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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | Energy Prediction of Sensor Nodes using Deep Learning |
| Authors Name: | Yash Goswami , Fatima Inamdar , Mandar Mokashi , Namrata Thakur |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_199866
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| Published Paper Id: | IJNRD2306511 |
| Published In: | Volume 8 Issue 6, June-2023 |
| DOI: | |
| Abstract: | Wireless Sensor Networks (WSNs) have various applications, but the limited battery life of sensor nodes which makes energy consumption a crucial characteristic of sensor networks. Despite recent research focusing heavily on energy-conscious applications and operating systems, energy consumption remains a limiting factor. Once sensor nodes have already been deployed. It is challenging and sometimes even impossible to change batteries which may result in erroneous lifetime prediction of sensor network causing high costs and may render the network useless before its purpose is fulfilled. The models show high accuracy in predicting energy consumption, enabling the development of more efficient and sustainable WSNs. The significance of this approach extends to other domains such as IoT and cyber-physical systems, enabling accurate energy prediction and efficient resource management. The research demonstrates that the use of deep learning techniques significantly improves the accuracy of energy prediction compared to traditional machine learning techniques. This approach can assist researchers and engineers in developing energy-efficient WSNs, ensuring their sustainability. Moreover, the proposed method can be applied in a variety of applications such as energy-efficient routing and adaptive power management in WSNs. The study provides valuable insights into the energy consumption of the sensor nodes & opens up new avenues for developing sustainable and efficient WSNs. |
| Keywords: | sensor nodes, deep learning, feed-forward neural network, energy prediction, sensor network |
| Cite Article: | "Energy Prediction of Sensor Nodes using Deep Learning", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.8, Issue 6, page no.f98-f108, June-2023, Available :http://www.ijnrd.org/papers/IJNRD2306511.pdf |
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| ISSN: |
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID:IJNRD2306511 Registration ID: 199866 Published In: Volume 8 Issue 6, June-2023 DOI (Digital Object Identifier): Page No: f98-f108 Country: Nagpur, Maharashtra, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2306511 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2306511 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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