Energy efficient and secure routing in fog based WSN using deep learning based intrusion detection system

Abstract

In recent years, IoT has seen massive development in diverse applications ranging from smart homes, smart cities to smart space automations. The Wireless Sensor Networks (WSN) have become an integral component in many of the real time IoT applications due to their scalability, efficiency, flexibility and reliability. It has a group of spatially dedicated sensors for monitoring and recording the physical conditions of the environment and organizing the collected data at a central location. Tremendous amount of data being stored and processed in cloud resulting in higher latency and reduced speed. The latency, security and energy efficiency aspects especially in delay sensitive critical IoT applications need to be addressed. To address this issue, fog computing has been used where IoT devices are placed at the end users thereby performing all the storage and computations locally and avoiding the raw data transmission to cloud all the time. The fog computing with WSN is used to develop a secure, efficient model for data transmission and offers tremendous solutions in monitoring the surveillance-related environments through their affordable, energy-efficient, and low bandwidth sensors. The main objectives of this research to develop an energy efficient cluster head organization and multi objective optimal routing algorithm which includes intrusion detection mechanism using deep learning technique in fog based WSN environment. newline