A Market-based Approach to Sensor Management

Given the explosion in number and types of sensor nodes, the next generation of sensor management systems must focus on identifying and acquiring valuable information from this potential flood of sensor data. Thus an emerging problem is deciding what to produce, where, for whom, and when. Identifying and making tradeoffs involved in information production is a difficult problem that market-based systems can “solve” by allowing user values, or utilities, to drive the selection process. Essentially this transforms the traditional “data driven” approach (in which multiple sensors and information sources are used, with a focus on how to process the collected data) to a user-centered approach in which one or more users treat the information collection and distribution system as a market and vie to acquire goods and services (e.g., information collection, processing resources and network bandwidth). We describe our market-based approach to sensor management, and compare our prototype system to an information-theoretic system in a multi-sensor, multi-user simulation with promising results. This research is motivated in part, by rapid technology advances in network technology and in sensing. These advances allow near universal instrumentation and sensing with worldwide distribution. However while advances in service-oriented architectures and web-based tools have created “the plumbing” for data distribution and access, improvements in optimization of these distributed resources for effective decision making have lagged behind the collection and distribution advances. Key words— sensor management, resource allocation, combinatorial auctions, level 4 data fusion, genetic algorithms, computational market systems. 1 Manuscript received August 13, 2007; revised January 6, 2009; released for publication June 3, 2009. Refereeing of this contribution was handled by Chee Chong and Robert Lynch. 2 This material is based upon work supported by the Army Research Laboratory under contract number W911NF-04-2-0049 and Tennessee State University under subcontract number 332.77-07.361. Accepted to Journal of Advances in Information Fusion, April 2009.