{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T05:23:18Z","timestamp":1773465798321,"version":"3.50.1"},"reference-count":149,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2023,9,14]],"date-time":"2023-09-14T00:00:00Z","timestamp":1694649600000},"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":["ACM Comput. Surv."],"published-print":{"date-parts":[[2024,2,29]]},"abstract":"<jats:p>The pandemic caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has impacted the economy, health, and society. Emerging strains are making pandemic management challenging. There is an urge to collect epidemiological, clinical, and physiological data to make an informed decision on mitigation. Advances in the Internet of Things (IoT) and edge computing provide solutions for pandemic management through data collection and intelligent computation. While existing data-driven architectures operate on specific application domains and attempt to automate decision-making, they do not capture the multifaceted interaction among computational models, communication infrastructure, and data. In this article, we survey the existing approaches for pandemic management, including data repositories and contact-tracing applications. We envision a unified pandemic management architecture that leverages the IoT and edge computing paradigms to automate recommendations on vaccine distribution, dynamic lockdown, mobility scheduling, and pandemic trend prediction. We elucidate the data flow among the layers, namely, cloud, edge, and end device layers. Moreover, we address the privacy implications, threats, regulations, and solutions that may be adapted to optimize the utility of health data with security guarantees. The article ends with a discussion of the limitations of the architecture and research directions to enhance its practicality.<\/jats:p>","DOI":"10.1145\/3609324","type":"journal-article","created":{"date-parts":[[2023,7,14]],"date-time":"2023-07-14T12:04:44Z","timestamp":1689336284000},"page":"1-32","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Towards a Unified Pandemic Management Architecture: Survey, Challenges, and Future Directions"],"prefix":"10.1145","volume":"56","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6767-266X","authenticated-orcid":false,"given":"Satyaki","family":"Roy","sequence":"first","affiliation":[{"name":"University of North Carolina, Chapel Hill, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4079-8259","authenticated-orcid":false,"given":"Nirnay","family":"Ghosh","sequence":"additional","affiliation":[{"name":"Indian Institute of Engineering Science and Technology (IIEST), Shibpur, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8509-9596","authenticated-orcid":false,"given":"Nitish","family":"Uplavikar","sequence":"additional","affiliation":[{"name":"University of Missouri, Columbia, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3880-5886","authenticated-orcid":false,"given":"Preetam","family":"Ghosh","sequence":"additional","affiliation":[{"name":"Virginia Commonwealth University, Virginia, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,9,14]]},"reference":[{"key":"e_1_3_1_2_2","unstructured":"2023. World Health Organization (WHO) Coronavirus (COVID-19) Dashboard. Retrieved 26 July 2023 from https:\/\/covid19.who.int\/"},{"key":"e_1_3_1_3_2","doi-asserted-by":"crossref","unstructured":"S. Lopez-Leon Sandra T. Wegman-Ostrosky C. Perelman R. Sepulveda P. Rebolledo A. Cuapio and S. Villapol. 2021. More than 50 long-term effects of COVID-19: A systematic review and meta-analysis. Scientific Reports 11 1 (2021) 16144.","DOI":"10.1038\/s41598-021-95565-8"},{"key":"e_1_3_1_4_2","doi-asserted-by":"crossref","unstructured":"C. van Oosterhout N. Hall H. Ly and K. Tyler. 2021. COVID-19 evolution during the pandemic\u2013Implications of new SARS-CoV-2 variants on disease control and public health policies. Virulence 12 1 (2021) 507\u2013508.","DOI":"10.1080\/21505594.2021.1877066"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41565-020-0737-y"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1038\/d41586-021-00396-2"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3082108"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ssaho.2021.100163"},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/MCI.2020.3019873"},{"key":"e_1_3_1_10_2","article-title":"Artificial intelligence for COVID-19: Battling the pandemic with computational intelligence","author":"Xu Z.","year":"2021","unstructured":"Z. Xu, C. Su, Y. Xiao, and F. Wang. 2021. Artificial intelligence for COVID-19: Battling the pandemic with computational intelligence. Intelligent Medicine 2, 1 (2021), 13\u201329.","journal-title":"Intelligent Medicine"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1039\/D0CS01065K"},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-68936-0"},{"key":"e_1_3_1_13_2","first-page":"1","article-title":"Impacts of epidemic outbreaks on supply chains: Mapping a research agenda amid the COVID-19 pandemic through a structured literature review","year":"2020","unstructured":"M. Queiroz, D. Ivanov, A. Dolgui, and S. Wamba. 2020. Impacts of epidemic outbreaks on supply chains: Mapping a research agenda amid the COVID-19 pandemic through a structured literature review. Annals of Operations Research 319, 1 (2020), 1\u201338.","journal-title":"Annals of Operations Research"},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.iot.2020.100342"},{"key":"e_1_3_1_15_2","doi-asserted-by":"crossref","unstructured":"T. Hu S. Wang B. She M. Zhang X. Huang Y. Cui J. Khuri Y. Hu X. Fu X. Wang P. Wang X. Zhu S. Bao W. Guan and Z. Li. 2021. Human mobility data in the COVID-19 pandemic: Characteristics applications and challenges. Applications and Challenges 14 9 (2021) 1126\u20131147.","DOI":"10.1080\/17538947.2021.1952324"},{"key":"e_1_3_1_16_2","first-page":"3","volume-title":"Proceedings of the Computational Intelligence Methods in COVID-19: Surveillance, Prevention, Prediction and Diagnosis","year":"2021","unstructured":"K. Raza, Maryam, and S. Qazi. 2021. An introduction to computational intelligence in COVID-19: Surveillance, prevention, prediction, and diagnosis. In Proceedings of the Computational Intelligence Methods in COVID-19: Surveillance, Prevention, Prediction and Diagnosis. Springer, 3\u201318."},{"key":"e_1_3_1_17_2","first-page":"219","volume-title":"IDDM","author":"Yakovyna V.","year":"2020","unstructured":"V. Yakovyna, N. Shakhovska, K. Shakhovska, and J. Campos. 2020. Recommendation rules mining for reducing the spread of COVID-19 cases. In IDDM 2753 (2020), 219\u2013229."},{"key":"e_1_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.3390\/fi13050105"},{"key":"e_1_3_1_19_2","doi-asserted-by":"publisher","DOI":"10.1002\/ima.22552"},{"key":"e_1_3_1_20_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3053268"},{"key":"e_1_3_1_21_2","doi-asserted-by":"publisher","DOI":"10.1137\/S0036144500371907"},{"key":"e_1_3_1_22_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00466-020-01880-8"},{"key":"e_1_3_1_23_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jeconom.2020.07.038"},{"key":"e_1_3_1_24_2","doi-asserted-by":"publisher","unstructured":"S. Sanche Y. Lin C. Xu E. Romero-Severson N. Hengartner and R. Ke. 2020. Early release-high contagiousness and rapid spread of severe acute respiratory syndrome coronavirus 2. 26 7 (2020) 1470\u20131477. 10.3201\/eid2607.200282","DOI":"10.3201\/eid2607.200282"},{"key":"e_1_3_1_25_2","doi-asserted-by":"publisher","DOI":"10.1137\/S003614450342480"},{"key":"e_1_3_1_26_2","doi-asserted-by":"publisher","DOI":"10.7236\/JIWIT.2012.12.6.297"},{"key":"e_1_3_1_27_2","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2017.2750180"},{"key":"e_1_3_1_28_2","unstructured":"T. Luan L. Gao Z. Li Y. Xiang G. Wei and L. Sun. 2015. Fog computing: Focusing on mobile users at the edge. arXiv preprint arXiv:1502.01815."},{"key":"e_1_3_1_29_2","unstructured":"2021. WHO COVID publication repository. Retrieved May 13 2020 from https:\/\/datascience.nih.gov\/covid-19-open-access-resources"},{"key":"e_1_3_1_30_2","unstructured":"2021. NIST COVID-19 repository. Retrieved from https:\/\/covid19-data.nist.gov\/"},{"key":"e_1_3_1_31_2","unstructured":"2021. CDEI COVID-19 repository and public attitudes retrospective. Retrieved 5 March 2021 from https:\/\/www.gov.uk\/government\/publications\/covid-19-repository-and-public-attitudes-retrospective"},{"key":"e_1_3_1_32_2","unstructured":"2021. Elsevier Coronavirus Research Repository. Retrieved from https:\/\/coronavirus.1science.com\/search"},{"key":"e_1_3_1_33_2","unstructured":"2021. NIH Open-Access Data and Computational Res. Retrieved May 13 2020 from https:\/\/datascience.nih.gov\/covid-19-open-access-resources"},{"key":"e_1_3_1_34_2","unstructured":"2021. Google Health COVID-19 Open Data Repository. Retrieved May 13 2020 from https:\/\/datascience.nih.gov\/covid-19-open-access-resources"},{"key":"e_1_3_1_35_2","unstructured":"2021. iReceptor Repository. Retrieved from https:\/\/www.antibodysociety.org\/covid-19\/covid-19-data-repository-now-available\/"},{"key":"e_1_3_1_36_2","unstructured":"2021. Stanford Research Repository. Retrieved from https:\/\/med.stanford.edu\/starr-tools\/covid-19-starr-tools.html"},{"key":"e_1_3_1_37_2","unstructured":"2021. Humdata. Retrieved March 10 2023 from https:\/\/data.humdata.org\/dataset\/novel-coronavirus-2019-ncov-cases"},{"key":"e_1_3_1_38_2","unstructured":"2021. Johns Hopkins COVID resource center. Retrieved March 10 2023 from https:\/\/coronavirus.jhu.edu\/"},{"key":"e_1_3_1_39_2","unstructured":"2021. Our World in Data Coronavirus Source Data. Retrieved from https:\/\/ourworldindata.org\/coronavirus-source-data"},{"key":"e_1_3_1_40_2","unstructured":"2021. CDC Data Tracker. Retrieved from https:\/\/www.cdc.gov\/coronavirus\/2019-ncov\/covid-data\/covidview\/index.html"},{"key":"e_1_3_1_41_2","unstructured":"2021. Faith and COVID-19: Resource Repository. Retrieved from https:\/\/berkleycenter.georgetown.edu\/publications\/faith-and-covid-19-resource-repository"},{"key":"e_1_3_1_42_2","unstructured":"2021. COVID-19 WASH Resource. Retrieved from https:\/\/washcluster.net\/Covid-19-resources"},{"key":"e_1_3_1_43_2","unstructured":"2021. Sages COVID-19 Medical Device Repository. Retrieved from https:\/\/www.sages.org\/covid-19-medical-device-repository\/"},{"key":"e_1_3_1_44_2","unstructured":"2021. COVID-19 Toolkit: Federal Depository Library Program. Retrieved from https:\/\/www.fdlp.gov\/promotion\/covid-19-fdlp-toolkit"},{"key":"e_1_3_1_45_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3010226"},{"key":"e_1_3_1_46_2","doi-asserted-by":"publisher","DOI":"10.1177\/2056305120947657"},{"key":"e_1_3_1_47_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cosrev.2020.100307"},{"key":"e_1_3_1_48_2","doi-asserted-by":"publisher","DOI":"10.1109\/MCE.2020.3002492"},{"key":"e_1_3_1_49_2","doi-asserted-by":"publisher","DOI":"10.1136\/medethics-2020-106516"},{"key":"e_1_3_1_50_2","doi-asserted-by":"publisher","DOI":"10.1126\/science.abe2803"},{"key":"e_1_3_1_51_2","doi-asserted-by":"publisher","DOI":"10.1109\/BIBM52615.2021.9669682"},{"key":"e_1_3_1_52_2","first-page":"93","volume-title":"Proceedings of the 2020 11th International Conference on Information and Knowledge Technology","year":"2020","unstructured":"M. Jadidi, P. Moslemi, S. Jamshidiha, I. Masroori, A. Mohammadi, and V. Pourahmadi. 2020. Targeted vaccination for COVID-19 using mobile communication networks. In Proceedings of the 2020 11th International Conference on Information and Knowledge Technology. IEEE, 93\u201397."},{"key":"e_1_3_1_53_2","doi-asserted-by":"publisher","DOI":"10.1007\/s40258-021-00667-z"},{"key":"e_1_3_1_54_2","doi-asserted-by":"crossref","unstructured":"D. Bertsimas V. Digalakis A. Jacquillat M. Li and A. Previero. 2021. Where to locate COVID-19 mass vaccination facilities? Naval Research Logistics 69 2 (2021) 179\u2013200.","DOI":"10.1002\/nav.22007"},{"key":"e_1_3_1_55_2","doi-asserted-by":"publisher","DOI":"10.3390\/jcm10040591"},{"key":"e_1_3_1_56_2","doi-asserted-by":"crossref","unstructured":"M. Meehan D. Cocks J. Caldwell J. Trauer A. Adekunle R. Ragonnet and E. McBryde. 2020. Age-targeted dose allocation can halve COVID-19 vaccine requirements. medRxiv (2020).","DOI":"10.1101\/2020.10.08.20208108"},{"key":"e_1_3_1_57_2","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.2025786118"},{"key":"e_1_3_1_58_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.vaccine.2021.08.069"},{"key":"e_1_3_1_59_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.orhc.2016.02.003"},{"key":"e_1_3_1_60_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.envpol.2020.115920"},{"key":"e_1_3_1_61_2","doi-asserted-by":"publisher","DOI":"10.1007\/s41109-021-00437-9"},{"key":"e_1_3_1_62_2","doi-asserted-by":"crossref","unstructured":"J. Roux C. Massonnaud and P. Cr\u00e9pey. 2020. COVID-19: One-month impact of the French lockdown on the epidemic burden. MedRxiv (2020).","DOI":"10.1101\/2020.04.22.20075705"},{"key":"e_1_3_1_63_2","doi-asserted-by":"crossref","unstructured":"M. Rahman et\u00a0al. 2020. Data-driven dynamic clustering framework for mitigating the adverse economic impact of Covid-19 lockdown practices. 62 (2020) 102372.","DOI":"10.1016\/j.scs.2020.102372"},{"key":"e_1_3_1_64_2","doi-asserted-by":"publisher","DOI":"10.1057\/s10713-020-00052-1"},{"key":"e_1_3_1_65_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00148-021-00867-w"},{"key":"e_1_3_1_66_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmateco.2021.102488"},{"key":"e_1_3_1_67_2","doi-asserted-by":"publisher","DOI":"10.3386\/w27441"},{"key":"e_1_3_1_68_2","first-page":"1","volume-title":"Proceedings of the World Wide Web Conference","author":"Petrovi\u0107 N.","year":"2021","unstructured":"N. Petrovi\u0107, V. Dimovski, J. Peterlin, M. Me\u0161ko, and V. Roblek. 2021. Data-driven solutions in smart cities: The case of COVID-19 apps. In Proceedings of the World Wide Web Conference. 1\u20139."},{"key":"e_1_3_1_69_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-85607-6_59"},{"issue":"1","key":"e_1_3_1_70_2","first-page":"1","article-title":"The use of mobile phone data to inform analysis of COVID-19 pandemic epidemiology","volume":"11","year":"2020","unstructured":"K. Grantz, H. Meredith, D. Cummings, C. Metcalf, B. Grenfell, J. Giles, S. Mehta, S. Solomon, A. Labrique, N. Kishore, C. Buckee, and A. Wesolowski. 2020. The use of mobile phone data to inform analysis of COVID-19 pandemic epidemiology. Nature Communications 11, 1 (2020), 1\u20138.","journal-title":"Nature Communications"},{"key":"e_1_3_1_71_2","unstructured":"N. Oliver E. Letouz\u00e9 H. Sterly S. Delataille M. Nadai B. Lepri R. Lambiotte R. Benjamins C. Cattuto V. Colizza N. Cordes S. Fraiberger T. Koebe S. Lehmann J. Murillo A. Pentland P. Pham F. Pivetta A. Ali Salah J. Saram\u00e4ki S. Scarpino M. Tizzoni S. Verhulst and P. Vinck. 2020. Mobile phone data and COVID-19: Missing an opportunity? arXiv preprint arXiv:2003.12347 (2020)."},{"key":"e_1_3_1_72_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jth.2021.101135"},{"key":"e_1_3_1_73_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.tranpol.2021.01.006"},{"key":"e_1_3_1_74_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3058206"},{"key":"e_1_3_1_75_2","doi-asserted-by":"crossref","unstructured":"M. Serafino H. Monteiro S. Luo S. Reis C. Igual A. Neto M. Travizano J. Andrade and H. Makse. 2021. Superspreading k-cores at the center of COVID-19 pandemic persistence. Bulletin of the American Physical Society.","DOI":"10.1101\/2020.08.12.20173476"},{"key":"e_1_3_1_76_2","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.96.040601"},{"key":"e_1_3_1_77_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3095252"},{"key":"e_1_3_1_78_2","doi-asserted-by":"publisher","DOI":"10.1093\/jamia\/ocaa322"},{"key":"e_1_3_1_79_2","doi-asserted-by":"publisher","DOI":"10.3390\/math8060890"},{"key":"e_1_3_1_80_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCCNT49239.2020.9225319"},{"key":"e_1_3_1_81_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.chaos.2020.110023"},{"key":"e_1_3_1_82_2","article-title":"Machine learning prediction for covid 19 pandemic in india","author":"Ogundokun R.","year":"2020","unstructured":"R. Ogundokun and J. Awotunde. 2020. Machine learning prediction for covid 19 pandemic in india. medRxiv (2020).","journal-title":"medRxiv"},{"key":"e_1_3_1_83_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jiph.2020.06.001"},{"key":"e_1_3_1_84_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.techfore.2021.120602"},{"issue":"1","key":"e_1_3_1_85_2","first-page":"1","article-title":"Spatiotemporal tracing of pandemic spread from infection data","volume":"11","year":"2021","unstructured":"S. Roy, P. Biswas, and P. Ghosh. 2021. Spatiotemporal tracing of pandemic spread from infection data. Scientific Reports 11, 1 (2021), 1\u201312.","journal-title":"Scientific Reports"},{"key":"e_1_3_1_86_2","doi-asserted-by":"crossref","unstructured":"X. Zhang H. Liu H. Tang M. Zhang X. Yuan and X. Shen. 2021. The effect of population size for pathogen transmission on prediction of COVID-19 spread. Scientific Reports 11 1 (2021) 18024.","DOI":"10.1038\/s41598-021-97578-9"},{"key":"e_1_3_1_87_2","doi-asserted-by":"publisher","DOI":"10.2196\/19446"},{"key":"e_1_3_1_88_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.bdr.2021.100190"},{"key":"e_1_3_1_89_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.chaos.2020.110056"},{"key":"e_1_3_1_90_2","doi-asserted-by":"publisher","DOI":"10.5114\/aoms\/133522"},{"key":"e_1_3_1_91_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.sciaf.2022.e01374"},{"key":"e_1_3_1_92_2","doi-asserted-by":"publisher","DOI":"10.5194\/isprs-archives-XLVI-4-W5-2021-361-2021"},{"key":"e_1_3_1_93_2","doi-asserted-by":"publisher","DOI":"10.3390\/s21020484"},{"key":"e_1_3_1_94_2","doi-asserted-by":"publisher","DOI":"10.26719\/2020.26.6.626"},{"key":"e_1_3_1_95_2","doi-asserted-by":"publisher","DOI":"10.3390\/su13073797"},{"key":"e_1_3_1_96_2","first-page":"191","volume-title":"Proceedings of the Intelligent Interactive Multimedia Systems for e-Healthcare Applications","year":"2022","unstructured":"J. Awotunde, R. Jimoh, O. Matiluko, B. Gbadamosi, and G. Ajamu. 2022. Artificial intelligence and an edge-IoMT-based system for combating COVID-19 pandemic. In Proceedings of the Intelligent Interactive Multimedia Systems for e-Healthcare Applications. Springer, 191\u2013214."},{"key":"e_1_3_1_97_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jnca.2020.102886"},{"key":"e_1_3_1_98_2","doi-asserted-by":"publisher","DOI":"10.1145\/3471933"},{"key":"#cr-split#-e_1_3_1_99_2.1","unstructured":"L. Liquori E. Scarrone S. Wood L. Cees F. Dasilva M. Maass F. Bob Thomas. Kessler H. Taras and M. Vanetti. 2021. ETSI Technical Specification TS 103757. SmartM2M"},{"key":"#cr-split#-e_1_3_1_99_2.2","unstructured":"Asynchronous Contact Tracing System. (Dec. 2021). https:\/\/hal.inria.fr\/hal-02989793"},{"key":"e_1_3_1_100_2","unstructured":"2022. IBM is partnering with the Oxford Pandemic Sciences Institute. (2022). Retrieved from https:\/\/research.ibm.com\/blog\/ibm-partners-oxford-pandemic-sciences-inst"},{"key":"e_1_3_1_101_2","doi-asserted-by":"publisher","DOI":"10.1016\/S2213-2600(22)00056-X"},{"key":"e_1_3_1_102_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.seps.2021.101091"},{"key":"e_1_3_1_103_2","doi-asserted-by":"publisher","DOI":"10.1177\/18333583221104213"},{"issue":"6","key":"e_1_3_1_104_2","first-page":"e28269\u2013e28269","article-title":"An urban population health observatory system to support COVID-19 pandemic preparedness, response, and management: Design and development study.","volume":"7","year":"2021","unstructured":"W. Brakefield, N. Ammar, O. Olusanya, and A. Shaban-Nejad. 2021. An urban population health observatory system to support COVID-19 pandemic preparedness, response, and management: Design and development study. JMIR Public Health and Surveillance 7, 6 (2021), e28269\u2013e28269.","journal-title":"JMIR Public Health and Surveillance"},{"key":"e_1_3_1_105_2","doi-asserted-by":"publisher","DOI":"10.1145\/1496046.1496064"},{"key":"e_1_3_1_106_2","doi-asserted-by":"publisher","DOI":"10.1145\/1461928.1461944"},{"key":"e_1_3_1_107_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDCS.2011.43"},{"issue":"11","key":"e_1_3_1_108_2","first-page":"1","article-title":"Mobile edge computing\u2013A key technology towards 5G","volume":"11","year":"2015","unstructured":"Y. Hu, M. Patel, D. Sabella, N. Sprecher, and V. Young. 2015. Mobile edge computing\u2013A key technology towards 5G. ETSI White Paper 11, 11 (2015), 1\u201316.","journal-title":"ETSI White Paper"},{"key":"e_1_3_1_109_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jss.2021.111208"},{"key":"e_1_3_1_110_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.mbs.2021.108654"},{"key":"e_1_3_1_111_2","doi-asserted-by":"publisher","DOI":"10.1023\/A:1023949509487"},{"key":"e_1_3_1_112_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cell.2014.06.049"},{"key":"e_1_3_1_113_2","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btp543"},{"key":"e_1_3_1_114_2","doi-asserted-by":"publisher","DOI":"10.1038\/nmeth.2810"},{"key":"e_1_3_1_115_2","doi-asserted-by":"publisher","DOI":"10.5555\/3379017"},{"key":"e_1_3_1_116_2","first-page":"62","volume-title":"Proceedings of the European Business Intelligence Summer School","author":"Bontempi G.","year":"2012","unstructured":"G. Bontempi, S. Taieb, and Y. Le Borgne. 2012. Machine learning strategies for time series forecasting. In Proceedings of the European Business Intelligence Summer School. Springer, 62\u201377."},{"key":"e_1_3_1_117_2","doi-asserted-by":"publisher","DOI":"10.1109\/ISIT.2019.8849330"},{"key":"e_1_3_1_118_2","unstructured":"C. Hazay and L. Yehuda. 2010. A note on the relation between the definitions of security for semi-honest and malicious adversaries. Cryptology ePrint Archive (2010)."},{"key":"e_1_3_1_119_2","doi-asserted-by":"publisher","DOI":"10.1561\/0400000042"},{"key":"e_1_3_1_120_2","doi-asserted-by":"publisher","DOI":"10.1515\/popets-2018-0024"},{"key":"e_1_3_1_121_2","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-020-0186-1"},{"key":"e_1_3_1_122_2","doi-asserted-by":"publisher","DOI":"10.1109\/SFCS.1995.492461"},{"key":"e_1_3_1_123_2","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-48910-X_16"},{"key":"e_1_3_1_124_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-65610-2_8"},{"key":"e_1_3_1_125_2","unstructured":"H. Cho D. Ippolito and Y. Yu. 2020. Contact tracing mobile apps for COVID-19: Privacy considerations and related trade-offs. arXiv preprint arXiv:2003.11511 (2020)."},{"key":"e_1_3_1_126_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10916-014-0181-3"},{"key":"e_1_3_1_127_2","doi-asserted-by":"publisher","DOI":"10.1145\/3421509"},{"key":"e_1_3_1_128_2","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2019.2918749"},{"key":"e_1_3_1_129_2","doi-asserted-by":"publisher","DOI":"10.1109\/GLOCOM.2017.8253985"},{"key":"e_1_3_1_130_2","doi-asserted-by":"publisher","DOI":"10.6028\/NIST.SP.800-66r2.ipd"},{"key":"e_1_3_1_131_2","volume-title":"Proceedings of the CERC","author":"Akinsanya Opeoluwa Ore","year":"2019","unstructured":"Opeoluwa Ore Akinsanya, Maria Papadaki, and Lingfen Sun. 2019. Current cybersecurity maturity models: How effective in healthcare cloud?. In Proceedings of the CERC."},{"issue":"3","key":"e_1_3_1_132_2","first-page":"24","article-title":"Are the EU GDPR and the California CCPA becoming the de facto global standards for data privacy and protection?","volume":"15","author":"Barrett Catherine","year":"2019","unstructured":"Catherine Barrett. 2019. Are the EU GDPR and the California CCPA becoming the de facto global standards for data privacy and protection? Scitech Lawyer 15, 3 (2019), 24\u201329.","journal-title":"Scitech Lawyer"},{"key":"e_1_3_1_133_2","unstructured":"A. Soltani R. Calo and C. Bergstrom. 2020. Contact-tracing apps are not a solution to the COVID-19 crisis. Brookings.(April 27 2020) Available at https:\/\/www.brookings.edu\/techstream\/inaccurate-and-insecure-why-contact-tracing-apps-could-be-a-disaster. (2020)."},{"key":"e_1_3_1_134_2","doi-asserted-by":"crossref","unstructured":"N. Arora A. Banerjee and M. Narasu. 2020. The role of artificial intelligence in tackling COVID-19. Future Virology 15 11 (2020) 717\u2013724.","DOI":"10.2217\/fvl-2020-0130"},{"key":"e_1_3_1_135_2","unstructured":"2021. The COVID Tracking Project. Retrieved from https:\/\/github.com\/COVID19Tracking. (2021)."},{"key":"e_1_3_1_136_2","doi-asserted-by":"publisher","DOI":"10.3390\/s19061326"},{"key":"e_1_3_1_137_2","doi-asserted-by":"publisher","DOI":"10.1109\/GLOCOM.2018.8648047"},{"key":"e_1_3_1_138_2","first-page":"1","volume-title":"Proceedings of the International Conference on Distributed Computing and Networking","year":"2020","unstructured":"S. Roy, N. Ghosh, P. Ghosh, and S. Das. 2020. biomcs: A bio-inspired collaborative data transfer framework over fog computing platforms in mobile crowdsensing. In Proceedings of the International Conference on Distributed Computing and Networking. 1\u201310."},{"key":"e_1_3_1_139_2","doi-asserted-by":"publisher","DOI":"10.4108\/trans.sis.2013.01-03.e2"},{"key":"e_1_3_1_140_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICWS.2005.25"},{"key":"e_1_3_1_141_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0065-2458(08)60206-5"},{"key":"e_1_3_1_142_2","doi-asserted-by":"publisher","DOI":"10.1109\/POLICY.2003.1206966"},{"key":"e_1_3_1_143_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.hcc.2021.100008"},{"key":"e_1_3_1_144_2","unstructured":"A. Hard K. Rao R. Mathews S. Ramaswamy F. Beaufays S. Augenstein H. Eichner C. Kiddon and D. Ramage. Federated learning for mobile keyboard prediction. arXiv preprint arXiv:1811.03604. 2018 Nov 8."},{"key":"e_1_3_1_145_2","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2020.2986024"},{"key":"e_1_3_1_146_2","first-page":"520","volume-title":"Proceedings of the IEEE Workshops of International Conference on Advanced Information Networking and Applications","year":"2011","unstructured":"J. Park, H. Yu, K. Chung, and E. Lee. 2011. Markov chain-based monitoring service for fault tolerance in mobile cloud computing. In Proceedings of the IEEE Workshops of International Conference on Advanced Information Networking and Applications. Ieee, 520\u2013525."},{"key":"e_1_3_1_147_2","first-page":"1","volume-title":"Proceedings of the 2018 6th IEEE International Conference on Mobile Cloud Computing, Services, and Engineering","year":"2018","unstructured":"M. Tomasoni, A. Capponi, C. Fiandrino, D. Kliazovich, F. Granelli, and P. Bouvry. 2018. Profiling energy efficiency of mobile crowdsensing data collection frameworks for smart city applications. In Proceedings of the 2018 6th IEEE International Conference on Mobile Cloud Computing, Services, and Engineering. IEEE, 1\u20138."},{"key":"e_1_3_1_148_2","doi-asserted-by":"crossref","unstructured":"T. Mastelic A. Oleksiak H. Claussen I. Brandic J. Pierson and A. Vasilakos. 2014. Cloud computing: Survey on energy efficiency. ACM Computing Surveys 47 2 (2014) 1\u201336.","DOI":"10.1145\/2656204"},{"key":"e_1_3_1_149_2","doi-asserted-by":"publisher","DOI":"10.3390\/app9204367"}],"container-title":["ACM Computing Surveys"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3609324","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3609324","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:46:22Z","timestamp":1750178782000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3609324"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,14]]},"references-count":149,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2024,2,29]]}},"alternative-id":["10.1145\/3609324"],"URL":"https:\/\/doi.org\/10.1145\/3609324","relation":{},"ISSN":["0360-0300","1557-7341"],"issn-type":[{"value":"0360-0300","type":"print"},{"value":"1557-7341","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,14]]},"assertion":[{"value":"2022-01-28","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-07-11","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-09-14","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}