Constrained remote state estimation and quickest change detection in the context of Cyber-physical systems are studied in this thesis. The remote state estimation is studied from the perspective of both an estimator and an attacker. Three different problems are formulated, and the common theme is twofold: (i): The resources for the estimator/attacker/decision maker are limited, and efficient resources utilization policies are thus necessary. (ii): In each proposed (optimal) policy, decisions are made sequentially with real-time information.

In the remote estimation from the estimator's perspective problem, a sensor observes a dynamic process and sends its observations to a remote estimator over a wireless fading channel characterized by a time-homogeneous Markov chain. The suc...[ Read more ]

Constrained remote state estimation and quickest change detection in the context of Cyber-physical systems are studied in this thesis. The remote state estimation is studied from the perspective of both an estimator and an attacker. Three different problems are formulated, and the common theme is twofold: (i): The resources for the estimator/attacker/decision maker are limited, and efficient resources utilization policies are thus necessary. (ii): In each proposed (optimal) policy, decisions are made sequentially with real-time information.

In the remote estimation from the estimator's perspective problem, a sensor observes a dynamic process and sends its observations to a remote estimator over a wireless fading channel characterized by a time-homogeneous Markov chain. The successful transmission probability depends on both the channel gains and the transmission power used by the sensor. Jointly optimal transmission power controller and remote estimator, which minimize an infinite-horizon cost consisting of the power usage and the remote estimation error, are presented by formulating the problem as a partially observable Markov decision process (POMDP). Structural results are provided using the majorization theory when the monitored dynamic system is scalar. The remote estimation from the attacker's perspective is studied in multi-systems scenarios. Suppose there are M independent systems, each of which has a remote sensor monitoring the system and sending its local estimates to a fusion center over a packet-dropping channel. An attacker may generate noises to exacerbate the communication channels between sensors and the fusion center. Due to capacity limitation, at each time the attacker can exacerbate at most N of the M channels. The optimal attack policy, which maximizes the estimation error at the fusion center, is solved as a solution to a Markov decision process (MDP), of which a threshold structure is proved. To overcome the curse of dimensionality of MDP algorithms, we further provide an asymptotically optimal (as M and N go to infinity) policy, which is easy to compute and implement. The quickest change detection problem is to detect an abrupt change event as quickly as possible subject to constraints on false detection. Unlike the classical problem, where the decision maker can access only one sequence of observations, in this thesis work, the decision maker chooses one of two different sequences of observations at each time instant. The information quality and sampling cost of the two sequences of observations are different. We present an asymptotically optimal joint design of observation scheduling policy and stopping time such that the detection delay is minimized subject to constraints on both average run length to false alarm and average cost per sample. The decentralized case in a multi-channel setting is also studied.