@conference {
	title = {Optimal Stationary Self-Triggered Sampling for Estimation},
	author = {Soleymani, Touraj., Hirche, Sandra., Baras, John S.},
	year = {2016},
	month = {12/2016},
	abstract = {In this paper, we study optimal stationary sampling~for transmission of measurements of a stochastic process~from a source encoder to a source decoder through a costly communication~channel. We measure information transferred over~a time interval by the change in the decoder{\textquoteright}s entropy regarding~the state of the process given the transmitted measurements.~In our setting, the encoder employs a sampler to control the~information flow in the channel. The problem is casted as a~discounted infinite horizon optimization problem that takes into~account the transferred information and the paid price. We~derive the optimal stationary sampling policy, and propose two~computational methods with convergence guarantees by using~techniques from approximate dynamic programing. In addition,~we introduce two triggering mechanisms based on the value of~information and on the covariance threshold that can generate~the optimal policy. Finally, we present some numerical and~simulation results.},
	address = {Las Vegas, Nevada},
	booktitle = {55th IEEE Conference on Decision and Control },
	pages = {pp. 3084-3089}
}
@conference {
	title = {Optimal Information Control in Cyber-Physical Systems},
	author = {Soleymani, Touraj., Hirche, Sandra., Baras, John S.},
	year = {2016},
	month = {09/2016},
	abstract = {In this paper, we address optimal information control in cyber-physical systems. In~particular, we study optimal closed-loop policies for transmission of measurements of a stochastic~dynamical system through a communication channel given estimation and communication~costs. We develop a framework for optimizing an aggregate cost function that incorporates~the estimation and the communication costs over a finite time horizon. We obtain the optimal~closed-loop policy, and show that it can be expressed directly in terms of the value of information.~In addition, we propose an approximation algorithm that yields a suboptimal closed-loop policy.~Numerical and simulation results are presented for a simple system.},
	address = {Tokyo, Japan},
	booktitle = {6th IFAC Workshop on Distributed Estimation and Control in Networked Systems},
	pages = {pp. 1-6}
}
@conference {
	title = {Maximization of Information in Energy Limited Directed Communication},
	author = {Soleymani, Touraj., Hirche, Sandra., Baras, John S.},
	year = {2016},
	month = {06/2016},
	abstract = {This study is concerned with an observer who~desires to inform optimally a distant agent regarding a physical~stochastic process in the environment. We consider that the~observer has a constraint on the energy resource for the directed~communication to the agent. The information we are interested~in is the change in the knowledge possessed by the agent about~the state of the process. We find the maximum information~that can be transferred from the observer to the agent over~a finite horizon subject to a bound on the total energy of the~observer.We show that the maximum information is the optimal~value of a mixed-integer nonlinear optimization problem. We~obtain lower and upper bounds on the maximum information~and also a suboptimal admissible solution based on a semidefinite~programming. Moreover, we propose an optimized~event-triggering mechanism based on a linear matrix inequality~which yields event-driven sampling. Numerical and simulation~results are presented for simple stable and unstable systems.},
	address = {Aalborg, DK},
	booktitle = {European Control Conference (ECC16)},
	pages = {pp. 1001-1006}
}
@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}
}
