# Industry 4.0 towards Forestry 4.0: Fire Detection Use Case +
## 2 Related Work  
Traditionally, stream processing systems have managed the sudden changes in stream rate by elastically increasing available resources, or by discarding part of the data streams (i.e., load shedding) [12; 13; 14]. Much work on the potential of stream processing has been carried out in recent years due to the growth in IoT. Our work tackles change of stream rates and window-based stream processing, which are major challenges faced due to loosely couple nature of IoT data streams. The research work of forest environmental management and industry 4.0 including forest fire detection is discussed. Therefore, we divide the existing work into the following categories:  
For the stream processing adaptation, [PERSON] et al. have proposed an adaptive cloud-based approach for provisioning virtual machines with respect to stream rate change [15]. Figure 2: Forestry 4.0 and sustainability impacts. The proposed approach periodically estimates the number of virtual machines required to support the input stream data rate to maintain virtual machines overloaded and meet processing latency. The adaptive stream rate for smart grid applications on clouds has been studied to throttle the rate of generation of power events by smart meters [2]. Furthermore, one of the aspects of the fluctuated streams generated by various IoT devices is the out-of-order events problem. [PERSON] et al. have proposed a real-time query-matching algorithm to generate queries when the number of event types is large and query length is long by minimizing the overhead and reduce the response time [1]. The distributed Platform for Elastic Stream Processing (PESP) has been introduced to deal with changing rates of streaming data [3]. The PESP platform operates a cost-efficient stream processing engines due to a flexible adoption of processing nodes. With regard to query stream processing, STREAM, a Stanford's data stream management system, supports a large class of declarative continuous queries over continuous streams and traditional stored data sets [16]. [PERSON] et al. have proposed a robust algorithm to automatically adapt the batch size based on the data ingestion rates, variations in available resources, and workload characteristics [17]. [PERSON] et al. have leveraged adaptive batch sizing and block size to minimize the end-to-end latency of streaming system without prior knowledge of workloads specification [18]. The authors proposed a heuristic algorithm integrated with isotonic regression to automatically learn and adjust batch size and execution parallelism according to workloads and operating conditions. There has been much work in the area of stream-processing based on querying windows. SABER, a window-based hybrid stream processing system is proposed to adapt scheduling strategy on CPU and GPU with respect to increasing of the share of queries [19]. SPECTRE (SPECulaTive Runtime Environment) is a framework for speculative processing of multiple dependent windows in parallel [20]. The SPECTRE framework has addressed the speculative processing concept to allow the execution of multiple versions of multiple windows using different event sets in parallel. It has provided a probabilistic model to process different window versions that have the highest probability to be correct. Stream-processing based on sliding window has been extensively studied for different aspects such as aggregations and anomaly detection. For aggregation, the DABA algorithm has been proposed for incremental sliding window aggregation over stream data [21]. Scotty, which is window-based operator has been proposed for aggregation and discretization [22]. The key idea of the Scotty operator is splitting the streams into non-overlapping slices and computes shared partial aggregates per slice while supporting out-of-order processing. For anomaly detection, a sliding window-based strategy has been used for detecting faults over high dimensional streaming data [23].