Energy efficient approach to send data in cognitive radio wireless sensor networks(CRSN)

The adoption of cognitive radio wireless sensor networks be supposed to preserve developing, essentially in such a fields as a healthcare, logistic, scientific and a military applications. Yet, a sensor size or dimension represents a consequential constraint mainly in expressions of an energy autonomy and as a result of a life period for the batteries which is so too tiny. Thus, the reason why a concentrated research being conducted at the present time on how to control energy consumption in sensor within a network, taking communications into account as a precedence. For this intention, we propose a algorithm or method to send data from source to sink with a less energy consumption within cognitive wireless sensor networks, according to the number of nodes, data flow rate, and the distance between them. Moreover, we have succeeded in sinking energy utilization within a linear sensor network through up with nodes featuring with differing data flow rates between them.

[1]  Özgür B. Akan,et al.  Throughput maximization in electromagnetic energy harvesting cognitive radio sensor networks , 2016, Int. J. Commun. Syst..

[2]  Nan Zhao,et al.  A Novel Two-Stage Entropy-Based Robust Cooperative Spectrum Sensing Scheme with Two-Bit Decision in Cognitive Radio , 2011, Wirel. Pers. Commun..

[3]  Özgür B. Akan,et al.  Modeling of rate-based congestion control schemes in cognitive radio sensor networks , 2016, Ad Hoc Networks.

[4]  Özgür B. Akan,et al.  On the Utilization of Spectrum Opportunity in Cognitive Radio Networks , 2016, IEEE Communications Letters.

[5]  Kris Steenhaut,et al.  Low-Overhead Dynamic Multi-channel MAC for Wireless Sensor Networks , 2010, EWSN.

[6]  R.W. Brodersen,et al.  Implementation issues in spectrum sensing for cognitive radios , 2004, Conference Record of the Thirty-Eighth Asilomar Conference on Signals, Systems and Computers, 2004..

[7]  Mehmet Çunkas,et al.  Color image segmentation based on multiobjective artificial bee colony optimization , 2015, Appl. Soft Comput..

[8]  Kaigui Bian,et al.  MAC-Layer Misbehaviors in Multi-Hop Cognitive Radio Networks , 2022 .

[9]  Khaled Ben Letaief,et al.  Cooperative Communications for Cognitive Radio Networks , 2009, Proceedings of the IEEE.

[10]  Özgür B. Akan,et al.  A correlation‐based and spectrum‐aware admission control mechanism for multimedia streaming in cognitive radio sensor networks , 2017, Int. J. Commun. Syst..

[11]  Özgür B. Akan,et al.  Adaptive and cognitive communication architecture for next-generation PPDR systems , 2016, IEEE Communications Magazine.

[12]  Brandon F. Lo.  A survey of common control channel design in cognitive radio networks , 2011, Phys. Commun..

[13]  Özgür B. Akan,et al.  Dedicated Radio Utilization for Spectrum Handoff and Efficiency in Cognitive Radio Networks , 2015, IEEE Transactions on Wireless Communications.

[14]  Christian Callegari,et al.  Advances in Computing, Communications and Informatics (ICACCI) , 2015 .

[15]  Anant Sahai,et al.  SNR Walls for Signal Detection , 2008, IEEE Journal of Selected Topics in Signal Processing.