# 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
As the selected use case, IoT-based forest fire detection, considered one of the latency-sensitive applications, we

Figure 19: Dynamic experiment number 7. (**a**) Dynamic window size relative to input stream rate (**b**) dynamic window size relative to reduction of reporting time for output alerts (**c**) dynamic window size relative to End-to-End latency time improvement (E2 ELatency). Figure 18: Comparison of static and adaptive window sizes for queuing time, processing time, end-to-end latency. believe that losing 85% of red alerts is still reasonable to provide a timely warning about fires by using 15% of the expected alerts while reducing notification time to 74%. Finally, our experimental results over the generated forest fire dataset confirm that the dynamic window selector significantly reduces the end-to-end latency time for the reported fire alerts by sending fast danger alerts even though there are many missed alerts during the adaptation process. ## 7 Discussion

To bring the static window size experimental investigation to reality, the weather variables including temperature, humidity, CO, CO\({}_{2}\) and wind, are considered relying on the new technology of IoT, which can achieve 24-h monitoring of weather conditions. From this experimental investigation, we learn that the large window size is suitable for the low stream rate, which reports the green alerts in normal weather conditions. On the other hand, the small window size which reports fast alerts is good for the high stream rate, which leads to critical danger ratings (i.e., red alerts). In terms of forest fire detection, it is a widely accepted view that relies on how difficult to detect forest fires accurately using static streaming window configurations. Consequently, the window size needs to linearly adapt according to the increasing stream rate to keep maintaining the critical alerts even with a reasonable number of loss alerts that indicate forest fires. In particular, we need to deploy a suitable dynamic streaming window configuration to maintain the weather data changes' stream rates to avoid the potential fires in the broad forestry areas, especially in the summer season. In summer times which are usually called fire seasons, more than 80% forest fire occurred either in the spring or in the autumn with a slight increasing distribution due to the drier weather conditions [25]. According to this work, the weather variables, including temperature, humidity, CO, CO\({}_{2}\), and wind, are considered relying on IoT's new technology, which can achieve 24-h monitoring of weather conditions in forestry areas. The joined streaming results, which are the alerts, can illustrate the relationships between the weather alert thresholds and the wind speed. Therefore, based on the findings using our proposed dynamic window-based selection system, the reported alerts are very time-sensitive in case of high wind speed. In particular, in the autumn with the drier weather conditions, the orange alerts should be considered red alerts in high wind speed, which strongly warns of danger fires igniting. Consequently, our proposed dynamic window selection system

Figure 20: Summary of end-to-end latency time improvement (E2E) and loss alerts ratios for each alert type for adaptive window size experiment with respect to the static window experiment. can inform the forest fire authorities about the forest fires that rely on different weather conditions even not fire seasons. ### Challenges

In this paper, we present a novel dynamic query window-based processing system to recommend the optimal window size and type based on the dynamic context of IoFT applications. Regarding improving forestry 4.0 environmental sustainability, real-time network connectivity of IoFT devices and decision making will enhance forest resources to be continuously managed and monitored. Management of forestry 4.0 will be easy due to reducing resource damages and wastes, while harvesting will be coordinate to minimize gas emissions.