Autonomous Sustainability Monitoring

Paper

Frontiers in Sustainability · Oct 15, 2025

This study introduces a framework that integrates Artificial Intelligence (AI), Internet of Things (IoT) sensors, and graph-theoretic optimization for smart waste management. The system achieved 94.1% predictive accuracy for overflow events and reduced missed pickups by 72.7%, demonstrating the efficacy of autonomous monitoring in urban sustainability.

Paper

Scientific Reports · Oct 14, 2025

This study investigates the use of artificial intelligence to facilitate sustainable urban development, focusing on the creation of smart, carbon-free cities. It highlights how AI can optimize resource usage and environmental monitoring to ensure high quality of life amidst rapid urbanization.

Paper

EPFL Infoscience · Jul 4, 2025

This research explores the convergence of AIoT and Cyber-Physical Systems to enable Urban Brain and Digital Twin platforms. These systems facilitate real-time operational management and strategic predictive planning for environmental sustainability in smart cities.

Paper

Scientific Reports · Jul 2, 2025

This study explores the transformative role of IoT in sustainable urban planning, emphasizing real-time monitoring, disaster management, and energy/waste management. Results highlight significant mediated relationships between IoT integration and urban resilience.

Paper

Computers, Materials & Continua · Nov 10, 2025

Presents a hybrid AI-IoT framework integrated with Digital Twins for predictive management of urban infrastructure. The study emphasizes the role of these technologies in developing cognitive cities capable of autonomous monitoring and decision-making.

Article

mpls.ox.ac.uk

Humans have an immense impact on the natural world. Climate change, habitat destruction, direct exploitation (e.g., poaching) and the facilitation of invasive species threaten over one million species with extinction before 2100. As a result, we need ambitious, effective, and flexible solutions to aid in monitoring and protecting biodiversity. While remaining the essential core component of biodiversity research, direct monitoring of ecosystems by humans is expensive, inefficient, error-prone, and time-consuming at the spatial scales needed. Therefore, autonomous data collection and processing (e.g. via robots and sensor networks) has the potential to rapidly improve the cost-effectiveness of biodiversity monitoring.

Article

sciencedirect.com

• Applications of autonomous search for environmental monitoring are presented. • Different platforms used in environmental monitoring are reviewed. • Review of state of the art autonomous search methods. • Presented recent developments in distributed source localization algorithm. • There is a paradigm shift from single agent to cooperative multi-agent systems.

Article

energyinformatics.springeropen.com

There have recently been intensive efforts aimed at addressing the challenges of environmental degradation and climate change through the applied innovative solutions of AI, IoT, and Big Data. Given the synergistic potential of these advanced technologies, their convergence is being embraced and leveraged by smart cities in an attempt to make progress toward reaching the environmental targets of sustainable development goals under what has been termed “environmentally sustainable smart cities.” This new paradigm of urbanism represents a significant research gap in and of itself. To fill this gap, this study explores the key research trends and driving factors of environmentally sustainable smart cities and maps their thematic evolution. Further, it examines the fragmentation, amalgamation, and transition of their underlying models of urbanism as well as their converging AI, IoT, and Big Data technologies and solutions. It employs and combines bibliometric analysis and evidence synthesis methods. A total of 2,574 documents were collected from the Web of Science database and compartmentalized into three sub-periods: 1991–2015, 2016–2019, and 2020–2021. The results show that environmentally sustainable smart cities are a rapidly growing trend that markedly escalated during the second and third periods—due to the acceleration of the digitalization and decarbonization agendas—thanks to COVID-19 and the rapid advancement of data-driven technologies. The analysis also reveals that, while the overall priority research topics have been dynamic over time—some AI models and techniques and environmental sustainability areas have received more attention than others. The evidence synthesized indicates that the increasing criticism of the fragmentation of smart cities and sustainable cities, the widespread diffusion of the SDGs agenda, and the dominance of advanced ICT have significantly impacted the materialization of environmentally sustainable smart cities, thereby influencing the landscape and dynamics of smart cities. It also suggests that the convergence of AI, IoT, and Big Data technologies provides new approaches to tackling the challenges of environmental sustainability. However, these technologies involve environmental costs and pose ethical risks and regulatory conundrums. The findings can inform scholars and practitioners of the emerging data-driven technology solutions of smart cities, as well as assist policymakers in designing and implementing responsive environmental policies.

Article

ukras.org.uk

Article

books.google.com.br

Article

weforum.org

It’s a historic moment for Artificial Intelligence (AI). All the pieces are coming together: big data, advances in hardware, emerging powerful AI algorithms, and an open source community for tools that reduces barriers to entry for industry and start-ups alike. The result: AI is being propelled out of research labs and into our everyday lives, from navigating cities, ride shares, our energy networks, to the online world.

Article

mdpi.com

As the transformative power of AI crosses all economic and social sectors, the use of it as a modern technique for the simulation and/or forecast of various indicators must be viewed as a tool for sustainable development. The present paper reveals the results of research on modeling and simulating the influences of four economic indicators (the production in industry, the intramural research and development expenditure, the turnover and volume of sales and employment) on the evolution of European Economic Sentiment using artificial intelligence.

Article

recode.net

The potential of AI to clean up the environment is enormous — here’s how AI will accelerate sustainability efforts.

Article

se.com

Artificial intelligence is a vital part of our solutions that address business challenges. We deliver AI-based solutions leveraging our EcoStruxureTM architecture and platform to our four end markets — buildings, data centers, infrastructure, and industry.

Article

mdpi.com

The role of sustainable mobility and its impact on society and the environment is evident and recognized worldwide. Nevertheless, although there is a growing number of measures and projects that deal with sustainable mobility issues, it is not so easy to compare their results and, so far, there is no globally applicable set of tools and indicators that ensure holistic evaluation and facilitate replicability of the best practices. In this paper, based on the extensive literature review, we give a systematic overview of relevant and scientifically sound indicators that cover different aspects of sustainable mobility that are applicable in different social and economic contexts around the world. Overall, 22 sustainable mobility indicators have been selected and an overview of the applied measures described across the literature review has been presented.

Article

pure.tudelft.nl

Increasingly, we need to understand how ecosystems are responding to the pressures of climate change, habitat loss and degradation, exploitation, chemical and light pollution, and invasive species. Gaining this knowledge would provide a better understanding of the complex interwoven relationships between ecosystem functioning and human social and economic systems.

Article

mdpi.com

This paper deals with the definition and analysis of a complete electronic system for the detection and monitoring of stability characteristics in complex scenarios such as structural elements or environmental events. For instance, it may be successfully adopted to detect rockfall events on protection barriers, as well as to monitor landslides or the integrity of structures like bridges and buildings. The system is completely autonomous thanks to the implementation of an energy harvesting architecture and realizes a wireless sensor network whose nodes are auto-configurable, making it possible to freely arrange them in situ. The continuously collected data are relative to acceleration, inclination, position, and temperature of each node. These data are transmitted and stored on a remote web server devoted to the automatic management of alarms and accessible for data consulting. The proposed system is currently operating in different experimental fields in Italy.

Article

reliableplant.com

Next generation manufacturing solutions are making supply chains smarter, faster, more customer centric, and more sustainable. With the latter in mind, today’s post will be shining the light on some of this year’s upcoming sustainable trends that are set to shape the future of manufacturing processes in 2020 and beyond.

Article

planetarycomputer.microsoft.com

Supporting sustainability decision-making with the power of the cloud

Article

mdpi.com

GeoAI, or geographic artificial intelligence, is a powerful tool used for urban sustainability monitoring, analysis, and prediction by combining innovative artificial intelligence methods from space science, machine learning, deep learning, data mining, and cloud computing from big earth data. GeoAI plays a key role in pushing geographic information science (GIS) and earth observation toward a new stage of development by enhancing traditional geospatial analysis and mapping. By combining remote sensing data and GeoAI, we can classify and map land cover, track temporal changes in land use, and predict future trends regarding urban sustainability for better planning, management, and decision making. In summary, GeoAI exhibits vast potential to contribute to urban sustainability in the future.