WSN and Fuzzy Logic for Flash Flood and Traffic Congestion Detection

Floods are the most common natural disaster and source of significant damage to life, agriculture and economy. Flash Floods are particularly deadly because of short timescales on which they occur. Most flood casualties are caused by a lack of information. There is no dedicated flood sensing systems that monitor propagation of flash floods in cities. .Human being do not have power to totally uproot natural calamity but they can predict natural calamity & take major steps to prevent it. Wireless Sensor Network (WSN) and Internet of Things (IoT) technology is used for predicting & detecting flooding condition in this study. WSN is preferred due to its cost effectiveness, faster transfer of data & accurate computation of required parameter for flood prediction. IoT combines embedded system hardware techniques along with data science or machine learning models. The model uses a mesh network connection over ZigBee for the WSN to collect data, and a GPRS module to send data to the internet. Data sets are evaluated using fuzzy logic to detect floods then broadcast alerts. Floods rarely occur hence the system is dedicated for traffic congestion notifications

A Systematic Literature Review of Path-Planning Strategies for Robot Navigation in Unknown Environment

The Many industries, including ports, space, surveillance, military, medicine and agriculture have benefited greatly from mobile robot technology.  An autonomous mobile robot navigates in situations that are both static and dynamic. As a result, robotics experts have proposed a range of strategies. Perception, localization, path planning, and motion control are all required for mobile robot navigation. However, Path planning is a critical component of a quick and secure navigation. Over the previous few decades, many path-planning algorithms have been developed. Despite the fact that the majority of mobile robot applications take place in static environments, there is a scarcity of algorithms capable of guiding robots in dynamic contexts. This review compares qualitatively mobile robot path-planning systems capable of navigating robots in static and dynamic situations. Artificial potential fields, fuzzy logic, genetic algorithms, neural networks, particle swarm optimization, artificial bee colonies, bacterial foraging optimization, and ant-colony are all discussed in the paper. Each method's application domain, navigation technique and validation context are discussed and commonly utilized cutting-edge methods are analyzed. This research will help researchers choose appropriate path-planning approaches for various applications including robotic cranes at the sea ports as well as discover gaps for optimization