Smart City Gnosys
Smart city article details
| Title | Internet Of Things-Enabled Optimal Data Aggregation Approach For The Intelligent Surveillance Systems |
|---|---|
| ID_Doc | 33069 |
| Authors | Rahmani M.K.I.; Khan F.; Muzaffar A.W.; Jan M.A. |
| Year | 2022 |
| Published | Mobile Information Systems, 2022 |
| DOI | http://dx.doi.org/10.1155/2022/4681583 |
| Abstract | The Internet of Things (IoT)-based intelligent surveillance systems in smart cities are a challenging issue as various devices capture the data. These devices, deployed close to the underlined phenomenon, such as cameras, are duplicated or redundant as accuracy is the main requirement of these systems. For this purpose, sensor nodes are deployed to provide 24/7 monitoring of a smart city, which minimizes the security risks and enables quick response in case of any disaster. However, due to a large number of devices, huge data are generated; thus, controlling traffic congestion in case of undesirable circumstances, for example, in case of accident or intention blockage of the road, is desperately needed. Numerous data aggregation mechanisms were reported in the literature to address this issue with smart city surveillance systems. However, these approaches were designed for either specific application environments or complex environments, making the implementation process hard. In this article, we have developed an Internet of Things-enabled optimal data aggregation approach, specifically designed for the intelligent surveillance systems in smart cities, to convert raw data values into the refined ones with minimum possible data loss ratio. Moreover, the proposed scheme bounds every server to perform or carry out the data refinement process to maintain the expected ratio of accuracy and precision. In this approach, ordinary devices are forced to capture and forward data in raw form, preferably without any or minimum possible processing. This reduces the load on ordinary devices in intelligent surveillance systems. Additionally, we have developed a novel approach to eliminate or reduce (if elimination is not possible) noisy data or outliers. It is implemented along with existing state-of-the-art techniques to verify the exceptional performance of the proposed data aggregation approach. These algorithms were compared using various performance evaluation metrics such as refinement ratio, data loss ratio, energy efficiency, and lifetime. The simulation results have verified that the proposed scheme's performance is better than the existing approaches. © 2022 Mohammad Khalid Imam Rahmani et al. |
| Author Keywords |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
51476  | 0.886 | Sharma H.; Kanwal N. | Smart Surveillance Using Iot: A Review; [Розумне Відеоспостереження За Допомогою Інтернету Речей: Огляд] | Radioelectronic and Computer Systems, 2024, 1(109) (2024) |
| 22787 | 0.878 | Rani S. | Emerging Technologies And The Application Of Wsn And Iot: Smart Surveillance, Public Security, And Safety Challenges | Emerging Technologies and the Application of WSN and IoT: Smart Surveillance, Public Security, and Safety Challenges (2024) |
| 23238 | 0.876 | Wala T.; Chand N.; Sharma A.K. | Energy Efficient Data Collection In Smart Cities Using Iot | Advances in Intelligent Systems and Computing, 1132 (2020) |
| 5243 | 0.873 | Cengiz B.; Adam I.Y.; Ozdem M.; Das R. | A Survey On Data Fusion Approaches In Iot-Based Smart Cities: Smart Applications, Taxonomies, Challenges, And Future Research Directions | Information Fusion, 121 (2025) |
| 44380 | 0.872 | Pati A.; Praharaj A.; Swain K.; Lenka M.R. | Real-Time Incident Monitoring For Smart Cities Using Iot | ESIC 2024 - 4th International Conference on Emerging Systems and Intelligent Computing, Proceedings (2024) |
| 6197 | 0.871 | Ibrahim A.S.; Youssef K.Y.; Eldeeb A.H.; Abouelatta M.; Kamel H. | Adaptive Aggregation Based Iot Traffic Patterns For Optimizing Smart City Network Performance | Alexandria Engineering Journal, 61, 12 (2022) |
| 43228 | 0.87 | Butt A.U.R.; Saba T.; Khan I.; Mahmood T.; Khan A.R.; Singh S.K.; Daradkeh Y.I.; Ullah I. | Proactive And Data-Centric Internet Of Things-Based Fog Computing Architecture For Effective Policing In Smart Cities | Computers and Electrical Engineering, 123 (2025) |
| 14138 | 0.87 | Wang M.; Perera C.; Jayaraman P.P.; Zhang M.; Strazdins P.; Shyamsundar R.K.; Ranjan R. | City Data Fusion: Sensor Data Fusion In The Internet Of Things | The Internet of Things: Breakthroughs in Research and Practice (2017) |
| 60106 | 0.868 | Rathore, MM; Ahmad, A; Paul, A; Rho, S | Urban Planning And Building Smart Cities Based On The Internet Of Things Using Big Data Analytics | COMPUTER NETWORKS, 101 (2016) |
| 30379 | 0.867 | Lakshmi D.; Jeyarani J.; Suguna R.; Muneeshwari P.; Valantina G.M.; Jayaraman S. | Impact Of Iot Data Integration On Real-Time Analytics For Smart City Management | Proceedings of the 2024 10th International Conference on Communication and Signal Processing, ICCSP 2024 (2024) |