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Cognitive Radio Networks for Internet of Things and Wireless Sensor Networks

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: closed (1 September 2020) | Viewed by 21773

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Special Issue Editors

Dr. Heejung Yu

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Guest Editor

Department of Electronics and Information Engineering, Korea University, Sejong 30019, Korea
Interests: statistical signal processing; communication theory; cognitive radio; physical layer security; 5G systems
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Yousaf Bin Zikria

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Guest Editor

Department of Information and Communication Engineering, Yeungnam University, Gyeongsan, Korea
Interests: IoT, 5G, wireless communications and networks, opportunistic communications, wireless sensor networks, routing protocols, cognitive radio ad hoc networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent advancements, developments, and deployments of IoT are transforming people’s everyday life. In the near future, 5G networks will be widely deployed around the world, and IoT technologies and applications will be developed extensively. Next-generation wireless sensors networks (WSN) are also creating a path to achieve the goal of next-generation IoT. Challenges for the next-generation IoT networks remain to decrease the overall network delay and increase the throughput without compromising the reliability. The co-existence of networks operating in a limited frequency band is one of the viable options. However, data bandwidth support and spectrum availability are the major challenges. Therefore, cognitive radio networks (CRN) are the best available technology to cater to all these challenges for the co-existence of IoT, next-generation wireless networks, WSN, 5G, and beyond 5G networks.

This Special Issue invites research papers to share the latest research insights and present emerging results on CRN for IoT networks and WSNs. Topics of interest for this Special Issue include but are not limited to the following.

CRN for Internet of Multimedia Things (IoMT)
CRN for Internet of Health Things (IoHT)
CRN for Internet of Nano Things (IoNT)
CRN for Internet of Vehicular Things (IoVT)
CRN for Smart Home
CRN for Smart City
CRN for Smart Grid
CRN for Smart Factory
CRN for Tactile Internet
CRN for WSN
Machine Learning based CRN solutions for IoT and WSN

Prof. Heejung Yu
Prof. Yousaf Bin Zikria
Guest Editors

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

CRN
IoT
WSN
spectrum sensing
spectrum sharing
machine learning

Published Papers (7 papers)

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18 pages, 834 KiB

Open AccessArticle

A Joint Power, Delay and Rate Optimization Model for Secondary Users in Cognitive Radio Sensor Networks

by Bernard Ssajjabbi Muwonge, Tingrui Pei, Julianne Sansa Otim and Fred Mayambala

Sensors 2020, 20(17), 4907; https://doi.org/10.3390/s20174907 - 31 Aug 2020

Cited by 9 | Viewed by 2131

Abstract

To maximize the limited spectrum among primary users and cognitive Internet of Things (IoT) users as we save the limited power and energy resources available, there is a need to optimize network resources. Whereas it is quite complex to study the impact of transmission rate, transmission power or transmission delay alone, the complexity is aggravated by the simultaneous consideration of all these three variables jointly in addition to a channel selection variable, since it creates a non-convex problem. Our objective is to jointly optimize the three major variables; transmission power, rate and delay under constraints of Bit Error Rate (BER), interference and other channel limitations. We analyze how total power, rate and delay vary with packet size, network size, BER and interference. The resulting problem is solved using a branch-and-cut polyhedral approach. For simulation of results, we use MATLAB together with the state-of-the-art BARON software. It is observed that an increase in packet size generally leads to an increase in total rate, total power and total transmission delay. It is also observed that increasing the number of secondary users on the channel generally leads to an increased power, delay and rate. Full article

(This article belongs to the Special Issue Cognitive Radio Networks for Internet of Things and Wireless Sensor Networks)

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19 pages, 15440 KiB

Open AccessArticle

Residual Energy Analysis in Cognitive Radios with Energy Harvesting UAV under Reliability and Secrecy Constraints

by Waqas Khalid, Heejung Yu and Song Noh

Sensors 2020, 20(10), 2998; https://doi.org/10.3390/s20102998 - 25 May 2020

Cited by 14 | Viewed by 3241

Abstract

The integration of unmanned aerial vehicles (UAVs) with a cognitive radio (CR) technology can improve the spectrum utilization. However, UAV network services demand reliable and secure communications, along with energy efficiency to prolong battery life. We consider an energy harvesting UAV (e.g., surveillance drone) flying periodically in a circular track around a ground-mounted primary transmitter. The UAV, with limited-energy budget, harvests radio frequency energy and uses the primary spectrum band opportunistically. To obtain intuitive insight into the performance of energy-harvesting, and reliable and secure communications, the closed-form expressions of the residual energy, connection outage probability, and secrecy outage probability, respectively, are analytically derived. We construct the optimization problems of residual energy with reliable and secure communications, under scenarios without and with an eavesdropper, respectively, and the analytical solutions are obtained with the approximation of perfect sensing. The numerical simulations verify the analytical results and identify the requirements of length of sensing phase and transmit power for the maximum residual energy in both reliable and secure communication scenarios. Additionally, it is shown that the residual energy in secure communication is lower than that in reliable communication. Full article

(This article belongs to the Special Issue Cognitive Radio Networks for Internet of Things and Wireless Sensor Networks)

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11 pages, 961 KiB

Open AccessArticle

A Cooperative Phase-Steering Technique with On-Off Power Control for Spectrum Sharing-Based Wireless Sensor Networks

by Sangku Lee, Janghyuk Yoon and Bang Chul Jung

Sensors 2020, 20(7), 1942; https://doi.org/10.3390/s20071942 - 30 Mar 2020

Cited by 6 | Viewed by 2008

Abstract

With the growth of the number of Internet of Things (IoT) devices, a wide range of wireless sensor networks (WSNs) will be deployed for various applications. In general, WSNs are constrained by limitations in spectrum and energy resources. In order to circumvent these technical challenges, we propose a novel cooperative phase-steering (CPS) technique with a simple on-off power control for generic spectrum sharing-based WSNs, which consists of a single secondary source (SS) node, multiple secondary relay (SR) nodes, a single secondary destination (SD) node, and multiple primary destination (PD) nodes. In the proposed technique, each SR node that succeeds in packet decoding from the SS and for which its interference power to the PD nodes is lower than a certain threshold is allowed to transmit the signal to the SD node. All SR nodes that are allowed to transmit signals to the SD node adjust the phase of their transmit signal such that the phase of received signals at the SD node from the SR nodes is aligned to a certain angle. Moreover, we mathematically analyze the outage probability of the proposed scheme. Our analytical and simulation results show that the proposed technique outperforms the conventional cooperative relaying schemes in terms of outage probability. Through extensive computer simulations, it is shown that the analytical results match well with the simulated outage probability as a lower bound. Full article

(This article belongs to the Special Issue Cognitive Radio Networks for Internet of Things and Wireless Sensor Networks)

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14 pages, 1978 KiB

Open AccessArticle

Resource Allocation in Cognitive Radio Wireless Sensor Networks with Energy Harvesting

by Haitao Xu, Hongjie Gao, Chengcheng Zhou, Ruifeng Duan and Xianwei Zhou

Sensors 2019, 19(23), 5115; https://doi.org/10.3390/s19235115 - 22 Nov 2019

Cited by 25 | Viewed by 3230

Abstract

The progress of science and technology and the expansion of the Internet of Things make the information transmission between communication infrastructure and wireless sensors become more and more convenient. For the power-limited wireless sensors, the life time can be extended through the energy-harvesting technique. Additionally, wireless sensors can use the unauthored spectrum resource to complete certain information transmission tasks based on cognitive radio. Harvesting enough energy from the environments, the wireless sensors, works as the second users (SUs) can lease spectrum resource from the primary user (PU) to finish their task and bring additional transmission cost to themselves. To minimize the overall cost of SUs and to maximize the spectrum profit of the PU during the information transmission period, we formulated a differential game model to solve the resource allocation problem in the cognitive radio wireless sensor networks with energy harvesting, considering the SUs as the game players. By solving the proposed resource allocation game model, we found the open loop Nash equilibrium solutions and feedback Nash equilibrium solutions for all SUs as the optimal control strategies. Ultimately, series numerical simulation experiments have been made to demonstrate the rationality and effectiveness of the game model. Full article

(This article belongs to the Special Issue Cognitive Radio Networks for Internet of Things and Wireless Sensor Networks)

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15 pages, 2716 KiB

Open AccessArticle

Cognitive Interference Cancellation with Digital Channelizer for Satellite Communication

by Byounghak Kim, Heejung Yu and Song Noh

Sensors 2020, 20(2), 355; https://doi.org/10.3390/s20020355 - 08 Jan 2020

Cited by 7 | Viewed by 3616

Abstract

The concept of Internet of Things (IoT) has attracted much research attention for the realization of a smart society. However, the radio transmission coverage of the existing IoT solutions is not enough to connect lots of devices deployed over wide areas. Therefore, satellite networks have been considered as one of the most attractive solutions to wide cell coverage of IoT, i.e., global-scaled IoT. In satellite communication, a digital channelizer is one of the most significant parts that support multiple transponders. Owing to their wide coverage, satellite communication systems are more vulnerable to interference than other types of wireless communication systems. In this study, a cognitive interference cancellation using the inherent properties of a digital channelizer is considered. The proposed method detects a subchannel corrupted by interference and omits it. A simple energy detection method and a modified version are proposed for detection of interference. In the modified (i.e., improved) method, the number of required signal blocks to achieve the target detection performance can be reduced, i.e., the detection performance is improved with the same number of blocks, by exploiting the property of the fast Fourier transform (FFT) algorithm. Detection performance such as false alarm and detection probabilities are analyzed, and the validity of the analysis is verified with numerical results. It is also shown that an interference lower than a certain level in the proposed approach does not need to be cancelled. Full article

(This article belongs to the Special Issue Cognitive Radio Networks for Internet of Things and Wireless Sensor Networks)

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15 pages, 686 KiB

Open AccessArticle

Enhanced Sensing and Sum-Rate Analysis in a Cognitive Radio-Based Internet of Things

by Md. Sipon Miah, Kazi Mowdud Ahmed, Md. Khairul Islam, Md. Ashek Raihan Mahmud, Md. Mahbubur Rahman and Heejung Yu

Sensors 2020, 20(9), 2525; https://doi.org/10.3390/s20092525 - 29 Apr 2020

Cited by 8 | Viewed by 2695

Abstract

Spectrum sensing plays a vital role in cognitive radio networks (CRNs) for identifying the spectrum hole. However, an individual cognitive radio user in a CRN does not obtain sufficient sensing performance and sum rate of the primary and secondary links to support the future Internet of Things (IoT) using conventional detection techniques such as the energy detection (ED) technique in a noise-uncertain environment. In an environment comprising noise uncertainty, the performance of conventional energy detection techniques is significantly degraded owing to the noise fluctuation caused by the noise temperature, interference, and filtering. To mitigate this problem, we present a cooperative spectrum sensing technique that comprises the use of the Kullback–Leibler divergence (KLD) in cognitive radio-based IoT (CR-IoT). In the proposed method, each unlicensed IoT device that is capable of spectrum sensing, which is called a CR-IoT user, makes a local decision using the KLD technique. The spectrum sensing performed with the KLD requires a smaller number of samples than other conventional approaches, e.g., energy detection, for reliable sensing even in a noise uncertain environment. After the local decision is made, each CR-IoT user sends its own local decision result to the corresponding fusion center, which makes a global decision using the soft fusion rule. The results obtained through simulations show that the proposed KLD scheme achieves a better sensing performance, i.e., higher detection and lower false-alarm probabilities, enhances the sum rate, and reduces the total time as compared to the conventional ED scheme under various fading channels. Full article

(This article belongs to the Special Issue Cognitive Radio Networks for Internet of Things and Wireless Sensor Networks)

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6 pages, 178 KiB

Open AccessEditorial

Cognitive Radio Networks for Internet of Things and Wireless Sensor Networks

by Heejung Yu and Yousaf Bin Zikria

Sensors 2020, 20(18), 5288; https://doi.org/10.3390/s20185288 - 16 Sep 2020

Cited by 36 | Viewed by 3813

Abstract

Recent innovation, growth, and deployment of internet of things (IoT) networks are changing the daily life of people. 5G networks are widely deployed around the world, and they are important for continuous growth of IoT. The next generation cellular networks and wireless sensor networks (WSN) make the road to the target of the next generation IoT networks. The challenges of the next generation IoT networks remain in reducing the overall network latency and increasing throughput without sacrificing reliability. One feasible alternative is coexistence of networks operating on different frequencies. However, data bandwidth support and spectrum availability are the major challenges. Therefore, cognitive radio networks (CRN) are the best available technology to cater to all these challenges for the co-existence of IoT, WSN, 5G, and beyond-5G networks. Full article

(This article belongs to the Special Issue Cognitive Radio Networks for Internet of Things and Wireless Sensor Networks)

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