Enhanced cooperative behavior and fair spectrum allocation for intelligent IoT devices in cognitive radio networks

Khalifa, Ahmed H.; Shehata, Mohamed K.; Gasser, Safa M.; El-Mahallawy, Mohamed S.

doi:10.1016/j.phycom.2020.101190

Cited by 6 publications

(3 citation statements)

References 21 publications

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“…Ziyun Xin et al [22] proposed the binary adaptive cuckoo search algorithm for the optimized spectrum allocation in the CRN. The initialization was done by the reverse learning algorithm thus making the improved cuckoo search algorithm.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning based Spectrum Utilization in Cognitive Radio Networks using ASAR Algorithm

Kumaran,

Roobert

et al. 2023

Preprint

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In cognitive radio networks (CRN), primary users (PU) are the licensed users and secondary users (SU) are the unlicensed users. PUs can use the spectrum in the primary network (PN) which is licensed, but the SU can only use the spectrum that is not used by the PU. Also, the SU should not affect the transmission of PU. Thus, the spectrum allocation for SU is a major problem in CRN. Therefore, in this paper, cooperative spectrum sensing (CSS) is considered with adaptive search and rescue (ASAR) optimization and auto-encoder deep learning. For the effective sharing of spectrum, a novel classification method based on deep learning is proposed. Here, for the detection of PU, multiple SUs are considered. The implementation tool used for the proposed method is NS2. Assessment metrics are performed based on delay, throughput, network lifetime, etc. Also, the balance between the transmitted power and interference is maintained to achieve high throughput.

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Section: Related Workmentioning

confidence: 99%

Deep Learning based Spectrum Utilization in Cognitive Radio Networks using ASAR Algorithm

Kumaran,

Roobert

et al. 2023

Preprint

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“…A cognitive radio (CR) is a smart technology that can efficiently solve limited access issues in wireless networks. Its concept was first proposed by Joseph Mitola III in 1998 and several entities such as the IEEE, the International Telecommunications Unit (ITU) and the National Telecommunications and Information Administration (NTIA) have since established a general definition for the same [1][2][3].…”

Section: General Contextmentioning

confidence: 99%

Spectrum Decision-Making in Collaborative Cognitive Radio Networks

2020

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Spectral decision making is a function of the cognitive cycle. It aims to select spectral opportunities within a set of finite possibilities. Decision-making methodologies, based on collaborative information exchanges, are used to improve the selection process. For collaborative decision making to be efficient, decisions need to be analyzed based on the amount of information. Using little data can produce inefficient decisions, and taking a lot of data can result in high computational costs and delays. This document presents three contributions: the incorporation of a collaborative strategy for decision making, the implementation of real data, and the analysis of the amount of information through the number of failed handoffs. The collaborative model acts as a two-way information node, the information it coordinates corresponds to the Global System for Mobile Communications (GSM) band, and the amount of information to be shared is selected according to five levels of collaboration: 10%, 20%, 50%, 80%, and 100%, in which each percentage represents the total number of users that will be part of the process. The decision-making process is carried out by using two multi-criteria techniques: Feedback Fuzzy Analytical Hierarchical Process (FFAHP) and Simple Additive Weighting (SAW). The results are presented in two comparative analyses. The first performs the analysis using the number of failed handoffs. The second quantifies the level of collaboration for the number of failed handoffs. Based on the obtained percentage ratios, the information shared, and the average of increase rates, the level of collaboration that leads to efficient results is determined to be between 20% and 50% for the given number of failed handoffs.

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“…Industrial cyber-physical system (CPS) is an emergent technology that focuses on the integration of computational applications with physical devices [1][2][3]. Industrial CPSs facilitate the remote control of large-scale heterogeneous systems, big data analysis, and condition monitoring, which has a high impact on various industrial fields [2,[4][5][6][7][8]. Industrial CPSs contain many edge devices which collect a huge amount of data, which is very helpful for developing deep learningbased methods to solve difficult industrial tasks, such as fault diagnosis [9,10], intelligent control [11], degradation prediction [12], smart city [13], etc.…”

Section: Introductionmentioning

confidence: 99%

Accelerating privacy-preserving momentum federated learning for industrial cyber-physical systems

Zhang

Guan

2021

Complex Intell. Syst.

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Federated learning (FL) is a distributed learning approach, which allows the distributed computing nodes to collaboratively develop a global model while keeping their data locally. However, the issues of privacy-preserving and performance improvement hinder the applications of the FL in the industrial cyber-physical systems (ICPSs). In this work, we propose a privacy-preserving momentum FL approach, named PMFL, which uses the momentum term to accelerate the model convergence rate during the training process. Furthermore, a fully homomorphic encryption scheme CKKS is adopted to encrypt the gradient parameters of the industrial agents’ models for preserving their local privacy information. In particular, the cloud server calculates the global encrypted momentum term by utilizing the encrypted gradients based on the momentum gradient descent optimization algorithm (MGD). The performance of the proposed PMFL is evaluated on two common deep learning datasets, i.e., MNIST and Fashion-MNIST. Theoretical analysis and experiment results confirm that the proposed approach can improve the convergence rate while preserving the privacy information of the industrial agents.

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Enhanced cooperative behavior and fair spectrum allocation for intelligent IoT devices in cognitive radio networks

Cited by 6 publications

References 21 publications

Deep Learning based Spectrum Utilization in Cognitive Radio Networks using ASAR Algorithm

Deep Learning based Spectrum Utilization in Cognitive Radio Networks using ASAR Algorithm

Spectrum Decision-Making in Collaborative Cognitive Radio Networks

Accelerating privacy-preserving momentum federated learning for industrial cyber-physical systems

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