Wireless Cyber-Physical System Performance Evaluation Through a Graph Database Approach

Abstract Despite the huge efforts to deploy wireless communications technologies in smart manufacturing scenarios, some manufacturing sectors are still slow to massive adoption. This slowness of widespread adoption of wireless technologies in cyber-physical systems (CPSs) is partly due to not fully understanding the detailed impact of wireless deployment on the physical processes especially in the cases that require low latency and high reliability communications. In this article, we introduce an approach to integrate wireless network traffic data and physical processes data to evaluate the impact of wireless communications on the performance of a manufacturing factory work cell. The proposed approach is introduced through the discussion of an engineering use case. A testbed that emulates a robotic manufacturing factory work cell is constructed using two collaborative-grade robot arms, machine emulators, and wireless communication devices. All network traffic data are collected and physical process data, including the robots and machines states and various supervisory control commands, is also collected and synchronized with the network data. The data are then integrated where redundant data are removed and correlated activities are connected in a graph database. A data model is proposed, developed, and elaborated; the database is then populated with events from the testbed, and the resulting graph is presented. Query commands are then presented as a means to examine and analyze network performance and relationships within the components of the network. Moreover, we detail the way by which this approach is used to study the impact of wireless communications on the physical processes and illustrate the impact of various wireless network parameters on the performance of the emulated manufacturing work cell. This approach can be deployed as a building block for various descriptive and predictive wireless analysis tools for CPS.

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Connectivity as a Design Feature for Industry 4.0 Production Equipment: Application for the Development of an In-Line Metrology System

Applied Sciences ◽

10.3390/app11031312 ◽

2021 ◽

Vol 11 (3) ◽

pp. 1312

Author(s):

Ana Pamela Castro-Martin ◽

Horacio Ahuett-Garza ◽

Darío Guamán-Lozada ◽

Maria F. Márquez-Alderete ◽

Pedro D. Urbina Coronado ◽

...

Keyword(s):

Industry 4.0 ◽

Design Feature ◽

Production Equipment ◽

Smart Manufacturing ◽

Process Data ◽

Industrial Sectors ◽

Advanced Manufacturing Technologies ◽

Manufacturing Technologies ◽

Equipment Application ◽

The Impact

Industry 4.0 (I4.0) is built upon the capabilities of Internet of Things technologies that facilitate the recollection and processing of data. Originally conceived to improve the performance of manufacturing facilities, the field of application for I4.0 has expanded to reach most industrial sectors. To make the best use of the capabilities of I4.0, machine architectures and design paradigms have had to evolve. This is particularly important as the development of certain advanced manufacturing technologies has been passed from large companies to their subsidiaries and suppliers from around the world. This work discusses how design methodologies, such as those based on functional analysis, can incorporate new functions to enhance the architecture of machines. In particular, the article discusses how connectivity facilitates the development of smart manufacturing capabilities through the incorporation of I4.0 principles and resources that in turn improve the computing capacity available to machine controls and edge devices. These concepts are applied to the development of an in-line metrology station for automotive components. The impact on the design of the machine, particularly on the conception of the control, is analyzed. The resulting machine architecture allows for measurement of critical features of all parts as they are processed at the manufacturing floor, a critical operation in smart factories. Finally, this article discusses how the I4.0 infrastructure can be used to collect and process data to obtain useful information about the process.

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A Novel Insider Attack and Machine Learning Based Detection for the Internet of Things

ACM Transactions on Internet of Things ◽

10.1145/3466721 ◽

2021 ◽

Vol 2 (4) ◽

pp. 1-23

Author(s):

Morshed Chowdhury ◽

Biplob Ray ◽

Sujan Chowdhury ◽

Sutharshan Rajasegarar

Keyword(s):

Machine Learning ◽

Internet Of Things ◽

Network Traffic ◽

The Internet ◽

Traffic Data ◽

Lossy Networks ◽

Insider Attack ◽

Iot Devices ◽

The Impact ◽

The Internet Of Things

Due to the widespread functional benefits, such as supporting internet connectivity, having high visibility and enabling easy connectivity between sensors, the Internet of Things (IoT) has become popular and used in many applications, such as for smart city, smart health, smart home, and smart vehicle realizations. These IoT-based systems contribute to both daily life and business, including sensitive and emergency situations. In general, the devices or sensors used in the IoT have very limited computational power, storage capacity, and communication capabilities, but they help to collect a large amount of data as well as maintain communication with the other devices in the network. Since most of the IoT devices have no physical security, and often are open to everyone via radio communication and via the internet, they are highly vulnerable to existing and emerging novel security attacks. Further, the IoT devices are usually integrated with the corporate networks; in this case, the impact of attacks will be much more significant than operating in isolation. Due to the constraints of the IoT devices, and the nature of their operation, existing security mechanisms are less effective for countering the attacks that are specific to the IoT-based systems. This article presents a new insider attack, named loophole attack , that exploits the vulnerabilities present in a widely used IPv6 routing protocol in IoT-based systems, called RPL (Routing over Low Power and Lossy Networks). To protect the IoT system from this insider attack, a machine learning based security mechanism is presented. The proposed attack has been implemented using a Contiki IoT operating system that runs on the Cooja simulator, and the impacts of the attack are analyzed. Evaluation on the collected network traffic data demonstrates that the machine learning based approaches, along with the proposed features, help to accurately detect the insider attack from the network traffic data.

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The Impact of Hands-On Simulation Laboratories on Teaching of Wireless Communications

Journal of Information Technology Education Innovations in Practice ◽

10.28945/3674 ◽

2017 ◽

Vol 16 ◽

pp. 069-090

Author(s):

Te-Shun Chou ◽

Aaron Vanderbye

Keyword(s):

Wireless Communications ◽

Wireless Network ◽

Learning Experience ◽

Wireless Networking ◽

Successful Outcome ◽

Practical Skills ◽

Local Networks ◽

Hands On ◽

Networking Labs ◽

The Impact

Aim/Purpose: To prepare students with both theoretical knowledge and practical skills in the field of wireless communications. Background: Teaching wireless communications and networking is not an easy task because it involves broad subjects and abstract content. Methodology: A pedagogical method that combined lectures, labs, assignments, exams, and readings was applied in a course of wireless communications. Contribution: Five wireless networking labs, related to wireless local networks, wireless security, and wireless sensor networks, were developed for students to complete all of the required hands-on lab activities. Findings: Both development and implementation of the labs achieved a successful outcome and provided students with a very effective learning experience. Students expressed that they had a better understanding of different wireless network technologies after finishing the labs. Recommendations for Practitioners: Detailed instructional lab manuals should be developed so that students can carry out hands-on activities in a step-by-step fashion. Recommendation for Researchers: Hands-on lab exercises can not only help students understand the abstract technical terms in a meaningful way, but also provide them with hands-on learning experience in terms of wireless network configuration, implementation, and evaluation. Impact on Society: With the help of a wireless network simulator, students have successfully enhanced their practical skills and it would benefit them should they decide to pursue a career in wireless network design or implementation. Future Research: Continuous revision of the labs will be made according to the feedback from students. Based on the experience, more wireless networking labs and network issues could be studied in the future.

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On the impact of wireless network traffic location and access technology on mobile device energy consumption

37th Annual IEEE Conference on Local Computer Networks ◽

10.1109/lcn.2012.6423608 ◽

2012 ◽

Cited By ~ 14

Author(s):

Ramona Trestian ◽

Olga Ormond ◽

Gabriel-Miro Muntean

Keyword(s):

Energy Consumption ◽

Wireless Network ◽

Mobile Device ◽

Network Traffic ◽

Access Technology ◽

The Impact

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Ocean resurge-induced impact melt dynamics on the peak-ring of the Chicxulub impact structure, Mexico

International Journal of Earth Sciences ◽

10.1007/s00531-021-02008-w ◽

2021 ◽

Author(s):

Felix M. Schulte ◽

◽

Axel Wittmann ◽

Stefan Jung ◽

Joanna V. Morgan ◽

...

Keyword(s):

Impact Structure ◽

Physical Processes ◽

Hydrothermal Conditions ◽

Chemical Examination ◽

Impact Melt ◽

Target Rock ◽

Single Process ◽

Chicxulub Impact ◽

The Impact

AbstractCore from Hole M0077 from IODP/ICDP Expedition 364 provides unprecedented evidence for the physical processes in effect during the interaction of impact melt with rock-debris-laden seawater, following a large meteorite impact into waters of the Yucatán shelf. Evidence for this interaction is based on petrographic, microstructural and chemical examination of the 46.37-m-thick impact melt rock sequence, which overlies shocked granitoid target rock of the peak ring of the Chicxulub impact structure. The melt rock sequence consists of two visually distinct phases, one is black and the other is green in colour. The black phase is aphanitic and trachyandesitic in composition and similar to melt rock from other sites within the impact structure. The green phase consists chiefly of clay minerals and sparitic calcite, which likely formed from a solidified water–rock debris mixture under hydrothermal conditions. We suggest that the layering and internal structure of the melt rock sequence resulted from a single process, i.e., violent contact of initially superheated silicate impact melt with the ocean resurge-induced water–rock mixture overriding the impact melt. Differences in density, temperature, viscosity, and velocity of this mixture and impact melt triggered Kelvin–Helmholtz and Rayleigh–Taylor instabilities at their phase boundary. As a consequence, shearing at the boundary perturbed and, thus, mingled both immiscible phases, and was accompanied by phreatomagmatic processes. These processes led to the brecciation at the top of the impact melt rock sequence. Quenching of this breccia by the seawater prevented reworking of the solidified breccia layers upon subsequent deposition of suevite. Solid-state deformation, notably in the uppermost brecciated impact melt rock layers, attests to long-term gravitational settling of the peak ring.

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Measuring The “Impact” of People, Films, And TV using the IMDb Graph Database

SoutheastCon 2021 ◽

10.1109/southeastcon45413.2021.9401938 ◽

2021 ◽

Author(s):

Griffin Maxwell ◽

Michael Kronmueller ◽

Dar-jen Chang ◽

Ahmed Desoky

Keyword(s):

Graph Database ◽

The Impact

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Simulation on the Physical Process of Neural Electromagnetic Signal Generation Based on a Simple but Functional Bionic Na+ Channel

Chinese Physics B ◽

10.1088/1674-1056/ac3cac ◽

2021 ◽

Author(s):

Fan Wang ◽

Jingjing Xu ◽

Yanbin Ge ◽

Shengyong Xu ◽

Yanjun Fu ◽

...

Keyword(s):

Dynamic Equilibrium ◽

Electromagnetic Signal ◽

Na Channel ◽

Physical Processes ◽

Flux Transport ◽

Ionic Flux ◽

Concentration Difference ◽

Voltage Gated ◽

Electromagnetic Signals ◽

The Impact

Abstract The physical processes occurring at open Na+ channels in neural fibers are essential for understanding the nature of neural signals and the mechanism by which the signals are generated and transmitted along nerves. However, there is less generally accepted description of these physical processes. We studied changes in the transmembrane ionic flux and the resulting two types of electromagnetic signals by simulating the Na+ transport across a bionic nanochannel model simplified from voltage-gated Na+ channels. Results show that the Na+ flux can reach a steady state in approximately 10 ns owing to the dynamic equilibrium of Na+ ions concentration difference between the both sides of membrane. After characterizing the spectrum and transmission of these two electromagnetic signals, the low-frequency transmembrane electric field is regarded as the physical quantity transmitting in waveguide-like lipid dielectric layer and triggering the neighboring voltage-gated channels. Factors influencing the Na+ flux transport are also studied. The impact of the Na+ concentration gradient is found higher than that of the initial transmembrane potential on the Na+ transport rate, and introducing the surface-negative charge in the upper third channel could increase the transmembrane Na+ current. This work can be further studied by improving the simulation model; however, the current work helps to better understand the electrical functions of voltage-gated ion channels in neural systems.

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