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

Author(s):  
Y. Zhang ◽  
I.-L. Yen ◽  
F. B. Bastani ◽  
A.T. Tai ◽  
S. Chau
Author(s):  
Salman Shamshad ◽  
Khalid Mahmood ◽  
Shafiq Hussain ◽  
Sahil Garg Ashok Kumar Das ◽  
Neeraj Kumar Joel J. P. C. Rodrigues

Author(s):  
Krishna K. Venkatasubramanian ◽  
Sidharth Nabar ◽  
Sandeep K. S. Gupta ◽  
Radha Poovendran

With a rapidly aging population, the healthcare community will soon face severe medical personnel shortage and rising costs. Pervasive Health Monitoring Systems (PHMS) can help alleviate this situation. PHMS provides continuous real-time monitoring of a person’s health using a (usually wireless) network of medical and ambient sensors/devices on the host (patients), called Body Area Networks (BANs). The sensitive nature of health information collected by PHMS mandates that patient’s privacy be protected by securing the medical data from any unauthorized access. The authors’ approach for addressing these issues focuses on a key observation that PHMS are cyber-physical systems (CPS). Cyber-physical systems are networked, computational platforms, deeply embedded in specific physical processes for monitoring and actuation purposes. In this work, they therefore present a novel perspective on securing PHMS, called Cyber Physical Security (CYPSec) solutions. CYPSec solutions are environmentally-coupled security solutions, which operate by combining traditional security primitives along with environmental features. Its use results in not only secure operation of a system but also the emergence of additional “allied” properties which enhance its overall capabilities. The principal focus of this chapter is the development of a new security approach for PHMS called CYPsec that leverages their cyber-physical nature. The authors illustrate the design issues and principals of CYPSec through two specific examples of this generic approach: (a) Physiological Signal based key Agreement (PSKA) is designed to enable automated key agreement between sensors in the BAN based on physiological signals from the body; and (b) Criticality Aware Access Control (CAAC) which has the ability to provide controlled opening of the system for emergency management. Further, they also discuss aspects such as altered threat-model, increased complexity, non-determinism, and mixed critical systems, that must be addressed to make CYPSec a reality.


2020 ◽  
Vol 14 (1) ◽  
pp. 1457-1467
Author(s):  
Lantian Shangguan ◽  
Swaminathan Gopalswamy

2017 ◽  
Vol 1 (4) ◽  
pp. 1-26 ◽  
Author(s):  
Md Zakirul Alam Bhuiyan ◽  
Jie Wu ◽  
Guojun Wang ◽  
Jiannong Cao ◽  
Wenjun Jiang ◽  
...  

2020 ◽  
Vol 4 (3) ◽  
pp. 1-27
Author(s):  
Vuk Lesi ◽  
Ilija Jovanov ◽  
Miroslav Pajic

Author(s):  
Márton Búr ◽  
Gábor Szilágyi ◽  
András Vörös ◽  
Dániel Varró

Abstract Smart cyber-physical systems (CPSs) have complex interaction with their environment which is rarely known in advance, and they heavily depend on intelligent data processing carried out over a heterogeneous and distributed computation platform with resource-constrained devices to monitor, manage and control autonomous behavior. First, we propose a distributed runtime model to capture the operational state and the context information of a smart CPS using directed, typed and attributed graphs as high-level knowledge representation. The runtime model is distributed among the participating nodes, and it is consistently kept up to date in a continuously evolving environment by a time-triggered model management protocol. Our runtime models offer a (domain-specific) model query and manipulation interface over the reliable communication middleware of the Data Distribution Service (DDS) standard widely used in the CPS domain. Then, we propose to carry out distributed runtime monitoring by capturing critical properties of interest in the form of graph queries, and design a distributed graph query evaluation algorithm for evaluating such graph queries over the distributed runtime model. As the key innovation, our (1) distributed runtime model extends existing publish–subscribe middleware (like DDS) used in real-time CPS applications by enabling the dynamic creation and deletion of graph nodes (without compile time limits). Moreover, (2) our distributed query evaluation extends existing graph query techniques by enabling query evaluation in a real-time, resource-constrained environment while still providing scalable performance. Our approach is illustrated, and an initial scalability evaluation is carried out on the MoDeS3 CPS demonstrator and the open Train Benchmark for graph queries.


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