A Fog Computing-Based Collaborative Information Resource System for Smart Cities: TSAS and KLED Algorithms for Data Transmission and Service Deployment

Effective integration and scheduling of information resources play a pivotal role in realizing intelligent management within the framework of smart city development. This research endeavors to overcome two significant hurdles: the redundancy in data transmission during the acquisition phase at the n...

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Veröffentlicht in:Informatica (Ljubljana) Jg. 49; H. 25; S. 27 - 42
Hauptverfasser: Xiao, Ya, Jung, Jinyeol
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Ljubljana Slovenian Society Informatika / Slovensko drustvo Informatika 03.07.2025
Schlagworte:
Accuracy
Algorithms
Cities
Cloud computing
Collaboration
Completion time
Data acquisition
Data collection
Data compression
Data transmission
Edge computing
Efficiency
False alarms
Information resources
Information sources
Multiple objective analysis
Optimization
Redundancy
Scheduling
Smart cities
Synchronism
Task scheduling
ISSN:0350-5596, 1854-3871
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Zusammenfassung:Effective integration and scheduling of information resources play a pivotal role in realizing intelligent management within the framework of smart city development. This research endeavors to overcome two significant hurdles: the redundancy in data transmission during the acquisition phase at the network's edge and the inefficiencies encountered when deploying analytical services across diverse edge devices. To address these challenges, a collaborative system architecture rooted in fog computing is introduced. A prediction mechanism, driven by spatiotemporal correlations, is incorporated to dynamically modulate data transmission intervals. This adjustment effectively curtails unnecessary synchronization, thereby enhancing the efficiency of data acquisition. Moreover, a deployment strategy based on multi-objective optimization is devised to allocate analytical tasks among edge devices constrained by limited resources, aiming to minimize the overall execution time. Experimental evaluations carried out on a real-world dataset encompassing 54 sensing terminals reveal that the proposed synchronization mechanism outperforms two traditional methods, reducing the false alarm rate by 58.90% and 31.35%, respectively, with a minimum mean absolute error of 2.6×10-5. Additionally, the deployment strategy achieves an average reduction of 13.12% in service completion time across four standard scientific workflow structures. The system adeptly alleviates bandwidth constraints and computational limitations inherent in edge networks, providing a practical and effective solution for efficient data transmission and task scheduling in extensive smart city environments.
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ISSN:0350-5596
1854-3871
DOI:10.31449/inf.v49i25.8057