Underwater Wireless Sensor Networks: An Energy-Efficient Clustering Routing Protocol Based on Data Fusion and Genetic Algorithms
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 rout...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-12-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/11/1/312 |
| _version_ | 1797543017080422400 |
|---|---|
| author | Xingxing Xiao Haining Huang Wei Wang |
| author_facet | Xingxing Xiao Haining Huang Wei Wang |
| author_sort | Xingxing Xiao |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T13:38:44Z |
| format | Article |
| id | doaj.art-6e9bb79015ed4404986374dd35e640e5 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T13:38:44Z |
| publishDate | 2020-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-6e9bb79015ed4404986374dd35e640e52023-11-21T03:12:03ZengMDPI AGApplied Sciences2076-34172020-12-0111131210.3390/app11010312Underwater Wireless Sensor Networks: An Energy-Efficient Clustering Routing Protocol Based on Data Fusion and Genetic AlgorithmsXingxing Xiao0Haining Huang1Wei Wang2Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, ChinaInstitute of Acoustics, Chinese Academy of Sciences, Beijing 100190, ChinaInstitute of Acoustics, Chinese Academy of Sciences, Beijing 100190, ChinaDue 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.https://www.mdpi.com/2076-3417/11/1/312data fusionunderwater wireless sensor networkback propagation neural networkclustering routing protocolgenetic algorithmnetwork lifespan |
| spellingShingle | Xingxing Xiao Haining Huang Wei Wang Underwater Wireless Sensor Networks: An Energy-Efficient Clustering Routing Protocol Based on Data Fusion and Genetic Algorithms Applied Sciences data fusion underwater wireless sensor network back propagation neural network clustering routing protocol genetic algorithm network lifespan |
| title | Underwater Wireless Sensor Networks: An Energy-Efficient Clustering Routing Protocol Based on Data Fusion and Genetic Algorithms |
| title_full | Underwater Wireless Sensor Networks: An Energy-Efficient Clustering Routing Protocol Based on Data Fusion and Genetic Algorithms |
| title_fullStr | Underwater Wireless Sensor Networks: An Energy-Efficient Clustering Routing Protocol Based on Data Fusion and Genetic Algorithms |
| title_full_unstemmed | Underwater Wireless Sensor Networks: An Energy-Efficient Clustering Routing Protocol Based on Data Fusion and Genetic Algorithms |
| title_short | Underwater Wireless Sensor Networks: An Energy-Efficient Clustering Routing Protocol Based on Data Fusion and Genetic Algorithms |
| title_sort | underwater wireless sensor networks an energy efficient clustering routing protocol based on data fusion and genetic algorithms |
| topic | data fusion underwater wireless sensor network back propagation neural network clustering routing protocol genetic algorithm network lifespan |
| url | https://www.mdpi.com/2076-3417/11/1/312 |
| work_keys_str_mv | AT xingxingxiao underwaterwirelesssensornetworksanenergyefficientclusteringroutingprotocolbasedondatafusionandgeneticalgorithms AT haininghuang underwaterwirelesssensornetworksanenergyefficientclusteringroutingprotocolbasedondatafusionandgeneticalgorithms AT weiwang underwaterwirelesssensornetworksanenergyefficientclusteringroutingprotocolbasedondatafusionandgeneticalgorithms |