Wireless Sensor Networks (WSN) are being developed in the agriculture and weather station sectors to monitor and manage a number of objects. Sensor network hardware platforms are low-power embedded systems featuring a variety of sensors, such as on-board sensors and analogue I/O ports for adding sensors. The design of genuine environmental monitoring and communication systems is greatly aided by the creation of a WSN. A WSN is a collection of sensors that are geographically spread and specialized for monitoring and recording environmental variables and storing the findings in a central location. Limited power consumption as sensor nodes works on batteries, Nodes are mobile opposed to conventional wires, Usage in harsh environment, Nodes can be deployed in any fashion over an area, Easy to use and multi-layer design operation these are the main characteristics of WSN. Limits imposed on the energy storage device by their applications and deployment settings are less relevant to WSN environment-monitoring applications than controlling power consumption and optimizing data transmission. A WSN is used to record real-time traffic and environmental data in order to develop descriptive and predictive models for determining the optimum routes to reduce traffic congestion and, as a result, urban pollution. C

This article describes how social media has emerged as a main vehicle of information diffusion among people. They often share their experience, feelings and knowledge through these channels. Some pieces of information quickly reach a large number of people, while others not. The authors analyzed this variation by collecting tweets on 2016 U.S. presidential election. This article gives a comprehensive understanding of how sentiment encoded in the textual contents can affects the information diffusion, along with the effect of content features, i.e., URLs, hashtags, and contextual features, i.e., number of followers, followees, tweets generated by the user so far, account age, tweet age. In order to explore the relationship between sentiment content and information diffusion, the authors first checked the features' significance as an indicator of diffusibility by using random forests. Finally, support vectors and k-Neighbors regression models are used to capture the complete dynamics of information diffusion. Experiments and results clearly reveal that sentiment prominently helps in making a better prediction of information diffusion.