THESIS
2021
1 online resource (xvi, 97 pages) : illustrations (some color)
Abstract
The broadcasting nature of the fast developing wireless networking technologies
has aroused concerns about data privacy since neighboring illegitimate users are
able to overhear confidential information contained in the transmitted data. At
the same time, due to the limited resources of the remote sides, how to use fewer
resources without sacrificing too much system performance has also attracted
increasing interest.
In this thesis, we focus on privacy-aware and resource-saving network control
problems in wireless networks. Specifically, we investigate an optimal encryption
scheduling under an operation constraint, introduce an event-triggered
mechanism into cognitive radio sensor networks (CRSNs), and propose a joint
sensor and actuator placement to minimize an infinite linear-quadrati...[ Read more ]
The broadcasting nature of the fast developing wireless networking technologies
has aroused concerns about data privacy since neighboring illegitimate users are
able to overhear confidential information contained in the transmitted data. At
the same time, due to the limited resources of the remote sides, how to use fewer
resources without sacrificing too much system performance has also attracted
increasing interest.
In this thesis, we focus on privacy-aware and resource-saving network control
problems in wireless networks. Specifically, we investigate an optimal encryption
scheduling under an operation constraint, introduce an event-triggered
mechanism into cognitive radio sensor networks (CRSNs), and propose a joint
sensor and actuator placement to minimize an infinite linear-quadratic Gaussian
(LQG) cost.
For privacy-aware network control, we investigate the optimal encryption
scheduling for remote state estimation under an operation constraint. As the
information about eavesdroppers is unknown to the estimator, we introduce the concept of eavesdropper-invariant schedules and derive associated structural
results. In addition, we propose a practical algorithm that compares a finite
number of points to obtain an ε-optimal encryption schedule.
For resource-saving network control, we introduce a stochastic event-triggered
mechanism into CRSNs. To achieve a better trade-off between the estimation
performance and communication consumption, we propose both open-loop and
closed-loop schedulers. The parameter design problems in both schedules are
efficiently solved by convex programming. We also consider the joint sensor
and actuator (SaA) placement to minimize the infinite-horizon LQG cost for
a discrete dynamic noisy system. This problem is first reformulated as a joint
bilinear problem by relaxing the Boolean constraints. After deriving a compact
search region that the optimal solution of the relaxed problem belongs to, we
introduce a branch and bound (B&B) algorithm to obtain the global optimal
solution of the relaxed problem. We then generate a suboptimal solution to the
original problem from the relaxed one and further analyze the optimality gap.
Finally, we illustrate the results with numerical examples for the above problems.
At the end of this thesis, we also provide some possible directions for
future work.
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