Online learning in autonomic multi-hop wireless networks for transmitting mission-critical applications

Abstract—In this paper, we study how to optimize the transmission decisions of nodes aimed at supporting mission-critical applications, such as surveillance, security monitoring, and military operations, etc. We focus on a network scenario where multiple source nodes transmit simultaneously mission-critical data through relay nodes to one or multiple destinations in multi-hop wireless Mission-Critical Networks (MCN). In such a network, the wireless nodes can be modeled as agents that can acquire local information from their neighbors and, based on this available information, can make timely transmission decisions to minimize the end-to-end delays of the mission-critical applications. Importantly, the MCN needs to cope in practice with the time-varying network dynamics. Hence, the agents need to make transmission decisions by considering not only the current network status, but also how the network status evolves over time, and how this is influenced by the actions taken by the nodes...

• Type: Journal
• Year: 2010
• Where: JSAC
• Authors: Hsien-Po Shiang, Mihaela van der Schaar
• Keywords: JSAC 2010, Mission-critical Applications, Model-based Reinforcement, Transmission Decisions