A Biologically Inspired QoS-Aware Architecture for Scalable, Adaptive, and Survivable Network Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . PASKORN CHAMPRASERT AND JUNICHI SUZUKI

2016 ◽  
pp. 499-538
Keyword(s):  
Network Systems ◽  
Biologically Inspired ◽  
Survivable Network

Survivable Network Systems: An Emerging Discipline

1997 ◽  
Author(s):  
R. J. Ellison ◽  
D. A. Fisher ◽  
R. C. Linger ◽  
H. F. Lipson ◽  
T. Longstaff
Keyword(s):  
Network Systems ◽  
Survivable Network

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.

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