Lifetime Maximization by Dynamic Threshold and Sensor Selection in Multi-channel Cognitive Sensor Network
محورهای موضوعی : Wireless Network
Asma Bagheri
1
,
Ataollah Ebrahimzadeh
2
,
مریم نجیمی
3
1 - but
2 - BUT
3 - استادیار
کلید واژه: Cognitive sensor network , Detection probability , False alarm probability , Lifetime , Multi-channel cooperative spectrum sensing,
چکیده مقاله :
The tiny and low-cost sensors cannot simultaneously sense more than one channel since they do not have high-speed Analog-to-Digital-Convertors (ADCs) and high-power batteries. It is a critical problem when they are used for multi-channel sensing in cognitive sensor networks (CSNs). One solution for this problem is that the sensors sense various channels at different sensing periods. Due to the energy limitation in these scenarios, the lifetime maximization will become an important issue. In this paper, maximizing the lifetime of a CSN is investigated by selecting both the cooperative sensors and their detector threshold, such that the desired detection performance constraints are satisfied. This is a NP-complete problem, and obtaining the optimum solution needs exhaustive search with exponential complexity order. Here we have proposed two convex-based optimization algorithms with low order of complexity. First algorithm applies the known instantaneous Signal-to-Noise-Ratio (SNR) and obtains the proper detector thresholds by solving an equation for every channel. Investigation the effect of detector thresholds on the energy consumption, the false alarm probability and the detection probability shows that we can minimize the detector thresholds such that the detection constraints are met. In the second algorithm in order to reduce the complexity of the problem it is proposed the Bisection method for determining detector thresholds. Because knowing the instantaneous SNR is difficult, we have investigated the performance of the second algorithm by average value of SNR. Simulation results show that the proposed algorithms improve the performance of the network in case of lifetime and energy consumption.
The tiny and low-cost sensors cannot simultaneously sense more than one channel since they do not have high-speed Analog-to-Digital-Convertors (ADCs) and high-power batteries. It is a critical problem when they are used for multi-channel sensing in cognitive sensor networks (CSNs). One solution for this problem is that the sensors sense various channels at different sensing periods. Due to the energy limitation in these scenarios, the lifetime maximization will become an important issue. In this paper, maximizing the lifetime of a CSN is investigated by selecting both the cooperative sensors and their detector threshold, such that the desired detection performance constraints are satisfied. This is a NP-complete problem, and obtaining the optimum solution needs exhaustive search with exponential complexity order. Here we have proposed two convex-based optimization algorithms with low order of complexity. First algorithm applies the known instantaneous Signal-to-Noise-Ratio (SNR) and obtains the proper detector thresholds by solving an equation for every channel. Investigation the effect of detector thresholds on the energy consumption, the false alarm probability and the detection probability shows that we can minimize the detector thresholds such that the detection constraints are met. In the second algorithm in order to reduce the complexity of the problem it is proposed the Bisection method for determining detector thresholds. Because knowing the instantaneous SNR is difficult, we have investigated the performance of the second algorithm by average value of SNR. Simulation results show that the proposed algorithms improve the performance of the network in case of lifetime and energy consumption.
[1] I. F. Akyildiz, B. F. Lo and R. Balakrishnan, "Cooperative spectrum sensing in cognitive radio networks: A survey," Physical Communication Elsevier, vol. 4, no. 1, pp. 40-62, 2011. #
[2] J. Soa and T. Kwon, "Limited reporting-based cooperative spectrum sensing for multiband cognitive radio networks," International Journal of Electronics and Communications (AEU), vol. 70, no. 4, pp. 386-397, 2016. #
[3] G. P. Joshi, S. Y. Nam and S. W. Kim, "Cognitive Radio Wireless Sensor Networks: Applications, Challenges and Research Trends," Sensors , vol. 13, no. 9, pp. 11196-11228, Sep 2013. #
[4] S. K. Jayaweera, "Wideband spectrum sensing," in Signal Processing for Cognitive Radios, John Wiley & Sons, Inc, 2015, pp. 323-376. #
[5] H. Sun, A. Nallanathan, C.-X. Wang and Y. Chen, "Wideband spectrum sensing for cognitive radio networks: a survey," IEEE Wireless Communications, vol. 20, no. 2, pp. 74-81, 2013. #
[6] Z. Quan, S. Cui, A. H. Sayed and H. V. Poor, "Optimal multiband joint detection for spectrum sensing in cognitive radio network," IEEE Transactions on Signal Processing, vol. 57, no. 3, pp. 1128-1140, 2009. #
[7] D. M. M. Plata and Á. G. A. Reátiga, "Evaluation of energy detection for spectrum sensing based on the dynamic selection of detection-threshold," Procedia Engineering, vol. 35, no. 1, pp. 135-143, 2012. #
[8] K. Cicho´n, A. Kliks and H. Bogucka, "Energy-efficient cooperative spectrum sensing: a survay," IEEE Communications Survay & Tutorials, vol. 18, no. 3, pp. 1861-1886, 2016. #
[9] A. Ghasemi and S. Sousa, "Spectrum sensing in cognitive radio networks: requirements, challenges and design trade-offs," IEEE Communications Magazine, vol. 46, no. 4, pp. 32-39, April 2008. #
[10] S. Maleki, A. Pandharipande and G. Leus, "Energy-efficient distributed spectrum sensing for cognitive sensor networks," IEEE Sensors Journal, vol. 11, no. 3, pp. 565-573, 2011. #
[11] S. Maleki, G. Leus, S. Chatzinotas and B. Ottersten, "To AND or to OR: on energy-efficient distributed spectrum sensing with combined censoring and sleeping," IEEE Transactions on Wireless Communications, vol. 14, no. 8, pp. 4508-4521, 2015. #
[12] S. Maleki, S. P. Chepuri and L. Geert, "Optimization of hard fusion based spectrum sensing for energy-constrained cognitive radio networks," Physical Communication, vol. 9, pp. 193-198, 2013. #
[13] M. Najimi, A. Ebrahimzadeh, S. M. Hosseini Andargoli and A. Fallahi, "Energy-efficient sensor selection for cooperative spectrum sensing in the lack or partial information," IEEE Sensor Journal, vol. 15, no. 7, pp. 3807-3818, 2015. #
[14] M. Najimi, A. Ebrahimzadeh, S. M. Hosseini Andargoli and A. Fallahi, "A novel sensing node and decision node selection method for energy efficiency of cooperative spectrum sensing in cognitive radio networks," IEEE Sensor Journal, vol. 13, no. 5, pp. 1610-1621, 2013. #
[15] M. Najimi, A. Ebrahimzadeh, S. Hosseni Andargoli and A. Fallahi, "Lifetime Maximization in Cognitive Sensor Networks Based on the Node Selection," IEEE Sensors Journal, 2014. #
[16] M. Basharat, W. Ejaz, K. Raahemifar and A. Anpalagan, "Multi-Band Cooperative Spectrum Sensing in RF Powered Cognitive Radio Networks," in IEEE 84th Vehicular Technology Conference(VTC-Fall), Montreal, 2016. #
[17] X. Liu, F. Li and Z. Na, "Optimal resource allocation in simultaneous cooperative spectrum sensing and energy harvesting for multichannel cognitive radio," IEEE Access, vol. 5, pp. 3801-3812, 2017. #
[18] Y. Ma, Y. Gao, Y. C. Liang and S. Cui, "Reliable and efficient sub-Nyquist wideband spectrum sensing in cooperative cognitive radio networks," IEEE Journal on Selected Areas in Communications, vol. 34, no. 10, pp. 2750-2762, 2016. #
[19] A. Celik and A. Kamal, "Multi-objective clustering optimization for multi-channel cooperative spectrum sensing in heterogeneous green CRNs," IEEE Transactions on Cognitive Communications and Networking, vol. 2, no. 2, pp. 150-161, 2016. #
[20] H. Pham, Y. Zhang, T. Skeie, P. Engelstad and F. Eliassen, "Joint energy-efficient cooperative spectrum sensing and power allocation in Cognitive Machine-to-Machine Communications," in IEEE International Wireless Communications and Mobile Computing Conference (IWCMC), Paphos, Cyprus, 2016. #
[21] A. Ebrahimzadeh, M. Najimi, S. M. Hosseini Andargoli and A. Fallahi, "Sensor selection and optimal energy detection threshold for efficient cooperative spectrum sensing," IEEE Transactions on Vehicular Technology, vol. 64, no. 4, pp. 1565 - 1577, 2015.#
[22] P. Kaligineedi and V. Bhargava, "Sensor allocation and quantization schemes for multi-band cognitive radio cooperative sensing system,IEEE Transaction on Wireless Communications, vol. 10, no. 1, pp. 284-293, 2011.#
[23] B. Sklar, "Rayleigh fading channels in mobile digital communication systems part1:Characterization," IEEE Communication Magzaine, vol. 35, no. 7, pp. 90-100, 1997.#
[24] M. Noori and M. Ardakani, "Lifetime analysis of random event-driven clustered wireless sensor networks," IEEE Transactions on Mobile Computing, vol. 10, no. 10, pp. 1448-1458, 2011.#
[25] P. Li, S. Guo and Z. Cheng, "Max-min lifetime optimization for cooperative communications in cognitive radio networks," IEEE Transactions on Parallel and Distributed Systems, vol. 25, no. 6, pp. 1533-1542, 2014.#
[26] S. Boyd and L. Vandenberghe, Convex Optimization, Cambridge, U.K.: Cambridge University Press, 2004.#
[27] N. Rastegardoost and B. Jabbari, "On channel selection schemes for spectrum sensing in cognitive radio networks," in IEEE Wireless Communications and Networking Conference (WCNC) , 2015.#
[28] I. Gradshteyn and I. Ryzhik, Table of integrals, series, and products, Elsevier , 2007.#
[29] D. Han and J. h. Lim, "Smart home energy management system using IEEE 802.15.4 and zigbee," IEEE Transactions on Consumer Electronics, vol. 56, no. 3, pp. 1403-1410, 2010.#
[30] Y. Xu, Z. Gao and W. Tian, "Optimal channel selection for cooperative spectrum sensing using coordination game," in International ICST conference on communications and networking in China (CHINACOM), 2012.#
