Linguistic interval type 2 fuzzy logic-based Exigency Vehicle routing: IoT system development for smart city applications with soft computing-based optimization

Abstract

An Exigency Vehicle (EV) has to go more quickly to increase the likelihood that someone in danger would survive. Construction projects, strikes, and accidents may all be avoided with an effective vehicle routing solution. The Internet of Things (IoT) network simulation for Exigency Vehicle routing is proposed in this research utilizing a linguistic interval type 2 fuzzy logic system (LIT2FLS) based data fusion approach. The predicted effectiveness of our model for intelligent city applications, including emergency vehicle routing, is well demonstrated by the LIT2FLS correlation coefficients of 0.994%. This data fusion approach determines the exact level of congestion for a given place by combining sensory data with crowd reactions. In addition, OSRM employs a road communication system to detect real-time traffic variations and choose the least congested route. Furthermore, a sensor station for gathering the speeds and pollutants of moving automobiles on the road has also been established. An Android app has been developed to collect public information on blockages A driver of an Exigency Vehicle (EV) is directed towards a medical facility with the quickest, congestion-aware path by means of the Application software service. We have created an IT2FLS system that assists with decisions to calculate traffic congestion. Analyze the ability to scale and quickness of response associated with the suggested routing strategy.

Keywords

• LIT2FLS
• IoT
• ARM
• Senor node
• OSRM
• Exigency Vehicle routing