Green Resilience of Cyber-Physical Systems
- URL: http://arxiv.org/abs/2311.05201v1
- Date: Thu, 9 Nov 2023 08:29:55 GMT
- Title: Green Resilience of Cyber-Physical Systems
- Authors: Diaeddin Rimawi
- Abstract summary: Cyber-Physical System (CPS) represents systems that join both hardware and software components to perform real-time services.
The need for a recovery technique is highly needed to achieve resilience in the system.
This proposal suggests a game theory solution to achieve resilience and green in CPS.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: Cyber-Physical System (CPS) represents systems that join both hardware and
software components to perform real-time services. Maintaining the system's
reliability is critical to the continuous delivery of these services. However,
the CPS running environment is full of uncertainties and can easily lead to
performance degradation. As a result, the need for a recovery technique is
highly needed to achieve resilience in the system, with keeping in mind that
this technique should be as green as possible. This early doctorate proposal,
suggests a game theory solution to achieve resilience and green in CPS. Game
theory has been known for its fast performance in decision-making, helping the
system to choose what maximizes its payoffs. The proposed game model is
described over a real-life collaborative artificial intelligence system (CAIS),
that involves robots with humans to achieve a common goal. It shows how the
expected results of the system will achieve the resilience of CAIS with
minimized CO2 footprint.
Related papers
- Sustainable Diffusion-based Incentive Mechanism for Generative AI-driven Digital Twins in Industrial Cyber-Physical Systems [65.22300383287904]
Industrial Cyber-Physical Systems (ICPSs) are an integral component of modern manufacturing and industries.
By digitizing data throughout the product life cycle, Digital Twins (DTs) in ICPSs enable a shift from current industrial infrastructures to intelligent and adaptive infrastructures.
mechanisms that leverage sensing Industrial Internet of Things (IIoT) devices to share data for the construction of DTs are susceptible to adverse selection problems.
arXiv Detail & Related papers (2024-08-02T10:47:10Z) - Towards an Engineering Discipline for Resilient Cyber-Physical Systems [0.0]
The term resilience refers to the ability to cope with unexpected inputs while delivering correct service.
My thesis aims to pioneer an engineering discipline for resilient cyber-physical systems.
arXiv Detail & Related papers (2024-07-22T11:50:01Z) - Disentangling the Causes of Plasticity Loss in Neural Networks [55.23250269007988]
We show that loss of plasticity can be decomposed into multiple independent mechanisms.
We show that a combination of layer normalization and weight decay is highly effective at maintaining plasticity in a variety of synthetic nonstationary learning tasks.
arXiv Detail & Related papers (2024-02-29T00:02:33Z) - GResilience: Trading Off Between the Greenness and the Resilience of
Collaborative AI Systems [1.869472599236422]
We propose an approach to automatically evaluate CAIS recovery actions for their ability to trade-off between resilience and greenness.
Our approach aims to attack the problem from two perspectives: as a one-agent decision problem through optimization, and as a two-agent decision problem through game theory.
arXiv Detail & Related papers (2023-11-08T10:01:39Z) - Causal Semantic Communication for Digital Twins: A Generalizable
Imitation Learning Approach [74.25870052841226]
A digital twin (DT) leverages a virtual representation of the physical world, along with communication (e.g., 6G), computing, and artificial intelligence (AI) technologies to enable many connected intelligence services.
Wireless systems can exploit the paradigm of semantic communication (SC) for facilitating informed decision-making under strict communication constraints.
A novel framework called causal semantic communication (CSC) is proposed for DT-based wireless systems.
arXiv Detail & Related papers (2023-04-25T00:15:00Z) - Optimization for Infrastructure Cyber-Physical Systems [3.0646173923933446]
Cyber-physical systems (CPS) are systems where a decision making (cyber/control) component is tightly integrated with a physical system (with sensing/actuation) to enable real-time monitoring and control.
Some examples of infrastructure CPS include electrical power grids; water distribution networks; transportation and logistics networks; heating, and air conditioning ( ventilation) in buildings.
For control optimization, an infrastructure CPS is typically viewed as a system of semi-autonomous sub-systems with a network of sensors.
arXiv Detail & Related papers (2022-05-31T00:58:54Z) - Learning Physical Concepts in Cyber-Physical Systems: A Case Study [72.74318982275052]
We provide an overview of the current state of research regarding methods for learning physical concepts in time series data.
We also analyze the most important methods from the current state of the art using the example of a three-tank system.
arXiv Detail & Related papers (2021-11-28T14:24:52Z) - Beyond Robustness: A Taxonomy of Approaches towards Resilient
Multi-Robot Systems [41.71459547415086]
We analyze how resilience is achieved in networks of agents and multi-robot systems.
We argue that resilience must become a central engineering design consideration.
arXiv Detail & Related papers (2021-09-25T11:25:02Z) - Multi Agent System for Machine Learning Under Uncertainty in Cyber
Physical Manufacturing System [78.60415450507706]
Recent advancements in predictive machine learning has led to its application in various use cases in manufacturing.
Most research focused on maximising predictive accuracy without addressing the uncertainty associated with it.
In this paper, we determine the sources of uncertainty in machine learning and establish the success criteria of a machine learning system to function well under uncertainty.
arXiv Detail & Related papers (2021-07-28T10:28:05Z) - Constraints Satisfiability Driven Reinforcement Learning for Autonomous
Cyber Defense [7.321728608775741]
We present a new hybrid autonomous agent architecture that aims to optimize and verify defense policies of reinforcement learning (RL)
We use constraints verification (using satisfiability modulo theory (SMT)) to steer the RL decision-making toward safe and effective actions.
Our evaluation of the presented approach in a simulated CPS environment shows that the agent learns the optimal policy fast and defeats diversified attack strategies in 99% cases.
arXiv Detail & Related papers (2021-04-19T01:08:30Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.