2022/11/03 by Ioannis Mavromatis, Aftab Aslam Parwaz Khan, Mavromatis, Ioannis +1
Computer Science · #Cryptography and Security (cs.CR) #Data Stream Mining Techniques #FOS: Computer and information sciences #Machine Learning (cs.LG) #Mobile Crowdsensing and Crowdsourcing #Opportunistic and Delay-Tolerant Networks #Software Engineering (cs.SE)
paper · pdf · doi:10.48550/arxiv.2211.01827
openalex publication_date 2022/11/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This paper presents LE3D; a novel data drift detection framework for preserving data integrity and confidentiality. LE3D is a generalisable platform for evaluating novel drift detection mechanisms within the Internet of Things (IoT) sensor deployments. Our framework operates in a distributed manner, preserving data privacy while still being adaptable to new sensors with minimal online reconfiguration. Our framework currently supports multiple drift estimators for time-series IoT data and can easily be extended to accommodate new data types and drift detection mechanisms. This demo will illustrate the functionality of LE3D under a real-world-like scenario.