{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T04:35:32Z","timestamp":1782362132128,"version":"3.54.5"},"reference-count":53,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2023,5,30]],"date-time":"2023-05-30T00:00:00Z","timestamp":1685404800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The Internet of Things (IoT) plays a fundamental role in monitoring applications; however, existing approaches relying on cloud and edge-based IoT data analysis encounter issues such as network delays and high costs, which can adversely impact time-sensitive applications. To address these challenges, this paper proposes an IoT framework called Sazgar IoT. Unlike existing solutions, Sazgar IoT leverages only IoT devices and IoT data analysis approximation techniques to meet the time-bounds of time-sensitive IoT applications. In this framework, the computing resources onboard the IoT devices are utilised to process the data analysis tasks of each time-sensitive IoT application. This eliminates the network delays associated with transferring large volumes of high-velocity IoT data to cloud or edge computers. To ensure that each task meets its application-specific time-bound and accuracy requirements, we employ approximation techniques for the data analysis tasks of time-sensitive IoT applications. These techniques take into account the available computing resources and optimise the processing accordingly. To evaluate the effectiveness of Sazgar IoT, experimental validation has been conducted. The results demonstrate that the framework successfully meets the time-bound and accuracy requirements of the COVID-19 citizen compliance monitoring application by effectively utilising the available IoT devices. The experimental validation further confirms that Sazgar IoT is an efficient and scalable solution for IoT data processing, addressing existing network delay issues for time-sensitive applications and significantly reducing the cost related to cloud and edge computing devices procurement, deployment, and maintenance.<\/jats:p>","DOI":"10.3390\/s23115211","type":"journal-article","created":{"date-parts":[[2023,5,31]],"date-time":"2023-05-31T02:57:10Z","timestamp":1685501830000},"page":"5211","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Sazgar IoT: A Device-Centric IoT Framework and Approximation Technique for Efficient and Scalable IoT Data Processing"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0588-5931","authenticated-orcid":false,"given":"Ali","family":"Yavari","sequence":"first","affiliation":[{"name":"6G Research and Innovation Lab, Swinburne University of Technology, Melbourne, VIC 3122, Australia"},{"name":"School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Melbourne, VIC 3122, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2394-8027","authenticated-orcid":false,"given":"Harindu","family":"Korala","sequence":"additional","affiliation":[{"name":"Institute of Railway Technology, Monash University, Melbourne, VIC 3800, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7880-2140","authenticated-orcid":false,"given":"Dimitrios","family":"Georgakopoulos","sequence":"additional","affiliation":[{"name":"School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Melbourne, VIC 3122, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9699-9418","authenticated-orcid":false,"given":"Jonathan","family":"Kua","sequence":"additional","affiliation":[{"name":"School of Information Technology, Deakin University, Geelong, VIC 3220, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1403-4554","authenticated-orcid":false,"given":"Hamid","family":"Bagha","sequence":"additional","affiliation":[{"name":"School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Melbourne, VIC 3122, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,30]]},"reference":[{"key":"ref_1","unstructured":"Yavari, A. (2019). Internet of Things Data Contextualisation for Scalable Information Processing, Security, and Privacy. [Ph.D. Thesis, RMIT University]."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3510411","article-title":"A Survey of Techniques for Fulfilling the Time-Bound Requirements of Time-Sensitive IoT Applications","volume":"54","author":"Korala","year":"2022","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Korala, H., Georgakopoulos, D., Jayaraman, P.P., and Yavari, A. (2021). Managing time-sensitive iot applications via dynamic application task distribution and adaptation. Remote Sens., 13.","DOI":"10.3390\/rs13204148"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2427","DOI":"10.1109\/TITS.2019.2918328","article-title":"Methodology and Mobile Application for Driver Behavior Analysis and Accident Prevention","volume":"21","author":"Kashevnik","year":"2020","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_5","unstructured":"Korala, H., Jayaraman, P.P., Yavari, A., and Georgakopoulos, D. (December, January 30). APOLLO: A Platform for Experimental Analysis of Time Sensitive Multimedia IoT Applications. Proceedings of the 18th International Conference on Advances in Mobile Computing & Multimedia (MoMM\u201920), Chiang Mai, Thailand."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Yavari, A., Bagha, H., Korala, H., Mirza, I., Dia, H., Scifleet, P., Sargent, J., and Shafiei, M. (2022). ParcEMon: IoT Platform for Real-Time Parcel Level Last-Mile Delivery Greenhouse Gas Emissions Reporting and Management. Sensors, 22.","DOI":"10.3390\/s22197380"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Korala, H., Georgakopoulos, D., Jayaraman, P.P., and Yavari, A. (2021, January 5). A Time-Sensitive IoT Data Analysis Framework. Proceedings of the Hawaii International Conference on System Sciences (HICSS 54), Kauai, HI, USA.","DOI":"10.24251\/HICSS.2021.865"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Taneja, M., and Davy, A. (2017, January 8\u201312). Resource aware placement of IoT application modules in Fog-Cloud Computing Paradigm. Proceedings of the 2017 IFIP\/IEEE Symposium on Integrated Network and Service Management (IM), Lisbon, Portugal.","DOI":"10.23919\/INM.2017.7987464"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Yousefpour, A., Patil, A., Ishigaki, G., Kim, I., Wang, X., Cankaya, H.C., Zhang, Q., Xie, W., and Jue, J.P. (2018). FogPlan: A Lightweight QoS-aware Dynamic Fog Service Provisioning. CoRR.","DOI":"10.1109\/JIOT.2019.2896311"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1185","DOI":"10.1109\/JIOT.2017.2701408","article-title":"QoS-Aware Deployment of IoT Applications Through the Fog","volume":"4","author":"Brogi","year":"2017","journal-title":"IEEE Internet Things J."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Chandan, G., Jain, A., Jain, H. (2018, January 11\u201312). Real time object detection and tracking using Deep Learning and OpenCV. Proceedings of the 2018 International Conference on Inventive Research in Computing Applications (ICIRCA), Coimbatore, India.","DOI":"10.1109\/ICIRCA.2018.8597266"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1145\/2184319.2184337","article-title":"Real-time computer vision with OpenCV","volume":"55","author":"Pulli","year":"2012","journal-title":"Commun. ACM"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Gr\u00f6nman, J., Sillberg, P., Rantanen, P., and Saari, M. (2019, January 20\u201324). People Counting in a Public Event\u2014Use Case: Free-to-Ride Bus. Proceedings of the 2019 42nd International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), Opatija, Croatia.","DOI":"10.23919\/MIPRO.2019.8756921"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"3599","DOI":"10.1109\/TITS.2019.2911128","article-title":"Benchmark data and method for real-time people counting in cluttered scenes using depth sensors","volume":"20","author":"Sun","year":"2019","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Nieto-Rodr\u00edguez, A., Mucientes, M., and Brea, V.M. (2015, January 17\u201319). System for medical mask detection in the operating room through facial attributes. Proceedings of the Iberian Conference on Pattern Recognition and Image Analysis, Santiago de Compostela, Spain.","DOI":"10.1007\/978-3-319-19390-8_16"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Skarlat, O., Schulte, S., Borkowski, M., and Leitner, P. (2016, January 4\u20136). Resource Provisioning for IoT Services in the Fog. Proceedings of the 2016 IEEE 9th International Conference on Service-Oriented Computing and Applications (SOCA), Macau, China.","DOI":"10.1109\/SOCA.2016.10"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Mirza, I.B., Georgakopoulos, D., and Yavari, A. (2023). Cyber-Physical-Social Awareness Platform for Comprehensive Situation Awareness. Sensors, 23.","DOI":"10.3390\/s23020822"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Michal\u00e1k, P., and Watson, P. (2017, January 11\u201314). PATH2iot: A Holistic, Distributed Stream Processing System. Proceedings of the 2017 IEEE International Conference on Cloud Computing Technology and Science (CloudCom), Hong Kong, China.","DOI":"10.1109\/CloudCom.2017.35"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Bagha, H., Yavari, A., and Georgakopoulos, D. (2022). Hybrid Sensing Platform for IoT-Based Precision Agriculture. Future Internet, 14.","DOI":"10.3390\/fi14080233"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Skarlat, O., Nardelli, M., Schulte, S., and Dustdar, S. (2017, January 14\u201315). Towards qos-aware fog service placement. Proceedings of the 2017 IEEE 1st international conference on Fog and Edge Computing (ICFEC), Madrid, Spain.","DOI":"10.1109\/ICFEC.2017.12"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Yavari, A., Jayaraman, P.P., and Georgakopoulos, D. (2016, January 12\u201314). Contextualised service delivery in the Internet of Things: Parking recommender for smart cities. Proceedings of the 2016 IEEE 3Rd world forum on internet of things (WF-iot), Reston, VA, USA.","DOI":"10.1109\/WF-IoT.2016.7845479"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1497","DOI":"10.1109\/TII.2014.2306782","article-title":"QoS-aware scheduling of services-oriented internet of things","volume":"10","author":"Li","year":"2014","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"153479","DOI":"10.1109\/ACCESS.2020.3018140","article-title":"A comprehensive survey of enabling and emerging technologies for social distancing\u2014Part I: Fundamentals and enabling technologies","volume":"8","author":"Nguyen","year":"2020","journal-title":"IEEE Access"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"154209","DOI":"10.1109\/ACCESS.2020.3018124","article-title":"A comprehensive survey of enabling and emerging technologies for social distancing\u2014Part II: Emerging technologies and open issues","volume":"8","author":"Nguyen","year":"2020","journal-title":"IEEE Access"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"128776","DOI":"10.1109\/ACCESS.2020.3007939","article-title":"AI techniques for COVID-19","volume":"8","author":"Hussain","year":"2020","journal-title":"IEEE Access"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Pham, Q.V., Nguyen, D.C., Huynh-The, T., Hwang, W.J., and Pathirana, P.N. (2021). Artificial intelligence (AI) and big data for coronavirus (COVID-19) pandemic: A survey on the state-of-the-arts. arXiv.","DOI":"10.20944\/preprints202004.0383.v1"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1109\/MNET.011.2000458","article-title":"Explainable AI and mass surveillance system-based healthcare framework to combat COVID-I9 like pandemics","volume":"34","author":"Hossain","year":"2020","journal-title":"IEEE Netw."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"3545","DOI":"10.1109\/ACCESS.2022.3232461","article-title":"Chaotic-Map Based Encryption for 3D Point and 3D Mesh Fog Data in Edge Computing","volume":"11","author":"Raghunandan","year":"2022","journal-title":"IEEE Access"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"109581","DOI":"10.1109\/ACCESS.2020.3001973","article-title":"Artificial intelligence and COVID-19: Deep learning approaches for diagnosis and treatment","volume":"8","author":"Jamshidi","year":"2020","journal-title":"Ieee Access"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1109\/MNET.011.2000353","article-title":"B5G and explainable deep learning assisted healthcare vertical at the edge: COVID-I9 perspective","volume":"34","author":"Rahman","year":"2020","journal-title":"IEEE Netw."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Nguyen, T.T., Nguyen, Q.V.H., Nguyen, D.T., Hsu, E.B., Yang, S., and Eklund, P. (2020). Artificial intelligence in the battle against coronavirus (COVID-19): A survey and future research directions. arXiv.","DOI":"10.36227\/techrxiv.12743933"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.future.2020.08.046","article-title":"A drone-based networked system and methods for combating coronavirus disease (COVID-19) pandemic","volume":"115","author":"Kumar","year":"2021","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"90225","DOI":"10.1109\/ACCESS.2020.2992341","article-title":"A comprehensive review of the COVID-19 pandemic and the role of IoT, drones, AI, blockchain, and 5G in managing its impact","volume":"8","author":"Chamola","year":"2020","journal-title":"IEEE Access"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1109\/MNET.011.2000439","article-title":"Blockchain-envisioned softwarized multi-swarming uavs to tackle covid-i9 situations","volume":"35","author":"Gupta","year":"2020","journal-title":"IEEE Netw."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1109\/EMR.2020.3017451","article-title":"The fight against the COVID-19 pandemic with 5G technologies","volume":"48","author":"Siriwardhana","year":"2020","journal-title":"IEEE Eng. Manag. Rev."},{"key":"ref_36","unstructured":"World Health Organization (2020). Coronavirus Disease 2019 (COVID-19): Situation Report, 72, World Health Organization."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"434","DOI":"10.1016\/S2213-2600(20)30134-X","article-title":"Rational use of face masks in the COVID-19 pandemic","volume":"8","author":"Feng","year":"2020","journal-title":"Lancet Respir. Med."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"m1435","DOI":"10.1136\/bmj.m1435","article-title":"Face masks for the public during the COVID-19 crisis","volume":"369","author":"Greenhalgh","year":"2020","journal-title":"Bmj"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"105585","DOI":"10.1016\/j.jaerosci.2020.105585","article-title":"Influence of wind and relative humidity on the social distancing effectiveness to prevent COVID-19 airborne transmission: A numerical study","volume":"147","author":"Feng","year":"2020","journal-title":"J. Aerosol Sci."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.puhe.2020.06.005","article-title":"Is one-or two-meters social distancing enough for COVID-19? Evidence for reassessing","volume":"185","author":"Zhao","year":"2020","journal-title":"Public Health"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1740","DOI":"10.3201\/eid2608.201093","article-title":"Evaluating the effectiveness of social distancing interventions to delay or flatten the epidemic curve of coronavirus disease","volume":"26","author":"Matrajt","year":"2020","journal-title":"Emerg. Infect. Dis."},{"key":"ref_42","unstructured":"Punn, N.S., Sonbhadra, S.K., and Agarwal, S. (2020). Monitoring COVID-19 social distancing with person detection and tracking via fine-tuned YOLO v3 and Deepsort techniques. arXiv."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"863","DOI":"10.1002\/jmv.25883","article-title":"Failure in initial stage containment of global COVID-19 epicenters","volume":"92","author":"Khosrawipour","year":"2020","journal-title":"J. Med. Virol."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"102202","DOI":"10.1016\/j.apgeog.2020.102202","article-title":"Rapid surveillance of COVID-19 in the United States using a prospective space-time scan statistic: Detecting and evaluating emerging clusters","volume":"118","author":"Desjardins","year":"2020","journal-title":"Appl. Geogr."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"245","DOI":"10.15585\/mmwr.mm6909e1","article-title":"Active monitoring of persons exposed to patients with confirmed COVID-19\u2014United States, January\u2013February 2020","volume":"69","author":"Burke","year":"2020","journal-title":"MMWR Morb. Mortal. Wkly. Rep."},{"key":"ref_46","unstructured":"Lienhart, R., and Maydt, J. (2002, January 22\u201325). An extended set of haar-like features for rapid object detection. Proceedings of the International Conference on Image Processing, Rochester, NY, USA."},{"key":"ref_47","unstructured":"Viola, P., and Jones, M. (2001, January 8\u201314). Rapid object detection using a boosted cascade of simple features. Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2001, Kauai, HI, USA."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.websem.2012.05.003","article-title":"The SSN ontology of the W3C semantic sensor network incubator group","volume":"17","author":"Compton","year":"2012","journal-title":"J. Web Semant."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Yavari, A., Jayaraman, P.P., Georgakopoulos, D., and Nepal, S. (2017, January 4\u20137). ConTaaS: An approach to internet-scale contextualisation for developing efficient internet of things applications. Proceedings of the Hawaii International Conference on System Sciences 2017 (HICSS-50), Hilton Waikoloa Village, HI, USA.","DOI":"10.24251\/HICSS.2017.715"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Bykov, S., Geller, A., Kliot, G., Larus, J.R., Pandya, R., and Thelin, J. (2011, January 9). Orleans: Cloud computing for everyone. Proceedings of the 2nd ACM Symposium on Cloud Computing, Santa Cruz, CA, USA.","DOI":"10.1145\/2038916.2038932"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Yavari, A., Panah, A.S., Georgakopoulos, D., Jayaraman, P.P., and van Schyndel, R. (2017, January 5\u20138). Scalable role-based data disclosure control for the internet of things. Proceedings of the 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS), Atlanta, GA, USA.","DOI":"10.1109\/ICDCS.2017.307"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1842","DOI":"10.1109\/COMST.2017.2685630","article-title":"A survey of rate adaptation techniques for dynamic adaptive streaming over HTTP","volume":"19","author":"Kua","year":"2017","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"2149","DOI":"10.1109\/COMST.2014.2375213","article-title":"Reducing Internet Latency: A Survey of Techniques and Their Merits","volume":"18","author":"Briscoe","year":"2016","journal-title":"IEEE Commun. Surv. Tutor."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/11\/5211\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:45:29Z","timestamp":1760125529000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/11\/5211"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,30]]},"references-count":53,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2023,6]]}},"alternative-id":["s23115211"],"URL":"https:\/\/doi.org\/10.3390\/s23115211","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,30]]}}}