It was found in this paper that the sensor node model used in the previous underwater sensor network simulation has the defects that are difficult to overcome. Boolean model has sacrificed more coverage performance to enhance the efficiency of simulation calculation, while continuous probability model(CPM) has given up the computational efficiency to approach the actual coverage performance simulation.In order to find a sensor node model which can not only meet the need of the covering performance but improve the calculation efficiency, the paper first proposes the concept of perception factor (PF) from the point of view of the effective detection of targets; and then puts forward a sensor node discrete model based on PF. After that, it is hypothesized that the nodes can be divided without limit; discrete perceptual factor model (DPFM) is extended to continuous perceptual factor model (CPFM), and the validity of CPFM is proved. Finally,CPFM is further optimized for multi ring perceptual factor model (MRPFM) to avoid the calculation of the logarithm.In this paper, the coverage performance and computational efficiency of MRPFM are simulated and compared with other models. The same model parameters and environmental parameters are set up by simulation.First, the coverage performance of CPFM in the condition of positive triangle, regular and regular hexagon deployment is simulated. It is found that the best way is not a regular hexagon deployment, but a positive triangle deployment.Then, this paper simulates the positive triangle optimal deployment of MRPFM and continuous probability model, and compares the coverage performance of the three models. It is obvious that the performance of MRPFM is a bit lower than that of continuous exponential probability model(CEPM), but significantly higher than Boolean model;Finally, this paper simulates the time of the deployment of Boolean model, CEPM, multi ring probability model(MRPM) and MRPFM, based on different node numbers. The simulation map clearly shows that, compared with using CEPM, the time to complete the deployment calculation of using the MRPFM is obviously reduced, and the more the number of nodes, the more obvious the effect of time reduction; and compared with using Boolean model and MRPM, the time is also reduced to some extent. Therefore, the MRPFM has practical value in underwater sensor network simulation
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
References
[1].罗强. 水下无线传感器网络的部署研究．. 长沙: 国防科学技术大学, 2011.
[2].方伟,宋鑫宏.基于Voronoi图盲区的无线传感器网络覆盖控制部署策略.物理学报, 2014, 63(22):220701:1-10.
[3].陈娟,徐汀荣,兰希.基于网格的分布式能量有效无线传感器网络k覆盖多连通部署算法.计算机应用研究, 2014, 31(8):2466-2468.
[4].邹长忠.基于多目标遗传算法的无线传感器网络重新部署方法.福州大学学报（自然科学版）, 2015, 43(3):317-321.
[5].Shiei Li, Dongqian Ma, Qiangyi Li, et al. Nodes deployment algorithm based on perceived probability of heterogeneous wireless sensor network. In:2013 International Conference on Advanced Mechatronic Systems. IEEE Computer Society, 2013:374-378.
[6].Xiaoxiang Han. Mobile node deployment based on improved probability model and dynamic particle swarm algorithm. Journal of Networks, 2014,9(1):131-137.
[7].Ningning Wu, Juwei Zhang, Qiangyi Li, et al. Mobile nodes deployment scheme design based on perceived probability model in heterogeneous wireless sensor network. Journal of Robotics and Mechatronics, 2014, 26(5) : 616-621.
[8].Dan Tao, Shaojie Tang, Liang Liu. A coverage enhancement algorithm based on constrained artificial fish-swarm in directional sensor networks. Journal of Internet Technology, 2014,15,(1):43-52.
[9].Chakrabarty K, Iyengar S, Qi H, et al. Grid coverage for surveillance and target location in distributed sensor networks [J]. IEEE Transactions on Computers, 2002, 51(12): 1448-1453.
[10].Chakrabarty K, Iyengar S S, Hairong Q, et al. Coding theory framework for target location in distributed sensor networks[A].Proceedings of the International Conference on Information Technology: Coding and Computing[C], Washington, DC, USA IEEE Computer Society 2001:130-134.
[11].Y. Zou, K. Chakrabarty. Uncertainty-aware and coverage-oriented deployment for sensor networks [J]. Journal of Parallel and Distributed Computing, 2004, 64(7): 788-798.
[12].Y. Zou, K. Chakrabarty. Sensor Deployment and target localization in distributed sensor networks [J]. ACM Transactions on Embedded Computing Systems, 2004, 3(1):61-91.
[13].Jingbin Zhang, Ting Yan, S. H. Son. Deployment strategies for differentiated detection in wireless sensor networks[C].2006 3rd Annual IEEE Communications Society on sensor and Ad-Hoc Communications and Networks.2006, 1:316-325.
[14].屈玉贵,翟羽佳,蔺智挺等.一种新的无线传感器网络传感器放置模型[J],北京邮电大学学报,2004, 27(6):1-5.
[15].李石坚,徐从富,吴朝晖等.面向目标跟踪的传感器网络布局优化及保护策略[J].电子学报, 2006, 34(1): 71-76.
[16].Ertan Onur, Cem Ersoy. Sensing coverage and breach paths in surveillance wireless sensor networks, in IEEE Monograph Sensor Network Operations(chapter 12), Shashi Phoha and Thomas F. La Porta and Christopher Griffin, Eds. IEEE Press, 2004.
[17].M. Hefeeda, H. Ahmadi. A Probabilistic Coverage Protocol for Wireless Sensor Networks, In Proc. of IEEE International Conference on Network Protocols (ICNP’07), Beijing, China, 2007.
[18].万强.无线传感器网络的两点联合检测模型及空间覆盖的研究.硕士学位论文:西安电子科技大学,2013.
[19].Seapahn Meguerdichian, Farinaz Koushanfar. Exposure in wireless ad hoc sensor networks, in ACM International Conference on Mobile Computing and Networking(MobiCom) 2001:139-150.
[20].曹峰,刘丽萍,王智. 能量有效的无线传感器网络部署. 信息与控制. 2006,35(2):147-153.
[21].赵仕俊,张朝晖.无线传感器网络正六边形节点覆盖模型研究.计算机工程.2006,36(20):113-118.
{{custom_fnGroup.title_en}}
Footnotes
{{custom_fn.content}}
PDF(2363945 KB)
