{"id":160050,"date":"2006-09-01T00:00:00","date_gmt":"2006-09-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/analyzing-the-mac-level-behavior-of-wireless-networks-in-the-wild\/"},"modified":"2018-10-16T20:06:17","modified_gmt":"2018-10-17T03:06:17","slug":"analyzing-the-mac-level-behavior-of-wireless-networks-in-the-wild","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/analyzing-the-mac-level-behavior-of-wireless-networks-in-the-wild\/","title":{"rendered":"Analyzing the MAC-level Behavior of Wireless Networks in the Wild"},"content":{"rendered":"<div class=\"asset-content\">\n<p>We present Wit, a non-intrusive tool that builds on passive monitoring to analyze the detailed MAC-level behavior of operational wireless networks. Wit uses three processing steps to construct an enhanced trace of system activity. First, a robust merging procedure combines the necessarily incomplete views from multiple, independent monitors into a single, more complete trace of wireless activity. Next, a novel inference engine based on formal language methods reconstructs packets that were not captured by any monitor and determines whether each packet was received by its destination. Finally, Wit derives network performance measures from this enhanced trace; we show how to estimate the number of stations competing for the medium. We assess Wit with a mix of real traces and simulation tests. We \ufb01nd that merging and inference both signi\ufb01cantly enhance the originally captured trace. We apply Wit to multi-monitor traces from a live network to show how it facilitates 802.11 MAC analyses that would otherwise be dif\ufb01cult or rely on less accurate heuristics.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>We present Wit, a non-intrusive tool that builds on passive monitoring to analyze the detailed MAC-level behavior of operational wireless networks. Wit uses three processing steps to construct an enhanced trace of system activity. First, a robust merging procedure combines the necessarily incomplete views from multiple, independent monitors into a single, more complete trace of [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13547],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-160050","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-systems-and-networking","msr-locale-en_us"],"msr_publishername":"Association for Computing Machinery, Inc.","msr_edition":"SIGCOMM","msr_affiliation":"","msr_published_date":"2006-09-01","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"","msr_volume":"","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"","msr_organization":"","msr_how_published":"","msr_notes":"","msr_highlight_text":"","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":1,"msr_main_download":"208975","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"file","title":"sigcomm2006-wit.pdf","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/02\/sigcomm2006-wit.pdf","id":208975,"label_id":0}],"msr_related_uploader":"","msr_attachments":[{"id":208975,"url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/02\/sigcomm2006-wit.pdf"}],"msr-author-ordering":[{"type":"user_nicename","value":"ratul","user_id":33351,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=ratul"},{"type":"text","value":"Maya Rodrig","user_id":0,"rest_url":false},{"type":"text","value":"David Wetherall","user_id":0,"rest_url":false},{"type":"text","value":"John Zahorjan","user_id":0,"rest_url":false}],"msr_impact_theme":[],"msr_research_lab":[],"msr_event":[],"msr_group":[144899],"msr_project":[170535],"publication":[],"video":[],"download":[],"msr_publication_type":"inproceedings","related_content":{"projects":[{"ID":170535,"post_title":"Measurement-based models of wireless networks","post_name":"measurement-based-models-of-wireless-networks","post_type":"msr-project","post_date":"2010-08-19 11:57:00","post_modified":"2017-06-19 14:46:30","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/measurement-based-models-of-wireless-networks\/","post_excerpt":"Based on detailed measurements of wireless behavior in the wild, we build practical models that aid in understanding and predicting network performance. Our goal is to have a level of predictability that is similar to that in wired networks. Talks Measurement-based models enable predictable wireless behavior KAIST, June 2009 Effects of Interference of Wireless Mesh Networks: Pathologies and a Preliminary Solution HotNets, Nov 2007 Analyzing the MAC-level behavior of Wireless Networks in the Wild SIGCOMM,&hellip;","_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/170535"}]}}]},"_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/160050"}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-research-item"}],"version-history":[{"count":1,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/160050\/revisions"}],"predecessor-version":[{"id":522490,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/160050\/revisions\/522490"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=160050"}],"wp:term":[{"taxonomy":"msr-content-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-content-type?post=160050"},{"taxonomy":"msr-research-highlight","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-highlight?post=160050"},{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=160050"},{"taxonomy":"msr-publication-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-publication-type?post=160050"},{"taxonomy":"msr-product-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-product-type?post=160050"},{"taxonomy":"msr-focus-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-focus-area?post=160050"},{"taxonomy":"msr-platform","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-platform?post=160050"},{"taxonomy":"msr-download-source","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-download-source?post=160050"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=160050"},{"taxonomy":"msr-post-option","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-post-option?post=160050"},{"taxonomy":"msr-field-of-study","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-field-of-study?post=160050"},{"taxonomy":"msr-conference","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-conference?post=160050"},{"taxonomy":"msr-journal","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-journal?post=160050"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=160050"},{"taxonomy":"msr-pillar","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-pillar?post=160050"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}