Traffic congestion and accidents are increasing exponentially worldwide. More vehicles are sold every year which leads to more traffic fatalities and congestion. There have been several efforts worldwide for mobile Cyber Physical Systems (CPS) to address a range of problems including traffic congestion, accidents, unnecessary time spent in traffic jams, and overall infotainment by using onboard communicating and computing technologies. However, when we use peer-to-peer network-based communication for mobile CPS, malicious users/vehicles could mislead the mobile CPS by not reporting their true periodic status data to their neighbors on the road. In this paper, we study a data validation and correction approach for resiliency in mobile CPS that uses a diverse set of data for reducing false information. Numerical results obtained from Monte Carlo simulation are used to evaluate the proposed approach. Results show that the proposed approach minimizes the false data in the mobile CPS to enhance the resiliency.
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On the machine learning for minimizing the negative influence in mobile cyber physical systems
Emerging cyber physical system (CPS) are expected to enhance the overall performance of the networked systems to provide reliable services and applications to their users. However, massive number of connectivities in CPS bring security vulnerabilities and the mobility adds more complexity for securing the mobile CPS. Any mobile CPS can be represented as a graph with connectivity as well as with interactions among a group of mobile CPS nodes that plays a major role as a medium for the propagation of wrong/right information, and influence its members in the mobile CPS. This problem has wide spread applications in viral information disseminating in mobile CPS, where a malicious mobile CPS node may wish to spread the rumor via the most influential individuals in mobile CPS. In this paper, we design, develop and evaluate a machine learning approach that is based on a set theoretic approach for optimizing the influence in mobile CPS. This problem has applications in civilian and military systems.
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- Award ID(s):
- 1828811
- NSF-PAR ID:
- 10094356
- Date Published:
- Journal Name:
- SPIE Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications
- Page Range / eLocation ID:
- 60
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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ABSTRACT: This paper explores the use of cyber-physical systems (CPS) for optimal design in wind engineering. The approach combines the accuracy of physical wind tunnel testing with the ability to efficiently explore a solution space using numerical optimization algorithms. The approach is fully automated, with experiments executed in a boundary layer wind tunnel (BLWT), sensor feedback monitored by a high-performance computer, and actuators used to bring about physical changes in the BLWT. Because the model is undergoing physical change as it approaches the optimal solution, this approach is given the name “loop-in-the-model” testing. The building selected for this study is a low-rise structure with a parapet wall of variable height. Parapet walls alter the location of the roof corner vortices, alleviating large suction loads on the windward facing roof corner and edges and setting up an interesting optimal design problem. In the BLWT, the model parapet height is adjusted using servo-motors to achieve a particular design. The model surface is instrumented with pressure taps to measure the envelope pressure loading. The taps are densely spaced on the roof to provide sufficient resolution to capture the change in roof corner vortex formation. Experiments are conducted using a boundary BLWT located at the University of Florida Natural Hazard Engineering Research Infrastructure (NHERI) Experimental Facility. The proposed CPS approach enables the optimal solution to be found quicker than brute force methods, in particular for complex structures with many design variables. The parapet wall provides a proof-of-concept study with a single design variable that has a non-monotonic influence on a structure’s wind load. This study focuses on envelope load effects, seeking the parapet height that minimizes roof and parapet wall suction loading. Implications are significant for more complex structures where the optimal solution may not be obvious and cannot be reasonably determined with traditional experimental or computational methods. KEYWORDS: Cyber-physical systems, optimization, boundary-layer wind tunnel, parapet wall, NHERImore » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1117/12.2519598
