Applications of Independent Component Analysis in Cognitive Radio Sensor Networks

2016 ◽  
pp. 1173-1202
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.

Applications of Independent Component Analysis in Cognitive Radio Sensor Networks

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.

Black Hole Attack Detection in Healthcare Wireless Sensor Networks Using Independent Component Analysis Machine Learning Technique

2020 ◽  
Vol 15 (1) ◽  
pp. 56-64
Author(s):  
A. John Clement Sunder ◽  
A. Shanmugam
Keyword(s):  
Wireless Sensor Networks ◽  
Black Hole ◽  
Sensor Networks ◽  
Independent Component Analysis ◽  
Component Analysis ◽  
Attack Detection ◽  
Independent Component ◽  
Wireless Sensor ◽  
Physiological Data ◽  
Black Hole Attack

Background: Wireless Sensor Networks (WSNs) are self-configured infrastructure-less networks are comprising of a number of sensing devices used to monitor physical or environmental quantities such as temperature, sound, vibration, pressure, motion etc. They collectively transmit data through the network to a sink where it is observed and analyzed. Materials and Methods: The major issues in WSN are interference, delay and attacks that degrade their performance due to their distributed nature and operation. Timely detection of attacks is imperative for various real time applications like healthcare, military etc. To improve the Black hole attack detection in WSN, Projected Independent Component Analysis (PICA) technique is proposed herewith, which detects black hole attack by analyzing collected physiological data from biomedical sensors. Results: The PICA technique performs attack detection through Mutual information to measure the dependence in the joint distribution. The dependence among the nodes is identified based on the independent probability distribution functions and mutual probability function. Conclusion: The black hole attack isolation is then performed through the distribution of the attack separation message. This supports to improve Packet Delivery Ratio (PDR) with minimum delay. The simulation is carried out based on parameters such as black hole attack detection rate (BHADR), Black Hole Attack Detection Time (BHADT), False Positive Rate (FPR), PDR and delay.

scholarly journals Cognitive Radio Networks for Internet of Things and Wireless Sensor Networks

Sensors ◽  
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.

Spectrum Sensing in Cognitive Radio Sensor Networks

2014 ◽  
pp. 48-72 ◽  
Author(s):  
Farooq Alam ◽  
Zahooruddin ◽  
Ayaz Ahmad ◽  
Muhammad Iqbal
Keyword(s):  
Wireless Sensor Networks ◽  
Cognitive Radio ◽  
Sensor Networks ◽  
Spectrum Sensing ◽  
Wireless Sensor ◽  
Research Issues ◽  
Comprehensive Review ◽  
Cognitive Radio Sensor Networks ◽  
Cognitive Radio Sensor Network ◽  
Core Issues

In this chapter, the authors provide a comprehensive review of spectrum sensing in cognitive radio sensor networks. Firstly, they focus on general techniques utilized for spectrum sensing in wireless sensor networks. To have good understanding of core issues of spectrum sensing, the authors then give a brief description of cognitive radio networks. Then they give a thorough description of the main techniques that can be helpful in doing spectrum sensing in cognitive radio sensor network. The authors conclude this chapter with open research issues and challenges that need to be addressed to provide efficient spectrum sensing in order to minimize the limitations in cognitive radio sensor networks.

scholarly journals An anomaly node detection method for distributed time synchronization algorithm in cognitive radio sensor networks

2018 ◽  
Vol 14 (5) ◽  
pp. 155014771877446
Author(s):  
Qi Yang ◽  
Xuan Zhang ◽  
Jingfeng Qian ◽  
Qiang Ye
Keyword(s):  
Wireless Sensor Networks ◽  
Cognitive Radio ◽  
Sensor Networks ◽  
Time Synchronization ◽  
Detection Method ◽  
Wireless Sensor ◽  
Synchronization Process ◽  
Cognitive Radio Sensor Networks ◽  
Synchronization Algorithm ◽  
Global Time

In wireless sensor networks, time synchronization is an important issue for all nodes to have a unified time. The wireless sensor network nodes should cooperatively adjust their local time according to certain distributed synchronization algorithms to achieve global time synchronization. Conventionally, it is assumed that all nodes in the network are cooperative and well-functioned in the synchronization process. However, in cognitive radio wireless sensor networks, the global time synchronization process among secondary users is prone to fail because the communication process for exchanging synchronization reference may be frequently interrupted by the primary users. The anomaly nodes that failed to synchronize will significantly affect the global convergence performance of the synchronization algorithm. This article proposes an anomaly node detection method for distributed time synchronization algorithm in cognitive radio sensor networks. The proposed method adopts the statistical linear correlation analysis approach to detect anomaly nodes through the historical time synchronization information stored in local nodes. Simulation results show that the proposed method can effectively improve the robustness of the synchronization algorithm in distributed cognitive radio sensor networks.

scholarly journals Delay optimal opportunistic pipeline routing scheme for cognitive radio sensor networks

2018 ◽  
Vol 14 (4) ◽  
pp. 155014771877253 ◽  
Author(s):  
Anfeng Liu ◽  
Wei Chen ◽  
Xiao Liu
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Cognitive Radio ◽  
Sensor Networks ◽  
Duty Cycle ◽  
Data Transmission ◽  
Transmission Delay ◽  
Wireless Sensor ◽  
Cognitive Radio Sensor Networks ◽  
Routing Scheme

In order to solve the problem of spectrum scarcity in wireless sensor networks, cognitive radio technology can be introduced into wireless sensor networks, giving rising to cognitive radio sensor networks. Delay-sensitive data applications in cognitive radio sensor networks require efficient real-time communication. Opportunistic pipeline routing is a potential technology to reduce the delay, which can use nodes outside the main forwarding path forward data opportunistically when the transmission fails. However, the energy efficiency of cognitive radio sensor networks with opportunistic pipeline routing is low, and the data transmission delay can be further optimized. In view of this situation, we propose the delay optimal opportunistic pipeline routing scheme named Variable Duty Cycle for Opportunistic Pipeline Routing (VDCOPR). In the Variable Duty Cycle for Opportunistic Pipeline Routing scheme, the nodes employ high duty cycle in the area far from the sink, and low duty cycle in the area near to the sink, which can achieve the balance of energy consumption and reduce the data transmission delay while not affecting network lifetime. The theoretical analysis and experimental results show that, compared with previous opportunistic pipeline routing, energy consumption of network is relatively balanced and the data transmission delay can be reduced by 36.6% in the Variable Duty Cycle for Opportunistic Pipeline Routing scheme.

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