Mobilized ad-hoc networks: A reinforcement learning approach

Research in mobile ad-hoc networks has focused on situations in which nodes have no control over their movements. We investigate an important but overlooked domain in which nodes do have control over their movements. Reinforcement learning methods can be used to control both packet routing decisions and node mobility, dramatically improving the connectivity of the network. We first motivate the problem by presenting theoretical bounds for the connectivity improvement of partially mobile networks and then present superior empirical results under a variety of different scenarios in which the mobile nodes in our ad-hoc network are embedded with adaptive routing policies and learned movement policies.

RamboNodes for the Metropolitan Ad Hoc Network

We present an algorithm to store data robustly in a large, geographically distributed network by means of localized regions of data storage that move in response to changing conditions. For example, data might migrate away from failures or toward regions of high demand. The PersistentNode algorithm provides this service robustly, but with limited safety guarantees. We use the RAMBO framework to transform PersistentNode into RamboNode, an algorithm that guarantees atomic consistency in exchange for increased cost and decreased liveness. In addition, a half-life analysis of RamboNode shows that it is robust against continuous low-rate failures. Finally, we provide experimental simulations for the algorithm on 2000 nodes, demonstrating how it services requests and examining how it responds to failures.