Localization and Context Determination for Cyber-Physical Systems Based on 3D Imaging

2018 ◽  
pp. 1-26 ◽  
Author(s):  
Hannes Plank ◽  
Josef Steinbaeck ◽  
Norbert Druml ◽  
Christian Steger ◽  
Gerald Holweg
Keyword(s):  
High Performance ◽  
3D Imaging ◽  
Rapid Development ◽  
Cyber Physical Systems ◽  
Consumer Electronics ◽  
Context Aware ◽  
Physical Systems ◽  
Depth Sensors ◽  
Location Aware ◽  
Novel Concept

In recent years, consumer electronics became increasingly location and context-aware. Novel applications, such as augmented and virtual reality have high demands in precision, latency and update rate in their tracking solutions. 3D imaging systems have seen a rapid development in the past years. By enabling a manifold of systems to become location and context-aware, 3D imaging has the potential to become a part of everyone's daily life. In this chapter, we discuss 3D imaging technologies and their applications in localization, tracking and 3D context determination. Current technologies and key concepts are depicted and open issues are investigated. The novel concept of location-aware optical communication based on Time-of-Flight depth sensors is introduced. This communication method might close the gap between high performance tracking and localization. The chapter finally provides an outlook on future concepts and work-in progress technologies, which might introduce a new set of paradigms for location-aware cyber-physical systems in the Internet of Things.

scholarly journals Cyber–physical systems and context-aware sensing and computing

2017 ◽  
Vol 117 ◽  
pp. 1-4 ◽  
Author(s):  
Reza Malekian ◽  
Kui Wu ◽  
Gianluca Reali ◽  
Ning Ye ◽  
Kevin Curran
Keyword(s):  
Cyber Physical Systems ◽  
Context Aware ◽  
Physical Systems

scholarly journals Algorithm Design for Resilient Cyber-Physical Systems using an Automated Attack Generative Model

2021 ◽  
Author(s):  
Yu Zheng ◽  
Ali Sayghe ◽  
Olugbenga Anubi
Keyword(s):  
High Performance ◽  
Water Distribution ◽  
Algorithm Design ◽  
Cyber Physical Systems ◽  
Generative Model ◽  
Training Data ◽  
Pruning Algorithm ◽  
Physical Systems ◽  
Injection Attacks ◽  
Localization Algorithms

<div>This paper presents a suite of algorithms for detecting and localizing attacks in cyber-physical systems, and performing improved resilient state estimation through a pruning algorithm. High performance rates for the underlying detection and localization algorithms are achieved by generating training data that cover large region of the attack space. An unsupervised generative model trained by physics-based discriminators is designed to generate successful false data injection attacks. Then the generated adversarial examples are used to train a multi-class deep neural network which detects and localizes the attacks on measurements. Next, a pruning algorithm is included to improve the precision of localization result and provide performance guarantees for the resulting resilient observer. The performance of the proposed method is validated using the numerical simulation of a water distribution cyber-physical system.</div>

scholarly journals Algorithm Design for Resilient Cyber-Physical Systems using an Automated Attack Generative Model

2021 ◽  
Author(s):  
Yu Zheng ◽  
Ali Sayghe ◽  
Olugbenga Anubi
Keyword(s):  
High Performance ◽  
Water Distribution ◽  
Algorithm Design ◽  
Cyber Physical Systems ◽  
Generative Model ◽  
Training Data ◽  
Pruning Algorithm ◽  
Physical Systems ◽  
Injection Attacks ◽  
Localization Algorithms

<div>This paper presents a suite of algorithms for detecting and localizing attacks in cyber-physical systems, and performing improved resilient state estimation through a pruning algorithm. High performance rates for the underlying detection and localization algorithms are achieved by generating training data that cover large region of the attack space. An unsupervised generative model trained by physics-based discriminators is designed to generate successful false data injection attacks. Then the generated adversarial examples are used to train a multi-class deep neural network which detects and localizes the attacks on measurements. Next, a pruning algorithm is included to improve the precision of localization result and provide performance guarantees for the resulting resilient observer. The performance of the proposed method is validated using the numerical simulation of a water distribution cyber-physical system.</div>

FPGA on Cyber-Physical Systems for the Implementation of Internet of Things

2020 ◽  
pp. 82-96
Author(s):  
Rajit Nair ◽  
Preeti Nair ◽  
Vidya Kant Dwivedi
Keyword(s):  
High Performance ◽  
Management Strategies ◽  
Cyber Physical Systems ◽  
Edge Computing ◽  
Reconfigurable Systems ◽  
Smart Power ◽  
Physical Systems ◽  
Computing Performance ◽  
High Level ◽  
Processing Architecture

Today, in cyber-physical systems, there is a transformation in which processing has been done on distributed mode rather than performing on centralized manner. Usually this type of approach is known as Edge computing, which demands hardware time to time when requirements in computing performance get increased. Considering this situation, we must remain energy efficient and adaptable. So, to meet the above requirements, SRAM-based FPGAs and their inherent run-time reconfigurability are integrated with smart power management strategies. Sometimes this approach fails in the case of user accessibility and easy development. This chapter presents an integrated framework to develop FPGA-based high-performance embedded systems for Edge computing in cyber-physical systems. The processing architecture will be based on hardware that helps us to manage reconfigurable systems from high level systems without any human intervention.

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