Sea Route Monitoring System Using Wireless Sensor Network Based on the Data Compression Algorithm
Sea Route Monitoring System Using Wireless Sensor Network Based on the Data Compression Algorithm
摘要
The wireless sensor network （WSN） plays an important role in monitoring the environment near the harbor in order to make the ships nearby out of dangers and to optimize the utilization of limited sea routes. Based on the historical data collected by the buoys with sensing capacities, a novel data compression algorithm called adaptive time piecewise constant vector quantization （ATPCVQ） is proposed to utilize the principal components. The proposed system is capable of lowering the budget of wireless communication and enhancing the lifetime of sensor nodes subject to the constrain of data precision. Furthermore, the proposed algorithm is verified by using the practical data in Qinhuangdao Port of China.
基金
key project of the National Natural Science Foundation of China，Information Acquirement and Publish System of Shipping Lane in Harbor，the fund of Beijing Science and Technology Commission Network Monitoring and Application Demonstration in Food Security,the Program for New Century Excellent Talents in University，National Natural Science Foundation of ChinaProject，Fundamental Research Funds for the Central Universities
参考文献13
-
1M. Johannessen, Ola, G. Babich, Nikolay, A. Vitali, Y. Frolov, Ivan, and S. Stein, Remote Sensing of Sea Ice in the Northern Sea Route: Studies and Applications. Springer-Verlag Berlin and Heidelberg GmbH & Co. K, 2005.
-
2I. F. Akyildiz, W. Su, Y. Sankarasu- bramaniam, and E. Cayirci, Wireless sensor networks: a survey, Computer networks, 2002, 38(4): 393-422.
-
3J. Yick, B. Mukherjee, and D. Ghosal, Wireless sensor network survey. Computer networks, 2008, 52(12): 2292-2330.
-
4ZHANG Zhongshan, LONG Keping, WANG Jianping and F. Dressier, On swarm intelligence inspired self-organized networking: its bionic mech-anisms, designing principles and optimi-zation approaches. IEEE Commun. Surv, Tut., 2014,16(1 ): 513-537.
-
5ZHANG Zhongshan, HUANGFU Wei, LONG Keping, ZHANG Xu, LIU Xiaoyuan and ZHONG Bin, On the designing principles and optimization approaches of bio-inspired self- organized network: a survey. SCIENCE CHINA Information Sciences, 2013, 56(7). p. 71301.
-
6N. Kimura and S. Latifi, A survey on data compression in wireless sensor networks. in Information Technology: Coding and Computing, 2005. ITCC 2005. International Conference on, vol. 2. IEEE, 2005, pp. 8-13.
-
7ZHU XiaoRong, WANG Yong, ZHU HongBo, Analysis on life model of large sensor networks. SCIENCE CHINA Information Sciences, 2013, 56(4), p. 042304.
-
8D. Petrovic, R. C. Shah, K. Ramchandran, and J. Rabaey, Data funneling: routing with aggregation and compression for wireless sensor networks, in Sensor Network Protocols and Applications. Proceedings of the FirstIEEE. 2003 IEEE International Workshop on, pp. 156-162.
-
9N. Xu, S. Rangwala, K. K. Chintalapudi, D. Ganesan, A. Broad, R. Govindan, and D. Estrin, A wireless sensor network for structural monitoring, in Proceedings of the 2nd international conference on Embedded networked sensor systems. ACM, 2004, pp. 13-24.
-
10W. H. Equitz, A new vector quantization clustering algorithm, Acoustics, Speech and Signal Processing, IEEE Trans-actions on, 1989, 37(10):1568-1575.
-
1田毅,汪克念,郭冲宇,赵长啸.基于CUDA的并行视觉背景提取算法[J].电光与控制,2017,24(5):40-43.
-
2金虎.无线传感器网络中一种自适应时间同步算法[J].黑龙江大学工程学报,2012,3(1):111-114. 被引量：2
-
3崔庆渝,黄学武,向阳.舵的自适应时间最优控制设计[J].中南大学学报（自然科学版）,2003,34(z1):34-36. 被引量：1
-
4邱天爽,王宏禹.自适应时间延迟估计的原理和应用：第六讲 时间延迟估计的应用[J].电子与仪表,1995(6):35-39.
-
5郭冲宇,杜慧敏,蒋忭忭,季凯柏.视觉背景提取算法关键技术研究[J].计算机应用研究,2017,34(5):1548-1552. 被引量：7
-
6王越,万洪.一种节能的无线传感器网络多跳自适应时间同步算法[J].传感技术学报,2013,26(11):1557-1563. 被引量：8
-
7陈泽宇,翁默颖.一种新的神经网络畸变不变模式识别方法[J].华东师范大学学报（自然科学版）,1994(2):47-52.
-
8陈昶,陈玮,邓则名,张小花.管道泄漏检测中自适应时间延迟估计方法的设计[J].工业仪表与自动化装置,2003(6):25-27. 被引量：3
-
9吕军晖,周刚,金毅.一种基于时间序列的自适应网络异常检测算法[J].北京航空航天大学学报,2009,35(5):636-639. 被引量：11
-
10杜旋,姜学峰,王正敏,李威,董惠良.分布式网络监控系统中网链路负荷均衡方法研究[J].微电子学与计算机,2017,34(2):101-104. 被引量：6
