Distributed Learning and Reasoning in Cognitive Networks: Methods and Design Decisions

Friend, Daniel H.; Thomas, Ryan W.; MacKenzie, Allen B.; DaSilva, Luiz A.

doi:10.1002/9780470515143.ch9

Cited by 8 publications

(4 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It enriches the cognitive engine's knowledge base in order to improve the efficacy of future reasoning [7]. Most of the learning mechanisms are closely related with reasoning methods [10]. For instance, Qlearning is associated with distributed constraint optimization reasoning, meta-heuristic learning for Bayesian networks and case-based learning for parallel meta-heuristic reasoning.…”

Section: Figure 2 -(I) Cognitive Radio Modes and (Ii) Monitoring Modementioning

confidence: 99%

Onboard cognitive radio architecture for space assets communication

Raghunandan¹,

Sayed²,

Bose³

et al. 2010

2010 IEEE Aerospace Conference

View full text Add to dashboard Cite

Modeling the space environment of a planet including three dimensional dynamic "imaging" of the magnetosphere warrants the use of constellations of many spacecrafts. Communication among the various space assets and exchange of mission data for more efficient storage and analysis call for a dynamic and reliable technology capable of managing ad-hoc requests subjected to system and environmental constraints. In this paper 12 , we propose a cognitive radio architecture that can be used as an extension to space based software defined radio. Simulink is used to demonstrate the behavioral model of a system that represents the executable specifications. In order to illustrate some of the key features of the architecture, a basic model has been created in Simulink. This modeling would enable future implementation of the architecture and support any debugging or restructuring.

show abstract

Section: Figure 2 -(I) Cognitive Radio Modes and (Ii) Monitoring Modementioning

confidence: 99%

Onboard cognitive radio architecture for space assets communication

Raghunandan¹,

Sayed²,

Bose³

et al. 2010

2010 IEEE Aerospace Conference

View full text Add to dashboard Cite

show abstract

“…There are different frameworks for learning and reasoning in the process cognition. Distributed reasoning or decision making in a distributed system have different element [10] and there are several method for distributed reasoning. Evolutionary or genetic algorithm can be used for distributed decision making.…”

Section: Cognition In Networkmentioning

confidence: 99%

“…Distributed learning is different from decision making in a distributed system. Distributed learning is either supervised, unsupervised or reward based [10]. Three methods are commonly used for distributed learning and these methods can be applied in cases of cognitive routing system.…”

Section: Cognition In Networkmentioning

confidence: 99%

Cognition in a Cognitive Routing System for Mobile Ad-Hoc Network through Leaning Automata and Neural Network

Afridi

2013

AMM

View full text Add to dashboard Cite

A cognitive routing system intelligently selects one protocol at a time for specific routing conditions and environment in MANET. Cognition or self-learning can be achieved in a cognitive routing system for mobile ad-hoc network (MANET) through a learning system like learning automata or neural networks. This article covers the application of learning automata and neural network to achieve cognition in MANET routing system. Mobile Ad-hoc networks are dynamic in nature and lack any fixed infrastructure, so the implementation of cognition enhances the performance of overall routing system in these networks. In learning automata the process of learning is different from reasoning or decision making. Learning automata require little knowledge to take decisions. Neural network can be improved by increasing the number of neurons and changing parameters. Self-training enhance neural network performance and it select suitable protocol for a given network environment. Cognition in MANET is either based upon learning automata as in some wireless sensor networks or specialized cognitive neural networks like Elman network. Learning automata do not follow predetermine rules and has the ability to learn and evolve. The interaction of learning automata with the MANET environment results in the evolution of cognition system.

show abstract

“…Fundamental aspects of this optimization include learning and reasoning. Some of the possible techniques for distributed learning and reasoning in a CN environment are examined in [79].…”

Section: Cognitive Networkingmentioning

confidence: 99%

Cognitive Radio and Networking Research at Virginia Tech

et al. 2009

View full text Add to dashboard Cite

A large research team with a wide range of expertiseVfrom ICs and reconfigurable computing to wireless networkingVworks to achieve the promise of cognitive radio. ABSTRACT | More than a dozen Wireless @ Virginia Tech faculty are working to address the broad research agenda of cognitive radio and cognitive networks. Our core research team spans the protocol stack from radio and reconfigurable hardware to communications theory to the networking layer.Our work includes new analysis methods and the development of new software architectures and applications, in addition to work on the core concepts and architectures underlying cognitive radios and cognitive networks. This paper describes these contributions and points towards critical future work that remains to fulfill the promise of cognitive radio. We briefly describe the history of work on cognitive radios and networks at Virginia Tech and then discuss our contributions to the core cognitive processing underlying these systems, focusing on our cognitive engine. We also describe developments that support the cognitive engine and advances in radio technology that provide the flexibility desired in a cognitive radio node. We consider securing and verifying cognitive systems and examine the challenges of expanding the cognitive paradigm up the protocol stack to optimize end-to-end network performance.Lastly, we consider the analysis of cognitive systems using game theory and the application of cognitive techniques to problems in dynamic spectrum sharing and control of multipleinput multiple-output radios.

show abstract

Distributed Learning and Reasoning in Cognitive Networks: Methods and Design Decisions

Cited by 8 publications

References 0 publications

Onboard cognitive radio architecture for space assets communication

Onboard cognitive radio architecture for space assets communication

Cognition in a Cognitive Routing System for Mobile Ad-Hoc Network through Leaning Automata and Neural Network

Cognitive Radio and Networking Research at Virginia Tech

Contact Info

Product

Resources

About