@conference {
	title = {Covariance-Based Transmission Power Control for Estimation over Wireless Sensor Networks},
	author = {Soleymani, Touraj., Zoppi, Samuel, Vilgelm, Mikhail, Hirche, Sandra., kellerer, Wolfgang, John S. Baras},
	year = {2018},
	month = {06/2018},
	abstract = {In this study, we would like to design a transmission power control mechanism for minimizing the transmit power of a wireless sensor node subject to a constraint on the quality of estimation at a remote estimator. In particular, we measure the estimation distortion by a mean square error function, and model the transmit power as a function of the packet success rate using the IEEE 802.15.4 standard which was formed for the specification of low-data-rate and low-power wireless communication. We formulate the problem with an infinite horizon discounted cost function. We show that there is a separation between the designs of the optimal estimator and optimal transmit power policy. Then, we use dynamic programming to characterize the optimal transmit power policy in terms of the estimation error covariance. Finally, we propose approximate value iteration as an approximation algorithm to calculate a near-optimal transmit power policy.},
	address = {Limassol, Cyprus},
	booktitle = {European Control Conference 2018},
	pages = {pp. 857-862}
}
@conference {
	title = {LQG Control via Wireless sensor Networks with Minimal Transmission Power},
	author = {Soleymani, Touraj., Hirche, Sandra., Baras, John S.},
	year = {2018},
	month = {05/2018},
	abstract = {This study concerns control of dynamical systems via wireless sensor nodes with minimal transmission power. In particular, we jointly design a controller and a transmission power control mechanism that adjusts the transmit power of a wireless sensor node. We develop a framework for achieving the minimum expected transmit power required for a specific level of the LQG control performance.We model the communication channel between the wireless sensor node and the controller by an AWGN channel compatible with the IEEE 802.15.4 standard, which was formed for the specification of low-data-rate and low-power wireless communication. We use dynamic programming to characterize the optimal policies, and show that there exists a separation between the designs of the optimal estimator, optimal controller, and optimal transmission power control mechanism.},
	address = {Sorrento Coast, Italy},
	booktitle = {14th Workshop on Discrete Event systems(WODES 2018)},
	pages = {pp. 62-67}
}
@conference {
	title = {Networked H$\infty$ Control with Minimum Directed Information},
	author = {Soleymani, Touraj., Hirche, Sandra., Baras, John S.},
	year = {2017},
	month = {12/2017},
	address = {Melbourne, Australia},
	booktitle = {56th IEEE Conference on Decision and Control (CDC 2017)}
}
@conference {
	title = {Event-Triggered Output-Feedback H$\infty$ Control with Minimum Directed Information},
	author = {Soleymani, Touraj., Hirche, Sandra., Baras, John S.},
	year = {2017},
	month = {12/2017},
	abstract = {In this paper, the H$\infty$ control problem with minimum information exchange between a plant and a remote controller is considered. For transmission of the measured outputs, an event-triggering mechanism is proposed. We measure the transferred information satisfying the causality condition by the directed information, and measure the H$\infty$ control performance by the L2-gain. We achieve the minimum directed information required for a guaranteed level of control performance. In particular, corresponding to the worst case disturbances, we develop a filter with a recursive variable, and show that the optimal control policy is a linear function of this recursive variable. Finally, we define the value of information, and determine the structure of the optimal sampling policy based on the value of information.
~},
	address = {Melbourne, Australia},
	booktitle = {56th IEEE Conference on Decision and Control (CDC 2017)},
	pages = {pp. 6088-6094}
}
@conference {
	title = {Value of Information in Minimum-Rate LQG Control},
	author = {Soleymani, Touraj., Hirche, Sandra., Baras, John S.},
	year = {2017},
	month = {07/2017},
	abstract = {This study concerns fundamental limitations in control of mobile cyber-physical systems. In these systems, communication between a node and its base station due to limited power of the node is asymmetrical in terms of bandwidth and signal-to-noise ratio. The framework we develop in this paper is for partially observed linear quadratic Gaussian (LQG) control over communication networks in which the forward channel transporting measurements is modeled by a zero-delay packet-deletion channel and the feedback channel transporting control inputs is assumed ideal. Making use of dynamic programming, we characterize the optimal control and the optimal sampling policies that achieve the minimum data rate required for a guaranteed level of control performance. In particular, we prove that in the presence of event driven sampling the adopted filter is optimal and the separation principle between control and estimation holds. We show that the optimal control policy is a certainty equivalent policy and the optimal sampling policy is a threshold policy expressed in terms of the value of information. Furthermore, we prove that the value of information is a quadratic function of the innovation.},
	address = {Toulouse, France},
	booktitle = {IFAC 2017 World Conference}
}
@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}
}
@article {
	title = {Value of Information in Feedback Control},
	author = {Soleymani, Touraj., Baras, John S.},
	abstract = {In this article, we investigate the impact of information on networked control systems, and illustrate how to quantify a fundamental property of stochastic processes that can enrich our understanding about such systems. To that end, we develop a theoretical framework for the joint design of an event trigger and a controller in optimal event-triggered control. We cover two distinct information patterns: perfect information and imperfect information. In both cases, observations are available at the event trigger instantly, but are transmitted to the controller sporadically with one-step delay. For each information pattern, we characterize the optimal triggering policy and optimal control policy such that the corresponding policy profile represents a Nash equilibrium. Accordingly, we quantify the value of information VoIk as the variation in the cost-to-go of the system given an observation at time k. Finally, we provide an algorithm for approximation of the value of information, and synthesize a closed-form suboptimal triggering policy with a performance guarantee that can readily be implemented.},
	journal = {IEEE Transactions on Automatic Control}
}
