ABSTRACT
Traditional cloud computing of raw Electroencephalogram (EEG) data, particularly for continuous monitoring use-cases, consumes precious network resources and contributes to delay. Motivated by the paradigm shift of edge computing and Internet of Things (IoT) for continuous monitoring, we focus on this paper on the first step to carry out EEG edge analytics at the last frontier (i.e., the ultra-edge) of our considered cyber-physical system for ensuring users' convenience and privacy. To overcome challenges due to computational and energy resource constraints of IoT devices (e.g., EEG headbands/headsets), in this paper, we envision a smart, lightweight model, referred to as Logic-in-Headbands based Edge Analytics (LiHEA), which can be seamlessly incorporated with the consumer-grade EEG headsets to reduce delay and bandwidth consumption. By systematically investigating various traditional machine and deep learning models, we identify and select the best model for our envisioned LiHEA. We consider a use-case for detecting confusion, representing levels of distraction, during online course delivery which has become pervasive during the novel coronavirus (COVID-19) pandemic. We apply a unique feature selection technique to find out which features are triggered with confusion where delta waves, attention, and theta waves were announced as the three most important features. Among various traditional machine and deep learning models, our customized random forest model demonstrated the highest accuracy of 90%. Since the dataset size might have impacted the performance of deep learning-based approaches, we further apply the deep convolutional generative adversarial network (DCGAN) to generate synthetic traces with representative samples of the original EEG data, and thereby enhance the variation in the data. While the performances of the deep learning models significantly increase after the data augmentation, they still cannot outperform the random forest model. Furthermore, computational complexity analysis is performed for the three best-performing algorithms, and random forest emerges as the most viable model for our envisioned LiHEA.
ABSTRACT
Despite the severity of the second wave of the novel coronavirus disease (COVID-19) and the recent hope for vaccine roll-outs, many public and private institutions are forced to resume their activities subject to ensuring an adequate sterilization of their premises. The existing off-the-shelf drones for such environment sanitization have limited flight-time and payload-carrying capacity. In this paper, we address this challenge by formulating an optimization problem to minimize the energy consumed by drones equipped with ultraviolet-C band (UV-C) panels. To solve this computationally hard problem, we propose several heuristics, such as a randomized path selection algorithm whose solution is further improved with a genetic algorithm-based UV-C drone-based sterilization (UV-CDS) scheduling technique. We consider educational institutions, confronting increasing infections, as an important use-case for the problem. Due to the energy constraint of the drones, the number of drones required for sterilization of the campus is smartly altered for various campus scenarios. The respective energy-efficient paths in the proposed heuristics and our envisioned UV-CDS are estimated for the drones. The performance is evaluated through extensive computer-based simulations which clearly demonstrates the effectiveness of UV-CDS in terms of sub-optimal performance and much faster execution time in contrast with the other methods. IEEE