ML-Based Handover Prediction and AP Selection in Cognitive Wi-Fi Networks
<p>Device mobility in dense Wi-Fi networks offers several challenges. Two well-known problems related to device mobility are handover prediction and access point selection. Due to the complex nature of the radio environment, analytical models may not characterize the wireless channel, which ma...
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2022
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| _version_ | 1864513567605129216 |
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| author | Muhammad Asif Khan (7367468) |
| author2 | Ridha Hamila (7006457) Adel Gastli (14151273) Serkan Kiranyaz (3762058) Nasser Ahmed Al-Emadi (14151276) |
| author2_role | author author author author |
| author_facet | Muhammad Asif Khan (7367468) Ridha Hamila (7006457) Adel Gastli (14151273) Serkan Kiranyaz (3762058) Nasser Ahmed Al-Emadi (14151276) |
| author_role | author |
| dc.creator.none.fl_str_mv | Muhammad Asif Khan (7367468) Ridha Hamila (7006457) Adel Gastli (14151273) Serkan Kiranyaz (3762058) Nasser Ahmed Al-Emadi (14151276) |
| dc.date.none.fl_str_mv | 2022-11-22T21:13:40Z |
| dc.identifier.none.fl_str_mv | 10.1007/s10922-022-09684-2 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/ML-Based_Handover_Prediction_and_AP_Selection_in_Cognitive_Wi-Fi_Networks/21597354 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Strategy, management and organisational behaviour Distributed computing and systems software Strategy and Management Computer Networks and Communications Hardware and Architecture Information Systems |
| dc.title.none.fl_str_mv | ML-Based Handover Prediction and AP Selection in Cognitive Wi-Fi Networks |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>Device mobility in dense Wi-Fi networks offers several challenges. Two well-known problems related to device mobility are handover prediction and access point selection. Due to the complex nature of the radio environment, analytical models may not characterize the wireless channel, which makes the solution of these problems very difficult. Recently, cognitive network architectures using sophisticated learning techniques are increasingly being applied to such problems. In this paper, we propose data-driven machine learning (ML) schemes to efficiently solve these problems in wireless LAN (WLAN) networks. The proposed schemes are evaluated and results are compared with traditional approaches to the aforementioned problems. The results report significant improvement in network performance by applying the proposed schemes. The proposed scheme for handover prediction outperforms traditional methods i.e. received signal strength method and traveling distance method by reducing the number of unnecessary handovers by 60% and 50% respectively. Similarly, in AP selection, the proposed scheme outperforms the strongest signal first and least loaded first algorithms by achieving higher throughput gains up to 9.2% and 8% respectively.</p><h2>Other Information</h2> <p> Published in: Journal of Network and Systems Management<br> License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="http://dx.doi.org/10.1007/s10922-022-09684-2" target="_blank">http://dx.doi.org/10.1007/s10922-022-09684-2</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_ed9f60340cb755dcc0fc4b16035f34e3 |
| identifier_str_mv | 10.1007/s10922-022-09684-2 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/21597354 |
| publishDate | 2022 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | ML-Based Handover Prediction and AP Selection in Cognitive Wi-Fi NetworksMuhammad Asif Khan (7367468)Ridha Hamila (7006457)Adel Gastli (14151273)Serkan Kiranyaz (3762058)Nasser Ahmed Al-Emadi (14151276)Strategy, management and organisational behaviourDistributed computing and systems softwareStrategy and ManagementComputer Networks and CommunicationsHardware and ArchitectureInformation Systems<p>Device mobility in dense Wi-Fi networks offers several challenges. Two well-known problems related to device mobility are handover prediction and access point selection. Due to the complex nature of the radio environment, analytical models may not characterize the wireless channel, which makes the solution of these problems very difficult. Recently, cognitive network architectures using sophisticated learning techniques are increasingly being applied to such problems. In this paper, we propose data-driven machine learning (ML) schemes to efficiently solve these problems in wireless LAN (WLAN) networks. The proposed schemes are evaluated and results are compared with traditional approaches to the aforementioned problems. The results report significant improvement in network performance by applying the proposed schemes. The proposed scheme for handover prediction outperforms traditional methods i.e. received signal strength method and traveling distance method by reducing the number of unnecessary handovers by 60% and 50% respectively. Similarly, in AP selection, the proposed scheme outperforms the strongest signal first and least loaded first algorithms by achieving higher throughput gains up to 9.2% and 8% respectively.</p><h2>Other Information</h2> <p> Published in: Journal of Network and Systems Management<br> License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="http://dx.doi.org/10.1007/s10922-022-09684-2" target="_blank">http://dx.doi.org/10.1007/s10922-022-09684-2</a></p>2022-11-22T21:13:40ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1007/s10922-022-09684-2https://figshare.com/articles/journal_contribution/ML-Based_Handover_Prediction_and_AP_Selection_in_Cognitive_Wi-Fi_Networks/21597354CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/215973542022-11-22T21:13:40Z |
| spellingShingle | ML-Based Handover Prediction and AP Selection in Cognitive Wi-Fi Networks Muhammad Asif Khan (7367468) Strategy, management and organisational behaviour Distributed computing and systems software Strategy and Management Computer Networks and Communications Hardware and Architecture Information Systems |
| status_str | publishedVersion |
| title | ML-Based Handover Prediction and AP Selection in Cognitive Wi-Fi Networks |
| title_full | ML-Based Handover Prediction and AP Selection in Cognitive Wi-Fi Networks |
| title_fullStr | ML-Based Handover Prediction and AP Selection in Cognitive Wi-Fi Networks |
| title_full_unstemmed | ML-Based Handover Prediction and AP Selection in Cognitive Wi-Fi Networks |
| title_short | ML-Based Handover Prediction and AP Selection in Cognitive Wi-Fi Networks |
| title_sort | ML-Based Handover Prediction and AP Selection in Cognitive Wi-Fi Networks |
| topic | Strategy, management and organisational behaviour Distributed computing and systems software Strategy and Management Computer Networks and Communications Hardware and Architecture Information Systems |