Using virtual markets to program global behavior in sensor networks

Abstract

This 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.