SURETY-Fog: Secure Data Query and Storage Processing in Fog Driven IoT Environment

Abstract

Fog computing is an important paradigm in the current scenario among many sensing application services based on the Internet of Things (IoT). A traditional IoT environment suffers from a significant latency where all the devices access data from the cloud. To overcome this problem, fog computing is introduced to reduce the latency. However, several security limitations associated with fog computing have not been addressed. This research proposed the Secure Data Query and Storage Processing (SURETY-fog) method, which overcomes the security limitation. The proposed work has different processes to enhance security and efficiency including IoT device and user registration based on the Naor Reingold generator and Prince algorithm, Authentication by using a Multi-Factor Authentication model, secure optimized fog node selection and secure sensed data storage by using Deer Hunting Optimization (DHO) algorithm, Deep Q learning based secure data storage with improved trust in fog, and Lightweight based secure data transmission in fog layer by using bliss signature. The simulation is conducted by using iFogSim and evaluating the performance based on the following metrics, response time, attack detection rate, resource utilization, number of queries processed, transmission latency, and processing latency.