Deterministic blind radio networks

Ad-hoc radio networks and multiple access channels are classical and well-studied models of distributed systems, with a large body of literature on deterministic algorithms for fundamental communications primitives such as broadcasting and wake-up. However, almost all of these algorithms assume knowledge of the number of participating nodes and the range of possible IDs, and often make the further assumption that the latter is linear in the former. These are very strong assumptions for models which were designed to capture networks of weak devices organized in an ad-hoc manner. It was believed that without this knowledge, deterministic algorithms must necessarily be much less efficient. In this paper we address this fundamental question and show that this is not the case. We present deterministic algorithms for blind networks (in which nodes know only their own IDs), which match or nearly match the running times of the fastest algorithms which assume network knowledge (and even surpass the previous fastest algorithms which assume parameter knowledge but not small labels)

Radio network algorithms for global communication

Radio networks are a distributed computing model capturing the behavior of devices that communicate via wireless transmissions. Applications of wireless networks have expanded hugely in recent decades due to their convenience and versatility. However, wireless communication presents practical difficulties, particularly in avoiding interference between transmissions. The radio network model provides a theoretical distillation of the behavior of such networks, in order to better understand and facilitate communication. This thesis concerns fundamental global communication tasks in the radio network model: that is, tasks that require relaying messages throughout the entire network. Examples include broadcasting a message to all devices in a network, or reaching agreement on a single device to act as a coordinator. We present algorithms to perform global tasks efficiently, and show improved asymptotic running times over a range of environments and model variants. Our results demonstrate an advance over the state of the art in radio network research, and in many cases reach or approach known lower bounds