ICTACT Journal on Communication Technology

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

A Dynamic Spectrum Access Optimization Model for Cognitive Radio Wireless Sensor Network


Affiliations
1 1Department of Computer Engineering, Pacific Academy of Higher Education and Research University, India
2 Department of Computer Engineering, Fr. Conceicao Rodrigues Institute of Technology, India
3 Modern Institute of Technology and Research Centre, India
     

   Subscribe/Renew Journal


The availability of low cost and tiny sensor devices have resulted in increased adoption of wireless sensor network (WSN) in various industries and organization. The WSN is expected to play a significant role in future internet based application services. WSN has been adopted in healthcare, disaster management, environment monitoring and so on. The low-cost availability of smart devices has led to increased use of wireless devices such as Bluetooth, Wi-Fi etc. Therefore, cognitive radio network plays a significant role in handling spectrum efficiently. The emerging internet access technology such as 4G and 5G network which is expected to come in near future is going to make cognitive spectrum access more challenging. The existing cognitive radio based WSN is not efficient in utilizing spectrum. They induce high collision due to interference and improper channel state information. To address, this work present an efficient distributed opportunistic spectrum access for wireless sensor network. The channel availability of likelihood distribution is computed using continuous-time Markov chain considering primary transmitting users temporal channel usage channel pattern and spatial distribution. The simulation outcome shows the proposed model achieves significant performance improvement over existing model. The proposed model improves the overall spectrum efficiency of cognitive radio wireless sensor network in terms of throughput, packet transmission and collision.

Keywords

Cognitive Radio Network, Wireless Sensor Network, MAC, Spectrum.
Subscription Login to verify subscription
User
Notifications
Font Size

  • I.F. Akyildiz, W. Su, Y. Sankarasubramaniam and E. Cayirci, “Wireless Sensor Networks: A Survey”, Computer Networks, Vol. 38, No. 4, pp. 393-422, 2002.

  • Mainetti Luca et al., “Evolution of wireless sensor networks towards the Internet of Things: A survey”, Proceedings of IEEE International Conference on Software, Telecommunications and Computer Networks, pp. 15-17, 2011.

  • Tao Hua et al., “Intelligent Photovoltaic Monitoring based on Solar Irradiance Big Data and Wireless Sensor Networks”, Ad Hoc Networks, Vol. 35, No. 5, pp. 127-136, 2015.

  • Bin He and Yonggang Li, “Big Data Reduction and Optimization in Sensor Monitoring Network”, Journal of Applied Mathematics, Vol. 2014, pp. 1-8, 2014.

  • Jiachen Yang, Yancong Lin and Zhihan Lv, “A Self-Assessment Stereo Capture Model Applicable to the Internet of Things”, Sensors, Vol. 15, No. 8, pp. 20925-20944, 2015.

  • Jiachen Yang, Shudong He, Yancong Lin and Zhihan Lv, “Multimedia Cloud Transmission and Storage System based on Internet of Things”, Multimedia Tools and Applications, Vol. 76, No. 17, pp. 1-16, 2015.

  • Elias Yaacoub et al., “Cooperative Wireless Sensor Networks for Green Internet of Things”, Proceedings of 8th ACM Symposium on QoS and Security for Wireless and Mobile Networks, pp. 79-80, 2012.

  • Jiachen Yang, Shudong He, Yancong Lin and Zhihan Lv, “A Real-Time Monitoring System of Industry Carbon Monoxide based on Wireless Sensor Networks”, Sensors, Vol. 15, No. 11, pp. 29535-29546, 2015.

  • D. Jiang et al., “A Collaborative Multi-Hop Routing Algorithm for Maximum Achievable Rate”, Journal of Network and Computer Applications, Vol. 57, pp. 182-191, 2015.

  • H Zain Eldin, M.A. Elhosseini and H.A. Ali, “Image Compression Algorithms in Wireless Multimedia Sensor Networks: A Survey”, Ain Shams Engineering Journal, Vol. 6, No. 2, pp. 481-490, 2015.

  • Paola G. Vinueza Naranjo et al., “P-SEP: A Prolong Stable Election Routing Algorithm for Energy-Limited Heterogeneous Fog-Supported Wireless Sensor Networks”, The Journal of Supercomputing, Vol. 73, No. 2, pp. 1-23, 2016.

  • G. Zhou, J.A. Stankovic and S.H. Son, “Crowded Spectrum in Wireless Sensor Networks”, Proceedings of 3rd Workshop on Embedded Networked Sensors, pp. 1-5, 2006.

  • J. Yang et al., “A Low-Power and Portable Biomedical Device for Respiratory Monitoring with a Stable Power Source”, Sensors, Vol. 15, No. 8, pp. 19618-19632, 2015.

  • S. Maghsudi and S. Stanczak, “Hybrid Centralized-Distributed Resource Allocation for Device-to-Device Communication Underlaying Cellular Networks”, IEEE Transactions on Vehicular Technology, Vol. 65, No. 4, pp. 2481-2495, 2016.

  • D. Jiang, Y. Wang and C. Yao, “An Effective Dynamic Spectrum Access Algorithm for Multi-Hop Cognitive Wireless Networks”, Computer Networks, Vol. 84, No. 19, pp. 1-16, 2015.

  • T.A. Myrvoll and J.E. Hakegard, “Dynamic Spectrum Access in Realistic Environments using Reinforcement Learning”, Proceedings of International Symposium on Communications and Information Technologies, pp. 465-470, 2012.

  • Yuhua Xu, Jinlong Wang, Qihui Wu, A. Anpalagan and Yu-Dong Yao. “Opportunistic Spectrum Access in Unknown Dynamic Environment: A Game-Theoretic Stochastic Learning Solution”, IEEE Transactions on Wireless Communications, Vol. 11, No. 4, pp. 1380-1391, 2012.

  • S. Vakili, K. Liu and Q. Zhao, “Deterministic Sequencing of Exploration and Exploitation for Multi-Armed Bandit Problems”, IEEE Journal of Selected Topics in Signal Processing, Vol. 59, No. 3, pp. 1902–1916, 2013.

  • Y. Gai and B. Krishnamachari, “Decentralized Online Learning Algorithms for Opportunistic Spectrum Access”, Proceedings of IEEE Global Communication Conference, pp. 1-6, 2011.

  • A. Anandkumar, N. Michael, A. Tang and A. Swami, “Distributed Algorithms for Learning and Cognitive Medium Access with Logarithmic Regret”, IEEE Journal on Selected Areas in Communications, Vol. 29, No. 4, pp. 731-745, 2011.

  • Marjan Zandi, Min Dong and Ali Grami, “Decentralized Spectrum Learning and Access Adaptive to Channel Availability Distribution in Primary Network”, Proceedings of 14th Workshop on Signal Processing Advances in Wireless Communications, pp. 130-134, 2013.

  • M. Lelarge, A. Proutiere and M S. Talebi, “Spectrum Bandit Optimization”, Proceedings of Information Theory Workshop, pp. 1-5, 2013.

  • J. Zhu, Y. Song, D. Jiang and H. Song, “Multi-Armed Bandit Channel Access Scheme with Cognitive Radio Technology in Wireless Sensor Networks for the Internet of Things”, IEEE Access, Vol. 4, pp. 4609-4617, 2016.

  • Y. Xu, Q. Wu, J. Wang, L. Shen and A. Anpalagan, “Robust Multiuser Sequential Channel Sensing and Access in Dynamic Cognitive Radio Networks: Potential Games and Stochastic Learning”, IEEE Transactions on Vehicular Technology, Vol. 64, No. 8, pp. 3594-3607, 2015.

  • A. Jamal, C.K. Tham and W.C. Wong, “CR-WSN MAC: An Energy Efficient and Spectrum Aware MAC Protocol for Cognitive Radio Sensor Network”, Proceedings of 9th International Conference on Cognitive Radio Oriented Wireless Networks and Communications, pp. 67-72, 2014.

  • M. Hefnawi, “Large-Scale Multi-Cluster MIMO Approach for Cognitive Radio Sensor Networks”, IEEE Sensors Journal, Vol. 16, No. 11, pp. 4418-4424, 2016.

  • A. Ahmad, S. Ahmad, M.H. Rehmani and N.U. Hassan, “A Survey on Radio Resource Allocation in Cognitive Radio Sensor Networks”, IEEE Communications Surveys and Tutorials, Vol. 17, No. 2, pp. 888-917, 2015.

  • Ibrahim Mustapha, Borhanuddin M. Ali, A. Sala, M.F.A. Rasid and H. Mohamad, “An Energy Efficient Reinforcement Learning based Cooperative Channel Sensing for Cognitive Radio Sensor Networks”, Pervasive and Mobile Computing, Vol. 35, pp. 165-184, 2017.

  • Yan Jiao and Inwhee Joe, “Markov Model-Based Energy Efficiency Spectrum Sensing in Cognitive Radio Sensor Networks”, Journal of Computer Networks and Communications, Vol. 6, pp. 1-8, 2016.

  • P. Spachos and D. Hatzinakos, “Real-Time Indoor Carbon Dioxide Monitoring Through Cognitive Wireless Sensor Networks”, IEEE Sensors Journal, Vol. 16, No. 2, pp. 506-514, 2016.


Abstract Views: 413

PDF Views: 2




  • A Dynamic Spectrum Access Optimization Model for Cognitive Radio Wireless Sensor Network

Abstract Views: 413  |  PDF Views: 2

Authors

T. V. Saroja
1Department of Computer Engineering, Pacific Academy of Higher Education and Research University, India
Lata L. Ragha
Department of Computer Engineering, Fr. Conceicao Rodrigues Institute of Technology, India
Satyendra Kumar Sharma
Modern Institute of Technology and Research Centre, India

Abstract


The availability of low cost and tiny sensor devices have resulted in increased adoption of wireless sensor network (WSN) in various industries and organization. The WSN is expected to play a significant role in future internet based application services. WSN has been adopted in healthcare, disaster management, environment monitoring and so on. The low-cost availability of smart devices has led to increased use of wireless devices such as Bluetooth, Wi-Fi etc. Therefore, cognitive radio network plays a significant role in handling spectrum efficiently. The emerging internet access technology such as 4G and 5G network which is expected to come in near future is going to make cognitive spectrum access more challenging. The existing cognitive radio based WSN is not efficient in utilizing spectrum. They induce high collision due to interference and improper channel state information. To address, this work present an efficient distributed opportunistic spectrum access for wireless sensor network. The channel availability of likelihood distribution is computed using continuous-time Markov chain considering primary transmitting users temporal channel usage channel pattern and spatial distribution. The simulation outcome shows the proposed model achieves significant performance improvement over existing model. The proposed model improves the overall spectrum efficiency of cognitive radio wireless sensor network in terms of throughput, packet transmission and collision.

Keywords


Cognitive Radio Network, Wireless Sensor Network, MAC, Spectrum.

References