Leveraging Wireless Traffic Forecasting to Enhance Wearable-based Localization in Enterprise Networks: An FCESN Strategy

Leveraging Wireless Traffic Forecasting to Enhance Wearable-based Localization in Enterprise Networks: An FCESN Strategy | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 28 June 2025 V1 Latest version Share on Leveraging Wireless Traffic Forecasting to Enhance Wearable-based Localization in Enterprise Networks: An FCESN Strategy Authors : Difei Cao 0000-0002-3638-7660 and Qiuyue Li [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.175109975.59492466/v1 288 views 88 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Accurate wireless traffic forecasting facilitates proactive adaptation in wearable-based localization systems, enhancing reliability under dynamic conditions. We propose fully-connected echo state network (FCESN), a lightweight prediction model to strengthen wearable localization performance by anticipating communication trends in enterprise networks. FCESN integrates reservoir computing with a fully-connected layer, achieving high accuracy and efficiency in predictions. Evaluated on real-world data from specific access points for day-ahead forecasting, our proposal surpasses state-of-the-art models in precision and trend preservation while demanding significantly lower computation. The predicted traffic patterns are leveraged to proactively adjust localization strategies, enabling robust, context-aware positioning in dynamic enterprise environments. Supplementary Material File (leveraging_wireless_traffic_forecasting_to_enhance_wearable_based_localization_in_enterprise_networks__an_fcesn_strategy.pdf) Download 4.10 MB Information & Authors Information Version history V1 Version 1 28 June 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords echo state network fully connected layer wearable-based localization wireless traffic prediction Authors Affiliations Difei Cao 0000-0002-3638-7660 University of Science and Technology Beijing School of Computer and Communication Engineering View all articles by this author Qiuyue Li [email protected] Chongqing Vocational Institute of Safety Technology View all articles by this author Metrics & Citations Metrics Article Usage 288 views 88 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Difei Cao, Qiuyue Li. Leveraging Wireless Traffic Forecasting to Enhance Wearable-based Localization in Enterprise Networks: An FCESN Strategy. Authorea . 28 June 2025. DOI: https://doi.org/10.22541/au.175109975.59492466/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. 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