# Industry 4.0 towards Forestry 4.0: Fire Detection Use Case +
## 1 Introduction  
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. However, the information is required to adjust the process for producing decision-making dynamically. Smart forest is one of the domains that is drastically becoming technologicalized with data-gathering sites, processing, transportation and analysis. Industrial IoT data-processing poses some unique challenges when endeavouring to make stream processinga reliable solution. Some specific challenges that the IoT industry faces when it comes to data-processing are (1) the retrieval and processing overheads for the huge amount of heterogeneous streaming data generated from a large number of IoT devices, and (2) the missing and delayed events due to unpredictability of data which affects the required rapid response time in the IoT industry [1]. In particular, 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. Mainly, the true vision of IoT can only be realized if the underlying technologies for stream processing can handle large amounts of data. Realizing the necessity, recently, a few stable and scalable have emerged to facilitate real-time building applications for IoT by processing large IoT data at scale, such as Apache Spark, Apache Flink, Apache Samza and Apache Storm [3] etc. The rapid development of IoT has resulted in the emergence of a few sophisticated query language tailor-made for performing analytics over streaming data, such as Spark SQL, Flink Table API, KSQL, SamzaSQL, and StromSQL. All of these streaming data queries are relying on the concept of windowing to convert continuous infinite data streams into chunks of finite data sets sliced according to a pre-defined time (e.g., minutes, seconds, milliseconds) [4]. Our previous work has addressed the study of the impact of changes in IoT data streams rate over query window configurations [5]. Our evaluation results have indicated a direct impact of any changes in stream rate and window size over the engines' performance. For instance, consider a fire alarm detection query scenario that processes two input data streams and observes their values at a fixed interval for any fire hazard detection. Input streams/sensors are configured so that they change their stream rate with the changing values (e.g., if the temperature rises beyond a certain threshold, data observation rate is increased to ensure early detection of any fire event). Larger window size facilitates streaming query engines for optimal processing of data with high velocity. Still, it impedes the query engine's performance due to large intermediate result size or delay events. The smaller window size can lead to short-latency, but it misses events in case of high stream rates. Therefore, a fixed window size will either impede the query engine's performance due to a large number of intermediate results or miss the detection of fire events due to the small window size. One of the real use case of fire data monitoring is forest fire detection. The forest managers have gradually accepted IoT forest environmental monitoring technologies for taking forest inventory through remote IoT data collection [6]. The IoT-based forest fire monitoring data has the challenge of dynamic ecological changes, i.e., climate change due to unpredictable streaming data. In particular, the timeliness of acquired monitoring IoT data should be considered to avoid real-time data being affected by sudden factors. Internet of Forest Things (IoTFT) is version revolution of IoT, which refers to the smart devices distributed in forest for monitoring, management and fire detection and protection.