Loading...
Please wait, while we are loading the content...
Similar Documents
RSSI-Based for Device-Free Localization Using Deep Learning Technique
| Content Provider | MDPI |
|---|---|
| Author | Sukor, Abdul Syafiq Abdull Kamarudin, Latifah Munirah Zakaria, Ammar Rahim, Norasmadi Abdul Sudin, Sukhairi Nishizaki, Hiromitsu |
| Copyright Year | 2020 |
| Description | Device-free localization (DFL) has become a hot topic in the paradigm of the Internet of Things. Traditional localization methods are focused on locating users with attached wearable devices. This involves privacy concerns and physical discomfort especially to users that need to wear and activate those devices daily. DFL makes use of the received signal strength indicator (RSSI) to characterize the user’s location based on their influence on wireless signals. Existing work utilizes statistical features extracted from wireless signals. However, some features may not perform well in different environments. They need to be manually designed for a specific application. Thus, data processing is an important step towards producing robust input data for the classification process. This paper presents experimental procedures using the deep learning approach to automatically learn discriminative features and classify the user’s location. Extensive experiments performed in an indoor laboratory environment demonstrate that the approach can achieve 84.2% accuracy compared to the other basic machine learning algorithms. |
| Ending Page | 455 |
| Page Count | 12 |
| Starting Page | 444 |
| e-ISSN | 26246511 |
| DOI | 10.3390/smartcities3020024 |
| Journal | Smart Cities |
| Issue Number | 2 |
| Volume Number | 3 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2020-06-01 |
| Access Restriction | Open |
| Subject Keyword | Smart Cities Industrial Engineering Device-free Localization Machine Learning Classifier Deep Learning Big Data Wireless Networks Classification Received Signal Strength |
| Content Type | Text |
| Resource Type | Article |
