NETS: Small: Machine Learning Based Algorithms for Quasi-Static Ad Hoc Wireless Networks

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Quasi-static ad hoc wireless networks are models for city-wide mesh networks, machine-to-machine networks deployed for control, and certain sensor networks, which are of growing importance in applications such as smart electricity grids, transportation grid control and infrastructure monitoring. The goal of this project is to design distributed online network protocols that learn the characteristics of such networks and self-optimize to achieve more predictable performance, such as delay guarantees. This is especially important for networks such as smart grid, where end-to-end delay must be predictable. A factor graph is used to model the probability distribution over the allowed network control actions. A novel information theoretic formulation is being investigated, which measures the information sharing required to coordinate network actions, between devices and across the network stack within each device. The solution to this problem will then be obtained by using statistical sampling techniques from machine learning, providing the online algorithm for network control. This algorithm will be distributed, because it is obtained by minimizing the information sharing required. Thus, the project will provide a formal mathematical basis for the probabilistic design of distributed protocols for quasi-static ad hoc networks, utilizing the information theoretic concepts of information, entropy, and side-information to quantify the value of distributed actions. Results from this project will be published and presented in major professional conferences and journals, and will be available to the wider public. The project will support the training of doctoral students in the important field of wireless networks. The theoretical framework obtained will be discussed in courses on statistical engineering methods and information theory. The MAC protocol concepts will be used to augment the PI?s under-graduate experimental set-up based on software radios, so that students of communication theory can obtain hands-on experience with system design.