A Survey on Wireless Sensor Network-Based IoT Designs For Gas Leakage Detection and Fire-Fighting Applications

A wildland fire is an uncontrolled fire that occurs mainly in forest areas, although it can also invade urban or agricultural areas. Among the main causes of wildfires, human factors, either intentional or accidental, are the most usual ones. The number and impact of forest fires are expected to grow as a consequence of the global warming. In order to fight against these disasters, it is necessary to adopt a comprehensive, multifaceted approach that enables a continuous situational awareness and instant responsiveness. This paper describes a hierarchical wireless sensor network aimed atearly fire detection in risky areas, integrated with the fire fighting command centres, geographical information systems, and fire simulators. This configuration has been successfully tested in two fire simulations involving all the key players in fire fighting operations: fire brigades, communication systems, and aerial, coordination, and land means.

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Hybrid TDR-MI Based Wireless Sensor Network for Underground Water Pipeline Leakage Detection and Localization Using Pressure Residuals and Classifiers.

10.21203/rs.3.rs-788366/v1 ◽

2021 ◽

Author(s):

Ramdas Vankdothu ◽

Hanumanthu Bhukya ◽

Raghu Ram Bhukya

Keyword(s):

Wireless Sensor Network ◽

Sensor Network ◽

Demand Uncertainty ◽

Leak Detection ◽

Time Domain Reflectometry ◽

Wireless Sensor ◽

Leakage Detection ◽

Clustering Method ◽

Underground Pipelines ◽

Leak Localization

Abstract The pipeline leakage detection and leak localization trouble is a highly demanding and dangerous issue. Underground pipelines are a critical mode of transporting enormous fluid volumes (e.g., water) across extended distances. Solving this problem will save the country much money and resources, but it will also protect the environment. On the other hand, present leak detection technologies are insufficient for monitoring underground pipelines due to the extreme subterranean environmental conditions. This study proposes a hybrid wireless sensor network based on TDR (time domain reflectometry) and magnetic induction for monitoring underground pipelines to solve these problems. In this scenario, TDR is deployed beneath an MI-based wireless sensor network. TDR precisely locates the leak and dramatically decreases the amount of time required for inspection. We offer a wireless sensor network based on MI technology for low-cost, real-time leak detection in subsurface pipes. MISE-PIPE identifies leaks by integrating data from a range of different types of sensors installed within and around underground pipelines. Ad-hoc WSNs are used to measure pressure. (WDNs) is a hot topic that has piqued researchers' interest in recent years. Time and accuracy are critical components of leak localization, as they substantially impact the human population and economy. Statistical classifiers acting in the residual space are offered as a general method for leak localization. Classifiers are trained on leak data from all network nodes, taking demand uncertainty, sensor preservative noise, and leak magnitude on the account. Following leak identification and localization, all monitoring data is forwarded to the CH using the K-means clustering method, which serves two critical functions: optimal clustering and prolonging the Network Lifetime (NL) and preserving the QoS. The clustering method is optimized using the K-Means approach .

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