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Smarter eco-cities and their leading-edge artificial intelligence of things solutions for environmental sustainability:A comprehensive systematic review

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Environmental Science and Ecotechnology 2024年第3期19卷 29-59页

作者： Simon Elias Bibri John Krogstie Amin Kaboli 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)LausanneSwitzerland Department of Computer Science Norwegian University of Science and Technology(NTNU)TrondheimNorway School of Engineering Institute of Mechanical EngineeringSwiss Federal Institute of Technology Lausanne(EPFL)LausanneSwitzerland

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 *** strides have,in turn,impacted smart eco-cities,catalyzing ongoing improvements and driving solutions to address complex environmental *** aligns with the visionary concept of smarter eco-cities,an emerging paradigm of urbanism characterized by the seamless integration of advanced technologies and environmental ***,there remains a significant gap in thoroughly understanding this new paradigm and the intricate spectrum of its multifaceted underlying *** bridge this gap,this study provides a comprehensive systematic review of the burgeoning landscape of smarter eco-cities and their leadingedge AI and AIoT solutions for environmental *** ensure thoroughness,the study employs a unified evidence synthesis framework integrating aggregative,configurative,and narrative synthesis *** 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 technologi

关键词： Smarter eco-cities Smart eco-cities Smart cities artificial intelligence artificial intelligence of things Machine learning Environmental sustainability Climate change

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Enhanced Hybrid Equilibrium Strategy in Fog-Cloud Computing Networks with Optimal Task Scheduling

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Computers, Materials & Continua 2024年第5期79卷 2647-2672页

作者： Muchang Rao Hang Qin School of Computer Science Yangtze UniversityJingzhou434000China

More devices in the Intelligent Internet of things(AIoT)result in an increased number of tasks that require low latency and real-time responsiveness,leading to an increased demand for computational *** computing’s low-latency performance issues in AIoT scenarios have led researchers to explore fog computing as a complementary ***,the effective allocation of resources for task execution within fog environments,characterized by limitations and heterogeneity in computational resources,remains a formidable *** tackle this challenge,in this study,we integrate fog computing and cloud *** begin by establishing a fog-cloud environment framework,followed by the formulation of a mathematical model for task ***,we introduce an enhanced hybrid Equilibrium Optimizer(EHEO)tailored for AIoT task *** overarching objective is to decrease both the makespan and energy consumption of the fog-cloud system while accounting for task *** proposed EHEO method undergoes a thorough evaluation against multiple benchmark algorithms,encompassing metrics likemakespan,total energy consumption,success rate,and average waiting *** experimental results unequivocally demonstrate the superior performance of EHEO across all assessed ***,in the most favorable conditions,EHEO significantly diminishes both the makespan and energy consumption by approximately 50%and 35.5%,respectively,compared to the secondbest performing approach,which affirms its efficacy in advancing the efficiency of AIoT task scheduling within fog-cloud networks.

关键词： artificial intelligence of things fog computing task scheduling equilibrium optimizer differential evaluation algorithm local search

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FedAdaSS: Federated Learning with Adaptive Parameter Server Selection Based on Elastic Cloud Resources

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Computer Modeling in Engineering & Sciences 2024年第10期141卷 609-629页

作者： Yuwei Xu Baokang Zhao Huan Zhou Jinshu Su School of Computer National University of Defense TechnologyChangsha410000China

The rapid expansion of artificial intelligence(AI)applications has raised significant concerns about user privacy,prompting the development of privacy-preserving machine learning(ML)paradigms such as federated learning(FL).FL enables the distributed training of ML models,keeping data on local devices and thus addressing the privacy concerns of ***,challenges arise from the heterogeneous nature of mobile client devices,partial engagement of training,and non-independent identically distributed(non-IID)data distribution,leading to performance degradation and optimization objective bias in FL *** the development of 5G/6G networks and the integration of cloud computing edge computing resources,globally distributed cloud computing resources can be effectively utilized to optimize the FL *** the specific parameters of the server through the selection mechanism,it does not increase the monetary cost and reduces the network latency overhead,but also balances the objectives of communication optimization and low engagement mitigation that cannot be achieved simultaneously in a single-server framework of existing *** this paper,we propose the FedAdaSS algorithm,an adaptive parameter server selection mechanism designed to optimize the training efficiency in each round of FL training by selecting the most appropriate server as the parameter *** approach leverages the flexibility of cloud resource computing power,and allows organizers to strategically select servers for data broadcasting and aggregation,thus improving training performance while maintaining cost *** FedAdaSS algorithm estimates the utility of client systems and servers and incorporates an adaptive random reshuffling strategy that selects the optimal server in each round of the training *** analysis confirms the convergence of FedAdaSS under strong convexity and L-smooth assumptions,and comparative experiments within the FLSim framework demonstra

关键词： Machine learning systems federated learning server selection artificial intelligence of things non-IID data

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The synergistic interplay of artificial intelligence and digital twin in environmentally planning sustainable smart cities:A comprehensive systematic review

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Environmental Science and Ecotechnology 2024年第4期20卷 79-104页

作者： Simon Elias Bibri Jeffrey Huang Senthil Kumar Jagatheesaperumal John Krogstie Swiss Federal Institute of Technology Lausanne(EPFL) Institute of Computer and Communication Sciences(IINFCOM)School of ArchitectureCivil and Environmental Engineering(ENAC)Media and Design Laboratory(LDM)1015LausanneSwitzerland Department of Electronics&Communication Engineering Mepco Schlenk Engineering CollegeSivakasi626005TamilnaduIndia Department of Computer Science Norwegian University of Science and Technology(NTNU)TrondheimNorway

The dynamic landscape of sustainable smart cities is witnessing a significant transformation due to the integration of emerging computational technologies and innovative *** advancements are reshaping data-driven planning strategies,practices,and approaches,thereby facilitating the achievement of environmental sustainability *** transformative wave signals a fundamental shift d marked by the synergistic operation of artificial intelligence(AI),artificial intelligence of things(AIoT),and urban digital twin(UDT)*** previous research has largely explored urban AI,urban AIoT,and UDT in isolation,a significant knowledge gap exists regarding their synergistic interplay,collaborative integration,and collective impact on data-driven environmental planning in the dynamic context of sustainable smart *** address this gap,this study conducts a comprehensive systematic review to uncover the intricate interactions among these interconnected technologies,models,and domains while elucidating the nuanced dynamics and untapped synergies in the complex ecosystem of sustainable smart *** to this study are four guiding research questions:*** theoretical and practical foundations underpin the convergence of AI,AIoT,UDT,data-driven planning,and environmental sustainability in sustainable smart cities,and how can these components be synthesized into a novel comprehensive framework?*** does integrating AI and AIoT reshape the landscape of datadriven planning to improve the environmental performance of sustainable smart cities?*** can AI and AIoT augment the capabilities of UDT to enhance data-driven environmental planning processes in sustainable smart cities?*** challenges and barriers arise in integrating and implementing AI,AIoT,and UDT in data-driven environmental urban planning,and what strategies can be devised to surmount or mitigate them?Methodologically,this study involves a rigorous analysis and synthesis of studies publis

关键词： Sustainable smart cities artificial intelligence artificial intelligence of things Urban digital twin Data-driven urban planning Environmental planning Environmental sustainability

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