Heterogeneous network handover algorithm for unmanned aerial vehicles based on categorical fuzzy inference
In order to meet the requirements of 5G connected unmanned aerial vehicle (UAV) communication, wireless local area network (WLAN) and satellite network are integrated into UAV communication link, and form UAV heterogeneous communication network together with 5G. In order to achieve fast and reliable...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2022-01-01
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| Series: | ITM Web of Conferences |
| Subjects: | |
| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2022/07/itmconf_cccar2022_02018.pdf |
| _version_ | 1818218237435838464 |
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| author | Zhou Yongjian Liu Kun |
| author_facet | Zhou Yongjian Liu Kun |
| author_sort | Zhou Yongjian |
| collection | DOAJ |
| description | In order to meet the requirements of 5G connected unmanned aerial vehicle (UAV) communication, wireless local area network (WLAN) and satellite network are integrated into UAV communication link, and form UAV heterogeneous communication network together with 5G. In order to achieve fast and reliable handover decisions and uninterrupted service, this paper presents a vertical handover (VHO) algorithm based on classification fuzzy reasoning. Here, this paper introduces six parameters of UAV terminal and divides them into two categories for decision-making. The received signal strength (RSS), remaining available bandwidth (RBW), and propagation delay were taken as the key variables, while the network cost, expected dwell time (EDT) and security were taken as the non-key variables. In the handover decision, the pre-screening based on the received signal strength threshold and the stabilization time is firstly carried out, and the classification fuzzy logic system is used to make the next decision on the candidate networks that meet the requirements. Finally, the output of each fuzzy system is weighted according to user requirements to obtain the final judgment index. Through simulations, it has been proved that the algorithm proposed in this paper can accurately make the handover decision according to the user’s requirements, avoid the ping-pong effect, and effectively improve the system performance. |
| first_indexed | 2024-12-12T07:20:34Z |
| format | Article |
| id | doaj.art-4fb94a74ae004e578844ef81b151f137 |
| institution | Directory Open Access Journal |
| issn | 2271-2097 |
| language | English |
| last_indexed | 2024-12-12T07:20:34Z |
| publishDate | 2022-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | ITM Web of Conferences |
| spelling | doaj.art-4fb94a74ae004e578844ef81b151f1372022-12-22T00:33:23ZengEDP SciencesITM Web of Conferences2271-20972022-01-01470201810.1051/itmconf/20224702018itmconf_cccar2022_02018Heterogeneous network handover algorithm for unmanned aerial vehicles based on categorical fuzzy inferenceZhou Yongjian0Liu Kun1School of Aeronautics and Astronautics, Sun Yat-Sen UniversitySchool of Aeronautics and Astronautics, Sun Yat-Sen UniversityIn order to meet the requirements of 5G connected unmanned aerial vehicle (UAV) communication, wireless local area network (WLAN) and satellite network are integrated into UAV communication link, and form UAV heterogeneous communication network together with 5G. In order to achieve fast and reliable handover decisions and uninterrupted service, this paper presents a vertical handover (VHO) algorithm based on classification fuzzy reasoning. Here, this paper introduces six parameters of UAV terminal and divides them into two categories for decision-making. The received signal strength (RSS), remaining available bandwidth (RBW), and propagation delay were taken as the key variables, while the network cost, expected dwell time (EDT) and security were taken as the non-key variables. In the handover decision, the pre-screening based on the received signal strength threshold and the stabilization time is firstly carried out, and the classification fuzzy logic system is used to make the next decision on the candidate networks that meet the requirements. Finally, the output of each fuzzy system is weighted according to user requirements to obtain the final judgment index. Through simulations, it has been proved that the algorithm proposed in this paper can accurately make the handover decision according to the user’s requirements, avoid the ping-pong effect, and effectively improve the system performance.https://www.itm-conferences.org/articles/itmconf/pdf/2022/07/itmconf_cccar2022_02018.pdfunmanned aerial vehicle (uav)heterogeneous networkfuzzy logicvariable classificationvertical handover (vho) |
| spellingShingle | Zhou Yongjian Liu Kun Heterogeneous network handover algorithm for unmanned aerial vehicles based on categorical fuzzy inference ITM Web of Conferences unmanned aerial vehicle (uav) heterogeneous network fuzzy logic variable classification vertical handover (vho) |
| title | Heterogeneous network handover algorithm for unmanned aerial vehicles based on categorical fuzzy inference |
| title_full | Heterogeneous network handover algorithm for unmanned aerial vehicles based on categorical fuzzy inference |
| title_fullStr | Heterogeneous network handover algorithm for unmanned aerial vehicles based on categorical fuzzy inference |
| title_full_unstemmed | Heterogeneous network handover algorithm for unmanned aerial vehicles based on categorical fuzzy inference |
| title_short | Heterogeneous network handover algorithm for unmanned aerial vehicles based on categorical fuzzy inference |
| title_sort | heterogeneous network handover algorithm for unmanned aerial vehicles based on categorical fuzzy inference |
| topic | unmanned aerial vehicle (uav) heterogeneous network fuzzy logic variable classification vertical handover (vho) |
| url | https://www.itm-conferences.org/articles/itmconf/pdf/2022/07/itmconf_cccar2022_02018.pdf |
| work_keys_str_mv | AT zhouyongjian heterogeneousnetworkhandoveralgorithmforunmannedaerialvehiclesbasedoncategoricalfuzzyinference AT liukun heterogeneousnetworkhandoveralgorithmforunmannedaerialvehiclesbasedoncategoricalfuzzyinference |