Using Virtual Markets to Program Global Behavior in Sensor Networks
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CitationMainland, Geoff, Laura Kang, Sébastien Lahaie, David C. Parkes, and Matt Welsh. 2004. Using virtual markets to program global behavior in sensor networks. In Proceedings of the 11th Workshop on ACM SIGOPS European workshop: September 19-22, 2004, Leuven, Belgium, ed. Y. Berbers, M. Castro, and ACM Special Interest Group on Operating Systems. New York, N.Y.: ACM Press.
AbstractThis paper presents market-based macroprogramming (MBM), a new paradigm for achieving globally efficient behavior in sensor networks. Rather than programming the individual, low-level behaviors of sensor nodes, MBM defines a virtual market where nodes sell "actions" (such as taking a sensor reading or aggregating data) in response to global price information. Nodes take actions to maximize their own utility, subject to energy budget constraints. The behavior of the network is determined by adjusting the price vectors for each action, rather than by directly specifying local node actions, resulting in a globally efficient allocation of network resources. We present the market-based macro-programming paradigm, as well as several experiments demonstrating its value for a sensor network vehicle tracking application.
Citable link to this pagehttp://nrs.harvard.edu/urn-3:HUL.InstRepos:4054439
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