<p dir="ltr">In recent years, a surge in data-driven computation has significantly impacted security analysis in cyber-physical systems (CPSs), especially in decentralized environments. This transformation can be attributed to the remarkable computational power offered by high-performance computers (HPCs), coupled with advancements in distributed computing techniques and sophisticated learning algorithms like deep learning and reinforcement learning. Within this context, wireless communication systems and decentralized computing systems emerge as highly suitable environments for leveraging data-driven computation in security analysis. Our research endeavors have focused on exploring the vast potential of various deep learning algorithms within the CPS domains. We have not only delved into the intricacies of existing algorithms but also designed novel approaches tailored to the specific requirements of CPSs. A pivotal aspect of our work was the development of a comprehensive decentralized computing platform prototype, which served as the foundation for simulating complex networking scenarios typical of CPS environments. Within this framework, we harnessed deep learning techniques such as restricted Boltzmann machine (RBM) and deep convolutional neural network (DCNN) to address critical security concerns such as the detection of Quality of Service (QoS) degradation and Denial of Service (DoS) attacks in smart grids. Our experimental results showcased the superior performance of deep learning-based approaches compared to traditional pattern-based methods. Additionally, we devised a decentralized computing system that encompassed a novel decentralized learning algorithm, blockchain-based learning automation, distributed storage for data and models, and cryptography mechanisms to bolster the security and privacy of both data and models. Notably, our prototype demonstrated excellent efficacy, achieving a fine balance between model inference performance and confidentiality. Furthermore, we delved into the integration of domain knowledge from CPSs into our deep learning models. This integration shed light on the vulnerability of these models to dedicated adversarial attacks. Through these multifaceted endeavors, we aim to fortify the security posture of CPSs while unlocking the full potential of data-driven computation in safeguarding critical infrastructures.</p>

<p dir="ltr">The urgent global shift towards decarbonization necessitates the development of robust frameworks to navigate the complex technological, financial, and regulatory challenges emerging in the clean energy transition. Furthermore, the increased adoption of renewable energy sources (RES) is correlated to the exponential growth in weather data research over the last few years. This circular relationship, where big data drives renewable growth, which feeds back the data pipeline, serves as the primary focus of this study: the development of computational tools across diverse spatial and temporal scales for the optimal design and operation of renewable energy-based systems. Two scales are considered, differentiated by their primary objectives and techniques used. </p><p dir="ltr"> In the first one, the integration of probabilistic forecasts into the operations of RES microgrids (MGs) is studied in detail. It is revealed that longer scheduling horizons can reduce dispatch costs but at the expense of forecast accuracy due to increased prediction accuracy decay (PAD). To address this, a novel method that determines how to split the time horizon into timeblocks to minimize dispatch costs and maximize forecast accuracy is proposed. This forms the basis of an optimal rolling horizon strategy (ORoHS) which schedules distributed energy resources over varying prediction/execution horizons. Results offer Pareto-optimal fronts, showing the trade-offs between cost and accuracy at varying confidence levels. Solar power proved more cost-effective than wind power due to lower variability, despite wind’s higher energy output. The ORoHS strategy outperformed common scheduling methods. In the case study, it achieved a cost of \$4.68 compared to \$9.89 (greedy policy) and \$9.37 (two-hour RoHS). The second study proposes the Caribbean Energy Corridor (CEC) project, a novel, ambitious initiative that aims to achieve total grid connectivity between the Caribbean islands. The analysis makes use of thorough data procedures and optimization methods for the resource assessment and design tasks needed to build such an infrastructure. Renewable energy potentials are quantified under different temporal and spatial coverages to maximize usage. Prioritizing offshore wind development, the CEC’s could significantly surpass anticipated growth in energy demand, with an estimated installed capacity of 34 GW of clean energy upon completion. The corridor is modeled as an HVDC grid with 32 nodes and 31 links. Underwater transmission is optimized with a Submarine-Cable-Dynamic-Programming (SCDP) algorithm that determines the best routes across the bathymetry of the region. It is found that the levelized cost of electricity remains on the low end at \$0.11/kWh, despite high initial capital investments. Projected savings reach \$ 100 billion when compared with ”business-as-usual” scenarios and the current social cost of carbon. Furthermore, this infrastructure has the potential to create around 50,000 jobs in construction, policy, and research within the coming decades, while simultaneously establishing a robust and sustainable energy-water nexus in the region. Finally, the broader implications of these works are explored, highlighting their potential to address global challenges such as energy accessibility, prosperity in conflict zones, and sharing these discoveries with the upcoming generations.</p>