Abstract
Fire detection techniques have received considerable critical attention over the past ten years. Regardless of the progress in the area of fire detection, questions have been raised about the cost, complexity, consumed power from a large number of sensors to analyze sensors' data. Debate continues about the best strategies for the management of consumed power and how to accelerate the processing of real-time data in fire detection through the internet of things. This paper presents a partial coverage and a power-aware IoT-based fire detection model with different multi-functional sensors in smart cities. In the proposed model, the sleep scheduling approach used for saving sensors energy. This approach will significantly help in saving consumed energy and thus the need for any extra number of nodes required for continuously covering the target area. Moreover, fog computing is applied to process real-time data aggregated from a large number of sensors for running systems more efficiently. Validation of the proposed model was carried out via simulation and experimental testbed implementation with Arduino, sensors, and Raspberry pi. The results obtained indicate how the proposed technique can efficiently determine the sensors to meet the constraints imposed. The most striking finding to emerge from this experimental and simulation study is that the proposed technique helps in ensuring excellent performance in terms of the number of active nodes and the network lifetime, over other state-of-the-art techniques. The most striking finding of this work compared to MWSAC and PCLA, while covering the same area, is the minimization of the number of active nodes by 64.33% and 15%. It raises the network's lifetime by 91.32% compared to MWSAC and 12% compared to PCLA, respectively.