CNS Core: Small: Wireless Network Control in Uncooperative and Adversarial Environments

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Recent growth in mobile and media-rich applications has greatly increased the demand for wireless capacity, straining wireless networks. This dramatic increase in demand poses a challenge for current wireless networks, and calls for new network algorithms that make better use of scarce wireless resources. Moreover, modern communication networks frequently operate in unfriendly environments, where some of the users may be uncooperative, or even malicious, and try to disrupt network services. This project develops network control algorithms that operate effectively in adversarial environments that increasingly characterize realistic network settings, thus leading to dramatic improvement in network performance and enabling emerging wireless applications.This project develops a new optimization framework for networks where some of the nodes, as well as the external dynamics (e.g., link rates, exogenous arrivals), may be uncooperative and exhibit adversarial or even malicious behavior. This novel framework envisions network control algorithms that are "secure-by-design", in the face of adversarial dynamics. Moreover, the developed control algorithms will have a "robust optimization" flavor, in the sense that they will be designed from the outset to perform well under worst-case conditions, while maintaining nearly optimal performance under normal conditions. The research agenda includes the following tasks: (i) Network Optimization in Uncooperative Environments: Use techniques from model-based reinforcement learning to develop control algorithms for networks where a subset of nodes are uncontrollable and use some unknown stationary control policy. (ii) Network Optimization in Adversarial Environments: Develop online learning algorithms for maximizing throughput and network utility in networks where uncontrollable nodes can take arbitrary and possibly non-stationary actions. (iii) Network Optimization in Malicious Environments: Characterize the network's performance in overflow due to adversarial flow injections, develop optimal flow injection policies for the adversary, and network control algorithms to mitigate the effect of such adversarial flow injections.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.