Development of secured data transmission using machine learning-based discrete-time partially observed Markov model and energy optimization in cognitive radio networks

Vimal, S.; Kalaivani, L.; Kaliappan, M.; Suresh, A.; Gao, Xiao‐Zhi; Varatharajan, R.

doi:10.1007/s00521-018-3788-3

Cited by 41 publications

(7 citation statements)

References 27 publications

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“…Therefore, this upcoming technology in the wireless networks’ domain is highly likely to incorporate a ground-breaking architecture that fulfils the demands of the next-generation networks. Consequently, Cognitive Radio Network (CRN) is one of the most significant contenders for 5G network [ 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

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Green communication for cognitive radio networks based on game and utility-pricing theories

et al. 2020

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The most crucial challenge in the functioning of the wireless networks is the efficient utilization of radio resources. A significant element of resource handling is power regulation. With increasing requirement of wireless data transmission services, it is essential to devise energy harvesting techniques for mobile devices. In this research, a new methodology has been proposed for distributed power regulation in cognitive radio, networks of CR are grounded on non-cooperation game phenomenon and pricing technique. QoS (Quality of service) of the user of CR is anticipated as a beneficial activity through pricing as well as dissemination of energy generating as an unbeneficial game wherein the consumers increase their overall efficacy. The price is defined as an actual function of transmission power to upraise the pricing of the most distant consumers. The proposed mathematical model shows that the proposed game model has a Nash equilibrium and is also unique. Furthermore, in order to make the proposed algorithm valid for green communication within the wireless network, the best response technique was proposed. Finally, simulation results showed that the proposed energy harvesting technique, grounded on a unique function of the utilization, reduces the consumption of transmission power and greatly improves the convergence speed; which are suitable for the vision of the 5G networks.

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Section: Introductionmentioning

confidence: 99%

“…Furthermore, with recent developments in wireless communication, energy harvesting is also used in CRNs as a "greenery" solution [22]. Hence, the selected performance metric in this work is derived from the energyefficiency definition as will be explained in Eq (6) and that is matched with the above definition of green CR.…”

Section: Introductionmentioning

confidence: 99%

Green communication for cognitive radio networks based on game and utility-pricing theories

et al. 2020

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“…However, it has also raised many concerns that cyber-criminals are taking advantage of Vimal et al 24 For example, attackers can hack these IoT devices and use them for malicious purposes. [25][26][27] RPL is a conventional routing protocol for IoT. 28 It is also vulnerable to many internal attacks, such as blackhole and sinkhole.…”

Section: Problem Statementmentioning

confidence: 99%

CTrust‐RPL: A control layer‐based trust mechanism for supporting secure routing in routing protocol for low power and lossy networks‐based Internet of Things applications

Hassan

Asim

Baker

et al. 2021

Trans Emerging Tel Tech

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Internet of Things (IoT) fostered a new epoch of innovation by interconnecting digital devices to make human life more convenient and attractive. These smart objects are largely deployed as low power and lossy networks (LLNs) and use routing protocol for LLNs (RPL) for routing. Unfortunately, it is extremely vulnerable to a large variety of external and internal attacks to cause devastating and calamitous effects. However, this article's scope revolves around internal attacks only, where nodes are already part of a legitimate network. Various trust-based mechanisms have been proposed to secure the RPL protocol from insider attackers. Existing trust mechanisms cause high energy depletion due to complex computation on the node level, which consequently decreases the performance of LLNs. Therefore, this article presents a novel hierarchical trust-based mechanism "CTrust-RPL" by assessing the trust of nodes based on their forwarding behaviors. This study ships complex trust-related computations to the higher layer, known as the controller, to save computational, storage, and energy resources at the node level. We also compare the proposed mechanism with a state-of-the-art technique called Sec-trust. Our mechanism demonstrates superior performance in detecting and isolating blackhole attacks. The results depict that CTrust-RPL detects and isolates 10% more malicious nodes than Sec-trust in the same time-lapse. The average packet loss ratio difference is less for our proposed mechanism, with 35% more energy efficiency. 1 INTRODUCTION Internet of Things (IoT) has become an essential part of our personal lives. It is emerging as an epoch of innovation, where devices belonging to digital and machine ecosystems are interconnected over the Internet to yield efficacy and convenience in academia, industries, and human lives. 1-3 Technologies like 5G and 6G enable the next generation of wireless communication systems in compliance with sophisticated techniques for security. 4,5 They support the huge IoT infrastructures, which can be defined as a link, management, and communication of a large number of smart and sensing things (also known as objects). These things are capable of interacting with each other, especially, for transferring information in a network. 6-8 They are largely deployed as low power and lossy networks (LLN). The LLN is a class of networks where Trans Emerging Tel Tech.

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“…The CR networks focus mainly on the ability to detect the spectrum hole. The low SNR ratio with AND-model, OR-model, counting model, double threshold model has been established in analyzing the sensing techniques [46,41].…”

Section: Information Technology and Controlmentioning

confidence: 99%

EECCRN: Energy Enhancement with CSS Approach Using Q-Learning and Coalition Game Modelling in CRN

Vimal¹,

Kalaivani²,

Kadry

et al. 2021

ITC

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The Cognitive radio network (CR) is a widespread technology in which the Secondary users are assumed to be of the winning users to acquire the spectrum by reducing the false alarm possibilities and the false detection of the user assumed to be original user in nature is restricted with the usage of Spectrum monitoring agents. The collaborative spectrum sensing (CSS) is an approach that will identify the false intruder in the CR networks, here it is proposed with the Enhanced Q-Learning model with Coalition Game approach (EQLCG) to outline the energy enhancement. Besides an approach on Greedy Bidding is used to allocate the spectrum to the winning secondary user (SU) based on the idle primary user to strengthen the spectrum sensing. The winning secondary user forms a communication establishment with the neighbouring SU to eradicate the miss detection probability based on group level cooperation. The simulation experiment analyses the cluster level security with energy monitoring that has been performed using the analysis of interference by applying the coalition game theory modelling and the information obscured by the attacker is reduced with the usage of enhanced Q-learning, and the results prove that overhead is substantially monitored. The proposed paper enhances the security in physical layer with energy conservation and maintains the spectrum usage for application purpose. The proposed simulation approach reduces the miss detection and false alarm probabilistic approach while compared with Stackelberg and Bayesian game models.

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Development of secured data transmission using machine learning-based discrete-time partially observed Markov model and energy optimization in cognitive radio networks

Cited by 41 publications

References 27 publications

Green communication for cognitive radio networks based on game and utility-pricing theories

Green communication for cognitive radio networks based on game and utility-pricing theories

CTrust‐RPL: A control layer‐based trust mechanism for supporting secure routing in routing protocol for low power and lossy networks‐based Internet of Things applications

EECCRN: Energy Enhancement with CSS Approach Using Q-Learning and Coalition Game Modelling in CRN

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