Reinforcement learning based Secure edge enabled multi task scheduling model for internet of everything applications
Thiruppathy Kesavan, R. Venkatesan, Wai Kit Wong, Poh Kiat Ng
Abstract
The fast growth of the Internet of Everything (IoE) has resulted in an exponential rise in network data, increasing the demand for distributed computing. Data collection and management with job scheduling using wireless sensor networks are considered essential requirements of the IoE environment; however, security issues over data scheduling on the online platform and energy consumption must be addressed. The Secure Edge Enabled Multi-Task Scheduling (SEE-MTS) model has been suggested to properly allocate jobs across machines while considering the availability of relevant data and copies. The proposed approach leverages edge computing to enhance the efficiency of IoE applications, addressing the growing need to manage the huge data generated by IoE devices. The system ensures user protection through dynamic updates, multi-key search generation, data encryption, and verification of search result accuracy. A MTS mechanism is employed to optimize energy usage, which allocates energy slots for various data processing tasks. Energy requirements are assessed to allocate tasks and manage queues, preventing node overloading and minimizing system disruptions. Additionally, reinforcement learning techniques are applied to reduce the overall task completion time using minimal data. Efficiency and security have been improved due to reduced energy, delay, reaction, and processing times. Results indicate that the SEE-MTS model achieves energy utilization of 4 J, a delay of 2s, a reaction time of 4s, energy efficiency at 89%, and a security level of 96%. With computation time at 6s, SEE-MTS offers improved efficiency and security, reducing energy, delay, reaction, and processing times, although real-world implementation may be limited due to the number of devices and incoming data.