Swarm Intelligence-Based Task Scheduling for Enhancing Security for IoT Devices

Abstract

Due to the great advancement in computation, communication and control technologies, the Internet of Things (IoT) can provide ubiquitous connectivity for anyone and anything at any time and any place, leading to a revolution in information society. Protecting devices against various security threats is one of the most important challenges in IoT since IoT applications are generally security-critical systems while IoT devices are often poorly secured. For IoT devices, employing security services provided by smart gateways or edge/cloud servers to defend against various threats is an effective way to enhance their security. However, the finite battery energy of devices and the limited fund of device users hinder the wide application of security services in IoT. This necessitates the demand for designing new methodologies to tackle the trade-off among security, energy and fund of IoT devices. Therefore, this paper attempts to optimize system security of IoT devices under energy and fund constraints. Specifically, to formulate the energy and fund constrained security optimization problem, we firstly propose a pricing model for the security services provided by the smart gateway. We then formulate the problem as a mixed-integer linear programming (MILP) problem. Since using a solver to address the MILP problem may be time-consuming, we leverage the swarm intelligence technique to design a new task scheduling scheme that can efficiently solve the optimization problem. Extensive experiments are conducted to validate our proposed MILP and swarm intelligence based task scheduling algorithms. Simulation results show that our scheme outperforms two state-of-the-art methods in improving system quality of security and guaranteeing schedule feasibility.