<div>Unmanned Aerial Vehicles (UAVs), commonly known as drones, are aircrafts without a human pilot aboard. The flight of drones can be controlled with a remote control by an operator located at the ground station, or fully autonomously by onboard computers. Drones are mostly found in the military. However, over the recent years, they have attracted the interest of industry and civilian sectors. <br></div><div>With the recent advance of sensor and embedded device technologies, various sensors will be embedded in city infrastructure to monitor various city-related information. In this context, drones can be effectively utilized in many safety-critical applications for collecting data from sensors on the ground and transmitting configuration instructions or task requests to these sensors.</div><div> <br></div><div>However, drones, like many networked devices, are vulnerable to cyber and physical attacks.<br></div><div>Challenges for secure drone applications can be divided in four aspects: 1) securing communication between drones and sensors, 2) securing sensor localization when drones locate sensors, 3) providing secure drone platforms to protect sensitive data against physical capture attacks and detect modifications to drone software, and 4) protecting secret keys in drones under white-box attack environments.<br></div><div> <br></div><div>To address the first challenge, a suite of cryptographic protocols is proposed. The protocols are based on certificateless cryptography and support authenticated key agreement, non-repudiation and user revocation. To minimize the energy required by a drone, a dual channel strategy is introduced.<br></div><div>To address the second challenge, a drone positioning strategy and a technique that can filter out malicious location references are proposed.<br></div><div>The third challenge is addressed by a solution integrating techniques for software-based attestation and data encryption.<br></div><div>For attestation, free memory spaces are filled with pseudo-random numbers, which are also utilized to encrypt data collected by the drone like a stream cipher.<br></div>A dynamic white-box encryption scheme is proposed to address the fourth challenge. Short secret key are converted into large look-up tables and the tables are periodically shuffled by a shuffling mechanism which is secure against white-box attackers.

Advances in the development of simple and cheap sensors give new possibilities with large sensor network deployments in monitoring and surveillance applications. Commonly, the sensor positions are not known, specifically, when sensors are randomly spread in a big area. Low cost sensors are constructed with as few components as possible to keep price and energy consumption down. This implies that self-positioning and communication capabilities are low. So the question: “How do you localize such sensors with good precision with a feasible approach?” is central. When no information is available a stable and robust localization algorithm is needed. In this thesis an acoustic sensor network is considered. With a movable acoustic source a well-defined and audible signal is transmitted at different spots. The sensors measure the time of arrival which corresponds to distance. A two-step sensor localization approach is applied that utilizes the estimated distances. A novel approach in the first step is presented to incorporate more measurements and gain more position information. Localization and ranging performance is evaluated with simulations and data collected at field trials. The results show that the novel approach attains higher accuracy and robustness.

Optimum Sensor Localization/Selection in A Diagnostic/Prognostic Architecture Guangfan Zhang 107 Pages Directed by Dr. George J. Vachtsevanos This research addresses the problem of sensor localization/selection for fault diagnostic purposes in Prognostics and Health Management (PHM)/Condition-Based Maintenance (CBM) systems. The performance of PHM/CBM systems relies not only on the diagnostic/prognostic algorithms used, but also on the types, location, and number of sensors selected. Most of the research reported in the area of sensor localization/selection for fault diagnosis focuses on qualitative analysis and lacks a uniform figure of merit. Moreover, sensor localization/selection is mainly studied as an open-loop problem without considering the performance feedback from the on-line diagnostic/prognostic system. In this research, a novel approach for sensor localization/selection is proposed in an integrated diagnostic/prognostic architecture to achieve maximum diagnostic performance. First, a fault detectability metric is defined quantitatively. A novel graph-based approach, the Quantified-Directed Model, is called upon to model fault propagation in complex systems and an appropriate figure-of-merit is defined to maximize fault detectability and minimize the required number of sensors while achieving optimum performance. Secondly, the proposed sensor localization/selection strategy is integrated into a diagnostic/prognostic system architecture while exhibiting attributes of flexibility and scalability. Moreover, the performance is validated and verified in the integrated diagnostic/prognostic architecture, and the performance of the integrated diagnostic/prognostic architecture acts as useful feedback for further optimizing the sensors considered. The approach is tested and validated through a five-tank simulation system. This research has led to the following major contributions: ??generalized methodology for sensor localization/selection for fault diagnostic purposes. ??quantitative definition of fault detection ability of a sensor, a novel Quantified-Directed Model (QDG) method for fault propagation modeling purposes, and a generalized figure of merit to maximize fault detectability and minimize the required number of sensors while achieving optimum diagnostic performance at the system level. ??novel, integrated architecture for a diagnostic/prognostic system. ??lidation of the proposed sensor localization/selection approach in the integrated diagnostic/prognostic architecture.

Higher Order Statistics (HOS) and Second Order Statistics (SOS) approaches have certain advantages and disadvantages in signal processing applications. HOS approach provides more statistical information for non-Gaussian signals. On the other hand, SOS approach is more robust to the estimation errors than the HOS approach, especially when the number of observations is small. In this thesis, HOS and SOS approaches are jointly used in order to take advantage of both methods. In this respect, the joint use of HOS and SOS approaches are introduced for online calibration of sensor arrays with arbitrary geometries. Three different problems in online array calibration are considered and new algorithms for each of these problems are proposed. In the first problem, the positions of the randomly deployed sensors are completely unknown except the two reference sensors and HOS and SOS approaches are used iteratively for the joint Direction of Arrival (DOA) and sensor position estimation. Iterative HOS-SOS algorithm (IHOSS) solves the ambiguity problem in sensor position estimation by observing the source signals at least in two different frequencies and hence it is applicable for wideband signals. The conditions on these frequencies are presented. IHOSS is the first algorithm in the literature which finds the DOA and sensor position estimations in case of randomly deployed sensors with unknown coordinates. In the second problem, narrowband signals are considered and it is assumed that the nominal sensor positions are known. Modified IHOSS (MIHOSS) algorithm uses the nominal sensor positions to solve the ambiguity problem in sensor position estimation. This algorithm can handle both small and large errors in sensor positions. The upper bound of perturbations for unambiguous sensor position estimation is presented. In the last problem, an online array calibration method is proposed for sensor arrays where the sensors have unknown gain/phase mismatches and mutual coupling coefficients. In this case, sensor positions are assumed to be known. The mutual coupling matrix is unstructured. The two reference sensors are assumed to be perfectly calibrated. IHOSS algorithm is adapted for online calibration and parameter estimation, and hence CIHOSS algorithm is obtained. While CIHOSS originates from IHOSS, it is fundamentally different in many aspects. CIHOSS uses multiple virtual ESPRIT structures and employs an alignment technique to order the elements of rows of the actual array steering matrix. In this thesis, a new cumulant matrix estimation technique is proposed for the HOS approach by converting the multi-source problem into a single source one. The proposed algorithms perform well even in the case of correlated source signals due to the effectiveness of the proposed cumulant matrix estimate. The iterative procedure in all the proposed algorithms is guaranteed to converge. Closed form expressions are derived for the deterministic Cram&acute / er-Rao bound (CRB) for DOA and unknown calibration parameters for non-circular complex Gaussian noise with unknown covariance matrix. Simulation results show that the performances of the proposed methods approach to the CRB for both DOA and unknown calibration parameter estimations for high SNR.