1
|
Bibri SE, Krogstie J, Kaboli A, Alahi A. Smarter eco-cities and their leading-edge artificial intelligence of things solutions for environmental sustainability: A comprehensive systematic review. Environ Sci Ecotechnol 2024; 19:100330. [PMID: 38021367 PMCID: PMC10656232 DOI: 10.1016/j.ese.2023.100330] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/07/2023] [Revised: 09/28/2023] [Accepted: 09/28/2023] [Indexed: 12/01/2023]
Abstract
The recent advancements made in the realms of Artificial Intelligence (AI) and Artificial Intelligence of Things (AIoT) have unveiled transformative prospects and opportunities to enhance and optimize the environmental performance and efficiency of smart cities. These strides have, in turn, impacted smart eco-cities, catalyzing ongoing improvements and driving solutions to address complex environmental challenges. This aligns with the visionary concept of smarter eco-cities, an emerging paradigm of urbanism characterized by the seamless integration of advanced technologies and environmental strategies. However, there remains a significant gap in thoroughly understanding this new paradigm and the intricate spectrum of its multifaceted underlying dimensions. To bridge this gap, this study provides a comprehensive systematic review of the burgeoning landscape of smarter eco-cities and their leading-edge AI and AIoT solutions for environmental sustainability. To ensure thoroughness, the study employs a unified evidence synthesis framework integrating aggregative, configurative, and narrative synthesis approaches. At the core of this study lie these subsequent research inquiries: What are the foundational underpinnings of emerging smarter eco-cities, and how do they intricately interrelate, particularly urbanism paradigms, environmental solutions, and data-driven technologies? What are the key drivers and enablers propelling the materialization of smarter eco-cities? What are the primary AI and AIoT solutions that can be harnessed in the development of smarter eco-cities? In what ways do AI and AIoT technologies contribute to fostering environmental sustainability practices, and what potential benefits and opportunities do they offer for smarter eco-cities? What challenges and barriers arise in the implementation of AI and AIoT solutions for the development of smarter eco-cities? The findings significantly deepen and broaden our understanding of both the significant potential of AI and AIoT technologies to enhance sustainable urban development practices, as well as the formidable nature of the challenges they pose. Beyond theoretical enrichment, these findings offer invaluable insights and new perspectives poised to empower policymakers, practitioners, and researchers to advance the integration of eco-urbanism and AI- and AIoT-driven urbanism. Through an insightful exploration of the contemporary urban landscape and the identification of successfully applied AI and AIoT solutions, stakeholders gain the necessary groundwork for making well-informed decisions, implementing effective strategies, and designing policies that prioritize environmental well-being.
Collapse
Affiliation(s)
- Simon Elias Bibri
- School of Architecture, Civil and Environmental Engineering (ENAC), Civil Engineering Institute (IIC), Visual Intelligence for Transportation (VITA), Swiss Federal Institute of Technology Lausanne (EPFL), Lausanne, Switzerland
| | - John Krogstie
- Department of Computer Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Amin Kaboli
- School of Engineering, Institute of Mechanical Engineering, Swiss Federal Institute of Technology Lausanne (EPFL), Lausanne, Switzerland
| | - Alexandre Alahi
- School of Architecture, Civil and Environmental Engineering (ENAC), Civil Engineering Institute (IIC), Visual Intelligence for Transportation (VITA), Swiss Federal Institute of Technology Lausanne (EPFL), Lausanne, Switzerland
| |
Collapse
|
2
|
Wang C, He T, Zhou H, Zhang Z, Lee C. Artificial intelligence enhanced sensors - enabling technologies to next-generation healthcare and biomedical platform. Bioelectron Med 2023; 9:17. [PMID: 37528436 PMCID: PMC10394931 DOI: 10.1186/s42234-023-00118-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Accepted: 06/17/2023] [Indexed: 08/03/2023] Open
Abstract
The fourth industrial revolution has led to the development and application of health monitoring sensors that are characterized by digitalization and intelligence. These sensors have extensive applications in medical care, personal health management, elderly care, sports, and other fields, providing people with more convenient and real-time health services. However, these sensors face limitations such as noise and drift, difficulty in extracting useful information from large amounts of data, and lack of feedback or control signals. The development of artificial intelligence has provided powerful tools and algorithms for data processing and analysis, enabling intelligent health monitoring, and achieving high-precision predictions and decisions. By integrating the Internet of Things, artificial intelligence, and health monitoring sensors, it becomes possible to realize a closed-loop system with the functions of real-time monitoring, data collection, online analysis, diagnosis, and treatment recommendations. This review focuses on the development of healthcare artificial sensors enhanced by intelligent technologies from the aspects of materials, device structure, system integration, and application scenarios. Specifically, this review first introduces the great advances in wearable sensors for monitoring respiration rate, heart rate, pulse, sweat, and tears; implantable sensors for cardiovascular care, nerve signal acquisition, and neurotransmitter monitoring; soft wearable electronics for precise therapy. Then, the recent advances in volatile organic compound detection are highlighted. Next, the current developments of human-machine interfaces, AI-enhanced multimode sensors, and AI-enhanced self-sustainable systems are reviewed. Last, a perspective on future directions for further research development is also provided. In summary, the fusion of artificial intelligence and artificial sensors will provide more intelligent, convenient, and secure services for next-generation healthcare and biomedical applications.
Collapse
Affiliation(s)
- Chan Wang
- Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117576, Singapore
- Center for Intelligent Sensors and MEMS (CISM), National University of Singapore, 5 Engineering Drive 1, Singapore, 117608, Singapore
| | - Tianyiyi He
- Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117576, Singapore
- Center for Intelligent Sensors and MEMS (CISM), National University of Singapore, 5 Engineering Drive 1, Singapore, 117608, Singapore
| | - Hong Zhou
- Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117576, Singapore
- Center for Intelligent Sensors and MEMS (CISM), National University of Singapore, 5 Engineering Drive 1, Singapore, 117608, Singapore
| | - Zixuan Zhang
- Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117576, Singapore
- Center for Intelligent Sensors and MEMS (CISM), National University of Singapore, 5 Engineering Drive 1, Singapore, 117608, Singapore
| | - Chengkuo Lee
- Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117576, Singapore.
- Center for Intelligent Sensors and MEMS (CISM), National University of Singapore, 5 Engineering Drive 1, Singapore, 117608, Singapore.
- NUS Suzhou Research Institute (NUSRI), Suzhou Industrial Park, Suzhou, 215123, China.
- NUS Graduate School for Integrative Science and Engineering, National University of Singapore, Singapore, 117456, Singapore.
| |
Collapse
|
3
|
Xue X, Wang H, Liu W. Matching sensor ontologies with unsupervised neural network with competitive learning. PeerJ Comput Sci 2021; 7:e763. [PMID: 34901425 PMCID: PMC8627238 DOI: 10.7717/peerj-cs.763] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Accepted: 10/08/2021] [Indexed: 06/14/2023]
Abstract
Sensor ontologies formally model the core concepts in the sensor domain and their relationships, which facilitates the trusted communication and collaboration of Artificial Intelligence of Things (AIoT). However, due to the subjectivity of the ontology building process, sensor ontologies might be defined by different terms, leading to the problem of heterogeneity. In order to integrate the knowledge of two heterogeneous sensor ontologies, it is necessary to determine the correspondence between two heterogeneous concepts, which is the so-called ontology matching. Recently, more and more neural networks have been considered as an effective approach to address the ontology heterogeneity problem, but they require a large number of manually labelled training samples to train the network, which poses an open challenge. In order to improve the quality of the sensor ontology alignment, an unsupervised neural network model is proposed in this work. It first models the ontology matching problem as a binary classification problem, and then uses a competitive learning strategy to efficiently cluster the ontologies to be matched, which does not require the labelled training samples. The experiment utilizes the benchmark track provided by the Ontology Alignment Evaluation Initiative (OAEI) and multiple real sensor ontology alignment tasks to test our proposal's performance. The experimental results show that the proposed approach is able to determine higher quality alignment results compared to other matching strategies under different domain knowledge such as bibliographic and real sensor ontologies.
Collapse
Affiliation(s)
- Xingsi Xue
- Intelligent Information Processing Research Center, Fujian University of Technology, Fuzhou, Fujian, China
- Guangxi Key Laboratory of Automatic Detecting Technology and Instruments, Guilin University of Electronic Technology, Guilin, Guangxi, China
| | - Haolin Wang
- Intelligent Information Processing Research Center, Fujian University of Technology, Fuzhou, Fujian, China
- School of Computer Science and Mathematics, Fujian University of Technology, Fuzhou, Fujian, China
| | - Wenyu Liu
- Intelligent Information Processing Research Center, Fujian University of Technology, Fuzhou, Fujian, China
- School of Computer Science and Mathematics, Fujian University of Technology, Fuzhou, Fujian, China
| |
Collapse
|