Wireless Sensor Data Transport, Aggregation and Security

abstract: Wireless sensor networks (WSN) and the communication and the security therein have been gaining further prominence in the tech-industry recently, with the emergence of the so called Internet of Things (IoT). The steps from acquiring data and making a reactive decision base on the acquired sensor measurements are complex and requires careful execution of several steps. In many of these steps there are still technological gaps to fill that are due to the fact that several primitives that are desirable in a sensor network environment are bolt on the networks as application layer functionalities, rather than built in them. For several important functionalities that are at the core of IoT architectures we have developed a solution that is analyzed and discussed in the following chapters. The chain of steps from the acquisition of sensor samples until these samples reach a control center or the cloud where the data analytics are performed, starts with the acquisition of the sensor measurements at the correct time and, importantly, synchronously among all sensors deployed. This synchronization has to be network wide, including both the wired core network as well as the wireless edge devices. This thesis studies a decentralized and lightweight solution to synchronize and schedule IoT devices over wireless and wired networks adaptively, with very simple local signaling. Furthermore, measurement results have to be transported and aggregated over the same interface, requiring clever coordination among all nodes, as network resources are shared, keeping scalability and fail-safe operation in mind. Furthermore ensuring the integrity of measurements is a complicated task. On the one hand Cryptography can shield the network from outside attackers and therefore is the first step to take, but due to the volume of sensors must rely on an automated key distribution mechanism. On the other hand cryptography does not protect against exposed keys or inside attackers. One however can exploit statistical properties to detect and identify nodes that send false information and exclude these attacker nodes from the network to avoid data manipulation. Furthermore, if data is supplied by a third party, one can apply automated trust metric for each individual data source to define which data to accept and consider for mentioned statistical tests in the first place. Monitoring the cyber and physical activities of an IoT infrastructure in concert is another topic that is investigated in this thesis.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

Optimierte Ladung von Elektrofahrzeugen als Markow Entscheidungsprozess mittels maschineller Lernalgorithmen

Die Elektromobilität mit teils hohen Ladeleistungen ist für den sicheren Betrieb der elektrischen Verteilnetze zukünftig eine Herausforderung. Zur Reduzierung von Überlastungen in der Niederspannung werden daher Steueralgorithmen benötigt, um die Ladeleistung der Fahrzeuge zu steuern. Hierbei ergibt sich allerdings das Problem, dass die Niederspannungsnetze in der Regel messtechnisch nicht überwacht werden und so Eingangsdaten für Steueralgorithmen fehlen. In der Arbeit wird die Kombination von zwei maschinellen Lernalgorithmen untersucht. Die Steuerung der Ladeleistung von Elektrofahrzeugen ist als Markow-Entscheidungsprozess definiert, der mittels dem bestärkten Lernen gelöst wird. Für die Bereitstellung der Eingangsdaten wird ein künstliches neuronales Netz verwendet, das den Zustand eines Niederspannungsnetzes abschätzt. Durch das Zusammenspiel beider Algorithmen können die durch die Ladung von Elektrofahrzeugen ausgelösten Netzüberlastungen reduziert werden.The expansion of renewable energies and the charging of electric vehicles, in some cases with high power, pose a new challenge for the safe operation of electrical distribution grids in the future and, in some cases, today. Therefore, algorithms for controlling the charging power of electric vehicles are needed to prevent and reduce low-voltage equipment overloads. However, when implementing a control algorithm for the charging power of electric vehicles, the fact that low-voltage grids are not typically monitored by measurement technology, due to historical reasons, presents a problem. Therefore, it is necessary to be able to precisely estimate the condition of the low-voltage grid. This thesis presents an autonomous control of the charging power of distributed private electric vehicles to prevent and reduce equipment overloads in the low-voltage grid while maintaining a short charging time. This control utilizes a Markov decision process. Machine learning algorithms were used to solve the Markov decision process and generate its state. An artificial neural network was used to estimate the node voltage in real time and as a state for the Markov decision process. To solve the Markov decision process, reinforcement learning in a decentralized approach as a multiagent system was used as a machine learning algorithm. Each charging point was assigned a so-called agent that, using a defined reward function and action vector for controlling the charging power of the respective charging point, attempted to achieve the optimal balance between reducing equipment overload in the low-voltage grid and minimizing the charging time of electric vehicles. The estimation of node voltage and the autonomous decentralized control of electric vehicles using machine learning algorithms were validated and analyzed in three different scenarios of a grid model. This thesis investigates how the estimation of the node voltage as a state of the Markov decision process, which is subject to inaccuracies, affects the efficacy of the autonomous control. It also explores whether increasing the percentage of electric vehicles without expanding primary resources using the system of linear learning algorithms described is possible

ICT-Enabled Control and Energy Management of Community Microgrids for Resilient Smart Grid Operation

Our research has focused on developing novel controllers and algorithms to enhance the resilience of the power grid and increase its readiness level against major disturbances. The U.S. power grid currently encounters two main challenges: (1) the massive and extended blackouts caused by natural disasters, such as hurricane Sandy. These blackouts have raised a national call to explore innovative approaches for enhanced grid resiliency. Scrutinizing how previous blackouts initiated and propagated throughout the power grid, the major reasons are lack of situational awareness, lack of real-time monitoring and control, underdeveloped controllers at both the transmission and distribution levels, and lack of preparation for major emergencies; and (2) the projected high penetration of renewable energy resources (RES) into the electric grid, which is mainly driven by federal and state regulatory actions to reduce GHG emissions from new and existing power plants, and to encourage Non Wire Solutions (NWS). RESs are intermittent by nature imposing a challenge to forecast load and maintain generation/demand balance. The conceived vision of the smart grid is a cyber-physical system that amalgamates high processing power and increased dependence on communication networks to enable real-time monitoring and control. This will allow for, among other objectives, the realization of increased resilience and self-healing capabilities. This vision entails a hierarchical control architecture in which a myriad of microgrids, each locally controlled at the prosumer level, coordinates within the distribution level with their correspondent distribution system operator (i.e. area controllers). The various area controllers are managed by a Wide Area Monitoring, Protection and Control operator. The smart grid has been devised to address the grid main challenges; however, some technical barriers are yet to be overcome. These barriers include the need to develop new control techniques and algorithms that enable flexible transitions between operational modes of a single controller, and effective coordination between hierarchical control layers. In addition, there is a need to understand the reliability impacts of increased dependence on communication networks. In an attempt to tackle the aforementioned barriers, in my work, novel controllers to manage the prosumer and distribution networks were developed and analyzed. Specifically, the following has been accomplished at the prosumer level, we: 1) designed and implemented a DC MG testbed with minimal off-the-shelf components to enable testing new control techniques with significant flexibility and reconfiguration capability; 2) developed a communication-based hybrid state/event driven control scheme that aims at reducing the communication load and complexity, processor computations, and consequently system cost while maintaining resilient autonomous operation during all possible scenarios including major emergencies; and 3) analyzed the effect of communication latency on the performance of centralized ICT-based DC microgrids, and developed mathematical models to describe the behavior of microgrids during latency. In addition, we proposed a practical solution to mitigate severe impacts of latency. At the distribution level, we: 1) developed a model for an IEEE distribution test network with multiple MGs integrated[AM1] [PL2] ; 2) developed a control scheme to manage community MGs to mitigate RES intermittency and enhance the grid resiliency, deferring the need for infrastructure upgrade; and 3) investigated the optimal placement and operation of community MGs in distribution networks using complex network analysis, to increase distribution networks resilience. At the transmission level (T.L), New York State T.L was modeled. A case study was conducted on Long Island City to study the impact of high penetration of renewable energy resources on the grid resilience in the transmission level. These research accomplishments should pave the way and help facilitate a smooth transition towards the future smart grid.

Towards Artificial General Intelligence (AGI) in the Internet of Things (IoT): Opportunities and Challenges

Artificial General Intelligence (AGI), possessing the capacity to comprehend, learn, and execute tasks with human cognitive abilities, engenders significant anticipation and intrigue across scientific, commercial, and societal arenas. This fascination extends particularly to the Internet of Things (IoT), a landscape characterized by the interconnection of countless devices, sensors, and systems, collectively gathering and sharing data to enable intelligent decision-making and automation. This research embarks on an exploration of the opportunities and challenges towards achieving AGI in the context of the IoT. Specifically, it starts by outlining the fundamental principles of IoT and the critical role of Artificial Intelligence (AI) in IoT systems. Subsequently, it delves into AGI fundamentals, culminating in the formulation of a conceptual framework for AGI's seamless integration within IoT. The application spectrum for AGI-infused IoT is broad, encompassing domains ranging from smart grids, residential environments, manufacturing, and transportation to environmental monitoring, agriculture, healthcare, and education. However, adapting AGI to resource-constrained IoT settings necessitates dedicated research efforts. Furthermore, the paper addresses constraints imposed by limited computing resources, intricacies associated with large-scale IoT communication, as well as the critical concerns pertaining to security and privacy