Smart City Gnosys
Smart city article details
| Title | An Overview Of Iot Sensor Data Processing, Fusion, And Analysis Techniques |
|---|---|
| ID_Doc | 8925 |
| Authors | Krishnamurthi R.; Kumar A.; Gopinathan D.; Nayyar A.; Qureshi B. |
| Year | 2020 |
| Published | Sensors (Switzerland), 20, 21 |
| DOI | http://dx.doi.org/10.3390/s20216076 |
| Abstract | In the recent era of the Internet of Things, the dominant role of sensors and the Internet provides a solution to a wide variety of real-life problems. Such applications include smart city, smart healthcare systems, smart building, smart transport and smart environment. However, the real-time IoT sensor data include several challenges, such as a deluge of unclean sensor data and a high resource-consumption cost. As such, this paper addresses how to process IoT sensor data, fusion with other data sources, and analyses to produce knowledgeable insight into hidden data patterns for rapid decision-making. This paper addresses the data processing techniques such as data denoising, data outlier detection, missing data imputation and data aggregation. Further, it elaborates on the necessity of data fusion and various data fusion methods such as direct fusion, associated feature extraction, and identity declaration data fusion. This paper also aims to address data analysis integration with emerging technologies, such as cloud computing, fog computing and edge computing, towards various challenges in IoT sensor network and sensor data analysis. In summary, this paper is the first of its kind to present a complete overview of IoT sensor data processing, fusion and analysis techniques. © 2020 by the authors. Licensee MDPI, Basel, Switzerland. |
| Author Keywords | Data analysis; Data fusion; Data processing; Emerging technologies; Internet of Things |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
14138  | 0.895 | 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) |
| 5243 | 0.881 | 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) |
| 4324 | 0.878 | Pourghebleh B.; Hekmati N.; Davoudnia Z.; Sadeghi M. | A Roadmap Towards Energy-Efficient Data Fusion Methods In The Internet Of Things | Concurrency and Computation: Practice and Experience, 34, 15 (2022) |
| 23773 | 0.875 | Hu L.; Shu Y. | Enhancing Decision-Making With Data Science In The Internet Of Things Environments | International Journal of Advanced Computer Science and Applications, 14, 9 (2023) |
| 17329 | 0.871 | Hiriyannaiah S.; Khan Z.; Singh A.; Siddesh G.M.; Srinivasa K.G. | Data Reduction Techniques In Fog Data Analytics For Iot Applications | Studies in Big Data, 76 (2020) |
| 15579 | 0.871 | Davoli, L; Ferrari, G | Conclusions And Future Perspectives | Data Fusion in Wireless Sensor Networks (2019) |
| 30379 | 0.87 | 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) |
| 5278 | 0.869 | Sasaki Y. | A Survey On Iot Big Data Analytic Systems: Current And Future | IEEE Internet of Things Journal, 9, 2 (2022) |
| 9504 | 0.868 | Pflanzner T.; Kertesz A. | Analyzing Iot, Fog And Cloud Environments Using Real Sensor Data | Fog Computing: Concepts, Frameworks and Technologies (2018) |
| 26761 | 0.868 | Okay F.Y.; Kok I.; Guzel M.; Ozdemir S. | Fog Computing-Based Complex Event Processing For Internet Of Things | Big Data-Enabled Internet of Things (2020) |