Signal Processing and Machine Learning Methods for Internet of Things: Smart Energy Generation and Robust Indoor Localization

The application of Internet of Things (IoT) where sensors and actuators embedded in physical objects are linked through wired and wireless networks has shown a rapid growth over the past years in various domains with the benefits of improving efficiency and productivity, reducing cost, providing mobility and agility, etc. This dissertation focuses on developing signal processing and machine learning based techniques in IoT with applications to 1) smart energy generation and 2) robust indoor localization in smart city. Smart grids, in contrast to legacy grids, facilitate more efficient electricity generation and consumption by allowing two-way information exchange among various components in the grid and the users based on the measurements from numerous sensors located at different places. Due to the introduction of information communications, a smart grid is faced with the risk of external attacks which is aimed to take control of the grid. In particular, electricity generation from photovoltaic (PV) systems is a mature power generation technology utilizing renewable resources, owning to its advantages in clean production, reduced cost and high flexibility. However, the performance of a PV system can be susceptible and unstable due to various physical failures and dynamic environments (internal circuit faults, partial shading, etc.). To safeguard the system security, fault or attack detection technologies are of great importance for PV systems and smart grids. Existing approaches on fault or attack detection either rely on the prediction by a predetermined system model which acts as reference data for comparison or can be applied only within a certain set of component (e.g., several PV strings) based on local statistical properties without the capability of generalization. Furthermore, the output performance of a PV system is dynamic under different environmental conditions (irradiance level, temperature, etc.), which can be optimized by the technique of maximum power point tracking (MPPT). However, previous studies on MPPT usually require prior knowledge of the system model or high computational complexity for iterative optimization. Smart city, as another important application of IoT, relies on analysis of the measurement data from sensors located at users and environments to provider intelligent solutions in our daily life. One of the fundamental tasks for advanced location-based services is to accurately localize the user in a certain environment, e.g., on a certain floor inside a building. Indoor localization is faced with challenges of moving users, limited availability of sensors and noisy measurements due to hardware constraints and external interferences. This dissertation first describes our advanced fault/attack detection and localization methods for PV systems and smart grids, then develops our enhanced MPPT techniques for PV systems, and finally presents our robust indoor localization methods for smartphone users, based on statistical signal processing and machine learning approaches. In Chapter 2 and Chapter 3, we proposes fault/attack detection method in PV systems and smart grids respectively in the framework of abrupt change detection utilizing sequential output measurements without assuming any prior knowledge of the system characteristics or particular faulty/attack patterns, such that an alarm will triggered regardless of the magnitude or the type of faulty/attack signals. Starting from the proposed fault detection method in Chapter 2, we present our fault localization method for PV systems in Chapter 4 where the central controller is able to identify the faulty PV strings without full knowledge of each local measurements. Chapter 5 studies the MPPT method under dynamic shading conditions where we adopt neural networks to assist the identification of the global maximum power point by depicting the relationship between the system output power and the operating voltage. In Chapter 6, to tackle the challenge of accurate and robust indoor localization for smart city when sensors provides noisy measurement data, we propose a cooperative localization method which exploits the readings of the received strengths of Wi-Fi signals at the smartphone users and the relative distances among neighboring users to combat the deterioration due to aggregated measurement errors. Throughout the dissertation, our proposed methods are followed by simulations (of a PV system or a grid under various operating conditions) or experiments (of localizing moving users with smartphones to record sensors' measurements). The results demonstrate that our proposed fault/attack detection and localization methods and MPPT schemes can achieve higher adaptivity and efficiency with robustness against various external conditions an lower computational complexity, and our cooperative localization methods have high localization accuracy even given large measurement errors and limited measurement data

No Fault Found events in maintenance engineering Part 1: Current trends, implications and organizational practices

This paper presents the first part of a state of the art review on the No Fault Found (NFF) phenomenon. The aim has been to compile a systematic reference point for burgeoning NFF literature, and to provide a comprehensive overview for gaining an understanding of NFF knowledge and concepts. Increasing systems complexities have seen a rise in the number of unknown failures that are being reported during operational service. Units tagged as ‘NFF’ are evidence that a serviceable component was removed, and attempts to troubleshoot the root cause have been unsuccessful. There are many reasons on how these failures manifest themselves and these papers describe the prominent issues that have persisted across a variety of industrial applications and processes for decades. This article, in particular, deals with the impact of NFF from an organizational culture and human factors point of view. It also highlights recent developments in NFF standards, its financial implications and safety concerns

In-situ Data Analytics In Cyber-Physical Systems

Cyber-Physical System (CPS) is an engineered system in which sensing, networking, and computing are tightly coupled with the control of the physical entities. To enable security, scalability and resiliency, new data analytics methodologies are required for computing, monitoring and optimization in CPS. This work investigates the data analytics related challenges in CPS through two study cases: Smart Grid and Seismic Imaging System. For smart grid, this work provides a complete solution for system management based on novel in-situ data analytics designs. We first propose methodologies for two important tasks of power system monitoring: grid topology change and power-line outage detection. To address the issue of low measurement redundancy in topology identification, particularly in the low-level distribution network, we develop a maximum a posterior based mechanism, which is capable of embedding prior information on the breakers status to enhance the identification accuracy. In power-line outage detection, existing approaches suer from high computational complexity and security issues raised from centralized implementation. Instead, this work presents a distributed data analytics framework, which carries out in-network processing and invokes low computational complexity, requiring only simple matrix-vector multiplications. To complete the system functionality, we also propose a new power grid restoration strategy involving data analytics for topology reconfiguration and resource planning after faults or changes. In seismic imaging system, we develop several innovative in-situ seismic imaging schemes in which each sensor node computes the tomography based on its partial information and through gossip with local neighbors. The seismic data are generated in a distributed fashion originally. Dierent from the conventional approach involving data collection and then processing in order, our proposed in-situ data computing methodology is much more ecient. The underlying mechanisms avoid the bottleneck problem on bandwidth since all the data are processed distributed in nature and only limited decisional information is communicated. Furthermore, the proposed algorithms can deliver quicker insights than the state-of-arts in seismic imaging. Hence they are more promising solutions for real-time in-situ data analytics, which is highly demanded in disaster monitoring related applications. Through extensive experiments, we demonstrate that the proposed data computing methods are able to achieve near-optimal high quality seismic tomography, retain low communication cost, and provide real-time seismic data analytics

Bibliographical review on cyber attacks from a control oriented perspective

This paper presents a bibliographical review of definitions, classifications and applications concerning cyber attacks in networked control systems (NCSs) and cyber-physical systems (CPSs). This review tackles the topic from a control-oriented perspective, which is complementary to information or communication ones. After motivating the importance of developing new methods for attack detection and secure control, this review presents security objectives, attack modeling, and a characterization of considered attacks and threats presenting the detection mechanisms and remedial actions. In order to show the properties of each attack, as well as to provide some deeper insight into possible defense mechanisms, examples available in the literature are discussed. Finally, open research issues and paths are presented.Peer ReviewedPostprint (author's final draft

State of the art of cyber-physical systems security: An automatic control perspective

Cyber-physical systems are integrations of computation, networking, and physical processes. Due to the tight cyber-physical coupling and to the potentially disrupting consequences of failures, security here is one of the primary concerns. Our systematic mapping study sheds light on how security is actually addressed when dealing with cyber-physical systems from an automatic control perspective. The provided map of 138 selected studies is defined empirically and is based on, for instance, application fields, various system components, related algorithms and models, attacks characteristics and defense strategies. It presents a powerful comparison framework for existing and future research on this hot topic, important for both industry and academia

Wireless sensor data processing for on-site emergency response

This thesis is concerned with the problem of processing data from Wireless Sensor Networks (WSNs) to meet the requirements of emergency responders (e.g. Fire and Rescue Services). A WSN typically consists of spatially distributed sensor nodes to cooperatively monitor the physical or environmental conditions. Sensor data about the physical or environmental conditions can then be used as part of the input to predict, detect, and monitor emergencies. Although WSNs have demonstrated their great potential in facilitating Emergency Response, sensor data cannot be interpreted directly due to its large volume, noise, and redundancy. In addition, emergency responders are not interested in raw data, they are interested in the meaning it conveys. This thesis presents research on processing and combining data from multiple types of sensors, and combining sensor data with other relevant data, for the purpose of obtaining data of greater quality and information of greater relevance to emergency responders. The current theory and practice in Emergency Response and the existing technology aids were reviewed to identify the requirements from both application and technology perspectives (Chapter 2). The detailed process of information extraction from sensor data and sensor data fusion techniques were reviewed to identify what constitutes suitable sensor data fusion techniques and challenges presented in sensor data processing (Chapter 3). A study of Incident Commanders’ requirements utilised a goal-driven task analysis method to identify gaps in current means of obtaining relevant information during response to fire emergencies and a list of opportunities for WSN technology to fill those gaps (Chapter 4). A high-level Emergency Information Management System Architecture was proposed, including the main components that are needed, the interaction between components, and system function specification at different incident stages (Chapter 5). A set of state-awareness rules was proposed, and integrated with Kalman Filter to improve the performance of filtering. The proposed data pre-processing approach achieved both improved outlier removal and quick detection of real events (Chapter 6). A data storage mechanism was proposed to support timely response to queries regardless of the increase in volume of data (Chapter 7). What can be considered as “meaning” (e.g. events) for emergency responders were identified and a generic emergency event detection model was proposed to identify patterns presenting in sensor data and associate patterns with events (Chapter 8). In conclusion, the added benefits that the technical work can provide to the current Emergency Response is discussed and specific contributions and future work are highlighted (Chapter 9)