Smart-heating systems can reduce operational heat demand, but their net environmental benefit remains uncertain because these savings must compensate the life-cycle burdens of IoT hardware and communication infrastructure. This study assesses a smart heating control system for an 80\,m$^2$ classroom by combining life cycle assessment with thermal Monte Carlo simulation. The system is compared with two conventional reference scenarios: always-on heating and fixed-schedule heating. Across 50{,}000 scenarios, threshold emission factors per kWh of delivered heat were derived for multiple midpoint indicators to determine whether environmental break-even is reached within five years. The results show that break-even is much more likely when smart heating replaces an always-on scenario than when it replaces a fixed schedule. For climate change and several other indicators, many scenarios achieve break-even, particularly when the underlying heating system has high emission factors per kWh of delivered heat. In contrast, break-even is rare for ecotoxicity and resource use of minerals and metals, where the production burden of the IoT system dominates. Among scenarios that do reach break-even, the dominant drivers of time to break-even differ by reference case: air-change rate in the always-on comparison and occupancy patterns in the fixed-schedule comparison.

Small and medium-sized enterprises (SMEs) face growing pressure to transition toward circular economy practices, yet most available guidance on IoT-enabled circular strategies is designed for large firms and lacks regional grounding. This paper addresses that gap through a scoping review of academic literature and regional project documentation, towards developing an IoT-10R mapping framework that connects four IoT technology categories (asset tracking, sensor-based condition monitoring, data analytics platforms, and digital data-sharing platforms) to circular economy strategies R3 through R6 (Reuse, Repair, Refurbish, Remanufacture) of the 10R framework. The study is situated in the Lake Constance region, a cross-border industrial area spanning Austria, Germany, Switzerland, and Liechtenstein, where a cluster of Interreg- and Erasmus+-funded initiatives provides applied regional evidence. The reviewed literature and regional project documentation suggest that Repair and Reuse represent the most accessible entry points for SMEs, that data-sharing platforms function as a cross-cutting enabler across all four strategies, and that the primary barriers to adoption are organisational and institutional rather than purely technological. The paper contributes a scoping work towards a structured, SME-oriented framework for IoT-enabled circular economy implementation and focuses on the role of ecosystem-level support infrastructure in overcoming adoption barriers at regional scale.

Continuous operation of air purifiers and dehumidifiers in smart homes improves indoor air quality but increases energy demand and carbon emissions, undermining sustainability. Recent scientific trends show that the Internet of Things research increasingly recognizes heterogeneity and fragmentation of IoT ecosystems -situations where devices come from different manufacturers, follow diverse protocols, and lack integrated control logic. This fragmentation often causes redundant actuation and uncoordinated sensing, leading to significant energy waste. By focusing on the measurable impacts of fragmentation on decision accuracy, this study proposes analytical methods to reconcile health protection with energy sustainability in non-integrated environments. Using real-world data from a heterogeneous smart home deployment, the study employs multi-dimensional data analytics to identify environmental archetypes and isolate temperature-driven humidity fluctuations from actual moisture risks. Building on these findings, it was developed a Health-Sustainability–Technology model and a multi-sensor reconciliation algorithm. By suppressing responses to thermally driven noise, the algorithm treats data as a resource to prevent unnecessary mechanical cycles and compressor starts.

Internet of Things (IoT) technologies enable data-driven optimization of municipal services, particularly in waste management through real-time monitoring and dynamic collection planning. While simulation studies predict substantial efficiency gains from smart waste systems, empirical evidence from real-world implementations remains limited. This study investigates the deployment of an IoT-based smart waste management system developed in collaboration with ANTA SWISS AG and implemented in pilot municipalities. Integrating threshold theory with relational economics, the research analyzes how operational fill-level thresholds and stakeholder coordination influence system performance. Empirical results indicate that collection thresholds of approximately 70–75% bin fill levels can significantly reduce travel distances and working hours compared to traditional fixed-route collection. However, realized efficiency gains remain lower than those predicted by simulation models, highlighting the importance of organizational and relational factors in addition to technological optimization. The findings demonstrate that the effectiveness of smart waste infrastructures depends on coordinated interactions among municipalities, technology providers, and operational partners. Based on these insights, the paper proposes a relational business model framework to explain value creation in IoT-enabled municipal waste management ecosystems. Furthermore, the study highlights that IoT-driven optimization of waste collection contributes directly to sustainability goals by reducing emissions, resource consumption, and environmental impact in urban service systems. This underscores the role of smart waste management as a key enabler of more sustainable and circular urban infrastructures.

This paper investigates how IoT devices can be interoperably integrated into existing systems to reduce premature e‑waste. Long‑lasting smart home installations frequently encounter new communication standards during their lifetime, raising the question of whether defective or outdated devices can be replaced with modern devices using different communication technologies without requiring complete system replacement. To address this challenge, the historical development of interoperability concepts in computer science was analyzed, alongside recent approaches such as Matter. Based on this analysis, a set of requirements for achieving interoperability in IoT systems was derived. Two technical approaches were developed and implemented: a centralized universal gateway based on Home Assistant, and a decentralized solution using the Web of Things as a universal communication standard. Both implementations demonstrate that interoperability across heterogeneous smart home devices can be successfully achieved, thereby extending device lifetimes and contributing to e‑waste reduction.

Achieving accurate indoor positioning of IoT devices remains a key challenge, particularly under deployment conditions where fixed anchor nodes or additional infrastructure cannot be installed due to cost, installation effort, or operational constraints. Motivated by the requirements identified in an industrial use case, and the goal of infrastructure-efficient and sustainable IoT system design, this study conducts a systematic literature review to determine whether sub-meter indoor positioning accuracy can be achieved using received signal strength indicator (RSSI) measurements without fixed anchor nodes in existing IoT infrastructures. Following the PRISMA 2020 guidelines, 319 publications were retrieved from five scientific databases. An independent two-stage screening identified four studies most relevant to the research questions. The analysis shows that approaches labeled as anchorless often rely on implicit references, such as pre-localized nodes or existing wireless infrastructure. Despite advances in anchorless localization techniques, the reviewed evidence does not demonstrate reproducible sub-meter accuracy under the strict infrastructure-light conditions considered in this work. These findings suggest practical limitations for RSSI-based indoor localization in real-world IoT deployments and indicate that reliable positioning without some form of relational or cooperative reference information has not yet been consistently demonstrated in deployment-oriented studies. This review provides a foundation for future research on IoT localization systems aiming for accurate positioning while minimizing installation requirements.