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Ushaa Eswaran,
- Principal and Professor, Department of Electronics and Communication Engineering, Mahalakshmi Tech Campus Affiliated to Anna University, Chennai, Tamil Nadu, India
Abstract document.addEventListener(‘DOMContentLoaded’,function(){frmFrontForm.scrollToID(‘frm_container_abs_111551’);});Edit Abstract & Keyword
This paper introduces Dynamic Skip-Layer Trees (DSLTs), a novel hierarchical data structure specifically designed for processing and managing multi-layered IoT sensor data in smart city environments. DSLTs extend the traditional skip list concept by incorporating dynamic layer adjustment and spatial awareness, enabling efficient querying and updates across various geographical and temporal dimensions. Our experimental results demonstrate that DSLTs achieve up to 40% faster query processing and a 30% reduction in memory overhead compared to conventional data structures when handling large-scale IoT sensor networks. The implementation in a real-world smart traffic management system in Singapore showed significant improvements in real-time data processing capabilities.
Keywords: IoT Data Structures, Smart Cities, Dynamic Skip-Layer Trees, Spatial-Temporal Data Processing, Hierarchical Data Management, Sensor Networks
[This article belongs to International Journal of Data Structure Studies (ijdss)]
Ushaa Eswaran. Dynamic Skip-Layer Trees: A Novel Data Structure for Efficient Multi-Level IoT Data Processing in Smart Cities. International Journal of Data Structure Studies. 2024; 02(02):-.
Ushaa Eswaran. Dynamic Skip-Layer Trees: A Novel Data Structure for Efficient Multi-Level IoT Data Processing in Smart Cities. International Journal of Data Structure Studies. 2024; 02(02):-. Available from: https://journals.stmjournals.com/ijdss/article=2024/view=0
References
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[1] Nassereddine, Mohamed, and Alex Khang. “Applications of Internet of Things (IoT) in smart cities.” Advanced IoT technologies and applications in the industry 4.0 digital economy. CRC Press, 2024. 109-136.
[2] Al-Maqashi, Saleem, et al. “The impact of icts in the development of smart city: Opportunities and challenges.” (2024).
[3] Ullah, Amin, et al. “Smart cities: The role of Internet of Things and machine learning in realizing a data-centric smart environment.” Complex & Intelligent Systems 10.1 (2024): 1607-1637.
[4] Wang, Ze, et al. “A two-stage electricity consumption forecasting method integrated hybrid algorithms and multiple factors.” Electric Power Systems Research 234 (2024): 110600.
[5] Kang, Xiaoqiang. “Efficient data management in Internet of Things: A survey of data aggregation techniques.” Journal of Intelligent & Fuzzy Systems Preprint (2024): 1-17.
[6] Safaee, Shahab, et al. “A distributed B+ Tree indexing method for processing range queries over streaming data.” Cluster Computing 27.2 (2024): 1251-1274.
[7] Mersy, Gabriel, et al. “Optimizing Collections of Bloom Filters within a Space Budget.” Proceedings of the VLDB Endowment 17.11 (2024): 3551-3564.
[8] Hojati, Majid, Steven Roberts, and Colin Robertson. “DSTree: A Spatio-Temporal Indexing Data Structure for Distributed Networks.” Mathematical and Computational Applications 29.3 (2024): 42. [9] Fadhel, Mohammed A., et al. “Comprehensive systematic review of information fusion methods in smart cities and urban environments.” Information Fusion (2024): 102317.
[10] Cao, Yuanxin, et al. “Spatial distribution and partition of polycyclic aromatic hydrocarbons (PAHs) in the water and sediment of the southern Bohai Sea: Yellow River and PAH property influences.” Water Research 248 (2024): 120873.
[11] Kai, D. I., et al. “Spatial and temporal variation characteristics of the drought index in China grasslands in the recent 40 years (1982—2018).” National Remote Sensing Bulletin 26.12 (2024): 2629-2641.
[12] Zhang, Qi, et al. “Link Aggregation for Skip Connection–Mamba: Remote Sensing Image Segmentation Network Based on Link Aggregation Mamba.” Remote Sensing 16.19 (2024): 3622.
[13] Ray, Saikat Sinha, et al. “Leveraging ChatGPT and Bard: What does it convey for water treatment/desalination and harvesting sectors?.” Desalination 570 (2024): 117085.
[14] da Costa, Tiago André Queiroz Soares. “Enhanced multiview experiences through remote content selection and dynamic quality adaptation.” (2024).
[15] Sharifi, Abbas, et al. “Application of artificial intelligence in digital twin models for stormwater infrastructure systems in smart cities.” Advanced Engineering Informatics 61 (2024): 102485.
| Volume | 02 |
| Issue | 02 |
| Received | 24/10/2024 |
| Accepted | 26/10/2024 |
| Published | 07/11/2024 |
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