Now-a-days, fire accidents are occurring very frequently in public transport system which causes the loss of most valuable human lives and the government property. There are a number of methods to avoid fire accidents and to reduce the severity of loss in case of fire accidents in public transport system. But the damage is catastrophic as a rescue service could not reach at right time due to improper communication. So, we must further avoid and reduce the loss caused by fire accidents in buses. The main objective of our project is to detect the fire accidents and to inform the nearest respective authorities who can reach faster. Fire sensors are used to detect the fire when the crash or accidents caused. All the sensors are connected to a central controller whenever the fire is detected the controller activates the water sprinkles, sounds the alarms. A GPS module is integrated to the system through that the longitude and latitude of the location is send to the fire department authorities. Node MCU is used as the central controller in the proposed system. Final the data is send to the cloud through it will be available to all the govt. departments. Things speak cloud is used in the prototype.
Mobile Ad hoc Networks (MANET) have been highly vulnerable to attacks due to the dynamic nature of its network infrastructure. Among these attacks, routing attacks have received considerable attention since it could cause the most devastating damage to MANET. Even though there exist several intrusion response techniques to mitigate such critical attacks, existing solutions typically attempt to isolate malicious nodes based on binary or naı¨ve fuzzy response decisions. However, binary responses may result in the unexpected network partition, causing additional damages to the network infrastructure, and naı¨ve fuzzy responses could lead to uncertainty in countering routing attacks in MANET. In this paper, we propose a risk-aware response mechanism to systematically cope with the identified routing attacks. Our risk-aware approach is based on an extended Dempster-Shafer mathematical theory of evidence introducing a notion of importance factors. In addition, our experiments demonstrate the effectiveness of our approach with the consideration of several performance metrics
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