Decomposed Task Scheduling for Security-Critical Mobile Cyber–Physical Systems

With the recent advances in mobile sensing, computing, and communication technologies, mobile cyber–physical systems (MCPSs) become a promising networking paradigm that provides mobile users with various applications and services from cyber space to the physical world. One of the main challenges in the MCPSs is to defend security threats launched by adversaries. Exploiting security services to defend threats is energy consuming, whereas the energy of mobile devices is generally limited since most of mobile devices are battery powered. This necessitates the need to design new methodologies to tackle the tradeoff between security and energy of MCPSs. To this end, this article aims to maximize MCPSs’ security under the constraints of energy and deadline. In this article, we first formulate the MCPS security maximization problem as a mixed-integer nonlinear programming (MINLP) problem and then transform it into a mixed-integer linear programming (MILP) problem without performance degradation. To solve the transformed MILP problem efficiently, we propose a decomposed algorithm to derive the optimum task scheduling solution instead of using MILP solvers that may be very time consuming for MCPSs of large granularity. The derived task scheduling solution decides the assignment, operating frequency, execution order, as well as security service selection for all tasks. We implement a series of simulation-based experiments to validate the proposed decomposed task scheduling scheme. Simulation results demonstrate that the proposed scheme increases system security level by 20.38% and 65.11% on average as compared to a state-of-art approach and a baseline method.