基于增强加权质心定位的认知物联网用户频谱接入控制

doi:10.11959/j.issn.2096-3750.2023.00312

摘要/Abstract

摘要：

在认知物联网（CIoT, cognitive internet of things）中，由于主用户（PU, primary user）与次级用户（SU, secondary user）之间的非合作特性，单独依靠传统的频谱感知技术判断频谱接入机会存在一定的不可靠性。作为一种重要的辅助信息，PU与SU之间的相互位置信息可以协助判断授权频谱的二次接入可能性。提出了一种低复杂度的基于相邻关系的加权质心定位（NB-WCL, neighbor-based weighted centroid localization）算法，通过解决CIoT中 SU 的定位问题，从而完成 CIoT 中各个地理位置上是否能够进行频谱接入的决策。在理论层面分析了二维位置估计的均方根误差（RMSE, root mean square error）性能，通过仿真验证了通信半径、节点密集度、阴影影响、路径损失、连通性度量值以及发送数据次数等因素对于算法性能的影响。理论推导与实验结果表明，相对于传统的定位算法，所提方案为 CIoT 中的 SU 定位算法提供了更为强健和良好的定位误差性能，能够有效地增强认知物联网中用户频谱接入的可靠性。该方案可以作为认知物联网中的一种高效实用的定位感知方案。

关键词: 加权质心定位, 认知物联网, 相邻关系, 性能分析, 频谱接入

Abstract:

In the cognitive internet of things (CIoT), due to the non-cooperative characteristics between the primary user (PU) and the secondary user (SU), it is unreliable to seek the spectrum access opportunity by merely relying on traditional spectrum sensing technology.As an important type of auxiliary information, the mutual location information between PU and SU can assist in determining the possibility of secondary access to the licensed frequency band (LFB).A low-complexity neighbor-based weighted centroid localization (NB-WCL) algorithm was proposed to solve the localization problem of SUs in CIoT, so as to complete the decision of whether spectrum access can be performed at each geographical location in CIoT.The root mean square root error (RMSE) performance of two-dimensional position estimation was analyzed and the impacts of factors were verified such as communication radius, node density, shadowing influence, path loss exponent, connectivity metric, and the number of data transmitted on the algorithm performance in simulations.The theoretical derivation and experimental results show that the proposed scheme provides more robust and better localization error performance for the SU localization algorithm in CIoT than the traditional localization algorithms, which can effectively enhance the reliability of CIoT for spectrum access.The proposed scheme can serve as a practically effective candidate solution in the CIoT.

Key words: weighted centroid localization, cognitive internet of things, neighbor relationship, performance analysis, spectrum access

中图分类号:

TN929.5

申滨, 李银波, 梁枭伟. 基于增强加权质心定位的认知物联网用户频谱接入控制[J]. 物联网学报, 2023, 7(1): 93-108.

Bin SHEN, Yinbo LI, Xiaowei LIANG. Spectrum access control for cognitive internet of things users based on enhanced weighted centroid localization[J]. Chinese Journal on Internet of Things, 2023, 7(1): 93-108.

图/表 15

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表3

各定位算法计算复杂度比较"

算法	复杂度
NB-WCL	$O_{NB-WCL} = (2 K + 1) N M + K (2 M + N^{*})$
NB-CL	$O_{NB-CL} = (2 K + 1) N M + K (M + N^{*})$
WCL	$O_{WCL} = 3 K N M + K M$
CL	$O_{CL} = 3 K N M$
APIT	$O_{APIT} = 2 K N M + K (\frac{(M - 2) (M - 1) M}{6})$
LS-RSSI	$O_{LS-RSSI} = 2 K N M$
Bounding-Box	$O_{Bounding-Box} = 3 K N M + 2 K M$
DV-Hop	$O_{DV-Hop} = 2 K N M + K M$

表3

图12

参考文献 11

[1]	WANG X , QIAN Z H , WANG X ,et al. Robust localization for cognitive IoT via the mobile anchor node based on the diameter-varying spiral line[J]. IEEE Access, 2019(7): 28487-28497.
[2]	ZHANG X J , MA Y , QI H R ,et al. Distributed compressive sensing augmented wideband spectrum sharing for cognitive IoT[J]. IEEE Internet of Things Journal, 2018,5(4): 3234-3245.
[3]	ADU ANSERE J , HAN G J , WANG H ,et al. A reliable energy efficient dynamic spectrum sensing for cognitive radio IoT networks[J]. IEEE Internet of Things Journal, 2019,6(4): 6748-6759.
[4]	CHEN S , SHEN B , WANG X ,et al. Geo-location information aided spectrum sensing in cellular cognitive radio networks[J]. Sensors (Basel,Switzerland), 2019,20(1): E213.
[5]	LI T , YUAN J , TORLAK M . Network throughput optimization for random access narrowband cognitive radio internet of things(NB-CR-IoT)[J]. IEEE Internet of Things Journal, 2018,5(3): 1436-1448.
[6]	DEL PERAL-ROSADO J A , RAULEFS R , LóPEZ-SALCEDO J A , ,et al. Survey of cellular mobile radio localization methods:from 1G to 5G[J]. IEEE Communications Surveys ＆ Tutorials, 2018,20(2): 1124-1148.
[7]	HE T , HUANG C D , BLUM B M ,et al. Range-free localization schemes for large scale sensor networks[C]// Proceedings of the 9th Annual International Conference on Mobile Computing and Networking-MobiCom'03. NewYork:ACM Press, 2003: 81-95.
[8]	WANG H F , GAO Z , GUO Y ,et al. A survey of range-based localization algorithms for cognitive radio networks[C]// Proceedings of 2012 2nd International Conference on Consumer Electronics,Communications and Networks (CECNet). Piscataway:IEEE Press, 2012: 844-847.
[9]	XUNAUE C , SHAN Z G , LIU J J . Distributed localization for anchor-free sensor networks[J]. Journal of Systems Engineering and Electronics, 2008,19(3): 405-418.
[10]	ALIPPI C , VANINI G . A RSSI-based and calibrated centralized localization technique for wireless sensor networks[C]// Proceedings of Fourth Annual IEEE International Conference on Pervasive Computing and Communications Workshops. Piscataway:IEEE Press, 2006.
[12]	SAVARESE C , RABAEY J M , BEUTEL J . Location in distributed ad-hoc wireless sensor networks[C]// Proceedings of 2001 IEEE International Conference on Acoustics,Speech,and Signal Processing.Proceedings (Cat.No.01CH37221). Piscataway:IEEE Press, 2001: 2037-2040.
[13]	WANG J , URRIZA P , HAN Y X ,et al. Weighted centroid localization algorithm:theoretical analysis and distributed implementation[J]. IEEE Transactions on Wireless Communications, 2011,10(10): 3403-3413.
[14]	BULUSU N , HEIDEMANN J , ESTRIN D . GPS-less low-cost outdoor localization for very small devices[J]. IEEE Personal Communications, 2000,7(5): 28-34.
[15]	MARIANI A , KANDEEPAN S , GIORGETTI A ,et al. Cooperative weighted centroid localization for cognitive radio networks[C]// Proceedings of 2012 International Symposium on Communications and Information Technologies (ISCIT). Piscataway:IEEE Press, 2012: 459-464.
[16]	CHAUDHARI S , CABRIC D . Cyclic weighted centroid algorithm for transmitter localization in the presence of interference[J]. IEEE Transactions on Cognitive Communications and Networking, 2016,2(2): 162-177.
[17]	HADIR A , ZINE-DINE K , BAKHOUYA M . A weighted centroid localization algorithm for wireless sensor networks[C]// Proceedings of 2019 International Conference on High Performance Computing ＆Simulation (HPCS). Piscataway:IEEE Press, 2019: 92-98.
[18]	SAEED N , NAM H . Energy efficient localization algorithm with improved accuracy in cognitive radio networks[J]. IEEE Communications Letters, 2017,21(9): 2017-2020.
[19]	SAEED N , NAM H . Robust multidimensional scaling for cognitive radio network localization[J]. IEEE Transactions on Vehicular Technology, 2015,64(9): 4056-4062.
[20]	ARAGUES R , GONZáLEZ A , LóPEZ-NICOLáS G ,et al. Distributed relative localization using the multidimensional weighted centroid[J]. IEEE Transactions on Control of Network Systems, 2020,7(3): 1272-1282.
[21]	MAGOWE K , GIORGETTI A , KANDEEPAN S ,et al. Accurate analysis of weighted centroid localization[J]. IEEE Transactions on Cognitive Communications and Networking, 2019,5(1): 153-164.
[22]	MAGOWE K , GIORGETTI A , SITHAMPARANATHAN K . Closed-form approximation of weighted centroid localization performance[J]. IEEE Sensors Letters, 2019,3(12): 1-4.
[23]	HAN W J , LI J D , LIU Q ,et al. Spatial false alarms in cognitive radio[J]. IEEE Communications Letters, 2011,15(5): 518-520.
[24]	HAN W J , LI J D , LI Z ,et al. Spatial false alarm in cognitive radio network[J]. IEEE Transactions on Signal Processing, 2013,61(6): 1375-1388.
[25]	申滨, 王欣, 陈思吉 ,等. 基于机器学习主用户发射模式分类的蜂窝认知无线电网络频谱感知[J]. 电子与信息学报, 2021,43(1): 92-100.
	SHEN B , WANG X , CHEN S J ,et al. Machine learning based primary user transmit mode classification for spectrum sensing incellular cognitive radio network[J]. Journal of Electronics ＆ Information Technology, 2021,43(1): 92-100.
[26]	BAO Y , KAN R . On the moments of ratios of quadratic forms in normal random variables[J]. Journal of Multivariate Analysis, 2013,117: 229-245.
[27]	SHERMAN J , MORRISON W J . Adjustment of an inverse matrix corresponding to a change in one element of a given matrix[J]. The Annals of Mathematical Statistics, 1950,21(1): 124-127.
[28]	WANG Y P , YANG X , ZHAO Y T ,et al. Bluetooth positioning using RSSI and triangulation methods[C]// Proceedings of 2013 IEEE 10th Consumer Communications and Networking Conference. Piscataway:IEEE Press, 2013: 837-842.
[29]	SIMIC S , SASTRY S . Distributed localization in wireless Ad-Hoc networks:Technical Report UCB/ERL[R]. 2002.
[30]	DRAGOS , NICULESCUBADRI , NATH . DV-Based positioning in Ad-Hoc networks[J]. Telecommunication Systems, 2003,22(1-4): 267-280.
[11]	WEN C Y , HSIAO Y C . Decentralized anchor-free localization for wireless ad-hoc sensor networks[C]// Proceedings of 2008 IEEE International Conference on Systems,Man and Cybernetics. Piscataway:IEEE Press, 2008: 2777-2785.

变量	状态	含义
	0	待定位LUN
F_L	1	AN
	2	定位成功LUN

区域	F_u,Access	接入状态
白色	1	允许接入
深灰色	0	等待接入
浅灰色	-1	禁止接入