Research on Situational Awareness Security Defense of Intrusion Link Based on Data Element Characteristic Network Transmission Signal

2021 ◽  
pp. 696-708
Author(s):  
Chun Wang Wu ◽  
Lin Xia Li ◽  
Juan Wang
Keyword(s):  
Situational Awareness ◽  
Data Element ◽  
Network Transmission ◽  
Element Characteristic ◽  
Characteristic Network

scholarly journals Immersive telepresence on the operation of unmanned vehicles

2021 ◽  
Vol 18 (1) ◽  
pp. 172988142097854
Author(s):  
Eduardo Jose Fabris ◽  
Vicenzo Abichequer Sangalli ◽  
Leonardo Pavanatto Soares ◽  
Márcio Sarroglia Pinho
Keyword(s):  
Situation Awareness ◽  
User Study ◽  
Situational Awareness ◽  
Mixed Reality ◽  
Sensory Information ◽  
Vehicle Control ◽  
Unmanned Vehicles ◽  
Ground Vehicles ◽  
Network Transmission ◽  
Near Future

Unmanned ground vehicles are usually deployed in situations, where it is too dangerous or not feasible to have an operator onboard. One challenge faced when such vehicles are teleoperated is maintaining a high situational awareness, due to aspects such as limitation of cameras, characteristics of network transmission, and the lack of other sensory information, such as sounds and vibrations. Situation awareness refers to the understanding of the information, events, and actions that will impact the execution and the objectives of the tasks at the current and near future of the operation of the vehicle. This work investigates how the simultaneous use of immersive telepresence and mixed reality could impact the situation awareness of the operator and the navigation performance. A user study was performed to compare our proposed approach with a traditional unmanned vehicle control station. Quantitative data obtained from the vehicle’s behavior and the situation awareness global assessment technique were used to analyze such impacts. Results provide evidence that our approach is relevant when the task requires a detailed observation of the surroundings, leading to higher situation awareness and navigation performance.

Maintaining Situational Awareness: The Role of Visual Attention

1999 ◽  
Author(s):  
Alex Chaparro ◽  
Loren Groff ◽  
Kamala Tabor ◽  
Kathy Sifrit ◽  
Leo J. Gugerty
Keyword(s):  
Visual Attention ◽  
Situational Awareness

Review on Fog Based Spectrum Sensing for Artificial Intelligence

2018 ◽  
pp. 66-70
Author(s):  
A. Rethina Palin ◽  
I. Jeena Jacob
Keyword(s):  
Bandwidth Allocation ◽  
Situational Awareness ◽  
Cluster Head ◽  
Mesh Network ◽  
Network Systems ◽  
Markov Logic ◽  
Wireless Mesh ◽  
Local Environments ◽  
Reactive Routing ◽  
The Hierarchical Structure

Wireless Mesh Network (MWN) could be divided into proactive routing, reactive routing and hybrid routing, which must satisfy the requirements related to scalability, reliability, flexibility, throughput, load balancing, congestion control and efficiency. DMN (Directional Mesh Network) become more adaptive to the local environments and robust to spectrum changes. The existing computing units in the mesh network systems are Fog nodes, the DMN architecture is more economic and efficient since it doesn’t require architecture- level changes from existing systems. The cluster head (CH) manages a group of nodes such that the network has the hierarchical structure for the channel access, routing and bandwidth allocation. The feature extraction and situational awareness is conducted, each Fog node sends the information regarding the current situation to the cluster head in the contextual format. A Markov logic network (MLN) based reasoning engine is utilized for the final routing table updating regarding the system uncertainty and complexity.

Situational Awareness Applied to Dynamic Security in Power Systems

2017 ◽  
Vol 12 (1) ◽  
pp. 73
Author(s):  
Sandra Camila Garzon ◽  
Mario Alberto Rios ◽  
Oscar Gomez
Keyword(s):  
Power Systems ◽  
Situational Awareness ◽  
Dynamic Security

Context-Driven Proactive Decision Support for Hybrid Teams

AI Magazine ◽  
2019 ◽  
Vol 40 (3) ◽  
pp. 41-57
Author(s):  
Manisha Mishra ◽  
Pujitha Mannaru ◽  
David Sidoti ◽  
Adam Bienkowski ◽  
Lingyi Zhang ◽  
...  
Keyword(s):  
Decision Support ◽  
Situational Awareness ◽  
Feature Space ◽  
Human Interaction ◽  
Superior Performance ◽  
Uncertain Environments ◽  
Cognitive States ◽  
Contextual Elements ◽  
Human Operators ◽  
Smart Machine

A synergy between AI and the Internet of Things (IoT) will significantly improve sense-making, situational awareness, proactivity, and collaboration. However, the key challenge is to identify the underlying context within which humans interact with smart machines. Knowledge of the context facilitates proactive allocation among members of a human–smart machine (agent) collective that balances auto­nomy with human interaction, without displacing humans from their supervisory role of ensuring that the system goals are achievable. In this article, we address four research questions as a means of advancing toward proactive autonomy: how to represent the interdependencies among the key elements of a hybrid team; how to rapidly identify and characterize critical contextual elements that require adaptation over time; how to allocate system tasks among machines and agents for superior performance; and how to enhance the performance of machine counterparts to provide intelligent and proactive courses of action while considering the cognitive states of human operators. The answers to these four questions help us to illustrate the integration of AI and IoT applied to the maritime domain, where we define context as an evolving multidimensional feature space for heterogeneous search, routing, and resource allocation in uncertain environments via proactive decision support systems.

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