物联网频谱感知的人工物理学优化实现
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摘要
针对认知物联网中频谱感知的检测性能和能耗需求之间的相互制约性,将其建模为一个多目标优化问题,寻求性能与能耗之间的折衷解。进而提出一种基于人工物理学优化的解决方案,设计了适合问题求解的个体质量、微粒所受合力的计算方法。仿真实验表明,所提算法在基本不影响检测准确性的同时有效降低了能耗,适合认知物联网中的频谱感知。
Aiming at the conflict between detection performance and energy consumption of spectrum detection in cognitive Internet of Things, it is modeled as a multi-objective optimization problem, and the trade-off solutions between performance and energy consumption are to be found. Furthermore, an artificial physics optimization algorithm is proposed to solve it. The calculation method of individual mass and particle force are designed. The simulation results show that the proposed algorithm can effectively reduce the energy consumption while keeping high detection accuracy, and it is suitable for the spectrum detection in the cognitive Internet of Things.
Aiming at the conflict between detection performance and energy consumption of spectrum detection in cognitive Internet of Things, it is modeled as a multi-objective optimization problem, and the trade-off solutions between performance and energy consumption are to be found. Furthermore, an artificial physics optimization algorithm is proposed to solve it. The calculation method of individual mass and particle force are designed. The simulation results show that the proposed algorithm can effectively reduce the energy consumption while keeping high detection accuracy, and it is suitable for the spectrum detection in the cognitive Internet of Things.
引文
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[2]Aijaz A,Aghvami A H.Cognitive machine-to-machine communications for internet-of-things:a protocol stack perspective[J].IEEE Internet of Things Journal,2005,2(2):103-112.
[3]Cichon K,Kliks A,Bogucka H.Energy-efficient cooperative spectrum sensing:a survey[J].IEEE Communications Surveys and Tutorials,2016,18(3):1861-1886.
[4]Yuan Wei,You Xinge,Xu Jing,et al.Multiobjective optimization of linear cooperative spectrum sensing:pareto solutions and refinement[J].IEEE Transactions on Cybernetics,2016,46(1):96-108.
[5]Liu Weirong,Qin Gaorong,Li Shuo,et al.A multiobjective evolutionary algorithm for energy-efficient cooperative spectrum sensing in cognitive radio sensor network[J].International Journal of Distributed Sensor Networks,2015,11.
[6]Ren Ju,Zhang Yaoxue,Ye Qiang,et al.Exploiting secure and energy-efficient collaborative spectrum sensing for cognitive radio sensor networks[J].IEEE Transactions on Wireless Communications,2016,15(10):6813-6827.
[7]Syed T S,Safdar G A.History-assisted energy-efficient spectrum sensing for infrastructure-based cognitive radio networks[J].IEEE Transactions on Vehicular Technology,2017,66(3):2462-2473.
[8]柴争义,王秉,李亚伦.人工物理学优化的认知无线电网络频谱分配[J].物理学报,2014,63(22):433-438.
[9]Zhong Ming,Zhang Hailin,Ma Bei.APO-based parallel algorithm of channel allocation for cognitive networks[J]China Communication,2016(6):100-109.
[10]沈林成,王祥科,朱华勇,等.基于拟态物理法的无人机集群与重构控制[J].中国科学:技术科学,2017,47(3):266-285.
[11]柴争义,王秉,李亚伦,等.人工物理学多目标算法求解认知参数优化问题[J].电子学报,2015,43(8):1526-1530.
[12]Nakano R C S,Vicerra R R P,Lim L A G,et al.Utilization of the physicomimetics framework for achieving local,decentralized,and emergent behavior in a swarm of Quadrotor Unmanned Aerial Vehicles(QUAV)[J].Journal of Advanced Computational Intelligence and Intelligent Informatics,2017,21(2):189-196.
[13]柴争义,陈亮,朱思峰.混沌免疫多目标算法求解认知引擎参数优化问题[J].物理学报,2012,61(5).
[14]Teeparthi K,Kumar D M V.Security-constrained optimal power flow with wind and thermal power generators using fuzzy adaptive artificial physics optimization algorithm[J].Neural Computing and Applications,2018,29(3):855-871.
[15]高洪元,李晨琬.膜量子蜂群优化的多目标频谱分配[J].物理学报,2014,63(12).
[16]Xie Liping,Zeng Jianchao,Yang Qiongqiong.A predictable artificial physics optimisation algorithm[J].International Journal of Computing Science and Mathematics,2015,6(5):459-470.