Managing Multi-Modal Sensor Networks Using Price Theory

We propose a unified framework for sensor management in multi-modal sensor networks, which is inspired by the trading behavior of economic agents in commercial markets. Each sensor node (SN) acts as a seller who wants to sell the data it collects, to the sensor network manager (SM) who acts as a buyer. The resources and the data are priced by looking to balance global supply and demand, with the SN required to purchase resources for producing the data, and the SM required to purchase data to accomplish his tasks. We model this interaction as a double sided market, with both consumers and producers, and propose an iterative double auction mechanism for computing the equilibrium of such a market. We relate the equilibrium point to the solutions of sensor selection (SS), resource allocation (RA), and data fusion (DF) problems, which constitute the sensor management. The proposed framework will enable the system to determine the kind and the amount of data that should be produced, and to combine the data that is produced at each SN. To illustrate this framework, we consider the problem of multiple-target tracking as an example. Numerical examples demonstrate the effectiveness of the proposed method, and show that appropriate sensor management will result in an accurate estimate of the number of targets in the scene, higher correct identifications of the targets, and a lower mean-squared error in the estimates of their positions and velocities.