In event-driven IoT systems, real-time data is crucial for effective complex event processing (CEP) and automated decision-making. However, missing sensor events due to network disruptions, sensor failures, or inconsistencies can degrade system performance and analytical accuracy. This study explores event fabrication techniques to reconstruct missing data in an IoTenabled smart farming scenario. We evaluate two methods - Naive and Exponential Smoothing with Linear Trend (ESLT) - by analyzing their accuracy and precision under various nonideal conditions. Experimental results highlight the trade-offs between these approaches, demonstrating that the Naive method, representing memoryless interpolation techniques, is more reliable for short-term gaps and sparse data, while ESLT, as a trend-aware statistical method, performs better with sufficient historical data but struggles in temporally sparse contexts. This distinction serves as a key finding, illustrating fundamental differences in event fabrication strategies. The findings provide insights into optimizing event fabrication strategies for real-time IoT applications, ensuring the continuity in event-driven architectures. Future research will explore adaptive methodologies for dynamically selecting the most suitable fabrication technique based on real-time conditions proposed by this study.
As smart cities evolve, the efficient and sustainable distribution of communication infrastructure is critical for enabling real-time data sharing, intelligent traffic management, and enhanced passenger experiences. This paper presents a System of Systems Engineering (SoSE)-based ulti-objective optimization framework for the strategic placement of IoT gateways within a bus stop network. Considered optimization objectives are to minimize the number of gateways, balance the load across gateways, maximize network coverage, and ensure that all bus stops locations are covered. The proposed Genetic Algorithm (GA) is designed to handle the complex trade-offs between these objectives, providing a robust solution for real-world deployment scenarios. Simulations are conducted to evaluate the algorithm's performance of different GA parameters. The study enhances the understanding of gateway distribution optimization in smart city transportation networks, supporting urban planners and network designers in improving infrastructure efficiency.
The increasing integration of IoT components in systems engineering is driving a shift towards decentralized decision-making and ubiquitous interconnectivity. This transformation gives rise to modern systems-of-systems (SoS), where subsystems operate autonomously while exchanging information across interconnected domains. The complexity of engineering such systems intensifies when they span multiple domains, such as the integration of a Smart Grid Component in both Industry 4.0 and Smart Grid contexts. These cross-domain interactions necessitate new approaches to manage engineering and runtime complexity effectively. The Asset Administration Shell (AAS), introduced by Platform Industry 4.0, offers a structured framework to address these challenges. By organizing engineering information and capturing runtime data, the AAS enables a holistic approach to designing and managing cross-domain systems-ofsystems. This paper investigates how the AAS can facilitate cross-domain systems engineering, emphasizing its potential as a transformative tool for managing complexity. The proposed methodology is validated through an example, demonstrating the AAS's practical applicability in bridging engineering and operational requirements across domains.
Design thinking is a critical skill in 21st-century engineering education, fostering creative problem-solving and user-centered design approaches. When integrated with systems thinking, particularly the Distinctions, Systems, Relationships, and Perspectives (DSRP) framework, it equips engineers with a structured approach to tackling complex challenges. Sustainability, encompassing environmental, economic, and social dimensions, represents a crucial intersection of these frameworks. Despite the increasing incorporation of sustainability in engineering curricula, there is limited research on how students navigate trade-offs among these dimensions in practical applications. This study explores how engineering students internalize and apply sustainability principles in an energy-efficient housing design challenge. Using Energy3D software, students designed energy-efficient houses while balancing cost-effectiveness, environmental impact, and technical feasibility. We analyzed student reflections and design outcomes using OpenAI's ChatGPT-4.0 model to identify sustainability themes and assess trade-offs. Our findings reveal that while most students effectively balanced environmental and economic considerations, variations in prioritization led to distinct design strategies. The majority (52.4%) were classified as Balanced Sustainability Champions, excelling in both dimensions, while others emphasized either technological innovation or cost constraints. The results highlight disparities in environmental performance, suggesting opportunities for targeted instructional improvements. By leveraging large language models (LLMs) for thematic analysis, this study provides novel insights into sustainability education, demonstrating how integrating design thinking and systems thinking within experiential learning can enhance students' ability to address real-world sustainability challenges.
Sustainability Assessment by Fuzzy Evaluation (SAFE) is a model widely used to measure sustainability in the context of nations, cities, corporations and other entities. Its 2025 version presented here provides sustainability scores and rankings for 161 countries. SAFE uses time series for 68 basic indicators regarding environmental and socioeconomic national performance. Each time series is converted to a single sustainability score between 0 and 1 via appropriate smoothing, imputation and normalization algorithms to remove measurement noise and fill in missing values. A multistage rulebased fuzzy inference procedure aggregates the normalized indicators into composite variables which yield an ecological and human component per country. These two components are finally aggregated into an overall sustainability index. Next a sensitivity analysis uncovers the most important indicators that affect sustainability. Most countries have made modest progress towards sustainability over 2000-2024. Developed countries exhibit contradictory performance. Several of them occupy the top places of the ranking but others show a decline in both the ecological and human components.
