Efficient IoT-based sensor BIG Data collection-processing and analysis in smart buildings
By A. Plageras, Kostas E. Psannis, C. Stergiou, Haoxiang Wang, Brij Bhooshan Gupta. Future generations computer systems, 2017
534 citations recorded by the source corpus.
Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition, Water Science and Technology
View Paper: https://www.semanticscholar.org/paper/fb18e87bdfa27b3bc7a9d9337f02cd6b66d0c372
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In Computer Vision and Pattern Recognition
Citations in the corpus
- Performance Analysis of IoT-Based Sensor, Big Data Processing, and Machine Learning Model for Real-Time Monitoring System in Automotive Manufacturing (2018, background): “proposed a monitoring system using IoT-based sensors for smart buildings [55].”
- A review of machine learning in building load prediction (2021, unknown): “Plageras et al. [175] surveyed IoT, cloud computing, big data, and sensors technologies with the aim of finding their common operations and combining them.”
- AI-big data analytics for building automation and management systems: a survey, actual challenges and future perspectives (2022, background): “Thus, it is now possible to record and process big data about environmental satisfaction levels of buildings’ occupants in real-time and non-invasive manners (Plageras et al. 2018).”
- Progress in wearable electronics/photonics—Moving toward the era of artificial intelligence and internet of things (2020, background): “44,68,298 By matching the proper learning models with specific sensing applications, more comprehensive information can be extracted from these diversely designed sensors, leading to a progressive revolution of wearable electronics.”
- Multidiscipline Applications of Triboelectric Nanogenerators for the Intelligent Era of Internet of Things (2022, background): “AI techniques can not only assist sensors in detecting more complex and diverse sensor signals, but also automatically extract the special features from sensor signals that represent the internal relationship of data sets [168, 169].”
- Privacy-Preserved Data Sharing Towards Multiple Parties in Industrial IoTs (2020, background): “These works focus on the novel applications of shared data [13], the resource allocation during the data exchange [14], and the design of integrated system [15] where data can be freely shared.”