一种改进区域生长法的WSN数据采集算法研究
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摘要
在开展大规模区域数据采集的无线传感网中,由于相邻区域数据的相似性造成的冗余会使得传感器能量耗费过多而过早死亡.针对该问题,提出一种基于区域生长法的WSN数据采集算法(DAA-RGM),该算法划分传感器网络部署区域,划分后根据子区域采集的历史数据选择一个与该子区域的历史数据变化趋势相似性最高的节点为代表节点.上述方法能够保证区域监测正常的情况下减少大量的冗余数据,因此能够较大幅度的延长传感器网络的生存时间,实验结果验证了该算法的有效性和可靠性.
WSN is widely applied in data acquisition in a large-scale region. For the similarity of data in adjacent regions,the nodes will acquire a large amount of redundant data which easily cause the network failure for large consumption of energy. To solve this problem,we propose an improved data acquisition algorithm based on region growing method that called DAA-GGM. The algorithm firstly acquires the whole networks data,and set part of nodes as seed randomly. Then it uses region growing method to divide the deployment area into many subareas where representative nodes which is most similar to the trends of the historical data will be chosen.The result of simulation shows that the proposed method of this paper can effectively reduce a lot of redundant data and prolong the network's survival time on the normal circumstance in the monitoring area.
WSN is widely applied in data acquisition in a large-scale region. For the similarity of data in adjacent regions,the nodes will acquire a large amount of redundant data which easily cause the network failure for large consumption of energy. To solve this problem,we propose an improved data acquisition algorithm based on region growing method that called DAA-GGM. The algorithm firstly acquires the whole networks data,and set part of nodes as seed randomly. Then it uses region growing method to divide the deployment area into many subareas where representative nodes which is most similar to the trends of the historical data will be chosen.The result of simulation shows that the proposed method of this paper can effectively reduce a lot of redundant data and prolong the network's survival time on the normal circumstance in the monitoring area.
引文
[1]Zhu Lu,Liu Yuan-yuan,Ci Bai-shan,et al.The method of data aggregation for w ireless sensor netw ork based on cluster compressed sensing of multi-sparsity basis[J].Chinese Journal of Sensors and Actuators,2016,29(3):417-422.
[2]Xu Xiao-bin,Zhang Guang-wei,Sun Qi-bo,et al.Precision configurable data aggregation algorithm in WSNs[J].Acta Electronica Sinica,2014,42(6):1205-1209.
[3]Tan De-kun,Fu Xue-feng,Zhao Jia,et al.A data aggregation method based on abnormal data-driven in clusters of w ireless sensor netw orks[J].Chinese Journal of Sensors and Actuators,2017,30(2):306-312.
[4]Fu H,Liu Y,Zhang Z,et al.Hybrid data fusion method using Bayesian estimation and fuzzy cluster analysis for WSN[J].Lecture Notes in Electrical Engineering,2014,41(10):809-816.
[5]Li L,Li W J.The analysis of data fusion energy consumption in WSN[C].International Conference on System Science,Engineering Design and M anufacturing Informatization,IEEE,2011:310-313.
[6]Wu W,Xu B,Cao M.A data fusion method of WSNs based on glow w orm sw arm algorithm optimized BP neural netw orks[J].RISTI(Revista Iberica de Sistemas e Tecnologias de Informacao),2016,8(17A):73-84.
[7]Davood I,Abawajy J H,Sara G,et al.A data fusion method in w ireless sensor netw orks[J].Sensors,2015,15(2):2964-2979.
[8]Aarju E,Kapoor E S,Bansal E A.An optimal energy allocation scheme for WSN based on data fusion In the presence of transfaulty nodes[J].Imperial Journal of Interdisciplinary Research,2016,2(10):78:89.
[9]Chen X,Li Y.Optimal energy allocation to maximize network utility of w ireless sensor netw orks based on data fusion[C].Sixth International Conference on Intelligent Systems Design and Engineering Applications,IEEE Computer Society,2015,10(21):551-554.
[10]Izadi D,Abawajy J H,Ghanavati S,et al.A data fusion method in w ireless sensor netw orks[J].Sensors,2015,15(2):2964-2979.
[11]Boyinbode O,Le H,Takizawa M.A survey on clustering algorithms for w ireless sensor netw orks[J].International Journal of Space-Based and Situated Computing,2011,1(2-3):130-136.
[12]Wang Jian-hua,Luo Yan-hong,Wen Qian.Road segmentation recognition of improved region grow ing method in color image[J].Automation and Instrumentation,2014,22(5):158-159.
[13]Wang Xiao-hong,Zhao Yu-qian,Liao Miao,et al.Automated kidneys segmentation from abdominal CT scans based on spatial fuzzy C-means and region grow ing[J].Journal of Central South University(Science and Technology),2014,45(10):3463-3469.
[14]Wang Jian-wei,Li Xing-min.Region growing in color image based on quaternion multiplication vector product properties[J].Computer Science,2015,42(11A):166-168.
[15]Jiang Wei,Lv Xiao-qi,Ren Xiao-ying,et al.3D segmentation method combining region grow ing and graph cut for coronary arteries computed tomography angiography images[J].Journal of Computer Applications,2015,35(5):1462-1466.
[16]Zhai Shuang,Qian Zhi-hong,Liu Xiao-hui,et al.Data compression algorithm based on sequence correlation for WSN[J].Journal of Electronics&Information Technology,2016,38(3):713-719.
[17]Li Peng,Wang Jian-xin,Ding Chang-song.Energy-efficient data gathering scheme based on compressive sensing in w ireless sensor netw orks[J].Acta Automatica Sinica,2016,42(11):1648-1656.
[1]朱路,刘媛媛,慈白山,等.多稀疏基分簇压缩感知的WSN数据融合方法[J].传感技术学报,2016,29(3):417-422.
[2]徐晓斌,张光卫,孙其博,等.一种误差可控传输均衡的WSN数据融合算法[J].电子学报,2014,42(6):1205-1209.
[3]谭德坤,付雪峰,赵嘉,等.基于异常数据驱动的WSN簇内数据融合方法[J].传感技术学报,2017,30(2):306-312.
[12]王建华,骆岩红,文茜.彩色图像区域生长算法的道路分割识别[J].自动化与仪器仪表,2014,22(5):158-159.
[13]王晓红,赵于前,廖苗,等.基于空间模糊C均值与区域生长的腹部CT序列图像肾脏自动分割[J].中南大学学报(自然科学版),2014,45(10):3463-3469.
[14]王建伟,李兴民.基于四元数矢量积算法的彩色图像区域生长算法[J].计算机科学,2015,42(11A):166-168.
[15]姜伟,吕晓琪,任晓颖,等.结合区域生长与图割算法的冠状动脉CT血管造影图像三维分割[J].计算机应用,2015,35(5):1462-1466.
[16]翟双,钱志鸿,刘晓慧,等.无线传感器网络中基于序列相关性的数据压缩算法[J].电子与信息学报,2016,38(3):713-719.
[17]李鹏,王建新,丁长松.WSN中基于压缩感知的高能效数据收集方案[J].自动化学报,2016,42(11):1648-1656.
[2]Xu Xiao-bin,Zhang Guang-wei,Sun Qi-bo,et al.Precision configurable data aggregation algorithm in WSNs[J].Acta Electronica Sinica,2014,42(6):1205-1209.
[3]Tan De-kun,Fu Xue-feng,Zhao Jia,et al.A data aggregation method based on abnormal data-driven in clusters of w ireless sensor netw orks[J].Chinese Journal of Sensors and Actuators,2017,30(2):306-312.
[4]Fu H,Liu Y,Zhang Z,et al.Hybrid data fusion method using Bayesian estimation and fuzzy cluster analysis for WSN[J].Lecture Notes in Electrical Engineering,2014,41(10):809-816.
[5]Li L,Li W J.The analysis of data fusion energy consumption in WSN[C].International Conference on System Science,Engineering Design and M anufacturing Informatization,IEEE,2011:310-313.
[6]Wu W,Xu B,Cao M.A data fusion method of WSNs based on glow w orm sw arm algorithm optimized BP neural netw orks[J].RISTI(Revista Iberica de Sistemas e Tecnologias de Informacao),2016,8(17A):73-84.
[7]Davood I,Abawajy J H,Sara G,et al.A data fusion method in w ireless sensor netw orks[J].Sensors,2015,15(2):2964-2979.
[8]Aarju E,Kapoor E S,Bansal E A.An optimal energy allocation scheme for WSN based on data fusion In the presence of transfaulty nodes[J].Imperial Journal of Interdisciplinary Research,2016,2(10):78:89.
[9]Chen X,Li Y.Optimal energy allocation to maximize network utility of w ireless sensor netw orks based on data fusion[C].Sixth International Conference on Intelligent Systems Design and Engineering Applications,IEEE Computer Society,2015,10(21):551-554.
[10]Izadi D,Abawajy J H,Ghanavati S,et al.A data fusion method in w ireless sensor netw orks[J].Sensors,2015,15(2):2964-2979.
[11]Boyinbode O,Le H,Takizawa M.A survey on clustering algorithms for w ireless sensor netw orks[J].International Journal of Space-Based and Situated Computing,2011,1(2-3):130-136.
[12]Wang Jian-hua,Luo Yan-hong,Wen Qian.Road segmentation recognition of improved region grow ing method in color image[J].Automation and Instrumentation,2014,22(5):158-159.
[13]Wang Xiao-hong,Zhao Yu-qian,Liao Miao,et al.Automated kidneys segmentation from abdominal CT scans based on spatial fuzzy C-means and region grow ing[J].Journal of Central South University(Science and Technology),2014,45(10):3463-3469.
[14]Wang Jian-wei,Li Xing-min.Region growing in color image based on quaternion multiplication vector product properties[J].Computer Science,2015,42(11A):166-168.
[15]Jiang Wei,Lv Xiao-qi,Ren Xiao-ying,et al.3D segmentation method combining region grow ing and graph cut for coronary arteries computed tomography angiography images[J].Journal of Computer Applications,2015,35(5):1462-1466.
[16]Zhai Shuang,Qian Zhi-hong,Liu Xiao-hui,et al.Data compression algorithm based on sequence correlation for WSN[J].Journal of Electronics&Information Technology,2016,38(3):713-719.
[17]Li Peng,Wang Jian-xin,Ding Chang-song.Energy-efficient data gathering scheme based on compressive sensing in w ireless sensor netw orks[J].Acta Automatica Sinica,2016,42(11):1648-1656.
[1]朱路,刘媛媛,慈白山,等.多稀疏基分簇压缩感知的WSN数据融合方法[J].传感技术学报,2016,29(3):417-422.
[2]徐晓斌,张光卫,孙其博,等.一种误差可控传输均衡的WSN数据融合算法[J].电子学报,2014,42(6):1205-1209.
[3]谭德坤,付雪峰,赵嘉,等.基于异常数据驱动的WSN簇内数据融合方法[J].传感技术学报,2017,30(2):306-312.
[12]王建华,骆岩红,文茜.彩色图像区域生长算法的道路分割识别[J].自动化与仪器仪表,2014,22(5):158-159.
[13]王晓红,赵于前,廖苗,等.基于空间模糊C均值与区域生长的腹部CT序列图像肾脏自动分割[J].中南大学学报(自然科学版),2014,45(10):3463-3469.
[14]王建伟,李兴民.基于四元数矢量积算法的彩色图像区域生长算法[J].计算机科学,2015,42(11A):166-168.
[15]姜伟,吕晓琪,任晓颖,等.结合区域生长与图割算法的冠状动脉CT血管造影图像三维分割[J].计算机应用,2015,35(5):1462-1466.
[16]翟双,钱志鸿,刘晓慧,等.无线传感器网络中基于序列相关性的数据压缩算法[J].电子与信息学报,2016,38(3):713-719.
[17]李鹏,王建新,丁长松.WSN中基于压缩感知的高能效数据收集方案[J].自动化学报,2016,42(11):1648-1656.