This paper presents LE3D; a novel data drift detection framework for preserving data integrity and confidentiality.
no code implementations • 3 Nov 2022 • Ioannis Mavromatis, Adrian Sanchez-Mompo, Francesco Raimondo, James Pope, Marcello Bullo, Ingram Weeks, Vijay Kumar, Pietro Carnelli, George Oikonomou, Theodoros Spyridopoulos, Aftab Khan
Our framework is also generalisable, adapting to new sensor streams and environments with minimal online reconfiguration.
Data-enabled cities are recently accelerated and enhanced with automated learning for improved Smart Cities applications.
The CICIDS2017 dataset was used to train and evaluate the performance of our proposed DBN approach.
Ranked #2 on Network Intrusion Detection on CICIDS2017
Each UMBRELLA node is installed on the pole of a lamppost and is equipped with a Raspberry Pi Camera Module v1 facing upwards towards the sky and lamppost light bulb.
Finally, an ablation study of the training dataset shows that, in both office and sport hall scenarios, after reusing the feature extraction layers of the base model, only 55% of the training data is required to obtain the models' accuracy similar to the base models.
Meanwhile, using a well-organised architecture, the neural network models can be trained directly with raw data from the CSI and localisation features can be automatically extracted to achieve accurate position estimates.
High accuracy localisation technologies exist but are prohibitively expensive to deploy for large indoor spaces such as warehouses, factories, and supermarkets to track assets and people.
The LoRaWAN based Low Power Wide Area networks aim to provide long-range connectivity to a large number of devices by exploiting limited radio resources.
Networking and Internet Architecture