A Capitalist Scheme for Energy Management in Inferential Sensor Networks

Abstract

Suppose that the energy made available to a sensor network at the beginning of a time slot is proportional to the success of the network inferential task during the previous slot. And further, assume that such energy is to be apportioned to charge the individual sensors, such that the more energy one sensor receives, the better it does its job. Then, the information gathered by the network in the long run consequently obeys a multiplicative rule, which enables us to adapt some results from portfolio theory to design the optimal apportionment. Two regimes emerge, one in which the expected value of the long-run information is key and all the energy is assigned to the “best” sensor, and another - more tricky - where the expected logarithm of the long-run information matters, and the solution is given by Cover's log-optimal apportionment.