Optimized Energy Utilization in Cognitive Radio Networks with Congestion Control
Keywords:
CRN, Congestion, Energy optimization, NS2, Novel algorithms.Abstract
Various real-time applications can be handled through wireless sensor networks, which consist of a wide range of sensor nodes. A novel congestion control mechanism is proposed on optimized rates for energy-efficient transmissions. To reduce energy consumption across the network, a rate-based congestion control algorithm based on cluster routing is presented. By reducing the end-to-end delay, rate control improves the network life time over a large simulation period. Clustering is initially performed using novel routing algorithms. After that, rate control is implemented using an energy optimization strategy suitable for high packet delivery ratios. Finally, packets are sent with maximum throughput using region Optimization-driven routing. The simulation is performed on the NS2 simulation platform. Finally, performances are evaluated with respect to average delay in end to end nodes, delivery ratio of packets, throughput, energy efficiency, energy consumption and reliability. Novel routing with an energy optimal algorithm (NREOA) reduces energy consumption as the network progresses and a variation of 20% compared with existing protocols.
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Chen J, Díaz M, Llopis L, Rubio B, Troya JM. A survey on quality-of-service support in wireless sensor and actor networks: requirements and challenges in the context of critical infrastructure protection. J Netw Comput Appl 2011;34 (4):1225–39
S. L. Yadav and R. L. Ujjwal, “Sensor data fusion and clustering: a congestion detection and avoidance approach in wireless sensor networks,” Journal of Information and Optimization Sciences, vol. 41, no. 7, pp. 1673–1688, 2020.
S. Yadav, “A study on congestion control mechanisms in wireless sensor networks,” Journal of Advanced Research in Dynamical & Control Systems, vol. 10, pp. 842–850, 2015.
O. Kaiwartya, A. H. Abdullah, Y. Cao et al., “T-MQM: testbed-based multi-metric quality measurement of sensor deployment for precision agriculture—a case study,” IEEE Sensors Journal, vol. 16, no. 23, pp. 8649–8664, 2016.
P. K. Kashyap, S. Kumar, A. Jaiswal, M. Prasad, and A. H. Gandomi, “Towards precision agriculture: IoT-enabled intelligent irrigation systems using deep learning neural network,” IEEE Sensors Journal, 2021
N. Siddique and H. Adeli, “Nature inspired computing: an overview and some future directions,” Cognitive Computation, vol. 7, no. 6, pp. 706–714, 2015.
P. K. Kashyap, “Genetic-fuzzy based load balanced protocol for WSNs,” International Journal of Electrical & Computer Engineering, vol. 9, no. 2, pp. 2088–8708, 2019.
A. Jaiswal, S. Kumar, O. Kaiwartya et al., “Quantum learning enabled green communication for next generation wireless systems,” IEEE Transactions on Green Communications and Networking, 2021.
Yang, Geng & Xie, Li & Mäntysalo, Matti & Zhou, Xiaolin & Pang, Zhibo & Xu, Li & Kao-Walter, Sharon & Chen, Qiang & Zheng, Li-Rong. (2014). A Health-IoT Platform Based on the Integration of Intelligent Packaging, Unobtrusive Bio-Sensor and Intelligent Medicine Box. IEEE Transactions on Industrial Informatics, 10 (4). 1-1.
M. R. Abid, R. Lghoul and D. Benhaddou, (2017). ICT for renewable energy integration into smart buildings: IoT and big data approach. IEEE AFRICON, Cape Town, pp. 856-861.
Zanella, N. Bui, A. Castellani, L. Vangelista and M. Zorzi, (2014). Internet of Things for Smart Cities. Proceedings in IEEE Internet of Things Journal, 1, issue 1, pp. 22-32.
Al-Fuqaha, M. Guizani, M. Mohammadi, M. Aledhari and M. Ayyash, (2015). Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications. Proceedings in IEEE Communications Surveys & Tutorials, 17, no. 4, pp. 2347-2376.
Subramanian, A.K., Paramasivam, I. PRIN: A Priority-Based Energy Efficient MAC Protocol for Wireless Sensor Networks Varying the Sample Inter-Arrival Time. Wireless Pers Commun 92, 863–881 (2017). https://doi.org/10.1007/s11277-016-3581-5
Chen, Z., Liu, A., Li, Z. et al. Distributed duty cycle control for delay improvement in wireless sensor networks. Peer-to-Peer Netw. Appl. 10, 559–578 (2017). https://doi.org/10.1007/s12083-016-0501-0
Wang, J., Ren, X., Chen, Fj. et al. On MAC optimization for large-scale wireless sensor network. Wireless Netw 22, 1877–1889 (2016). https://doi.org/10.1007/s11276-015-1073-2
W. He, G. Yan and L. D. Xu, (2014). Developing Vehicular Data Cloud Services in the IoT Environment. Proceedings in IEEE Transactions on Industrial Informatics, 10, no. 2, pp. 1587-1595.
S. T. Bakhsh, (2017). Energy-efficient distributed relay selection in wireless sensor network for Internet of Things. IEEE 13th International Wireless Communications and Mobile Computing Conference (IWCMC), Valencia, pp. 1802-1807
N. Akhtar, M. A. Khan, A. Ullah and M. Y. Javed, (2019). Congestion Avoidance for Smart Devices by Caching Information in MANETS and IoT. Proceedings in IEEE Access, 7, pp. 71459-71471.
Sarad, A., Verma, A. and Badholia, A. (2020). An Approach providing Congestion Control & Avoidance using Priority based Energy Efficient mechanism for Internet of Things (IoT). International Journal on Emerging Technologies, 11(2): 1013–1025.
Kruger, C.P. & Hancke, G.P. (2014). Implementing the Internet of Things vision in industrial wireless sensor networks.12th IEEE International Conference on Industrial Informatics, INDIA 2014, pp-627-632.
S. Haykin, “Cognitive radio: brain-empowered wireless communications,” Sel. Areas Commun. IEEE J., vol. 23, no. 2, pp. 201–220, 2005.
J. Mitola, “Cognitive radio---an integrated agent architecture for software defined radio,” 2000.
L. Chhaya, P. Sharma, A. Kumar, and G. Bhagwatikar, “Integration of cognitive radio with heterogeneous smart grid communication architecture,” in Intelligent Communication, Control and Devices, Springer, 2018, pp. 981–989.
Q. Zhao and A. Swami, “A survey of dynamic spectrum access: signal processing and networking perspectives,”DTIC Document, 2007.
Ozger, O. Cetinkaya, and O. B. Akan, “Energy harvesting cognitive radio networking for IoT-enabled smart grid,” Mob. Networks Appl., vol. 23, no. 4, pp. 956–966, 2018.M. M.
Buddhikot, “Understanding dynamic spectrum access: Models, taxonomy and challenges,” in 2007 2nd IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, 2007, pp. 649–663.
B. Wang and K. J. Liu, “Advances in cognitive radio networks: A survey,” Sel. Top. Signal Process. IEEE J., vol. 5, no. 1, pp. 5–23, 2011.
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