Smart City Gnosys
Smart city article details
| Title | Robust Sensor Fusion Algorithms For Iot |
|---|---|
| ID_Doc | 46928 |
| Authors | Subramanian S.; Lokeshwari G.; Jagadeesan S.; Leela S.; Pratheep V.G.; Venkatasudhahar M. |
| Year | 2024 |
| Published | Proceedings of the 2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024 |
| DOI | http://dx.doi.org/10.1109/ICSES63760.2024.10910399 |
| Abstract | Sensor fusion is a critical component of the IoT ecosystem, as it combines a variety of data streams from multiple sensors to enhance the accuracy, reliability, and real-time decision-making. This research concentrates on the development of robust sensor fusion algorithms that are capable of effectively managing the heterogeneity, noise, and uncertainty that are inherent in data from Internet of Things (IoT) sensors. Our primary focus is on the ability of these algorithmic methodologies to manage data from multi-modal sensors. We investigate algorithms that are based on deep learning, Bayesian inference, and Kalman filtering. The proposed solutions aim to improve the performance of systems in sectors that are influenced by the Internet of Things (IoT), including healthcare, smart cities, industrial automation, and autonomous cars. This investigation investigates the scalability, energy efficiency, and computational burden of the Internet of Things (IoT) in the context of limited resources. Through simulations and practical applications, we demonstrate the enhancement of data precision, defect tolerance, and overall system dependability by these robust sensor fusion techniques. © 2024 IEEE. |
| Author Keywords | Bayesian inference; Deep Learning and Data Reliability; Internet of Things (IoT); Kalman Filter; Multi-sensory Integration; Sensor Fusion |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
17221  | 0.868 | Alam F.; Mehmood R.; Katib I.; Albogami N.N.; Albeshri A. | Data Fusion And Iot For Smart Ubiquitous Environments: A Survey | IEEE Access, 5 (2017) |
| 25051 | 0.863 | Darabkh K.A.; Al-Akhras M. | Evolutionary Cost Analysis And Computational Intelligence For Energy Efficiency In Internet Of Things-Enabled Smart Cities: Multi-Sensor Data Fusion And Resilience To Link And Device Failures | Smart Cities, 8, 2 (2025) |
| 8925 | 0.863 | Krishnamurthi R.; Kumar A.; Gopinathan D.; Nayyar A.; Qureshi B. | An Overview Of Iot Sensor Data Processing, Fusion, And Analysis Techniques | Sensors (Switzerland), 20, 21 (2020) |
| 48408 | 0.862 | Djenouri Y.; Belhadi A.; Srivastava G.; Houssein E.H.; Lin J.C.-W. | Sensor Data Fusion For The Industrial Artificial Intelligence Of Things | Expert Systems, 39, 5 (2022) |
| 15579 | 0.861 | Davoli, L; Ferrari, G | Conclusions And Future Perspectives | Data Fusion in Wireless Sensor Networks (2019) |
| 17959 | 0.86 | Sudhakar K.; Lakshmi M.; Chaithra A.S.; Niveditha S.; Vadivel R.; Rubini P.; Selvameena R. | Deep Learning-Based Multisensor Fusion For Safe And Efficient Autonomous Vehicle Operations In Iot-Enabled Smart Cities | AI's role in enhanced automotive safety (2025) |