Cognitive Location based Mobile Adhoc Networks Implementation with an Android Operating Systems

1
Rajaram
Rajaram
2
Dr. V. Sumathy
Dr. V. Sumathy
1 PCET

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GJCST Volume 14 Issue E4

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Cognitive Location based Mobile Adhoc Networks Implementation with an Android Operating Systems Banner
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Cognitive radio (CR) technology is envisaged to solve the problems in wireless networks resulting from the limited available spectrum and the inefficiency in the spectrum usage by exploiting the existing wireless spectrum opportunistically. CR networks, equipped with the intrinsic capacities of the cognitive radio, will provide an ultimate spectrumaware communication paradigm in wireless communications. Specifically, in cognitive radio ad hoc networks (CRAHNs), the distributed multihop architecture, the dynamic network topology, and the time and location varying spectrum availability are some of the key distinguishing factors. In this paper, intrinsic properties and current research challenges of the CRAHNs are presented. A particular emphasis is given to distributed coordination between CR users through the establishment of a common control channel. Lastly, a new commission called the park model is explained, where CRAHN users may independently determine their own performance based on pre-decided spectrum. The performance is comparable to MANET routing protocols In this system implementation through real time systems with Specialized ANDROID BASED OPERATING SYSTEMS.

10 Cites in Articles

References

  1. S Adibi,S Erfani (2006). A multipath routing survey for mobile ad hoc networks.
  2. Kemal Akkaya,Mohamed Younis (2005). A survey on routing protocols for wireless sensor networks.
  3. Ian Akyildiz,Won-Yeol Lee,Mehmet Vuran,Shantidev Mohanty (2006). NeXt generation/dynamic spectrum access/cognitive radio wireless networks: A survey.
  4. A Daoud,M Alanyali,D Starobinski (2007). Secondary pricing of spectrum in cellular CDMA networks.
  5. R Brodersen,A Wolisz,D Cabric,S Mishra,D Willkomm (2004). Corvus: a cognitive radio approach for usage of virtual unlicensed spectrum.
  6. D Cabric,S Mishra,R Brodersen (2004). Implementation issues in spectrum sensing for cognitive radios.
  7. D Cabric,A Tkachenko,R Brodersen (2006). Spectrum sensing measurements of pilot, energy, and collaborative detection.
  8. Berk Canberk,Ian Akyildiz,Sema Oktug (2008). Primary User Activity Modeling Using First-Difference Filter Clustering and Correlation in Cognitive Radio Networks.
  9. L Cao,H Zheng (2005). Distributed spectrum allocation via local bargaining.
  10. L Cao,H Zheng (2008). Distributed rule-regulated spectrum sharing.

Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

Rajaram. 2014. \u201cCognitive Location based Mobile Adhoc Networks Implementation with an Android Operating Systems\u201d. Global Journal of Computer Science and Technology - E: Network, Web & Security GJCST-E Volume 14 (GJCST Volume 14 Issue E4): .

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Issue Cover
GJCST Volume 14 Issue E4
Pg. 11- 15
Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

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v1.2

Issue date

July 26, 2014

Language

English

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Cognitive radio (CR) technology is envisaged to solve the problems in wireless networks resulting from the limited available spectrum and the inefficiency in the spectrum usage by exploiting the existing wireless spectrum opportunistically. CR networks, equipped with the intrinsic capacities of the cognitive radio, will provide an ultimate spectrumaware communication paradigm in wireless communications. Specifically, in cognitive radio ad hoc networks (CRAHNs), the distributed multihop architecture, the dynamic network topology, and the time and location varying spectrum availability are some of the key distinguishing factors. In this paper, intrinsic properties and current research challenges of the CRAHNs are presented. A particular emphasis is given to distributed coordination between CR users through the establishment of a common control channel. Lastly, a new commission called the park model is explained, where CRAHN users may independently determine their own performance based on pre-decided spectrum. The performance is comparable to MANET routing protocols In this system implementation through real time systems with Specialized ANDROID BASED OPERATING SYSTEMS.

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Cognitive Location based Mobile Adhoc Networks Implementation with an Android Operating Systems

Rajaram
Rajaram PCET
Dr. V. Sumathy
Dr. V. Sumathy

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