Fully integrated network of networks
| dc.contributor.author | LaMar, Suzanna, author | |
| dc.contributor.author | Jayasumana, Anura, advisor | |
| dc.contributor.author | Cale, Jim, committee member | |
| dc.contributor.author | Guo, Yanlin, committee member | |
| dc.contributor.author | Simske, Steve, committee member | |
| dc.date.accessioned | 2022-05-30T10:23:00Z | |
| dc.date.available | 2024-05-24T10:23:00Z | |
| dc.date.issued | 2022 | |
| dc.description.abstract | There are many different facets to developing a fully integrated network of networks system that can facilitate seamless information exchange between nodes within a complex network topology. As an example, individual link resiliency, enhanced waveform capabilities, spectral and spatial diversity are all critical features in providing communications that can enable connectivity and interoperability for a fully networked system extending into multiple domains (ground, surface, air, and space). Steps taken toward achieving such an architecture are introduced with emerging millimeter wave (mmW) and high-band antenna technologies that can be integrated with future tactical multifunction software defined radios (SDRs) to enable information distribution between vital networked participants, including 5th generation aircraft. Small, lightweight mmW and high-band antenna designs that will enable small unit tactical operations to persist under electronic warfare conditions will be discussed. These small units are typically fielded with multiple communications radios but are limited in function and do not enable rapid communication on the move, or high-capacity data transfers at the halt. Additionally, a revolutionary cognitive antenna (CA) is introduced where artificial intelligence (AI) techniques are proposed to aid in improving antenna functions, support self-healing attributes, and promote autonomous communication operations. A CA designed for future spacecraft (S/C) communications systems that is environmentally perceptive will be presented where it can sense and transmit radio frequency (RF) signals and cooperate with a cognitive radio (CR) to modify waveform and beam pattern characteristics for enhanced resiliency and communications. As an extrapolation to interoperability and information exchanges, data must be always secured. Common communications payload security architectures are presented as a basis for offering data protection to not only the system itself, but also to networks that are part of the larger enterprise solution. Similarly, machine learning methods are proposed to combat malicious cyber-attacks within an enterprise security space-based communications architecture to offer a more resilient, protective adaptive framework. Additionally, the machine learning algorithms seek to provide a viable solution for identifying, classifying, and detecting possible intrusions in a highly dynamic environment. Machine learning is also applied to networking strategies to predict congestion before it happens; thereby, preventing bottlenecks within the network. This is especially important for critical, high-value information. A CONgestion Aware Intent-based Routing (CONAIR) architecture that facilitates faster and more reliable data exchanges between end users is proposed. The CONAIR architecture leverages platform and mission information to derive quality of service (QoS) metrics that can be used to support network route optimizations by using a network controller (NC) with machine learning to predict future network behaviors. Finally, the CA, multifunction SDRs and NC subsystems are integrated into a robust architecture on unmanned aerial vehicles (UAVs) to form collaborative cognitive communications systems that are responsive to stressing operating conditions. Through collaborative behaviors and interactions, communications can be optimized. These discriminating technologies support the continued ambition for maturating military communications systems to benefit cooperative interactions and information exchanges between various users in multi-hop, complex networks. | |
| dc.format.medium | born digital | |
| dc.format.medium | doctoral dissertations | |
| dc.identifier | LaMar_colostate_0053A_17192.pdf | |
| dc.identifier.uri | https://hdl.handle.net/10217/235350 | |
| dc.language | English | |
| dc.language.iso | eng | |
| dc.publisher | Colorado State University. Libraries | |
| dc.relation.ispartof | 2020- | |
| dc.rights | Copyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright. | |
| dc.subject | cognition | |
| dc.subject | networking | |
| dc.subject | antennas | |
| dc.subject | software defined radios | |
| dc.subject | machine learning | |
| dc.title | Fully integrated network of networks | |
| dc.type | Text | |
| dcterms.embargo.expires | 2024-05-24 | |
| dcterms.embargo.terms | 2024-05-24 | |
| dcterms.rights.dpla | This Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s). | |
| thesis.degree.discipline | Systems Engineering | |
| thesis.degree.grantor | Colorado State University | |
| thesis.degree.level | Doctoral | |
| thesis.degree.name | Doctor of Philosophy (Ph.D.) |
Files
Original bundle
1 - 1 of 1
Loading...
- Name:
- LaMar_colostate_0053A_17192.pdf
- Size:
- 5.53 MB
- Format:
- Adobe Portable Document Format