Architecture and Performance of an Island Genetic Algorithm-Based Cognitive Network

Friend, Daniel H.; EINainay, M.Y.; Shi, Yongsheng; MacKenzie, Allen B.

doi:10.1109/ccnc08.2007.228

Cited by 26 publications

(15 citation statements)

References 17 publications

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“…A channel allocation architecture depending on the island genetic algorithm is presented in [43]. While using the island genetic algorithm for distributed optimization, a fuzzy logic controller is being considered to reduce the uncertainty and noise at the input.…”

Section: Proposed Architecturesmentioning

confidence: 99%

Collaborative and Cognitive Network Platforms: Vision and Research Challenges

Onur

Durmus

Hawas

et al. 2011

Wireless Pers Commun

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Document VersionPublisher's PDF, also known as Version of Record (includes final page, issue and volume numbers) Please check the document version of this publication:• A submitted manuscript is the author's version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website.• The final author version and the galley proof are versions of the publication after peer review.• The final published version features the final layout of the paper including the volume, issue and page numbers. General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. Link to publication• Users may download and print one copy of any publication from the public portal for the purpose of private study or research.• You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal ? Take down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. Abstract In this paper, we present a visionary concept referred to as Collaborative and Cognitive Network Platforms (CCNPs) as a future-proof solution for creating a dependable, self-organizing and self-managing communication substrate for effective ICT solutions to societal problems. CCNP creates a cooperative communication platform to support critical services across a range of business sectors. CCNP is based on the personal network (PN) technology which is an inherently cooperative environment prototyped in the Dutch Freeband PNP2008 and the European Union IST MAGNET projects. In CCNP, the cognitive control plane strives to exploit the resources to better satisfy the requirements of networked applications. CCNP facilitates collaboration inherently. Through cognition in the cognitive control plane, CCNP becomes a self-managed substrate. The self-managed substrate, in this paper, is defined as cognitive and collaborative middleware on which future applications run without user intervention. Endemic sensor networks may be incorporated into the CCNP concept to feed its cognitive control plane. In this paper, we present the CCNP concept and discuss the research challenges related to collaboration and cognition.

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Section: Proposed Architecturesmentioning

confidence: 99%

Collaborative and Cognitive Network Platforms: Vision and Research Challenges

Onur

Durmus

Hawas

et al. 2011

Wireless Pers Commun

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“…In the context of dynamic spectrum access and cognitive radio networks, channel assignment has been considered for improving spectrum utilization, e.g. [7], [8]. Distributed channel selection has been examined using game theory, e.g.…”

Section: Introductionmentioning

confidence: 99%

“…Yet, our formulation and framework is significantly different, e.g., from [3], [7]. The objective function of our optimization construct differs from that of [2], [4], [5].…”

Section: Introductionmentioning

confidence: 99%

Analyzing Selfish Topology Control in Multi-Radio Multi-Channel Multi-Hop Wireless Networks

Komali

MacKenzie

2009

2009 IEEE International Conference on Communications

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Abstract-Typically, topology control is perceived as a per-node transmit power control process that achieves certain networklevel objectives. We take an alternative approach of controlling the topology of a network purely by assigning channels to multiple radio interfaces on nodes. Specifically, we exploit the synergy between topology control and channel allocation to reduce the overall interference in multi-radio multi-channel wireless ad hoc networks.We formulate channel assignment as a non-cooperative game, with nodes selecting low interference channels while maintaining some degree of network connectivity. This game is shown to be a potential game, which ensures the existence of, and convergence to, a Nash Equilibrium (NE). Next, we evaluate the performance of NE topologies with respect to interference and connectivity objectives. By quantifying the impact of channel availability on interference performance, we illuminate the tradeoff between interference reduction that can be achieved by distributing interference over multiple channels and the cost of having additional channels. Finally, we study the spectral occupancy of steady state topologies, and show that despite the non-cooperative behavior, the NE topologies achieve load balancing.

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“…The FINS Framework can also be used for the implementation of a cognitive protocol stack, where a cognitive engine uses the meters and knobs feature to learn from the current and previous conditions of the network node as well as the local network neighborhood [33]. The engine learns, then makes decisions to adapt and sends control instructions to Figure 4 illustrates how the FINS Framework can be used to implement a cognitive engine running on an SDR platform.…”

Section: Use Cases and Candidate Experimentsmentioning

confidence: 99%

On software tools and stack architectures for wireless network experiments

Abdallah

MacKenzie

DaSilva

et al. 2011

2011 IEEE Wireless Communications and Networking Conference

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Abstract-Simulation is still the most widely adopted performance evaluation technique in mobile ad hoc network research, in spite of a growing number of questions about the fidelity of this technique. Implementation-based testing and evaluation of wireless networks tends to produce believable results, but the technique sometimes suffers from poor repeatability, high implementation cost, and complex experimental logistics. The topic of this paper is software tools to enable implementationbased experimental research on wireless networks. We review some of the existing tools and propose the Flexible Internetwork Stack (FINS) framework, our open-source solution for network protocol implementation, integration, and testing. FINS aims to provide researchers with monitoring, logging, and reconfiguring utilities similar to the ones provided by simulation environments or emulation testbeds.

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Architecture and Performance of an Island Genetic Algorithm-Based Cognitive Network

Cited by 26 publications

References 17 publications

Collaborative and Cognitive Network Platforms: Vision and Research Challenges

Collaborative and Cognitive Network Platforms: Vision and Research Challenges

Analyzing Selfish Topology Control in Multi-Radio Multi-Channel Multi-Hop Wireless Networks

On software tools and stack architectures for wireless network experiments

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