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Industry 4.0 towards Forestry 4.0: Fire Detection Use Case †

Confirm SFI Research Centre for Smart Manufacturing, National University of Ireland Galway, Galway, Ireland
Faculty of Computer Science and Engineering, Hodeidah University, Al Hodeidah 3114, Yemen
SMART 4.0 FELLOW, Software Research Institute, Athlone Institute of Technology, Athlone, Ireland
Faculty of Engineering, IBB University, Ibb 70270, Yemen
School of Electronic Engineering, Dublin City University, Dublin, Ireland
Author to whom correspondence should be addressed.
This manuscript is extension version of conference paper: R Sahal, J. Breslin, M.I Ali, “On Evaluating the Impact of Changes in IoT Data Streams Rate over Query Window Configurations”, Proceedings of the 13th ACM International Conference on Distributed and Event-based Systems, ACM: New York, NY, USA, 2019; DEBS ’19, pp. 262–263.
Sensors 2021, 21(3), 694;
Received: 7 December 2020 / Revised: 14 January 2021 / Accepted: 15 January 2021 / Published: 20 January 2021
(This article belongs to the Section Internet of Things)
Forestry 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. View Full-Text
Keywords: IoT; query; industry 4.0; stream processing; window size; forestry 4.0; internet of forestry things; forest fire detection; forest sustainability IoT; query; industry 4.0; stream processing; window size; forestry 4.0; internet of forestry things; forest fire detection; forest sustainability
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MDPI and ACS Style

Sahal, R.; Alsamhi, S.H.; Breslin, J.G.; Ali, M.I. Industry 4.0 towards Forestry 4.0: Fire Detection Use Case. Sensors 2021, 21, 694.

AMA Style

Sahal R, Alsamhi SH, Breslin JG, Ali MI. Industry 4.0 towards Forestry 4.0: Fire Detection Use Case. Sensors. 2021; 21(3):694.

Chicago/Turabian Style

Sahal, Radhya, Saeed H. Alsamhi, John G. Breslin, and Muhammad Intizar Ali. 2021. "Industry 4.0 towards Forestry 4.0: Fire Detection Use Case" Sensors 21, no. 3: 694.

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