A Survey on Testing for Cyber Physical System

Asadollah, Sara Abbaspour; Inam, Rafia; Hansson, Hans

doi:10.1007/978-3-319-25945-1_12

Cited by 45 publications

(20 citation statements)

References 20 publications

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“…In this section, we follow different levels of testing presented in [12] that is required to check and verify the safety system. It is a major challenge to test and check the validity of both physical and cyber elements of such a complex CPS.…”

Section: Methodsmentioning

confidence: 99%

Cyber-physical universal safety and crash detection system for autonomous robot

Pikner¹,

Malayjerdi²

2021

Robot. syst., appl.

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Multi-purpose mobile robots with a modular layout have become a hot research topic in recent years. The safe performance of these robot's operations is relying on the low-level cyber-physical system (CPS). In this paper, the scientific goals underscore the analysis of the computational (cyber) units for low-level real-time fault monitoring. The purpose of these units is to monitor control signals issued by other (cyber) units or sensors and if there is a safety-critical problem, then predefined actions can be triggered. The safety controller was built and tested on the TalTech iseAuto platform. Based on the results, a new multi-layer universal safety system was developed. In the first layer, all signals and messages are checked to be in a suitable range or order. The second layer detects if an accident is happening using crash sensors. The third layer includes remote control switches support. If a failure or malfunction occurs, the emergency action plan is executed to stop the vehicle safely.

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Section: Methodsmentioning

confidence: 99%

Cyber-physical universal safety and crash detection system for autonomous robot

Pikner¹,

Malayjerdi²

2021

Robot. syst., appl.

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“…The computational and physical components of such systems are tightly interconnected and coordinated to work effectively together, sometimes with humans in the loop" Despite their differences in length, detail and the semantics of some terms, there are some common characteristics that can be extracted from these definitions. More specifically, we argue that cyber-physical systems have the following inherent characteristics:  Integration of cyber elements (computation, software and networking), engineered elements (physical processes) [1][7] [8][9] [10] [11], and human factors [4]  Reciprocal feedback loops between physical processes and computations, (simulation and decision making), sensing and actuation elements, and monitoring and control elements [4][1] [8][9] [12]  It also encompasses a new generation of embedded control systems (i.e. networked embedded systems) consisting of networked CPS components and tightly coupled and interconnected cyber-physical processes that require of cooperation and coordination.…”

Section: Cyber Physical Systems: Definitions and Characteristicsmentioning

confidence: 99%

CPS data streams analytics based on machine learning for Cloud and Fog Computing: A survey

Fei

Shah

Verba

et al. 2019

Future Generation Computer Systems

101

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Cloud and Fog computing has emerged as a promising paradigm for the Internet of things (IoT) and cyber-physical systems (CPS). One characteristic of CPS is the reciprocal feedback loops between physical processes and cyber elements (computation, software and networking), which implies that data stream analytics is one of the core components of CPS. The reasons for this are: (i) it extracts the insights and the knowledge from the data streams generated by various sensors and other monitoring components embedded in the physical systems; (ii) it supports informed decision making; (iii) it enables feedback from the physical processes to the cyber counterparts; (iv) it eventually facilitates the integration of cyber and physical systems. There have been many successful applications of data streams analytics, powered by machine learning techniques, to CPS systems. Thus, it is necessary to have a survey on the particularities of the application of machine learning techniques to the CPS domain. In particular, we explore how machine learning methods should be deployed and integrated in cloud and fog architectures for better fulfilment of the requirements, e.g. mission criticality and time criticality, arising in CPS domains. To the best of our knowledge, this paper is the first to systematically study machine learning techniques for CPS data stream analytics from various perspectives, especially from a perspective that leads to the discussion and guidance of how the CPS machine learning methods should be deployed in a cloud and fog architecture.

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“…According to Asadollah et al [3], hardware testing consists in testing hardware components of CPS, including tests of each component's functionality, which descriptions are based on the system requirements. Amongst the most common and important variable in testing CPS hardware, Asadollah et al [3] lists memory size, speed, storage capacity, I/O interfaces (ports), synchronization capabilities, etc. They also point out that testing the hardware under specific conditions (e.g.…”

Section: Main Principles Of Hardware Testingmentioning

confidence: 99%