{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:14:31Z","timestamp":1750220071953,"version":"3.41.0"},"reference-count":10,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2022,3,30]],"date-time":"2022-03-30T00:00:00Z","timestamp":1648598400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["GetMobile: Mobile Comp. and Comm."],"published-print":{"date-parts":[[2022,3,30]]},"abstract":"<jats:p>Connected sensors, e.g., cameras, LiDARs, air sensors, installed in a mobile platform (e.g., a terrestrial or aerial vehicle, or special in-person devices) can provide broad views of wide-area environments quickly and efficiently. If many vehicles incorporate such sensing systems, they together can be composed into a unique crowd-sourced platform and can gather fine-grained, diverse, and noisy information at city-scales. However, these sensors can generate large amounts of data and such data is hard to aggregate in a central server. We explore the design of a \"roaming edge\" - the notion that generalpurpose computing be installed in these mobile platforms, which connect over wireless paths to the static infrastructure and to the static edge nodes, to support a broad range of applications. In particular, a roaming edge node allows different sensors and data sources in-range of a mobile platform to connect to it, and supports data processing for necessary local analytics, considering both efficiency and privacy. The roaming edge, of course, does not operate in isolation and we describe a three-tier architecture that integrates it with a static edge and cloudhosted services. This paper also outlines several applications that can leverage opportunities provided by the roaming edge, and focus, briefly, on one - a real-time video query application with public safety implications.<\/jats:p>","DOI":"10.1145\/3529706.3529708","type":"journal-article","created":{"date-parts":[[2022,3,30]],"date-time":"2022-03-30T22:24:07Z","timestamp":1648679047000},"page":"5-11","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["The Roaming Edge and its Applications"],"prefix":"10.1145","volume":"25","author":[{"given":"Suman","family":"Banerjee","sequence":"first","affiliation":[{"name":"University of Wisconsin-Madison, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Remzi","family":"Arpaci-Dusseau","sequence":"additional","affiliation":[{"name":"University of Wisconsin-Madison, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shenghong","family":"Dai","sequence":"additional","affiliation":[{"name":"University of Wisconsin-Madison, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kassem","family":"Fawaz","sequence":"additional","affiliation":[{"name":"University of Wisconsin-Madison, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohit","family":"Gupta","sequence":"additional","affiliation":[{"name":"University of Wisconsin-Madison, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kangwook","family":"Lee","sequence":"additional","affiliation":[{"name":"University of Wisconsin-Madison, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shivaram","family":"Venkataraman","sequence":"additional","affiliation":[{"name":"University of Wisconsin-Madison, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,3,30]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"crossref","unstructured":"M. Satyanarayanan P. Bahl R. Caceres and N. Davies. 2009. The case for vm-based cloudlets in mobile computing. IEEE Pervasive.  M. Satyanarayanan P. Bahl R. Caceres and N. Davies. 2009. The case for vm-based cloudlets in mobile computing. IEEE Pervasive.","DOI":"10.1109\/MPRV.2009.82"},{"volume-title":"ACM Symposium on Edge Computing.","author":"Liu P.","key":"e_1_2_1_2_1","unstructured":"P. Liu , D. Willis , and S. Banerjee . 2016. Paradrop: Enabling lightweight multi-tenancy at the network's extreme edge . ACM Symposium on Edge Computing. P. Liu, D. Willis, and S. Banerjee. 2016. Paradrop: Enabling lightweight multi-tenancy at the network's extreme edge. ACM Symposium on Edge Computing."},{"key":"e_1_2_1_3_1","doi-asserted-by":"crossref","unstructured":"J. Eriksson L. Girod B. Hull R. Newton S. Madden and H. Balakrishnan. June 2008. The pothole patrol: Using a mobile sensor network for road surface monitoring. ACM MobiSys.  J. Eriksson L. Girod B. Hull R. Newton S. Madden and H. Balakrishnan. June 2008. The pothole patrol: Using a mobile sensor network for road surface monitoring. ACM MobiSys.","DOI":"10.1145\/1378600.1378605"},{"volume-title":"IEEE\/ACM Symposium on Edge Computing (SEC). IEEE, 1--13","author":"Liu P.","key":"e_1_2_1_4_1","unstructured":"P. Liu , D. Willis , and S. Banerjee . 2016. Paradrop: Enabling lightweight multi-tenancy at the network's extreme edge . IEEE\/ACM Symposium on Edge Computing (SEC). IEEE, 1--13 . P. Liu, D. Willis, and S. Banerjee. 2016. Paradrop: Enabling lightweight multi-tenancy at the network's extreme edge. IEEE\/ACM Symposium on Edge Computing (SEC). IEEE, 1--13."},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.5555\/2600239.2600241"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2003.815165"},{"volume-title":"Proceedings of the 1st International Workshop on Edge Systems, Analytics and Networking, 1--6.","author":"Liu P.","key":"e_1_2_1_7_1","unstructured":"P. Liu , B. Qi , and S. Banerjee . 2018. Edgeeye: An edge service framework for real-time intelligent video analytics . Proceedings of the 1st International Workshop on Edge Systems, Analytics and Networking, 1--6. P. Liu, B. Qi, and S. Banerjee. 2018. Edgeeye: An edge service framework for real-time intelligent video analytics. Proceedings of the 1st International Workshop on Edge Systems, Analytics and Networking, 1--6."},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2017.2736066"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSAIT.2020.2983165"},{"key":"e_1_2_1_10_1","volume-title":"Foundations and Trends in Machine Learning","volume":"14","author":"Kairouz Peter","unstructured":"Peter Kairouz , H. Brendan McMahan , Brendan Avent , Aur\u00e9lien Bellet , Mehdi Bennis , Arjun Nitin Bhagoji , Kallista Bonawitz , Zachary Charles , Graham Cormode , Rachel Cummings , Rafael G. L. D'Oliveira , Hubert Eichner , Salim El Rouayheb , David Evans , Josh Gardner , Zachary Garrett , Adri\u00e0 Gasc\u00f3n , Badih Ghazi , Phillip B. Gibbons , Marco Gruteser , Zaid Harchaoui , Chaoyang He , Lie He , Zhouyuan Huo , Ben Hutchinson , Justin Hsu , Martin Jaggi , Tara Javidi , Gauri Joshi , Mikhail Khodak , Jakub Konecn\u00fd , Aleksandra Korolova , Farinaz Koushanfar , Sanmi Koyejo , Tancr\u00e8de Lepoint , Yang Liu , Prateek Mittal , Mehryar Mohri , Richard Nock , Ayfer \u00d6zg\u00fcr , Rasmus Pagh , Hang Qi , Daniel Ramage , Ramesh Raskar , Mariana Raykova , Dawn Song , Weikang Song , Sebastian U. Stich , Ziteng Sun , Ananda Theertha Suresh , Florian Tram\u00e8r , Praneeth Vepakomma , Jianyu Wang , Li Xiong , Zheng Xu , Qiang Yang , Felix X. Yu , Han Yu and Sen Zhao . Advances and open problems in federated learning. June 2021 . Foundations and Trends in Machine Learning , vol. 14 , no. 1. Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aur\u00e9lien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G. L. D'Oliveira, Hubert Eichner, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adri\u00e0 Gasc\u00f3n, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaid Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konecn\u00fd, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancr\u00e8de Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer \u00d6zg\u00fcr, Rasmus Pagh, Hang Qi, Daniel Ramage, Ramesh Raskar, Mariana Raykova, Dawn Song, Weikang Song, Sebastian U. Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tram\u00e8r, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X. Yu, Han Yu and Sen Zhao. Advances and open problems in federated learning. June 2021. Foundations and Trends in Machine Learning, vol. 14, no. 1."}],"container-title":["GetMobile: Mobile Computing and Communications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3529706.3529708","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3529706.3529708","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T18:09:13Z","timestamp":1750183753000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3529706.3529708"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,30]]},"references-count":10,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2022,3,30]]}},"alternative-id":["10.1145\/3529706.3529708"],"URL":"https:\/\/doi.org\/10.1145\/3529706.3529708","relation":{},"ISSN":["2375-0529","2375-0537"],"issn-type":[{"type":"print","value":"2375-0529"},{"type":"electronic","value":"2375-0537"}],"subject":[],"published":{"date-parts":[[2022,3,30]]},"assertion":[{"value":"2022-03-30","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}