Abstract
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.
Highlights
Information gathering and transmission are growing to the point where it is common to exchange information between participation in real-time and anywhere in the world.the information is required to adjust the process for producing decision-making dynamically
It is sensible to state that our work presents an adaptive stream processing prototype for Forestry 4.0 i.e., Internet of Forest Things (IoFT)-based forest fire detection can automatically adapt the window size according to several parameters and, improve the performance of the underlying IoFT
While most of the publications have focused mainly on the digital technologies, we have focused on applying the automation theme by proposing dynamic stream query processing using IoFT for forest fire detection use case
Summary
Information gathering and transmission are growing to the point where it is common to exchange information between participation in real-time and anywhere in the world.the information is required to adjust the process for producing decision-making dynamically. The missing and delayed events due to unpredictability of data which affects the required rapid response time in the IoT industry [1] These IoT devices produce data at a variable rate (e.g., unscheduled events) rather than at a fixed rate, which augments the difficulties for data stream processing platforms to cope with such a sudden increase in streaming rates [2]. Taming this massive streaming data is a very challenging task
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