为提高无线传感器网络数据融合精度，降低网络能耗，延长网络生存时间，提出基于改进灰狼算法优化BP神经网络的无线传感器网络数据融合算法（IGWOBPDA）。首先为平衡灰狼算法全局与局部搜索能力提出改进控制参数和动态权重更新位置的改进灰狼方案，利用改进灰狼算法对BP神经网络初始阈值和初始权值进行优化以解决数据融合中BP神经网络对初值敏感、易陷入局部最优的问题；其次考虑到无线传感网实际传输节点能耗和分簇情况，提出基于节点剩余能量参数和节点密度参数的分簇方案，通过调整参数的权重因子来适应网络数据融合传输过程中的实际情况。仿真实验结果表明，对比BPNDA算法和GAPSOBP算法，IGWOBPDA算法在不同数据集下有更好的数据融合精度和更快的收敛速度，并且能有效地减少数据传输量，降低节点能耗，延长网络生存时间，在不同网络规模下保持稳定性。
曹轲
,
谭冲
,
刘洪
,
郑敏
. 基于改进灰狼算法优化BP神经网络的无线传感器网络数据融合算法[J]. 中国科学院大学学报, 2022
, 39(2)
: 232
-239
.
DOI: 10.7523/j.ucas.2020.0004
In order to improve the accuracy of the fusion data in wireless sensor network, reduce the energy consumption, and extend the network lifetime, data fusion algorithm of wireless sensor network based on improved grey wolf optimizer to optimize BP neural network (IGWOBPDA) is proposed in this paper. Firstly to balance the global and local search capabilties, the improved considering the actual energy consumption and clustering of the wireless sensor network's transmitting nodes, a clustering scheme based on node residual energy parameters and node density parameters is proposed,which adjusts the weighting factors to adapt to the actual situation of the network data fusion transmission process. Compared with BPNDA algorithm and GAPSOBP algorithm, simulation results show that IGWOBPDA algorithm has better data fusion accuracy and convergence in different data sets, which can effectively reduce the amount of data transmission and node energy consumption, extend network survival time, and maintain stability under different network scales. control parameter and the method of dynamic weight update position is proposed in the paper, which aims at the problems that the initial value of BP neural network in wireless sensor network data fusion algorithm is sensitive and the result can easily be the local optimal solution. Secondly,
[1] Rawat P, Singh K D, Chaouchi H, et al. Wireless sensor networks: a survey on recent developments and potential synergies[J].The Journal of supercomputing, 2014, 68(1): 1-48.DOI:10.1007/s11227-013-1021-9.
[2] Manshahia M S. Wireless sensor networks: a survey[J]. International Journal of Scientific&Engineering Research, 2016, 7(4):710-716.
[3] Min R, Bhardwaj M, Cho S H, et al. Energy-centric enabling technologies for wireless sensor networks[J]. IEEE Wireless Communications, 2002, 9(4): 28.DOI:10.1109/MWC.2002.1028875.
[4] Bai X, Wang Z, Sheng L, et al. Reliable data fusion of hierarchical wireless sensor networks with asynchronous measurement for greenhouse monitoring[J]. IEEE Transactions on Control Systems Technology, 2019, 27(3): 1036-1046.DOI:10.1109/TCST.2018.2797920.
[5] Heinzelman W B, Chandrakasan A P, Balakrishnan H. An application-specific protocol architecture for wireless microsensor networks[J]. IEEE Transactions on Wireless Communications. 2002, 1(4):660-670.DOI:10.1109/TWC.2002.804190.
[6] Boyinbode O, Le H, Mbogho A, et al. A survey on clustering algorithms for wireless sensor networks[C]//2010 13th International Conference on Network-based Information Systems. Takayama: IEEE, 2010: 358-364.
[7] 成瑜娟, 张敏洪. 感应半径可调的无线传感器网络生命周期的研究[J].中国科学院研究生院学报, 2011, 28(6):768-775.DOI: 10.7523/j.issn.2095-6134.2011.6.010.
[8] Heinzelman W R, Chandrakasan A, Balakrishnan H. Energy-efficient communication protocols for wireless microsensor networks[C]//Proceedings of 33rd Hawaii International Conference on System Sciences.Hawaii:IEEE, 2000: 8020-8030.DOI:10.1109/HICSS.2000.926982.
[9] 孔玉静, 侯鑫, 华尔天, 等.基于BP神经网络的无线传感器网络路由协议的研究[J].传感技术学报, 2013, 26(2): 246-251.DOI:10.3969/j.issn.1004-1699.2013.02.020.
[10] Sung W T. Employed BPN to multi-sensors data fusion for environment monitoring services[C]//Proceedings of the 6th International Conference on Autonomic and Trusted Computing, Berlin:Springer, 2009: 149-163.
[11] 孙凌逸, 黄先祥, 蔡伟, 等.基于神经网络的无线传感器网络数据融合算法[J]. 传感技术学报, 2011, 24(1): 122-127.DOI:10.3969/j.issn.1004-1699.2011.01.025.
[12] 余修武, 刘琴, 李向阳, 等.基于改进蚁群的BP神经网络WSN数据融合算法[J]. 北京邮电大学学报, 2018, 41(4): 91-96.DOI:10.13190/j.jbupt.2017-262.
[13] 侯鑫, 张东文, 钟鸣. 基于事件驱动和神经网络的无线传感器网络数据融合算法研究[J]. 传感技术学报, 2014, 27(1):142-148.DOI:10.3969/j.issn.1004-1699.2014.01.026.
[14] 胡强, 王海涛, 底楠, 等.无线传感网中一种智能数据融合算法的实现及仿真分析[J]. 传感技术学报, 2018, 31(2): 283-288.DOI:10.3969/j.issn.1004-1699.2018.02.022.
[15] Mirjalili S. How effective is the Grey Wolf optimizer in training multi-layer perceptrons[J]. Applied Intelligence, 2015, 43(1): 150-161.DOI:10.1007/s10489-014-0645-7.
[16] Ragulskis M, Vainoras A, Smidtaite R, et al.The logistic map of matrices[J]. Discrete and Continuous Dynamical Systems, 2017, 16(3): 927-944.DOI:10.3934/dcdsb.2011.16.927.
[17] Mirjalili S, Mirjalili S M, Lewis A. Grey wolf optimizer[J]. Advances in Engineering Software,2014, 69:46-61.DOI:10.1016/j.advengsoft.2013.12.007.
[18] Fouad A. A Hybrid grey wolf optimizer and genetic algorithm for minimizing potential energy function[J]. Memetic Computing, 2017, 9(9):1-13.DOI:10.1007/s12293-017-0234-5.
[19] Rodríguez L, Castillo O, Soria J, et al. A fuzzy hierarchical operator in the grey wolf optimizer algorithm[J]. Applied Soft Computing, 2017, 57:315-328.DOI:10.1016/j.asoc.2017.03.048.
[20] Wei C J, Yang J J, Zhang Z M. A distributed energy-efficient clustering routing protocol for wireless sensor networks[J]. Chinese Journal of Sensors and Actuators, 2013, 26(7): 1014-1018.DOI:10.3969/j.issn.1004-1699.2013.07.23.
[21] 黄利晓, 王晖, 袁利永, 等.基于能量均衡高效WSN的LEACH协议改进算法[J].通信学报, 2017, 38(Z2): 164-169.DOI:10.11959/j.issn.1000-436x.2017270.
[22] Jang S, Kim H Y, Kim N U, et al. Energy-efficient clustering scheme with concentric hierarchy[C]//IEEE International RF and Microwave Conference. Seremban:IEEE, 2011: 79-82.DOI:10.1109/RFM.2011.6168700.
[23] Ishizuka M, Aida M. Performance study of node placement in sensor network[C]//24th International Conference on Distributed Computing Systems. Hachioji:IEEE, 2004: 598-603.DOI:10.1109/ICDCSW.2004.1284093.
