Energy Conservation in Wireless Sensor Networks Using Partly-Informed Sparse Autoencoder
The energy in wireless sensor networks is considered a scarce commodity, especially in scenarios where it is difficult or impossible to provide supplementary energy sources once the initially available energy is used up. Even in cases where energy harvesting is feasible, effective energy utilization...
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8716655/ |
| _version_ | 1819170114831908864 |
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| author | Babajide O. Ayinde Abdulaziz Y. Barnawi |
| author_facet | Babajide O. Ayinde Abdulaziz Y. Barnawi |
| author_sort | Babajide O. Ayinde |
| collection | DOAJ |
| description | The energy in wireless sensor networks is considered a scarce commodity, especially in scenarios where it is difficult or impossible to provide supplementary energy sources once the initially available energy is used up. Even in cases where energy harvesting is feasible, effective energy utilization is still a crucial step for prolonging the network lifetime. Enhancement of life-time through efficient energy management is one of the essential ingredients underlining the design of any credible wireless sensor network. In this paper, we propose a sensor selection method using a novel and unsupervised neural network structure referred to as partly-informed sparse autoencoder (PISAE) that aims to reconstruct all sensor readings from a select few. The PISAE comprises three submodules, namely: the gate (which selects the most important sensors), encoder (encodes and compresses the data from select sensors), and decoder (decodes the output of the encoder and regenerates the readings of all initial sensors). Our approach relies on the premise that many sensors are redundant because their readings are spatially and temporally correlated and are predictable from the readings of a few other sensors in the network. Thus, overall network reliability and lifetime are enhanced by putting sensors with redundant readings to sleep without losing significant information. We evaluate the efficacy of the proposed method on three benchmark datasets and compare with existing results. The experimental results indicate the superiority of our approach compared with existing approaches in terms of accuracy and lifetime extension factor. |
| first_indexed | 2024-12-22T19:30:15Z |
| format | Article |
| id | doaj.art-4ac0ed5f19034907be1958db7746a9df |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-22T19:30:15Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-4ac0ed5f19034907be1958db7746a9df2022-12-21T18:15:08ZengIEEEIEEE Access2169-35362019-01-017633466336010.1109/ACCESS.2019.29173228716655Energy Conservation in Wireless Sensor Networks Using Partly-Informed Sparse AutoencoderBabajide O. Ayinde0https://orcid.org/0000-0001-7341-8799Abdulaziz Y. Barnawi1Department of Systems Engineering, King Fahd University of Petroleum and Minerals, Dhahran, Saudi ArabiaDepartment of Computer Engineering, King Fahd University of Petroleum and Minerals, Dhahran, Saudi ArabiaThe energy in wireless sensor networks is considered a scarce commodity, especially in scenarios where it is difficult or impossible to provide supplementary energy sources once the initially available energy is used up. Even in cases where energy harvesting is feasible, effective energy utilization is still a crucial step for prolonging the network lifetime. Enhancement of life-time through efficient energy management is one of the essential ingredients underlining the design of any credible wireless sensor network. In this paper, we propose a sensor selection method using a novel and unsupervised neural network structure referred to as partly-informed sparse autoencoder (PISAE) that aims to reconstruct all sensor readings from a select few. The PISAE comprises three submodules, namely: the gate (which selects the most important sensors), encoder (encodes and compresses the data from select sensors), and decoder (decodes the output of the encoder and regenerates the readings of all initial sensors). Our approach relies on the premise that many sensors are redundant because their readings are spatially and temporally correlated and are predictable from the readings of a few other sensors in the network. Thus, overall network reliability and lifetime are enhanced by putting sensors with redundant readings to sleep without losing significant information. We evaluate the efficacy of the proposed method on three benchmark datasets and compare with existing results. The experimental results indicate the superiority of our approach compared with existing approaches in terms of accuracy and lifetime extension factor.https://ieeexplore.ieee.org/document/8716655/Autoencoderenergy conservationenergy managementunsupervised feature extractionfeature importancefeature ranking |
| spellingShingle | Babajide O. Ayinde Abdulaziz Y. Barnawi Energy Conservation in Wireless Sensor Networks Using Partly-Informed Sparse Autoencoder IEEE Access Autoencoder energy conservation energy management unsupervised feature extraction feature importance feature ranking |
| title | Energy Conservation in Wireless Sensor Networks Using Partly-Informed Sparse Autoencoder |
| title_full | Energy Conservation in Wireless Sensor Networks Using Partly-Informed Sparse Autoencoder |
| title_fullStr | Energy Conservation in Wireless Sensor Networks Using Partly-Informed Sparse Autoencoder |
| title_full_unstemmed | Energy Conservation in Wireless Sensor Networks Using Partly-Informed Sparse Autoencoder |
| title_short | Energy Conservation in Wireless Sensor Networks Using Partly-Informed Sparse Autoencoder |
| title_sort | energy conservation in wireless sensor networks using partly informed sparse autoencoder |
| topic | Autoencoder energy conservation energy management unsupervised feature extraction feature importance feature ranking |
| url | https://ieeexplore.ieee.org/document/8716655/ |
| work_keys_str_mv | AT babajideoayinde energyconservationinwirelesssensornetworksusingpartlyinformedsparseautoencoder AT abdulazizybarnawi energyconservationinwirelesssensornetworksusingpartlyinformedsparseautoencoder |