Optimal Adaptive System Health Monitoring and Diagnosis for Resource Constrained Cyber-Physical Systems

Cyber-physical systems (CPS) are complex net-centric hardware/software systems that can be applied to transportation, healthcare, defense, and other real-time applications. To meet the high reliability and safety requirements for these systems, proactive system health monitoring and management (HMM) techniques can be used. However, to be effective, it is necessary to ensure that the operation of the underlying HMM system does not adversely impact the normal operation of the system being monitored. In particular, it must be ensured that the operation of the HMM system will not lead to resource contentions that may prevent the system being monitored from timely completion of critical tasks. This paper presents an adaptive HMM system model that defines the fault diagnosis quality metrics and supports diagnosis requirement specifications. Based on the model, the sensor activation decision problem (SADP) is defined along with a steepest descent based heuristic algorithm to make the HMM configuration decisions that best satisfy the diagnosis quality requirements. Evaluation results show that the technique reduces the overall system resource consumption without adversely impacting the diagnosis capability of the HMM.

1 Introduction

Recently, there has been a growing interest in cyber-physical systems. A cyber-physical system is a type of embedded system that integrates a large number of distributed software and physical components to achieve specific tasks. Examples of cyber-physical systems include sensor and actuator networks, space exploration systems, highway and urban traffic coordination systems, etc. Individual hardware/software component failures can potentially lead to catastrophic failures in such safety-critical systems. Hence, it is necessary to consider the overall hardware/software system reliability and availability to ensure the continuous and robust operation of these systems. High dependability can be achieved by designing a redundant system with lower failure rate [18] or increasing system repair and/or recovery rate when failures occur. In this paper, we design an adaptive health monitoring and management (HMM) [1] [2] [8] system to detect abnormal system behaviors and diagnose faulty hardware/software components in real-time to significantly reduce the repair and/or recovery time.