An Adaptive Bayesian System for Context-Aware Data Fusion in Smart Environments

By Alessandra De Paola, Pierluca Ferraro, Salvatore Gaglio, Giuseppe Lo Re, Sajal K. Das. 2017

72 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition, Water Science and Technology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Athanasios V. Vasilakos
2. Yongrui Qin
3. Stephan Reiff-Marganiec
4. Júlio Cezar Estrella
5. Charith Perera
6. Adarsh Kumar

In Computer Vision and Pattern Recognition

1. Athanasios V. Vasilakos
2. Yongrui Qin
3. Stephan Reiff-Marganiec
4. Júlio Cezar Estrella
5. Charith Perera

Citations in the corpus

• Fog Computing for Sustainable Smart Cities (2017, background): “Context data also has a significant impact on data collection and fusion strategies [De Paola et al. 2016; Jiang et al. 2014].”
• An Overview of IoT Sensor Data Processing, Fusion, and Analysis Techniques (2020, methodology): “The probabilistic methods include Bayesian networks, maximum likelihood estimation methods, inference theory, Kalman filtering, etc. Statistical methods include covariance, cross variance, and other statistical analyses [54].”