62290I (9 May 2006); https://doi.org/10.1117/12.665153

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available