Understanding WiFi Cross-Technology Interference Detection in the Real World

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dc.contributor.author Pulkkinen, Teemu
dc.contributor.author Nurminen, Jukka K
dc.contributor.author Nurmi, Petteri
dc.date.accessioned 2021-11-01T10:50:02Z
dc.date.available 2021-11-01T10:50:02Z
dc.date.issued 2021-02-23
dc.identifier.citation Pulkkinen , T , Nurminen , J K & Nurmi , P 2021 , Understanding WiFi Cross-Technology Interference Detection in the Real World . in 2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS) . IEEE International Conference on Distributed Computing Systems , IEEE Computer Society Conference Publishing Services , pp. 954-964 , International Conference on Distributed Computing Systems , Singapore , 29/11/2020 . https://doi.org/10.1109/ICDCS47774.2020.00061
dc.identifier.citation conference
dc.identifier.other PURE: 133827572
dc.identifier.other PURE UUID: 72926194-74aa-4edc-a559-d6be26526c46
dc.identifier.other WOS: 000667971400087
dc.identifier.other ORCID: /0000-0001-5083-1927/work/102449345
dc.identifier.uri http://hdl.handle.net/10138/335911
dc.description.abstract WiFi networks are increasingly subjected to cross-technology interference with emerging IoT and even mobile communication solutions all crowding the 2.4 GHz ISM band where WiFi networks conventionally operate. Due to the diversity of interference sources, maintaining high level of network performance is becoming increasing difficult. Recently, deep learning based interference detection has been proposed as a potentially powerful way to identify sources of interference and to provide feedback on how to mitigate their effects. The performance of such approaches has been shown to be impressive in controlled evaluations; however, little information exists on how they generalize to the complexity of everyday environments. In this paper, we contribute by conducting a comprehensive performance evaluation of deep learning based interference detection. In our evaluation, we consider five orthogonal but complementary metrics: correctness, overfitting, robustness, efficiency, and interpretability. Our results show that, while deep learning indeed has excellent correctness (i.e., detection accuracy), it can be prone to noise in measurements (e.g., struggle when transmission power is dynamically adjusted) and suffers from poor interpretability. To compensate for weaknesses of deep learning, as our second contribution we propose a novel signal modeling approach for interference detection and compare it against the deep learning. Our results demonstrate that, in terms of errors, there are some differences across the two approaches, with signal modeling being better at identifying technologies that rely on frequency hopping or that have dynamic spectrum signatures but suffering in other cases. Based on our results, we draw guidelines for improving interference detection performance. en
dc.format.extent 11
dc.language.iso eng
dc.publisher IEEE Computer Society Conference Publishing Services
dc.relation.ispartof 2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS)
dc.relation.ispartofseries IEEE International Conference on Distributed Computing Systems
dc.relation.isversionof 978-1-7281-7002-2
dc.rights unspecified
dc.rights.uri info:eu-repo/semantics/openAccess
dc.subject 113 Computer and information sciences
dc.subject NETWORKS
dc.title Understanding WiFi Cross-Technology Interference Detection in the Real World en
dc.type Conference contribution
dc.contributor.organization Department of Computer Science
dc.description.reviewstatus Peer reviewed
dc.relation.doi https://doi.org/10.1109/ICDCS47774.2020.00061
dc.relation.issn 1063-6927
dc.rights.accesslevel openAccess
dc.type.version acceptedVersion

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