| Citation: |
XIANG Dan, ZHAI Chenkai, LIN Libin, HE Dengyu, WANG Huihua, GAO Pan, QIU Haiyang. Underwater Sensor Network Node Locator Based on Improved Whale Optimization Iterative Algorithm[J]. Journal of South China Normal University (Natural Science Edition), 2024, 56(2): 119-128. DOI: 10.6054/j.jscnun.2024030
|
To address the issues of low node localization accuracy caused by the limited number of anchor nodes and large iteration errors in underwater wireless sensor networks, an improved whale optimization-Newton iteration (IWONI) algorithm for underwater three-dimensional node localization was proposed. IWONI first uses the Newton iteration algorithm to establish a corresponding rule for the distance relationship between nodes, and utilizes the estimated target position and correction factor to provide a dynamic search area for the improved whale optimization algorithm. Secondly, a fitness function weighted by measurement error is established as the judgment criterion, and the improved whale optimization algorithm is used for iterative solution to obtain the optimal solution. Finally, the network node positions are calculated through the localization equation. To validate the performance of the IWONI algorithm, comparative experiments were conducted on localization error, convergence performance, and localization success rate against time difference of arrival algorithms (TDOA-Taylor, TDOA-CHAN), ranging algorithms (least squares method, Gauss-Newton iteration), and Newton iteration algorithm. The impact of the number of nodes on localization accuracy was also investigated. The comparison results show that: (1)The IWONI algorithm has significantly lower localization error and faster convergence speed than other compared algorithms. (2)The IWONI algorithm has a high localization success rate of 92% even in the presence of high measurement noise, which is significantly better than other compared algorithms. (3)In the case of a constant communication radius, employing 5 to 7 sensor nodes can achieve a balance between localization accuracy and cost effectiveness in the IWONI algorithm.
| [1] |
LUO J H, YANG Y, WANG Z Y, et al. Localization algorithm for underwater sensor network: a review[J]. IEEE Internet of Things Journal, 2021, 8(17): 13126-13144. doi: 10.1109/JIOT.2021.3081918
|
| [2] |
侯森林, 杜秀娟, 李梅菊, 等. 水下无线传感器网络节点混合定位与优化算法[J]. 计算机工程, 2018, 44(12): 134-139. https://www.cnki.com.cn/Article/CJFDTOTAL-JSJC201812022.htm
HOU S L, DU X J, LI M J, et al. Hybrid location and optimization algorithm for underwater wireless sensor network[J]. Computer Engineering, 2018, 44(12): 134-139. https://www.cnki.com.cn/Article/CJFDTOTAL-JSJC201812022.htm
|
| [3] |
相雯, 殷旭旺, 王宏诗. 提高海洋人才培养质量助力海洋强国建设[J]. 中国多媒体与网络教学学报, 2024(2): 118-121. https://www.cnki.com.cn/Article/CJFDTOTAL-JMNT202402030.htm
XIANG W, YIN X W, WANG H S. Improving the quality of Marine personnel training will help build China into a maritime power[J]. Chinese Journal of Multimedia and Network Teaching, 2024(2): 118-121. https://www.cnki.com.cn/Article/CJFDTOTAL-JMNT202402030.htm
|
| [4] |
LUO J H, FAN L Y, WU S, et al. Research on localization algorithms based on acoustic communication for underwater sensor networks[J]. Sensors, 2018, 18(1): 67-91. doi: 10.3390/s18010067
|
| [5] |
ZHAO H Y, YAN J, LUO X Y, et al. Privacy preserving solution for the asynchronous localization of underwater sensor networks[J]. IEEE/CAA Journal of Automatica Sinica, 2020, 7(6): 1511-1527. doi: 10.1109/JAS.2020.1003312
|
| [6] |
曾福庚, 郭新辰. 基于马氏链蒙特卡罗的WSN节点定位算法[J]. 海南热带海洋学院学报, 2019, 26(2): 82-86. https://www.cnki.com.cn/Article/CJFDTOTAL-QZDX201902014.htm
ZENG F G, GUO X C. Amarkov chain monte carlo node localization algorithm based on wireless sensors[J]. Journal of Hainan Tropical Ocean University, 2019, 26(2): 82-86. https://www.cnki.com.cn/Article/CJFDTOTAL-QZDX201902014.htm
|
| [7] |
GUO Y, HAN Q H, KANG X Y. Underwater sensor networks localization based on mobility-constrained beacon[J]. Wireless Networks, 2020, 26(4): 2585-2594. doi: 10.1007/s11276-019-02023-5
|
| [8] |
LI Y, LIU M Q, ZHANG S L, et al. Node dynamic localization and prediction algorithm for internet of underwater things[J]. IEEE Internet of Things Journal, 2022, 9(7): 5380-5390. doi: 10.1109/JIOT.2021.3108424
|
| [9] |
QIN Y H, SUN Y H, LIU H R, et al. Joint time synchronization and localization of underwater mobile node[J]. Wireless Networks, 2023, 29(8): 3737-3746. doi: 10.1007/s11276-023-03441-2
|
| [10] |
KULANDAIVEL M, NATARAJAN A, VELAYUTHAM S, et al. Compressive sensing node localization method using autonomous underwater vehicle network[J]. Wireless Personal Communications, 2022, 126(3): 2781-2799. doi: 10.1007/s11277-022-09841-5
|
| [11] |
ZHANG W B, HAN G J, WANG X, et al. A node location algorithm based on node movement prediction in underwater acoustic sensor networks[J]. IEEE Transactions on Vehicular Technology, 2020, 69(3): 3166-3178. doi: 10.1109/TVT.2019.2963406
|
| [12] |
LIN Y, TAO H X, TU Y, et al. A node self-localization algorithm with a mobile anchor node in underwater acoustic sensor networks[J]. IEEE Access, 2019, 7: 43773-43780. doi: 10.1109/ACCESS.2019.2904725
|
| [13] |
LI Y, LIU M Q, ZHANG S L, et al. Particle system-based ordinary nodes localization with delay compensation in UWSNs[J]. IEEE Sensors Journal, 2022, 22(7): 7157-7168. doi: 10.1109/JSEN.2022.3149823
|
| [14] |
LIU H M, XU B, LIU B. A novel predictive localization algorithm for underwater wireless sensor networks[J]. Wireless Networks, 2023, 29(1): 303-319. doi: 10.1007/s11276-022-03107-5
|
| [15] |
MIRJALILI S, LEWIS A. The whale optimization algo-rithm[J]. Advances in Engineering Software, 2016, 95: 51-67. doi: 10.1016/j.advengsoft.2016.01.008
|
| [16] |
梁婷蓉. 基于水下传感器网络的目标被动定位算法研究[D]. 天津: 天津城建大学, 2023.
LIANG T R. Research on passive target location algorithm based on underwater sensor network[D]. Tianjin: Tianjin Chengjian University, 2023.
|
| [17] |
鲍晶晶, 蒋志迪, 刘尉悦, 等. 基于Chan和粒子群的超短基线协同定位算法[J]. 科技通报, 2022, 38(1): 32-36;44. https://www.cnki.com.cn/Article/CJFDTOTAL-KJTB202201006.htm
BAO J J, JIANG Z D, LIU W Y, et al. Ultra-short baseline collaborative localization algorithm based on Chan and particle swarm optimization[J]. Bulletin of Science and Technology, 2022, 38(1): 32-36;44. https://www.cnki.com.cn/Article/CJFDTOTAL-KJTB202201006.htm
|
| [18] |
胡荣明, 苏瑞鹏, 竞霞, 等. 融合改进小波去噪与T-Taylor的井下定位算法[J]. 测绘通报, 2023(2): 46-51. https://www.cnki.com.cn/Article/CJFDTOTAL-CHTB202302008.htm
HU R M, SU R P, JING X, et al. A downhole localization algorithms incorporating improved wavelet denoising and T- Taylor[J]. Bulletin of Surveying and Mapping, 2023(2): 46-51. https://www.cnki.com.cn/Article/CJFDTOTAL-CHTB202302008.htm
|
| [19] |
YANG J M, DENG S H, LIN M M, et al. An adaptive calibration algorithm based on RSSI and LDPLM for indoor ranging and positioning[J]. Sensors, 2022, 22(15): 5689-5704. doi: 10.3390/s22155689
|
| [20] |
刘利利. 三维水下移动目标定位及路径规划技术研究[D]. 焦作: 河南理工大学, 2023.
LIU L L. Research on 3D underwater moving target location and path planning[D]. Jiaozuo: Henan Polytechnic University, 2023.
|
| [21] |
QU J S, SHI H N, QIAO N, et al. New three-dimensional positioning algorithm through integrating TDOA and Newton's method[J]. EURASIP Journal on Wireless Communications and Networking, 2020, 2020(1): 1-8. doi: 10.1186/s13638-019-1618-7
|
| 1. |
刘威，徐徐，万雅琼，卢晓强，刘立，李佳琦，王雪霁，王翊肖，刘燕. 以海口市为例分析热带地区城市化过程中不同生境鸟类多样性特征. 环境科学研究. 2025(01): 29-38 .
|
Supported by: Beijing Renhe Information Technology Co., Ltd. 
DownLoad: