Show simple item record

dc.contributor.authorSahal, Radhya
dc.contributor.authorAlsamhi, Saeed H.
dc.contributor.authorBreslin, John G.
dc.contributor.authorAli, Muhammad Intizar
dc.identifier.citationSahal, R.; Alsamhi, S.H.; Breslin, J.G.; Ali, M.I. (2021) Industry 4.0 towards Forestry 4.0: Fire Detection Use Case . Sensors, 21, 694.
dc.description.abstractForestry 4.0 is inspired by the Industry 4.0 concept, which plays a vital role in the next industrial generation revolution. It is ushering in a new era for efficient and sustainable forest management. Environmental sustainability and climate change are related challenges to promote sustainable forest management of natural resources. Internet of Forest Things (IoFT) is an emerging technology that helps manage forest sustainability and protect forest from hazards via distributing smart devices for gathering data stream during monitoring and detecting fire. Stream processing is a well-known research area, and recently, it has gained a further significance due to the emergence of IoFT devices. Distributed stream processing platforms have emerged, e.g., Apache Flink, Storm, and Spark, etc. Querying windowing is the heart of any stream-processing platform which splits infinite data stream into chunks of finite data to execute a query. Dynamic query window-based processing can reduce the reporting time in case of missing and delayed events caused by data drift.In this paper, we present a novel dynamic mechanism to recommend the optimal window size and type based on the dynamic context of IoFT application. In particular, we designed a dynamic window selector for stream queries considering input stream data characteristics, application workload and resource constraints to recommend the optimal stream query window configuration. A research gap on the likelihood of adopting smart IoFT devices in environmental sustainability indicates a lack of empirical studies to pursue forest sustainability, i.e., sustainable forestry applications. So, we focus on forest fire management and detection as a use case of Forestry 4.0, one of the dynamic environmental management challenges, i.e., climate change, to deliver sustainable forestry goals. According to the dynamic window selector’s experimental results, end-to-end latency time for the reported fire alerts has been reduced by dynamical adaptation of window size with IoFT stream rate changes.en_US
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.subjectIndustry 4.0en_US
dc.subjectStream processingen_US
dc.subjectWindow sizeen_US
dc.subjectForestry 4.0en_US
dc.subjectInternet of forestry thingsen_US
dc.subjectForest fire detectionen_US
dc.subjectForest sustainabilityen_US
dc.titleIndustry 4.0 towards Forestsry 4.0: fire dection use caseen_US
dc.contributor.affiliationAthlone Institute of Technologyen_US
dc.contributor.sponsorScience Foundation Ireland (SFI) under Grant Number SFI/16/RC/3918 (Confirm), and Marie Skłodowska- Curie grant agreement No. 847577 co-funded by the European Regional Development Fund.en_US
dc.subject.departmentSoftware Research Institute AITen_US
dc.relation.projectidGrant Number SFI/16/RC/3918/ No. 847577en_US

Files in this item


This item appears in the following Collection(s)

Show simple item record

Attribution-NonCommercial-NoDerivatives 4.0 International
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 International