An Improved Optimal Channel Sensing Algorithm in Cognitive Radio Networks Used for Video Surveillance
DOI:
https://doi.org/10.13052/jmm1550-4646.1927Keywords:
cognitive radio networks, Q Learning, Double DQN, Energy Detection, Signal to Noise RatioAbstract
With a rapid rise in the number of wireless devices and gadgets, a shortage in the spectrum bands for wireless communications has been observed. To overcome this problem of shortage of spectrum bands, a new technology called Cognitive Radio Networks (CRNs) was adopted. CRNs help us utilize the spectrum bands which are currently being underutilized by opportunistically and intelligently switching to these underutilized white spaces. Thus, CRNs aim to use the frequency spectrum in an opportunistic manner by allowing different users to operate in available frequency bands without interference. In this paper, Double Q Learning (DQN) with prioritized experience relay approach has been used to study the throughput of the network at different parameters and to draw a relationship between throughput and Probability of Undetectable User Transmission. Double Q Learning (DQN) with prioritized experience relay is a reinforcement learning based method that adds backward exploration to the forward exploration of Q Learning method. Both forward and backward exploration are used to update the Q values. Since the sensor nodes in the cognitive environment have limited energy, and sensing the spectrum band involves energy consumption, so the technique for sensing should be energy efficient so that the sensor nodes can be effectively used for various operations of video surveillance.
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References
Ahlam Saud Althobaiti et al. “Medium Access Control Protocol for Wireless Sensor Networks Classification and Cross-Layering”, ICCMIT 0125.
Amruta Lipare et al. “Energy efficient Routing Structure to avoid Energy Hole Problem in Multi-Layer Network Model” Wireless Personel Communication 23th January 2020.
C. Sha, H. Chen, C. Yao, Y. Liu, R. Wang, “A Type of Energy Hole Avoiding Method Based on Synchronization of Nodes in Adjacent Annuluses for Sensor Network” Hindawi Publishing Corporation International Journal of Distributed Sensor Networks, Volume 2016, Article ID 5828956.
M. Carlos-Mancilla, E. Lopez-Mallado, M Siler,” Wireless Sensor Network Formation approaches and Techniques”, Hindawi Publishing Corporation Journal of Sensors, Volume 2016, 1st February 2016.
A. Al-Saadi, R. Setchi, Y. Hicks, S.M. Allen, “Routing Protocol for Heterogeneous Wireless Mesh Networks”, IEEE Transactions on Vehicular Technology, Vol. 65, No. 12, December 2016.
S. Movassaghi, A. Majidi, A. Jamalipour, D. Saremith, M. Abolhasan, “Enabling Interference-Aware and Energy-Efficient Coexistence of Multiple Wireles Body Area Network with Unknown Dynamics” IEEE Access, Special section on Body Area Network for Interdisciplinary research, 7 July 2016.
N. Jan, N. Javaid, Q. Javaid, N. Alrajeh, et al., “A Balanced Energy-Consuming and Hole-Alleviating Algorithm for Wireless Sensor Networks”, IEEE Access, 17 May 2017.
V. Nguyen, P. LIN, R. Hwang, “Energy Depletion Attack in Low Power Wireless Networks”, IEEE Access 29 April 2019.
H.H.M. Mahmoud, T. Ismail, M.S. Darweesh, “Dynamic Traffic Model with Optimal Gateways Placement in IP Cloud Heterogenous CRAN”, IEEE Access, 8 July 2020.
S. Anamalamudi, A.R. Sangi, M. Alkatheiri, A.M. Ahmed, “AODV routing protocol for Cognitive Radio access-based Internet of Things (IoT)”, future Generation Computer Systems, 29 December 2017.
F.A. Awin, Y.M. Alginahi, E.A. Raheem and K. Tepe, Technical Issues on Cognitive Radio-Based Internet of Things System: A Survey”, IEEE Access.
F. Bouabdallah, C. Zidi, R. Boutaba, “Joint Routing and Energy Management in Underwater Acoustic Sensor Networks”, IEEE Transaction on Network and Service Management, 2017.
Y.C. Liang, K.C. Chen, G.Y. Li, P. Mahonen, “Cognitive Radio Networking and Communication: An Overview”, IEEE transaction on vehicular technology, Vol. 60, No. 7, September 2011.
H. Dang, H. Wu,” Clustering and Cluster-Based Routing Protocol for Delay-Tolerant Mobile Networks”, IEEE Transaction on Wireless Communication, Vol. 9, No. 6, 6 June, 2010.
S. Haykin, P. Setoodeh, “Cognitive Radio Network the Supply Chain Paradigm”, IEEE Transaction on Cognitive Communication and Networking, 2015.
S. Floyd, V. Jacobson, “Random Early Detection Gateways for Congestion Avoidance”, IEEE/ACM Transaction on Networking Vol. 1, No. 4, 4 August 1993.
T. Schaul, J. Quan, I. Antonoglou, and D. Silver, “Prioritized experience replay,” arXiv [cs.LG], 2015.
H. van Hasselt, A. Guez, and D. Silver, “Deep reinforcement learning with Double Q-learning,” arXiv [cs.LG], 2015.
Core.ac.uk. [Online]. Available: https://core.ac.uk/download/pdf/297012544.pdf. [Accessed: 26-Apr-2021] s
H. Urkowitz, “Energy detection of unknown deterministic signals,” IEEE Proceedings, vol. 55, no. 4, pp. 523–531, Apr. 1967.
P. Verma and B. Singh, “Throughput analysis in cognitive radio networks,” 2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2014, pp. 1199–1203, doi: 10.1109/ICACCI.2014.6968301.
J. Mitola III and G. Q. Maguire, “Cognitive radio: making software radios more personal”, Personal Communications, IEEE, 1999.
