@conference {
	title = {Strategies for Two-Player Differential Games with Costly Information},
	author = {Maity, Dipankar., Baras, John S.},
	year = {2016},
	month = {05/2016},
	abstract = {In this work, a two players nonzero-sum differential game is considered, where one player tries to minimize some predefined cost and the other tries to maximize the same. The game is described by a stochastic differential system and the actions of the players serve as the control inputs to the dynamical system. The cost being a function of the actions chosen by the players and the state of the dynamical system, the players aim to control the state in order to optimize the cost functional. However in this problem, the players do not have the access to the states for every time, rather the states are available at discrete time instances after some finite costs are paid by the players. The inclusion of the information-cost makes the structure of the cost functional non-classical. The work presents the strategies for the players under no-cost information access as well as under costly information access. Explicit time instances for the information access are also derived by solving certain finite dimensional optimization problems.},
	address = {Xi{\textquoteright}an, China},
	booktitle = {13th International Workshop on Discrete Event Systems },
	pages = {pp . 211-216}
}
@conference {
	title = {Optimal Self-Driven Sampling for Estimation Based on Value of Information},
	author = {Soleymani, Touraj., Hirche, Sandra., Baras, John S.},
	year = {2016},
	month = {05/2016},
	abstract = {Consider an observer (reporter) who desires to inform optimally a distant agent regarding a physical stochastic process in the environment while the directed communication of the observer to the agent has a price. We define a metric, from a task-oriented perspective, for the information transferred from the observer to the agent. We develop a framework for optimizing an augmented cost function which is a convex combination of the transferred information and the paid price over a finite horizon. We suppose that the decision making takes place inside a source encoder, and that the sampling schedule is the decision variable. Moreover, we assume that no measurement at the current time is available to the observer for the decision making. We derive the optimal self-driven sampling policy using dynamic programming, and we show that this policy corresponds to a self-driven sampling policy based on a quantity that is, in fact, the value of information at each time instant. In addition, we use a semi-definite programming relaxation to provide a suboptimal sampling policy. Numerical and simulation results are presented for a simple unstable system},
	address = {Xi{\textquoteright}an, China},
	booktitle = {13th International Workshop on Discrete Event Systems },
	pages = {pp.183-188}
}
@conference {
	title = {Joint Optimization for Social Content Delivery in Wireless Networks},
	author = {Weng, Xiangnan., Baras, John S.},
	year = {2016},
	month = {05/2016},
	abstract = {Over the past decade, success of social networks has significantly reshaped how people consume information. Recommendation of contents based on user profiles is wellreceived. However, as users become dominantly mobile in content consumption, little is done to consider the optimization regarding the wireless environment. In this paper, we investigate a centralized wireless content delivery system with heterogeneous base stations, aiming to optimize overall user rewards given the capacity constraints of the wireless networks. We propose a scalable two-phase scheduling framework, consisting of: 1) distributed delivery decisions by each base station, and 2) resource consolidation by the system. Results indicate this novel joint optimization approach is both efficient and scalable},
	address = {Kuala lumpur, Malaysia},
	booktitle = {IEEE International Conference on Communications (ICC 2016)}
}
@conference {
	title = {Joint Optimization for Social Content Delivery in Heterogeneous Wireless Networks},
	author = {Weng, Xiangnan., Baras, John S.},
	year = {2016},
	month = {05/2016},
	abstract = {Over the past decade, success of social networks has significantly reshaped how people consume information. Recommendation of contents based on user profiles is well received. However, as users become dominantly mobile in content consumption, little is done to consider the optimization regarding the wireless environment. In this paper, we investigate a centralized wireless content delivery system with heterogeneous base stations, aiming to optimize overall user rewards given the capacity constraints of the wireless networks. We propose a scalable two-phase scheduling framework, consisting of: 1) distributed delivery decisions by each base station, and 2) resource consolidation by the system. Results indicate this novel joint optimization approach is both efficient and scalable.},
	address = {Tempe, Arizona},
	booktitle = {14th International Symposium on Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks},
	pages = {pp. 155 - 162}
}
