Reduction of RSSI variations for indoor position estimation in wireless sensor networks
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)...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Khon Kaen University
2018-09-01
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| Series: | Engineering and Applied Science Research |
| Subjects: | |
| Online Access: | https://www.tci-thaijo.org/index.php/easr/article/download/83457/107552/ |
| _version_ | 1818930779927871488 |
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| author | Apidet Booranawong Jerawat Sopajarn Thantip Sittiruk Nattha Jindapetch |
| author_facet | Apidet Booranawong Jerawat Sopajarn Thantip Sittiruk Nattha Jindapetch |
| author_sort | Apidet Booranawong |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-20T04:06:08Z |
| format | Article |
| id | doaj.art-bff48619997748e99083a61be9ae4f8c |
| institution | Directory Open Access Journal |
| issn | 2539-6161 2539-6218 |
| language | English |
| last_indexed | 2024-12-20T04:06:08Z |
| publishDate | 2018-09-01 |
| publisher | Khon Kaen University |
| record_format | Article |
| series | Engineering and Applied Science Research |
| spelling | doaj.art-bff48619997748e99083a61be9ae4f8c2022-12-21T19:54:03ZengKhon Kaen UniversityEngineering and Applied Science Research2539-61612539-62182018-09-0145321222010.14456/easr.2018.31Reduction of RSSI variations for indoor position estimation in wireless sensor networksApidet BooranawongJerawat SopajarnThantip SittirukNattha JindapetchIn 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.https://www.tci-thaijo.org/index.php/easr/article/download/83457/107552/Indoor localizationRSSIVariationCC2500Log-normal shadowing modelTrilateration |
| spellingShingle | Apidet Booranawong Jerawat Sopajarn Thantip Sittiruk Nattha Jindapetch Reduction of RSSI variations for indoor position estimation in wireless sensor networks Engineering and Applied Science Research Indoor localization RSSI Variation CC2500 Log-normal shadowing model Trilateration |
| title | Reduction of RSSI variations for indoor position estimation in wireless sensor networks |
| title_full | Reduction of RSSI variations for indoor position estimation in wireless sensor networks |
| title_fullStr | Reduction of RSSI variations for indoor position estimation in wireless sensor networks |
| title_full_unstemmed | Reduction of RSSI variations for indoor position estimation in wireless sensor networks |
| title_short | Reduction of RSSI variations for indoor position estimation in wireless sensor networks |
| title_sort | reduction of rssi variations for indoor position estimation in wireless sensor networks |
| topic | Indoor localization RSSI Variation CC2500 Log-normal shadowing model Trilateration |
| url | https://www.tci-thaijo.org/index.php/easr/article/download/83457/107552/ |
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