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Reduction of RSSI variations for indoor position estimation in wireless sensor networks |
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| รหัสดีโอไอ | |
| Creator | 1. Apidet Booranawong 2. Jerawat Sopajarn 3. Thantip Sittiruk 4. Nattha Jindapetch |
| Title | Reduction of RSSI variations for indoor position estimation in wireless sensor networks |
| Publisher | Faculty of Engineering, Khon Kaen University |
| Publication Year | 2561 |
| Journal Title | Engineering and Applied Science Research |
| Journal Vol. | 45 |
| Journal No. | 3 |
| Page no. | 212-220 |
| Keyword | Indoor localization, RSSI, Variation, CC2500, Log-normal shadowing model, Trilateration |
| URL Website | https://www.tci-thaijo.org/index.php/easr/index |
| Website title | Engineering and Applied Science Research |
| ISSN | 2539-6161 |
| Abstract | In this paper, the reduction of RSSI (received signal strength indicator) variation for indoor position estimation in wireless sensor networks (WSNs) is studied through simulation. We demonstrate that using raw RSSI data (with high variation) to estimate a sensor position (i.e., an unknown position) is not appropriate due to a large estimation error. To cope with this problem, we propose a RSSI improvement method for reducing RSSI variation. The sum of the average RSSI value used at the previous step and the RSSI value measured at the current step are employed to determine the appropriate RSSI value (i.e., the smoothed RSSI value). The priority technique is also applied to such a function by assigning different weighted values. Simulation results show that using our proposed method with an optimal weighted value gives better estimation results than using raw RSSI data and a moving average method. With the proposed method, the position estimation by an original trilateration approach is more accurate. |
