| Title | WSNB: Wearable Sensors with Neural Networks Located in a Base Station for IoT Environment |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Mheisn, Alaa, Shurman, Mohammad, Al-Ma’aytah, Abdallah |
| Conference Name | 2020 7th International Conference on Internet of Things: Systems, Management and Security (IOTSMS) |
| Keywords | Accuracy, base station, Base stations, clustering, EEG signal, Human Behavior, Intelligent sensors, Internet of Things, Labeling, Metrics, Neural Network, Neural networks, privacy, pubcrawl, resilience, Resiliency, Scalability, wearable sensor, Wearable sensors, wearables security, Wireless sensor networks |
| Abstract | The Internet of Things (IoT) is a system paradigm that recently introduced, which includes different smart devices and applications, especially, in smart cities, e.g.; manufacturing, homes, and offices. To improve their awareness capabilities, it is attractive to add more sensors to their framework. In this paper, we propose adding a new sensor as a wearable sensor connected wirelessly with a neural network located on the base station (WSNB). WSNB enables the added sensor to refine their labels through active learning. The new sensors achieve an average accuracy of 93.81%, which is 4.5% higher than the existing method, removing human support and increasing the life cycle for the sensors by using neural network approach in the base station. |
| DOI | 10.1109/IOTSMS52051.2020.9340167 |
| Citation Key | mheisn_wsnb_2020 |
WSNB: Wearable Sensors with Neural Networks Located in a Base Station for IoT Environment