# 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
## 8 Conclusions

Industry 4.0 is gaining advantages from the implementation in different domains. Therefore, the transformation of Industry 4.0 towards Forestry 4.0 in the forest domain appears inevitable. The advantages of this transformation will significantly improve the social, environmental and economical. Therefore, Forestry 4.0 is a promising sector of Industry 4.0 to make a real sustainable revolution. We applied a dynamic window configuration recommendation techniques to handle variable stream rate and build an optimal query execution plan. This approach shows a significant enhancement on stream query optimization. Furthermore, it presents additional modules, such as input stream monitoring and creates an open knowledge base. This addition plays a vital role in evaluating the optimal window configuration. Regarding forest fire detection and management by using IoFT, we describe how IoFT generates streaming datasets for a rapidly changing ecological environment. The forest fire dataset is evaluated using static and dynamic window sizes configurations. The proposed dynamic window size selector shows its superiority to send timely alarming alerts by adapting window size based on the sudden stream rate changes. Forestry 4.0 has a bright and exciting future. In the future, the extent of this work will be in the augment our approach within top distributed stream processing platforms. Build an open knowledgebase/knowledge graph storing statistics of IoT devices' performance. Their stream rates can be utilized by multiple platforms and applications to build robust and adaptive IoT applications. Memory consumption and connectivity among smart IoFT devices are the limitation of our proposed approach. It will be our future direction to improve sustainability impacts and manage fire detection before it occurs. On the other hand, improving dynamic selectors to predict future environmental risks will help identify and detect risk before it occurs by collecting large dataset from high input stream rates and then applying powerful predictive analytics techniques such as deep machine learning. Moreover, the combination of deep learning and blockchain technology can be used in order to improve processing, resource utilization and connectivity among smart IoFT devices. Blockchain is a decentralized technology for smart devices and makes decisions locally instead of sending data to the center for analysis. While, deep learning can be implemented in smart contracts to process data locally and make decision making based on historical storage data in blockchain to produce fire alert immediately. R.S. : Conceptualization, Data curation, Formal analysis, Methodology, Software, Writing-original draft, Writing-review & editing. S.H.A. : Conceptualization, Methodology, Formal analysis, Writing-review & editing. J.G.B. : Conceptualization, Funding acquisition, Investigation, Project administration, M.I.A. : Conceptualization, Funding acquisition, Project administration, Supervision. All authors have read and agreed to the published version of the manuscript. This research has emanated from research supported by a research grant from Science Foundation Ireland (SFI) under Grant Number SFI/16/RC/3918 (Confirm), and Marie Sklodowska-Curie grant agreement No. 847577 co-funded by the European Regional Development Fund. Not applicable. Not Data available online. For further query email to corresponding author ([EMAIL_ADDRESS], [EMAIL_ADDRESS]). This work has emanated has emanated from research supported by a research grant from Science Foundation Ireland (SFI) under Grant Number SFI/16/RC/3918 (Confirm),and Marie Sklodowska-Curie grant agreement No. 847577 co-funded by the European Regional Development Fund. ## Conflicts of Interest

The authors declare no conflict of interest. * [PERSON] et al. (2017) [PERSON]; [PERSON]; [PERSON] Sliding window-based fault detection from high-dimensional data streams. _IEEE Trans. Syst. Man Cybern. Syst._**2017**, _47_, 289-303. [CrossRef]
* [PERSON] (2014) [PERSON] A review on forest fire detection techniques.