Smart city re-imagined: City planning and GeoAI in the age of big data

Mortaheb, Reza; Jankowski, Piotr

doi:10.1016/j.jum.2022.08.001

Cited by 42 publications

(27 citation statements)

References 52 publications

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“…Figure 2 illustrates the primary operations involved in the GAPSO algorithm. The GA-PSO method begins by producing a random population (Mortaheb and Jankowski, 2023 ), and one of the parameters of the algorithm is a certain number of iterations that must be completed before the process may proceed. The population demonstrates that there is more than one approach to solving the issue of workflow tasks, and each approach is a method for allocating all of the workflow jobs to the VMs that are now accessible.…”

Section: The Proposed Algorithmmentioning

confidence: 99%

“…The pace of data generation is increasing rapidly due to the widespread use of IoT sensors and devices, as well as people's increasing reliance on online services and social media (Chandra and Verma, 2023 ; Mortaheb and Jankowski, 2023 ). The phenomenon known as Big Data has had a significant impact on several aspects of modern life, such as healthcare, finance, and transportation (Mohanty, 2015 ; Fanelli et al, 2023 ).…”

Section: Introductionmentioning

confidence: 99%

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Optimizing multi-objective task scheduling in fog computing with GA-PSO algorithm for big data application

Saad,

Enam,

Qureshi

2024

Front. Big Data

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As the volume and velocity of Big Data continue to grow, traditional cloud computing approaches struggle to meet the demands of real-time processing and low latency. Fog computing, with its distributed network of edge devices, emerges as a compelling solution. However, efficient task scheduling in fog computing remains a challenge due to its inherently multi-objective nature, balancing factors like execution time, response time, and resource utilization. This paper proposes a hybrid Genetic Algorithm (GA)-Particle Swarm Optimization (PSO) algorithm to optimize multi-objective task scheduling in fog computing environments. The hybrid approach combines the strengths of GA and PSO, achieving effective exploration and exploitation of the search space, leading to improved performance compared to traditional single-algorithm approaches. The proposed hybrid algorithm results improved the execution time by 85.68% when compared with GA algorithm, by 84% when compared with Hybrid PWOA and by 51.03% when compared with PSO algorithm as well as it improved the response time by 67.28% when compared with GA algorithm, by 54.24% when compared with Hybrid PWOA and by 75.40% when compared with PSO algorithm as well as it improved the completion time by 68.69% when compared with GA algorithm, by 98.91% when compared with Hybrid PWOA and by 75.90% when compared with PSO algorithm when various tasks inputs are given. The proposed hybrid algorithm results also improved the execution time by 84.87% when compared with GA algorithm, by 88.64% when compared with Hybrid PWOA and by 85.07% when compared with PSO algorithm it improved the response time by 65.92% when compared with GA algorithm, by 80.51% when compared with Hybrid PWOA and by 85.26% when compared with PSO algorithm as well as it improved the completion time by 67.60% when compared with GA algorithm, by 81.34% when compared with Hybrid PWOA and by 85.23% when compared with PSO algorithm when various fog nodes are given.

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Section: The Proposed Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimizing multi-objective task scheduling in fog computing with GA-PSO algorithm for big data application

Saad,

Enam,

Qureshi

2024

Front. Big Data

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“…RS data is one example of traditional, authoritative information that big data typically complements. Big data may be voluntary, automated, or driven depending on the source, e.g., "passive" technological devices that can produce 2D or 3D (Mortaheb and Jankowski, 2023).…”

Section: Geospatial Techniques Roles In Smart City Applicationsmentioning

confidence: 99%

Geospatial Techniques for Preparing the Requirements of 3D Modeling for Smart City Planning- Review paper

Sadiqe,

Jasim,

Al-Bakri

2023

IQJAP

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Geospatial technique, 3D modelling, Smart city.Developing smart city planning requires integrating various techniques, including geospatial techniques, building information models (BIM), information and communication technology (ICT), and artificial intelligence, for instance, threedimensional (3D) building models, in enabling smart city applications. This study aims to comprehensively analyze the role and significance of geospatial techniques in smart city planning and implementation. The literature review encompasses (74) studies from diverse databases, examining relevant solutions and prototypes related to smart city planning. The focus highlights the requirements and preparation of geospatial techniques to support the transition to a smart city. The paper explores various aspects, such as the advantages and challenges of geospatial techniques, data collection and analysis methodologies, and case studies showcasing successful implementations of smart city initiatives. The research concludes that geospatial techniques are instrumental in driving the development of smart cities. By analyzing and synthesizing the outcomes of the reviewed articles, this study establishes the essential contribution of geospatial techniques in successfully realizing the vision of smart cities.

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“…By utilizing the state-of-the-art deep learning models, human perception of neighborhood playability for childhood development in cities (Kruse et al, 2021) and urban visual intelligence about the hidden neighborhood socioeconomic status, such as poverty status and health outcomes, can be extracted from street-view images (Fan et al, 2023;Kang et al, 2020); near real-time global land cover and land use patterns and temporal changes can be detected from 10m spatial resolution of Sentinel-2 remote sensing imagery (Brown et al, 2022). Urban geographers and planners can also extract valuable insights into population dynamics, traffic patterns, land use patterns, environmental changes, and the interactions between social and ecological systems, so as to optimize urban spatial structure, improving transportation efficiency and quality of living, and environmental sustainability (Mortaheb and Jankowski, 2023;Liu and Biljecki, 2022). AI-driven predictive modeling can assist in rooftop solar potential estimation for sustainable city design (Wu and Biljecki, 2021), and anticipating changes in population density and mobility, facilitating better resource allocation and disaster preparedness in (Zou et al, 2022;Vongkusolkit and Huang, 2021).…”

Section: Ai In Human Geographymentioning

confidence: 99%

Geospatial Artificial Intelligence (GeoAI)

Gao¹

2021

Geography

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Nowadays, artificial intelligence (AI) is bringing tremendous new opportunities and challenges to geospatial research. Its fast development is powered by theoretical advancement, big data, computer hardware (e.g., the graphics processing unit, or GPU), and high-performance computing platforms that support the development, training, and deployment of AI models within a reasonable amount of time. Recent years have witnessed significant advances in geospatial artificial intelligence (GeoAI), which is the integration of geospatial studies and AI, especially machine learning and deep learning methods and the latest AI technologies in both academia and industry. GeoAI can be regarded as a study subject to develop intelligent computer programs to mimic the processes of human perception, spatial reasoning, and discovery about geographical phenomena and dynamics; to advance our knowledge; and to solve problems in human environmental systems and their interactions, with a focus on spatial contexts and roots in geography or geographic information science (GIScience). Thus, it would require the knowledge of AI theory, programming and computation practices as well as geographic domain knowledge to be competent in GeoAI research. There have already been increasingly collaborative GeoAI studies for GIScience, remote sensing, physical environment, and human society. It is a good time to provide a key reference list for educators, students, researchers, and practitioners to keep up with the latest GeoAI research topics. This bibliographical entry will first review the historical roots for AI in geography and GIScience and then list up to ten selective recent works with annotations that briefly describe their importance for each topic of interest in the GeoAI landscape, ranging from fundamental spatial representation learning to spatial predictions and to various advancements in cartography, earth observation, social sensing, and geospatial semantics.

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Smart city re-imagined: City planning and GeoAI in the age of big data

Cited by 42 publications

References 52 publications

Optimizing multi-objective task scheduling in fog computing with GA-PSO algorithm for big data application

Optimizing multi-objective task scheduling in fog computing with GA-PSO algorithm for big data application

Geospatial Techniques for Preparing the Requirements of 3D Modeling for Smart City Planning- Review paper

Geospatial Artificial Intelligence (GeoAI)

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