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dc.contributor.authorYadav, Piyush
dc.contributor.authorSalwala, Dhaval
dc.contributor.authorCurry, Edward
dc.date.accessioned2021-07-28T13:29:26Z
dc.date.available2021-07-28T13:29:26Z
dc.date.issued2021-04-23
dc.identifier.citationYadav, Piyush, Salwala, Dhaval, & Curry, Edward. (2021). VID-WIN: Fast Video Event Matching With Query-Aware Windowing at the Edge for the Internet of Multimedia Things. IEEE Internet of Things Journal, 8(13), 10367-10389. doi:10.1109/JIOT.2021.3075336en_IE
dc.identifier.issn2327-4662
dc.identifier.urihttp://hdl.handle.net/10379/16883
dc.description.abstractEfficient video processing is a critical component in many IoMT applications to detect events of interest. Presently, many window optimization techniques have been proposed in event processing with an underlying assumption that the incoming stream has a structured data model. Videos are highly complex due to the lack of any underlying structured data model. Video stream sources, such as CCTV cameras and smartphones are resource-constrained edge nodes. At the same time, video content extraction is expensive and requires computationally intensive deep neural network (DNN) models that are primarily deployed at high-end (or cloud) nodes. This article presents VID-WIN, an adaptive 2-stage allied windowing approach to accelerate video event analytics in an edge-cloud paradigm. VID-WIN runs parallelly across edge and cloud nodes and performs the query and resource-aware optimization for state-based complex event matching. VID-WIN exploits the video content and DNN input knobs to accelerate the video inference process across nodes. This article proposes a novel content-driven microbatch resizing , query-aware caching, and microbatch-based utility filtering strategy of video frames under resource-constrained edge nodes to improve the overall system throughput, latency, and network usage. Extensive evaluations are performed over five real-world data sets. The experimental results show that VID-WIN video event matching achieves ∼2.3× higher throughput with minimal latency and ~99% bandwidth reduction compared to other baselines while maintaining query-level accuracy and resource bounds.en_IE
dc.formatapplication/pdfen_IE
dc.language.isoenen_IE
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_IE
dc.relation.ispartofIeee Internet Of Things Journalen
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Ireland
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/3.0/ie/
dc.subjectComplex event processing;en_IE
dc.subjectdeep neural network (DNN),en_IE
dc.subjectedge computing,event query,en_IE
dc.subjectInternet of Multimedia Things (IoMT),en_IE
dc.subjectstreaming windows,en_IE
dc.subjectvideo streamsen_IE
dc.titleVID-WIN: Fast video event matching with query-aware windowing at the edge for the internet of multimedia thingsen_IE
dc.typeArticleen_IE
dc.date.updated2021-07-28T13:10:23Z
dc.identifier.doi10.1109/JIOT.2021.3075336
dc.local.publishedsourcehttps://dx.doi.org/10.1109/JIOT.2021.3075336en_IE
dc.description.peer-reviewedpeer-reviewed
dc.internal.rssid26418894
dc.local.contactPiyush Yadav, Insight Center For Data Analytics, Nuig. - Email: p.yadav1@nuigalway.ie
dc.local.copyrightcheckedYes
dc.local.versionACCEPTED
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Attribution-NonCommercial-NoDerivs 3.0 Ireland
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 Ireland