Energy-Harvesting-Aided Spectrum Sensing and Data Transmission in Heterogeneous Cognitive Radio Sensor Network

The incorporation of cognitive radio (CR) and energy harvesting (EH) capabilities in wireless sensor networks enables spectrum and energy-efficient heterogeneous CR sensor networks (HCRSNs). The new networking paradigm of HCRSNs consists of EH-enabled spectrum sensors and battery-powered data sensors. Spectrum sensors can cooperatively scan the licensed spectrum for available channels, whereas data sensors monitor an area of interest and transmit sensed data to the sink over those channels. In this paper, we propose a resource-allocation solution for the HCRSN to achieve the sustainability of spectrum sensors and conserve the energy of data sensors. The proposed solution is achieved by two algorithms that operate in tandem: a spectrum sensor scheduling (SSS) algorithm and a data sensor resource allocation (DSRA) algorithm. The SSS algorithm allocates channels to spectrum sensors such that the average detected available time for the channels is maximized, while the EH dynamics are considered and primary user (PU) transmissions are protected. The DSRA algorithm allocates the transmission time, power, and channels such that the energy consumption of the data sensors is minimized. Extensive simulation results demonstrate that the energy consumption of the data sensors can be significantly reduced, while maintaining the sustainability of the spectrum sensors.

[1]  Janne J. Lehtomäki,et al.  On the Selection of the Best Detection Performance Sensors for Cognitive Radio Networks , 2010, IEEE Signal Processing Letters.

[2]  Xuemin Shen,et al.  Risk-Aware Cooperative Spectrum Access for Multi-Channel Cognitive Radio Networks , 2014, IEEE Journal on Selected Areas in Communications.

[3]  Yasir Saleem,et al.  Cognitive Radio Sensor Networks , 2015 .

[4]  Yiyang Pei,et al.  Energy-Efficient Design of Sequential Channel Sensing in Cognitive Radio Networks: Optimal Sensing Strategy, Power Allocation, and Sensing Order , 2011, IEEE Journal on Selected Areas in Communications.

[5]  Ataollah Ebrahimzadeh,et al.  Sensor Selection and Optimal Energy Detection Threshold for Efficient Cooperative Spectrum Sensing , 2015, IEEE Transactions on Vehicular Technology.

[6]  Sarma B. K. Vrudhula,et al.  Joint Optimization of Transmit Power-Time and Bit Energy Efficiency in CDMA Wireless Sensor Networks , 2006, IEEE Transactions on Wireless Communications.

[7]  Lang Tong,et al.  Asymptotically Efficient Multichannel Estimation for Opportunistic Spectrum Access , 2012, IEEE Transactions on Signal Processing.

[8]  Jiming Chen,et al.  Energy-efficient cooperative spectrum sensing in sensor-aided cognitive radio networks , 2012, IEEE Wireless Communications.

[9]  Xuedong Liang,et al.  Dynamic Spectrum Allocation in Wireless Cognitive Sensor Networks: Improving Fairness and Energy Efficiency , 2008, 2008 IEEE 68th Vehicular Technology Conference.

[10]  Hai Jiang,et al.  Energy Detection for Spectrum Sensing in Cognitive Radio , 2014, SpringerBriefs in Computer Science.

[11]  C. Floudas,et al.  A global optimization algorithm (GOP) for certain classes of nonconvex NLPs—I. Theory , 1990 .

[12]  Xuemin Shen,et al.  Lifetime and Energy Hole Evolution Analysis in Data-Gathering Wireless Sensor Networks , 2016, IEEE Transactions on Industrial Informatics.

[13]  R. Rubinstein The Cross-Entropy Method for Combinatorial and Continuous Optimization , 1999 .

[14]  Barry G. Evans,et al.  Energy-Efficient Sensor Scheduling Algorithm in Cognitive Radio Networks Employing Heterogeneous Sensors , 2015, IEEE Transactions on Vehicular Technology.

[15]  Hao Liang,et al.  Dynamic Spectrum Access in Multi-Channel Cognitive Radio Networks , 2014, IEEE Journal on Selected Areas in Communications.

[16]  Suzan Bayhan,et al.  Scheduling in Centralized Cognitive Radio Networks for Energy Efficiency , 2013, IEEE Transactions on Vehicular Technology.

[17]  Yuefeng Ji,et al.  A Dynamic Spectrum Access Strategy Based on Real-Time Usability in Cognitive Radio Sensor Networks , 2011, 2011 Seventh International Conference on Mobile Ad-hoc and Sensor Networks.

[18]  Özgür B. Akan,et al.  Cognitive radio sensor networks , 2009, IEEE Network.

[19]  Zhou Su,et al.  Big data in mobile social networks: a QoE-oriented framework , 2016, IEEE Network.

[20]  Shaoqian Li,et al.  Optimization of Cooperative Spectrum Sensing in Multiple-Channel Cognitive Radio Networks , 2011, 2011 IEEE Global Telecommunications Conference - GLOBECOM 2011.

[21]  Salim Eryigit,et al.  Energy-Efficient Multichannel Cooperative Sensing Scheduling With Heterogeneous Channel Conditions for Cognitive Radio Networks , 2013, IEEE Transactions on Vehicular Technology.

[22]  Shie Mannor,et al.  A Tutorial on the Cross-Entropy Method , 2005, Ann. Oper. Res..

[23]  Kang G. Shin,et al.  Efficient Discovery of Spectrum Opportunities with MAC-Layer Sensing in Cognitive Radio Networks , 2008, IEEE Transactions on Mobile Computing.

[24]  Cheng-Chun Chang,et al.  Spectrum Reconstruction for On-Chip Spectrum Sensor Array Using a Novel Blind Nonuniformity Correction Method , 2012, IEEE Sensors Journal.

[25]  Ian F. Akyildiz,et al.  Cooperative spectrum sensing in cognitive radio networks: A survey , 2011, Phys. Commun..

[26]  Zheng Wang,et al.  Distributed Consensus-Based Weight Design for Cooperative Spectrum Sensing , 2015, IEEE Transactions on Parallel and Distributed Systems.

[27]  D. Griffel Linear programming 2: Theory and extensions , by G. B. Dantzig and M. N. Thapa. Pp. 408. £50.00. 2003 ISBN 0 387 00834 9 (Springer). , 2004, The Mathematical Gazette.

[28]  Alagan Anpalagan,et al.  Energy-Efficient Cognitive Radio Sensor Networks: Parametric and Convex Transformations , 2013, Sensors.

[29]  Özgür B. Akan,et al.  Cognitive Adaptive Medium Access Control in Cognitive Radio Sensor Networks , 2015, IEEE Transactions on Vehicular Technology.

[30]  M. Alouini,et al.  Optimal power allocation of a single transmitter-multiple receivers channel in a cognitive sensor network , 2012, 2012 International Conference on Wireless Communications in Underground and Confined Areas.

[31]  Xuemin Shen,et al.  Dynamic Channel Access to Improve Energy Efficiency in Cognitive Radio Sensor Networks , 2016, IEEE Transactions on Wireless Communications.

[32]  Fernando J. Velez,et al.  Survey on the Characterization and Classification of Wireless Sensor Network Applications , 2014, IEEE Communications Surveys & Tutorials.

[33]  Dirk P. Kroese,et al.  Convergence properties of the cross-entropy method for discrete optimization , 2007, Oper. Res. Lett..

[34]  Jiming Chen,et al.  Data gathering optimization by dynamic sensing and routing in rechargeable sensor networks , 2013, 2013 IEEE International Conference on Sensing, Communications and Networking (SECON).

[35]  Yonghong Zeng,et al.  Sensing-Throughput Tradeoff for Cognitive Radio Networks , 2008, IEEE Transactions on Wireless Communications.

[36]  Stephen P. Boyd,et al.  Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.

[37]  Jiming Chen,et al.  Distributed Sampling Rate Control for Rechargeable Sensor Nodes with Limited Battery Capacity , 2013, IEEE Transactions on Wireless Communications.

[38]  Kathrin Klamroth,et al.  Biconvex sets and optimization with biconvex functions: a survey and extensions , 2007, Math. Methods Oper. Res..

[39]  Özgür B. Akan,et al.  A Spectrum-Aware Clustering for Efficient Multimedia Routing in Cognitive Radio Sensor Networks , 2014, IEEE Transactions on Vehicular Technology.

[40]  Jiming Chen,et al.  Energy-Efficient Cooperative Spectrum Sensing by Optimal Scheduling in Sensor-Aided Cognitive Radio Networks , 2012, IEEE Transactions on Vehicular Technology.