scholarly journals Improving 802.11p for Delivery of Safety-Critical Navigation Information in Robot-to-Robot Communication Networks

2021 ◽  
Vol 59 (1) ◽  
pp. 16-21
Author(s):  
Jennifer Gielis ◽  
Amanda Prorok
2021 ◽  
pp. 41-57
Author(s):  
Gregory Falco ◽  
Eric Rosenbach

The question “How do I assess our cyber risk?” addresses how to identify and characterize cyber risk unique to an organization’s critical systems, networks, and data. The chapter begins with a case study about a cyberattack on Ukraine’s electric grid. It details risk assessment for three types of critical systems: mission-critical systems, business-critical systems, and safety-critical systems. It explains the three types of networks critical to many organizations: business and administrative networks, operational and service delivery networks, and communication networks. In outlining the “CIA triad,” it shows how cyber risk can be characterized as a confidentiality, integrity, or availability issue relating to digital assets. Further, it describes how to assess the importance of different digital assets and how to prioritize them using a business impact analysis (BIA). The chapter concludes with real-world Embedded Endurance strategy lessons Rosenbach gained in Saudi Arabia in the wake of one of the world’s most destructive cyberattacks.


Author(s):  
Lokukaluge P. Perera ◽  
Brage Mo

Various emission control regulations enforce vessels to collect performance and navigation data and evaluate ship energy efficiency by implementing onboard sensors and data acquisition (DAQ) systems. These DAQ systems are designed to collect, store and communicate large amounts of performance and navigation information through complex data handling processes. It is suggested that this information should eventually be transferred to shore based data analysis centers for further processing and storage. However, the associated data transfer costs introduce additional challenges in this process and enforce to investigate cost effective data handling approaches in shipping. That mainly relates to the amount of data that are transferring through various communication networks (i.e. satellites & wireless networks) between vessels and shore based data centers. Hence, this study proposes to use a deep learning approach (i.e. autoencoder system architecture) to compress ship performance and navigation information, which can be transferred through the respective communication networks as a reduced data set. The compressed data set can be expanded in the respective data center requiring further analysis. Therefore, a data set of ship performance and navigation information is analyzed (i.e. compression and expansion) through an autoencoder system architecture in this study. The compressed data set represents a subset of ship performance and navigation information can also be used to evaluate energy efficiency type applications in shipping. Furthermore, the respective input and output data sets of the autoencoder are also compared as statistical distributions to evaluate the network performance.


2011 ◽  
Vol 10 (4) ◽  
pp. 1431-1458 ◽  
Author(s):  
Paskorn Champrasert ◽  
Junichi Suzuki ◽  
Tetsuo Otani

Author(s):  
Zhu Han ◽  
Dusit Niyato ◽  
Walid Saad ◽  
Tamer Basar ◽  
Are Hjorungnes

Sign in / Sign up

Export Citation Format

Share Document