Swarm Intelligence-Based Uplink Power Control in Cognitive Internet of Things (CIoT) for Underlay Environment
Babar Sultan,
Imran Shafi and
Jamil Ahmad
Additional contact information
Babar Sultan: Department of Electrical Engineering, Abasyn University, Islamabad, Pakistan
Imran Shafi: Department of Electrical Engineering, Abasyn University, Islamabad, Pakistan
Jamil Ahmad: Kohat University of Science and Technology, Pakistan
International Journal of Applied Metaheuristic Computing (IJAMC), 2021, vol. 12, issue 3, 180-194
Abstract:
Internet of things (IoT) aims to shift intelligence to things and tends to increase the spectrum utilization efficiency. However, in doing so, it might generate high interference to the primary users (PUs) due to massive data flow into the networks. Cognitive radio smartly addresses this challenge by enabling different spectrum sharing modes while guaranteeing the quality of service. Motivated by this fact, the incorporation of cognitive abilities in IoT has given birth to a new sub-domain in IoT, known as Cognitive IoT (CIoT). This paper considers a single cell scenario in which multiple CIoT users (CUs) coexist with a PU in an underlay environment, and their communication performance has been optimized while adhering to the transmit power and interference constraints. Furthermore, two swarm intelligence-based implementations of the proposed algorithm have been provided, one based on Artificial Bee Colony (ABC) and the other based on Particle Swarm Optimization (PSO), and their effectiveness to solve the constrained power allocation problem for CIoT networks has been proved through simulations.
Date: 2021
References: Add references at CitEc
Citations:
Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve. ... 018/IJAMC.2021070108 (application/pdf)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:igg:jamc00:v:12:y:2021:i:3:p:180-194
Access Statistics for this article
International Journal of Applied Metaheuristic Computing (IJAMC) is currently edited by Peng-Yeng Yin
More articles in International Journal of Applied Metaheuristic Computing (IJAMC) from IGI Global
Bibliographic data for series maintained by Journal Editor ().