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76
Sensor Scheduling Under Energy Constraints
, 2011
"... To my parents, my teachers and my friends ii ACKNOWLEDGEMENTS I would like to thank my advisors, Professors Mingyan Liu and Demosthenis Teneketzis, for their guidance and support throughout my Ph.D. program. I truely enjoyed their wisdom and insights during our weekly research meetings. Without the ..."
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. Shrutivandana Sharma, Ashutosh Nayyar for many useful discussions and suggestions about my research and my presentations. I am also grateful to my friends at Michigan who kept me company through various stages of my studies. Thanks for their encouragements during my difficult times. My
Optimal control strategies in delayed sharing information structures
 IEEE Trans. Automatic Control
"... ar ..."
Identifying tractable decentralized control problems on the basis of information structures
 in proceedings of the 46th Allerton conference on communication, control and computation
, 2008
"... Abstract—Sequential decomposition of two general models of decentralized systems with nonclassical information structures is presented. In model A, all agents have two observations at each step: a common observation that all agents observe and a private observation of their own. The control actions ..."
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Cited by 17 (13 self)
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Abstract—Sequential decomposition of two general models of decentralized systems with nonclassical information structures is presented. In model A, all agents have two observations at each step: a common observation that all agents observe and a private observation of their own. The control actions of each agent is based on all past common observations, the current private observation and the contents of its memory. At each step, each agent also updates the contents of its memory. A cost function, which depends on the state of the plant and the control actions of all agents, is given. The objective is to choose control and memory update functions for all agents to either minimize a total expected cost over a finite horizon or to minimize a discounted cost over an infinite horizon. In model B, the agents do not have any common observation, the rest is same as in model A. The key idea of our solution methodology is the following. From the point of view of a fictitious agent that observes all common observations, the system can be viewed as a centralized system with partial observations. This allows us to identify information states and obtain a sequential decomposition. When the system variables take values in finite sets, the optimality equations of the sequential decomposition are similar to those of partially observable Markov decision processes (POMDP) with finite state and action spaces. For such systems, we can use algorithms for POMDPs to compute optimal designs for models A and B. I.
1 Decentralized Stochastic Control with Partial History Sharing: A Common Information Approach
, 1209
"... A general model of decentralized stochastic control called partial history sharing information structure is presented. In this model, at each step the controllers share part of their observation and control history with each other. This general model subsumes several existing models of information s ..."
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Cited by 18 (11 self)
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A general model of decentralized stochastic control called partial history sharing information structure is presented. In this model, at each step the controllers share part of their observation and control history with each other. This general model subsumes several existing models of information sharing as special cases. Based on the information commonly known to all the controllers, the decentralized problem is reformulated as an equivalent centralized problem from the perspective of a coordinator. The coordinator knows the common information and select prescriptions that map each controller’s local information to its control actions. The optimal control problem at the coordinator is shown to be a partially observable Markov decision process (POMDP) which is solved using techniques from Markov decision theory. This approach provides (a) structural results for optimal strategies, and (b) a dynamic program for obtaining optimal strategies for all controllers in the original decentralized problem. Thus, this approach unifies the various adhoc approaches taken in the literature. In addition, the structural results on optimal control strategies obtained by the proposed approach cannot be obtained by the existing generic approach (the personbyperson approach) for obtaining structural results in decentralized problems; and the dynamic program obtained by the proposed approach is simpler than that obtained by the existing generic approach (the designer’s approach) for obtaining dynamic programs in decentralized problems.
A graphical modeling approach to simplifying sequential teams
 in Proc. International Symposium on Modeling and Optimization in Mobile , Ad Hoc, and Wireless Networks (WiOpt), Control over Communication Channels (ConCom) Workshop, Seoul, South Korea
, 2009
"... Abstract—A graphical model for sequential teams is presented. This model is easy to understand, and at the same time, is general enough to model any finite horizon sequential team with finite valued system variables and unconstrained decision rules. The model can also be represented as a directed ac ..."
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Cited by 3 (0 self)
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Abstract—A graphical model for sequential teams is presented. This model is easy to understand, and at the same time, is general enough to model any finite horizon sequential team with finite valued system variables and unconstrained decision rules. The model can also be represented as a directed acyclic factor graph. This representation makes it easier to visualize and understand the functional dependencies between different system variables. It also helps in identifying data that is irrelevant for a decision maker to take an optimal decision. Such irrelevant data can be identified using algorithms from graphical models. Thus, the structural properties of optimal decision makers in this model for a sequential team can be identified in an automated manner using the directed acyclic factor graph representation of the sequential team. I.
Structural results and explicit solution for twoplayer LQG systems on a finite time horizon
 In IEEE Conference on Decision and Control, pages 6542 – 6549
, 2013
"... It is wellknown that linear dynamical systems with Gaussian noise and quadratic cost (LQG) satisfy a separation principle. Finding the optimal controller amounts to solving separate dual problems; one for control and one for estimation. For the discretetime finitehorizon case, each problem is a ..."
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Cited by 6 (4 self)
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It is wellknown that linear dynamical systems with Gaussian noise and quadratic cost (LQG) satisfy a separation principle. Finding the optimal controller amounts to solving separate dual problems; one for control and one for estimation. For the discretetime finitehorizon case, each problem is a simple forward or backward recursion. In this paper, we consider a generalization of the LQG problem with two controllers and a partially nested information structure. Each controller is responsible for one of two system inputs, but has access to different subsets of the available measurements. Our paper has three main contributions. First, we prove a fundamental structural result: sufficient statistics for the controllers can be expressed as conditional means of the global state. Second, we give explicit statespace formulae for the optimal controller. These formulae are reminiscent of the classical LQG solution with dual forward and backward recursions, but with the important difference that they are intricately coupled. Lastly, we show how these recursions can be solved efficiently, with computational complexity comparable to that of the centralized problem. 1
Initial Analyses and Demonstration of a Soil
"... Initial analyses and demonstration of a soil moisture smart sensor web ..."
D.Teneketzis,“On Jointly Optimal RealTime Encoding and Decoding Strategies in MultiTerminal Communication Systems.”,Control Group Report CGR 0803, Department of EECS, U.of Michigan, Ann Arbor, MI481092122 P r(Z 1 t , Z 2 t /X t
 Z 1,t−1 , Z 2,t−1 , f 1,t , f 2,t , ξ 1 t−1, ξ 2 t−1). P r(Xt/Z 1,t−1 , Z 2,t−1 , f 1,t , f 2,t , ξ 1 t−1, ξ 2 t−1) (76
"... Abstract—We consider a communication system consisting of two encoders communicating with a single receiver over a noiseless channel. The two encoders make distinct partial observations of a discretetime Markov source. Each encoder must encode its observations into a sequence of discrete variables. ..."
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Cited by 7 (2 self)
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Abstract—We consider a communication system consisting of two encoders communicating with a single receiver over a noiseless channel. The two encoders make distinct partial observations of a discretetime Markov source. Each encoder must encode its observations into a sequence of discrete variables. The sequence is transmitted over a noiseless channel to a receiver which attempts to reproduce the output of the Markov source. The system must operate in realtime, that is, the encoding at each encoder and decoding at receiver must be performed without any delay. The goal is to find globally (jointly) optimal realtime encoding and decoding strategies to minimize an expected distortion metric over a finite time horizon. We determine qualitative properties of optimal realtime encoding and decoding strategies. Using these properties, we develop a sequential decomposition of the problem of finding jointly optimal realtime encoding and decoding strategies. Such a sequential decomposition reduces exponentially the complexity of the joint optimization problem.
InSitu Soil Moisture Sensing: Optimal Sensor Placement and Field Estimation
"... We study the problem of optimal sensor placement in the context of soil moisture sensing. The goal of sensor placement is to select a subset of locations to collect (point) observations, so as to minimize an error measure of the resulting estimate for the unobserved locations. Prior work on sensor p ..."
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Cited by 8 (0 self)
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We study the problem of optimal sensor placement in the context of soil moisture sensing. The goal of sensor placement is to select a subset of locations to collect (point) observations, so as to minimize an error measure of the resulting estimate for the unobserved locations. Prior work on sensor placement has often relied on the assumption that the underlying spatial random process is Gaussian. We show that soil moisture in general does not follow a Gaussian distribution; rather it exhibits a multimodal behavior. On the other hand, it possesses unique features that can be exploited. Specifically, there exists a coarsegrained monotonic ordering of locations in their soil moisture level over time, a feature much more stable than the soil moisture process itself at these locations. This motivates a clustered sensor placement scheme, where locations are classified into clusters based on this ordering. Extensive numerical experiments are performed using a large set of 3dimensional soil moisture data generated by a stateoftheart soil moisture simulator. We conclude that the coarsegrained ordering of locations is a far more stable feature inherent in the soil moisture data, and placement algorithms using this feature outperform those solely relying on the Gaussian assumption.
Results 1  10
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