Learning and Fairness in Energy Harvesting: A Maximin Multi-Armed Bandits Approach
This addresses energy efficiency and fairness in sensor networks, but it is an incremental extension of existing bandit methods to a specific domain.
The paper tackles the problem of learning the minimum energy harvested by sensor nodes in a wireless network to maximize network lifespan, proposing a Maximin Multi-Armed Bandits approach with a UCB-based algorithm that achieves performance guarantees similar to UCB1.
Recent advances in wireless radio frequency (RF) energy harvesting allows sensor nodes to increase their lifespan by remotely charging their batteries. The amount of energy harvested by the nodes varies depending on their ambient environment, and proximity to the source. The lifespan of the sensor network depends on the minimum amount of energy a node can harvest in the network. It is thus important to learn the least amount of energy harvested by nodes so that the source can transmit on a frequency band that maximizes this amount. We model this learning problem as a novel stochastic Maximin Multi-Armed Bandits (Maximin MAB) problem and propose an Upper Confidence Bound (UCB) based algorithm named Maximin UCB. Maximin MAB is a generalization of standard MAB and enjoys the same performance guarantee as that of the UCB1 algorithm. Experimental results validate the performance guarantees of our algorithm.