Underwater Wireless Sensor Networks: An Energy-Efficient Clustering Routing Protocol Based on Data Fusion and Genetic Algorithms
Source abstract
Abstract
Due to the limited battery energy of underwater wireless sensor nodes and the difficulty in replacing or recharging the battery underwater, it is of great significance to improve the energy efficiency of underwater wireless sensor networks (UWSNs). We propose a novel energy-efficient clustering routing protocol based on data fusion and genetic algorithms (GAs) for UWSNs. In the clustering routing protocol, the cluster head node (CHN) gathers the data from cluster member nodes (CMNs), aggregates the data through an improved back propagation neural network (BPNN), and transmits the aggregated data to a sink node (SN) through a multi-hop scheme. The effective multi-hop transmission path between the CHN and the SN is determined through the enhanced GA, thereby improving transmission efficiency and reducing energy consumption. This paper presents the GA based on a specific encoding scheme, a particular crossover operation, and an enhanced mutation operation. Additionally, the BPNN employed for data fusion is improved by adopting an optimized momentum method, which can reduce energy consumption through the elimination of data redundancy and the decrease of the amount of transferred data. Moreover, we introduce an optimized CHN selecting scheme considering residual energy and positions of nodes. The experiments demonstrate that our proposed protocol outperforms its competitors in terms of the energy expenditure, the network lifespan, and the packet loss rate.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Underwater Vehicles and Communication Systems · Energy Efficient Wireless Sensor Networks · Water Quality Monitoring Technologies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China
Key Laboratory of Science and Technology on Advanced Underwater Acoustic Signal Processing, Chinese Academy of Sciences, Beijing 100190, China
School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
Funding
National Key Research and Development Program of China
References (52)
Show source-provided references
1. Cellular clustering-based interference-aware data transmission protocol for underwater acoustic sensor networks10.1109/TVT.2020.2964564 ↗
2. Underwater wireless sensor networks: A survey on enabling technologies, localization protocols, and internet of underwater things10.1109/ACCESS.2019.2928876 ↗
3. Zanaj, E., Gambi, E., Zanaj, B., Disha, D., and Kola, N. (2020). Underwater wireless sensor networks: Estimation of acoustic channel in shallow water. Appl. Sci., 10.10.3390/app10186393 ↗
4. An energy optimization clustering scheme for multi-hop underwater acoustic cooperative sensor networks10.1109/ACCESS.2020.2993544 ↗
5. Energy-balanced unequal layering clustering in underwater acoustic sensor networks10.1109/ACCESS.2018.2854276 ↗
6. Ahmed, G., Zhao, X., Fareed, M.M.S., and Fareed, M.Z. (2019). An energy-efficient redundant transmission control clustering approach for underwater acoustic networks. Sensors, 19.10.3390/s19194241 ↗
7. Durrani, M.Y., Tariq, R., Aadil, F., Maqsood, M., Nam, Y., and Muhammad, K. (2019). Adaptive node clustering technique for smart ocean under water sensor network (SOSNET). Sensors, 19.10.3390/s19051145 ↗
8. Zhang, W., Wang, J., Han, G., Zhang, X., and Feng, Y. (2019). A cluster sleep-wake scheduling algorithm based on 3D topology control in underwater sensor networks. Sensors, 19.10.3390/s19010156 ↗
9. Cooperative routing for energy efficient underwater wireless sensor networks10.1109/ACCESS.2019.2941422 ↗
10. A survey on routing techniques in underwater wireless sensor networks10.1016/j.jnca.2011.06.009 ↗
11. Joint routing and energy management in underwater acoustic sensor networks10.1109/TNSM.2017.2679482 ↗
12. Cross-layer network lifetime maximization in underwater wireless sensor networks10.1109/JSYST.2019.2920681 ↗
13. An energy-aware and void-avoidable routing protocol for underwater sensor networks10.1109/ACCESS.2018.2805804 ↗
14. Clustering routing algorithm and simulation of internet of things perception layer based on energy balance10.1109/ACCESS.2019.2944669 ↗
15. Hybrid multihop partition-based clustering routing protocol for WSNs
16. An enhanced hierarchical clustering approach for mobile sensor networks using fuzzy inference systems10.1109/JIOT.2017.2711248 ↗
17. Energy-saving algorithm and simulation of wireless sensor networks based on clustering routing protocol10.1109/ACCESS.2019.2956068 ↗
18. Yin, Q., Liu, M., Cheng, J., Ke, Y., and Chen, X. (2019). Mapping paddy rice planting area in northeastern china using spatiotemporal data fusion and phenology-based method. Remote Sens., 11.10.3390/rs11141699 ↗
19. Xiao, L., Xu, M., Chen, Y., and Chen, Y. (2019). Hybrid grey wolf optimization nonlinear model predictive control for aircraft engines based on an elastic BP neural network. Appl. Sci., 9.10.3390/app9061254 ↗
20. Sun, L., Cai, W., and Huang, X. (2010, January 21–24). Data Aggregation Scheme Using Neural Networks in Wireless Sensor Networks. Proceedings of the 2010 2nd International Conference on Future Computer and Communication, Wuhan, China.10.1109/ICFCC.2010.5497335 ↗
21. A novel data fusion strategy based on extreme learning machine optimized by bat algorithm for mobile heterogeneous wireless sensor networks10.1109/ACCESS.2020.2967118 ↗
22. Energy consumption in relay underwater acoustic sensor networks for NDN10.1109/ACCESS.2019.2907693 ↗
23. An effective order-aware hybrid genetic algorithm for capacitated vehicle routing problems in internet of things10.1109/ACCESS.2019.2925831 ↗
24. Path planning for autonomous underwater vehicles10.1109/TRO.2007.895057 ↗
25. Wang, S., and Wu, Y. (2017, January 16–18). A Genetic Algorithm for Energy Minimization Vehicle Routing Problem. Proceedings of the 2017 International Conference on Service Systems and Service Management, Dalian, China.
26. Cao, J., Li, Y., Zhao, S., and Bi, X. (2016, January 10–11). Genetic-Algorithm-Based Global Path Planning for AUV. Proceedings of the 2016 9th International Symposium on Computational Intelligence and Design (ISCID), Hangzhou, China.10.1109/ISCID.2016.2027 ↗
27. Heinzelman, W.R., Chandrakasan, A., and Balakrishnan, H. (2000, January 7). Energy-Efficient Communication Protocol for Wireless Microsensor Networks. Proceedings of the 33rd Annual Hawaii International Conference on System Sciences, Maui, HI, USA.
28. Fuzzy-logic-based clustering approach for wireless sensor networks using energy predication10.1109/JSEN.2012.2204737 ↗
29. Fault tolerance in WSN through PE-LEACH protocol10.1049/iet-wss.2018.5229 ↗
30. An energy-efficient reliable data transmission scheme for complex environmental monitoring in underwater acoustic sensor networks10.1109/JSEN.2015.2428712 ↗
31. Energy-efficient clustering algorithm in underwater sensor networks based on fuzzy c means and moth-flame optimization method10.1109/ACCESS.2020.2997066 ↗
32. Fuzzy and PSO based clustering scheme in underwater acoustic sensor networks using energy and distance parameters10.1007/s11277-019-06483-y ↗
33. Energy-efficient clustering algorithm for magnetic induction-based underwater wireless sensor networks10.1109/ACCESS.2018.2889910 ↗
34. Islam, T., and Lee, Y.K. (2019). A cluster based localization scheme with partition handling for mobile underwater acoustic sensor networks. Sensors, 19.10.3390/s19051039 ↗
35. An energy-efficient multi-level adaptive clustering routing algorithm for underwater wireless sensor networks10.1007/s10586-018-2376-8 ↗
36. Bansal, R., Maheshwari, S., and Awwal, P. (2019, January 10–11). Energy-Efficient Multilevel Clustering Protocol for Underwater Wireless Sensor Networks. Proceedings of the 2019 9th International Conference on Cloud Computing, Data Science & Engineering (Confluence), Noida, India.10.1109/CONFLUENCE.2019.8776984 ↗
37. Zou, Z., Lin, X., and Sun, J. (2019, January 6–8). A Cluster-Based Adaptive Routing Algorithm for Underwater Acoustic Sensor Networks. Proceedings of the 2019 International Conference on Intelligent Computing, Automation and Systems (ICICAS), Chongqing, China.10.1109/ICICAS48597.2019.00072 ↗
38. Cao, Y., and Zhang, L. (2017, January 22–24). Data Fusion of Heterogeneous Network Based on BP Neural Network and Improved SEP. Proceedings of the 2017 9th International Conference on Advanced Infocomm Technology (ICAIT), Chengdu, China.10.1109/ICAIT.2017.8388903 ↗
39. Yue, Y., Fan, H., Li, J., and Qin, Q. (2016, January 12–14). Large-Scale Mobile Wireless Sensor Network Data Fusion Algorithm. Proceedings of the 2016 IEEE International Conference on Big Data Analysis (ICBDA), Hangzhou, China.10.1109/ICBDA.2016.7509832 ↗
40. Goyal, N., Dave, M., and Verma, A.K. (2014, January 13–14). Fuzzy Based Clustering and Aggregation Technique for Under Water Wireless Sensor Networks. Proceedings of the 2014 International Conference on Electronics and Communication Systems (ICECS), Coimbatore, India.10.1109/ECS.2014.6892804 ↗
41. An enhanced k-means and ANOVA-based clustering approach for similarity aggregation in underwater wireless sensor networks10.1109/JSEN.2015.2443380 ↗
42. Wang, S., Zhao, B., Li, D., and Du, T. (2019, January 27–30). Data Fusion Algorithm of Wireless Sensor Based on Combination between Cluster Head Election Improvement and Neural Network. Proceedings of the 2019 Chinese Control Conference (CCC), Guangzhou, China.10.23919/ChiCC.2019.8866153 ↗
43. Gang, W., Xiangyang, L., Guangen, W., Yong, G., and Simin, M. (2020, January 18–20). Research on Data Fusion Method Based on Rough Set Theory and BP Neural Network. Proceedings of the 2020 International Conference on Computer Engineering and Application (ICCEA), Guangzhou, China.10.1109/ICCEA50009.2020.00065 ↗
44. Joint Data Collection and Fusion Using Mobile Sink in Heterogeneous Wireless Sensor Networks10.1109/JSEN.2020.3019372 ↗
45. Optimal routing and traffic scheduling for multihop cellular networks using genetic algorithm10.1109/TMC.2012.204 ↗
46. Genetic algorithm for energy-efficient QoS multicast routing10.1109/LCOMM.2012.112012.121467 ↗
47. An efficient genetic algorithm for anycast routing in delay/disruption tolerant networks10.1109/LCOMM.2010.04.092066 ↗
48. Energy efficient multi-hop path in wireless sensor networks using an enhanced genetic algorithm10.1016/j.ins.2019.05.094 ↗
49. Utilization of compact genetic algorithm for optimal shortest path selection to improve the throughput in mobile Ad-Hoc networks10.1007/s10586-018-2238-4 ↗
50. Xin, J., Zhong, J., Yang, F., Cui, Y., and Sheng, J. (2019). An improved genetic algorithm for path-planning of unmanned surface vehicle. Sensors, 19.10.3390/s19112640 ↗
51. Localization algorithms of underwater wireless sensor networks: A survey10.3390/s120202026 ↗
52. Underwater acoustic networks10.1109/48.820738 ↗