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Title: ICBCA – Improved Cluster Based Channel Allocation in Cognitive Radio Sensor Networks, Journal of Telecommunications and Information Technology, 2020, nr 3

Creator:

Agarkhed, Jayashree ; Gatate, Veeranna

Date:

2020, nr 3

Resource Type:

artykuł

Description:

Wireless sensor networks (WSNs) operate in an overcrowded electromagnetic environment, as the spectrum is shared by various wireless communication technologies. This gives rise to various challenges related to optimized and efficient spectrum utilization. Cognitive radio (CR) has emerged as a solution satisfying this requirement, as it is capable of adapting to the dynamic radio spectrum. Thanks to the deployment of cognitive radio in WSNs, the spectrum may be utilized in a more efficient manner. CR may identify the vacant channels dynamically, allowing the sensor nodes to effectively communicate with each other. In this paper a clustering algorithm known as improved cluster-based channel assignment (ICBCA) is implemented, forming clusters of CR sensor nodes and then selecting vacant channels for data transmission purposes. Simulation results show that ICBCA outperforms existing clustering algorithms in CR sensor networks

Publisher:

Instytut Łączności - Państwowy Instytut Badawczy

Format:

application/pdf

Resource Identifier:

ISSN 1509-4553, on-line: ISSN 1899-8852 ; oai:bc.itl.waw.pl:2146

Source:

Journal of Telecommunications and Information Technology

Language:

ang

Rights Management:

Biblioteka Naukowa Instytutu Łączności

Subject and keywords:

cognitive radio channel overlapping co-channel interference Channel interference

Object collections:

Last modified:

Oct 22, 2020

In our library since:

Oct 22, 2020

Number of object content hits:

40

All available object's versions:

https://ribes-54.man.poznan.pl/publication/2442

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Edition name Date
Agarkhed, Jayashree; Gatate, Veeranna, ICBCA – Improved Cluster Based Channel Allocation in Cognitive Radio Sensor Networks, Journal of Telecommunications and Information Technology, 2020, nr 3 Oct 22, 2020

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