A task orchestration approach for efficient mountain fire detection based on microservice and predictive analysis in IoT environment

Mountains are attraction spots for tourists, and tourism contributes to the country's gross domestic product. Mountains have many benefits such as biodiversity, tourism, and the supplication of food, to name a few. However, there are challenges to protect mountain lives from hazards such as fire caused by tourist activities in mountains. The in-Time fire detection and notification to the authorities have always been the central point in literature studies, and different studies have been carried out to optimize the notification time. In this paper, we model the fire detection and notification as a real-Time internet of things application and uses task orchestration and task scheduling mechanism to provide scalability along with optimal latency. The proposed fire detection and prediction mechanism detect mountain fire at the earliest stage and provide predictive analysis to prevent damage to mountain life and tourists. The architecture is based on microservice-based IoT task orchestration mechanism and device virtualization, which is not only lightweight but also handles a single problem in parallel chunks, thus optimizes the latency. The in-Time information about the fire is used for predictive analysis and notified to safety authorities which helps them to make a more informed decisions to minimize the damage caused by mountain fire. The performance of the proposed mechanism is evaluated in terms of different measures such as RMSE, MAPE, MSE, and MAPE. The proposed work approaches the fire detection and notification as a collection of tasks, and thus those tasks are selected for deployment which are guaranteed to be executed and have minimum latency. This idea of pre-planing the latency and task execution is the first attempt to the best of the authors' knowledge.