The Design of a Defense Mechanism to Mitigate Sinkhole Attack in Software Defined Wireless Sensor Cognitive Radio Networks

Independent component analysis is extensively used for blind source separation of different signals in various engineering disciplines. It has its applications in several areas of communication, multiple input multiple output, orthogonal frequency division multiplexing, wireless sensor networks, and cognitive radio networks. In this chapter, the authors discuss the general theory of independent component analysis, wireless sensor networks, cognitive radio networks, and cognitive radio sensor networks. The main focus of the chapter is the application of independent component analysis in cognitive radio networks, wireless sensor networks, and cognitive radio sensor networks. The issues and challenges of these emerging technologies are discussed while applying independent component analysis. Cognitive radio sensor network is a promising technology to efficiently resolve the issues of spectrum usage in sensor networks. The authors are the first to discuss the applications of independent component analysis in cognitive radio sensor networks. At the end of this chapter, they discuss some future research problems regarding the applications of independent component analysis in cognitive radio sensor networks.

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Detection algorithm of sinkhole attack in software-defined wireless sensor cognitive radio networks

2017 Global Wireless Summit (GWS) ◽

10.1109/gws.2017.8300470 ◽

2017 ◽

Cited By ~ 2

Author(s):

Lanka Chris Sejaphala ◽

Mthulisi Velempini

Keyword(s):

Cognitive Radio ◽

Cognitive Radio Networks ◽

Detection Algorithm ◽

Radio Networks ◽

Wireless Sensor

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Applications of Independent Component Analysis in Cognitive Radio Sensor Networks

Advances in Wireless Technologies and Telecommunication - Cognitive Radio Sensor Networks ◽

10.4018/978-1-4666-6212-4.ch010 ◽

2014 ◽

pp. 259-287

Author(s):

Zahooruddin ◽

Ayaz Ahmad ◽

Muhammad Iqbal ◽

Farooq Alam ◽

Sadiq Ahmad

Keyword(s):

Wireless Sensor Networks ◽

Cognitive Radio ◽

Sensor Networks ◽

Independent Component Analysis ◽

Cognitive Radio Networks ◽

Component Analysis ◽

Radio Networks ◽

Independent Component ◽

Wireless Sensor ◽

Cognitive Radio Sensor Networks

Independent component analysis is extensively used for blind source separation of different signals in various engineering disciplines. It has its applications in several areas of communication, multiple input multiple output, orthogonal frequency division multiplexing, wireless sensor networks, and cognitive radio networks. In this chapter, the authors discuss the general theory of independent component analysis, wireless sensor networks, cognitive radio networks, and cognitive radio sensor networks. The main focus of the chapter is the application of independent component analysis in cognitive radio networks, wireless sensor networks, and cognitive radio sensor networks. The issues and challenges of these emerging technologies are discussed while applying independent component analysis. Cognitive radio sensor network is a promising technology to efficiently resolve the issues of spectrum usage in sensor networks. The authors are the first to discuss the applications of independent component analysis in cognitive radio sensor networks. At the end of this chapter, they discuss some future research problems regarding the applications of independent component analysis in cognitive radio sensor networks.

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A COMPREHENSIVE SURVEY ON CHANNEL BONDING TECHNIQUES IN WIRELESS SENSOR NETWORKS AND FUTURISTIC COGNITIVE RADIO NETWORKS

JOURNAL OF MECHANICS OF CONTINUA AND MATHEMATICAL SCIENCES ◽

10.26782/jmcms.2020.09.00007 ◽

2020 ◽

Vol 15 (9) ◽

Author(s):

Atif Ishtiaq

Keyword(s):

Wireless Sensor Networks ◽

Cognitive Radio ◽

Sensor Networks ◽

Cognitive Radio Networks ◽

Radio Networks ◽

Wireless Sensor ◽

Channel Bonding ◽

Comprehensive Survey

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Cognitive Radio Networks for Internet of Things and Wireless Sensor Networks

Sensors ◽

10.3390/s20185288 ◽

2020 ◽

Vol 20 (18) ◽

pp. 5288

Author(s):

Heejung Yu ◽

Yousaf Bin Zikria

Keyword(s):

Wireless Sensor Networks ◽

Cognitive Radio ◽

Sensor Networks ◽

Internet Of Things ◽

Cognitive Radio Networks ◽

Radio Networks ◽

Wireless Sensor ◽

Next Generation ◽

5G Networks ◽

Continuous Growth

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.

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HCOBASAA: Countermeasure Against Sinkhole Attacks in Software-Defined Wireless Sensor Cognitive Radio Networks

2018 International Conference on Advances in Big Data, Computing and Data Communication Systems (icABCD) ◽

10.1109/icabcd.2018.8465449 ◽

2018 ◽

Cited By ~ 1

Author(s):

Lanka Sejaphala ◽

Mthulisi Velempini ◽

Sabelo Velemseni Dlamini

Keyword(s):

Cognitive Radio ◽

Cognitive Radio Networks ◽

Radio Networks ◽

Wireless Sensor

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Comparing Adversary Defense Mechanisms in Cognitive Radio Networks

International Journal of Sensors Wireless Communications and Control ◽

10.2174/2210327911666210201104628 ◽

2021 ◽

Vol 11 ◽

Author(s):

Doaa Kiwan ◽

John P. Fonseka ◽

Rana A. Hassan

Keyword(s):

Deep Learning ◽

Cognitive Radio ◽

Cognitive Radio Networks ◽

Defense Mechanisms ◽

Defense Mechanism ◽

Radio Networks ◽

Soft Decision ◽

Power Budget ◽

Jamming Attacks ◽

Learning Classifier

BACKGROUND: In a cognitive radio network, the cognitive transmitter senses the medium to detect spectrum opportunities and transmits its own data if the channel is sensed to be idle. A jammer can also sense the medium and identify the slots of successful transmission. The jammer’s main objective is to reduce the throughput of the cognitive transmitter. METHODS: Towards this objective, the jammer builds a deep learning classifier in which the most recent sensing results of acknowledgments (ACKs) sent by the receiver are used to predict the slots of successful transmissions of the cognitive transmitter. This allows the attacker to reliably predict the successful transmissions and can effectively jam these transmissions. The deep learning classification soft decision probabilities are used by the jammer for power control subject to a certain power budget. A receiver-based defense mechanism is developed against the jamming attacks. The receiver purposely takes some wrong actions, i.e., sends ACK when transmission is not successful and vice versa, to poison the training process of the attacker. Results: We show that our receiver’s defense mechanism effectively enhances the throughput of the cognitive transmitter when compared to the transmitter’s defense mechanism, where the transmitter takes some wrong decisions when it accesses the medium. CONCLUSION: A novel defense mechanism against jamming attacks in cognitive radio networks is introduced.

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