# Industry 4.0 towards Forestry 4.0: Fire Detection Use Case +
## 5 Methodology
### Dynamic Query Window-Based Selector for Stream Rate Change
#### 5.3.2 The Proposed Dynamic Window-Based Selector
Autonomous IoFT devices, vehicles and robots will improve safety and also working environment. There are more complex queries with complex operations, e.g. ; multiple streams join since such cases/scenarios are non-trivial to solve and may consider for future work. Furthermore, The delay of reporting time in case of missed and delayed events could be reduced by identifying the optimal amount of resources to satisfy the required processing delay under specific stream rate change [59]. In integration with other adaptive streaming techniques, other adaptive techniques such as adaptive load shedding for windowed stream joins [13] and dynamic batch sizing [17] can be integrated with our proposed dynamic windowing based on stream rate change for industrial applications. For stream processing platforms, the streaming data processing results should be on-the-fly to support alerting applications that process new data at the speed with which it is generated. Event streams are potentially unbounded, infinite sequences of records and unpredictable that represent events or changes in real-time. Consequently, the in-memory stream processing platforms must be both fast and scalable to handle billions of tuples every second. Memory consumption is one of the scalability challenging aspects that affect stream processing performance, especially for alerting applications, i.e., publishing notifications to subscribers. The existing research of adaptive windowing lacks solutions that consider memory consumption with the variable stream rates generated from the event sources (e.g., applications and sensors) and the critical destinations such as an alerting system. Regarding our proposed system, changing the window size can reduce the trade-offs with memory consumption. For example, assigning small window sizes for high stream rates helps avoid data drift problems (i.e., memory crash) and meet analytics requirements such as latency constraints. For further investigation, we plan to address the bottleneck and performance problems briefly due to memory consumption during streaming data processing [60]. ### Opportunities

We believe that the proposed system has taken a number of steps in this direction. Furthermore, the future work will take us much closer to comprehensive solutions for variable size windowing over IoT-based stream rate change. Moreover, the proposed system able to manage the sudden large change in stream rate to establish a set of early warning mechanisms for the quick response for critical-latency applications. Gathering data and producing accurate alert of fire detection in Forestry 4.0 requires to distribute a massive number of smart devices and efficient processing techniques. Real-time fire detection and management alert is a critical issue for improving Forestry 4.0 application implementation. Therefore, this issue should be given high consideration to improve treating with a real situation and supporting needed real remedies to situations in real-time. Furthermore, distributed several kinds of smart devices could be used to monitor the forest fire. Heterogeneous smart devices gather different data. Applying a multi-model for gathered data is also a critical issue for forest fire detection and management. Transmission gathered data for long-distance is a critical issue for improving Forestry 4.0 monitoring. Therefore, processing gathered data locally should be taken into consideration for improving forest situation in real time. Another solution, drone technology may be taken into consideration as edge station for gathering data from closed distance to smart devices efficiently and line of sight [61; 62]. Furthermore, drones can process data nearest to smart devices and take action based on an algorithm implemented by using deep learning and store in the drone. in this case, drones can produce alerts of fire detection in forests. On the other hand, there is an opportunity to use edge technology to improve the economy and manage wood production (i.e., drone edge intelligence for gathering data from IoFT devices).