Maximizing energy efficiency in wireless sensor networks for data transmission: A Deep Learning-Based Grouping Model approach

Surenther, I.; Sridhar, K.P.; Kingston Roberts, Michaelraj

doi:10.1016/j.aej.2023.10.016

Cited by 19 publications

(1 citation statement)

References 35 publications

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“…The "Deep Learning based Grouping Model Approach" (DL-GMA) has been presented in Ref. 38 to optimize WSN energy consumption. DL-GMA utilizes cutting-edge "deep learning" methods, like "Recurrent Neural Network" (RNN) along with "Long Short-Term Memory" (LSTM), which improves energy utilization by selecting, forming, and maintaining Cluster Heads (CH) efficiently.…”

Section: Literature Review Of Energy Efficient Protocols For Wsnsmentioning

confidence: 99%

A comprehensive review of energy efficient routing protocols for query driven wireless sensor networks

Bekal,

Kumar,

Mane

et al. 2024

F1000Res

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In this current era of communications and networking, The Internet of things plays the main role in the making of smart communication and networking. In this article, we have focused on the literature survey on wireless sensor networks which are energy efficient. Various standard protocols are reviewed along with some enhanced protocols which makes the network energy efficient. The comparison of the standard and enhanced protocols with respect to various applications in wireless sensor networks is thoroughly done in this article. The outcomes of the enhanced protocols are also briefly discussed. For easier analysis to future researchers, a comparative table which lists the enhanced protocols which are compared with standard counterparts along with the factors for energy efficiency of the protocols. This article also comments on the issues and challenges of the protocols which can be further analyzed for making the wireless sensor network more energy efficient.

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Section: Literature Review Of Energy Efficient Protocols For Wsnsmentioning

confidence: 99%

A comprehensive review of energy efficient routing protocols for query driven wireless sensor networks

Bekal,

Kumar,

Mane

et al. 2024

F1000Res

View full text Add to dashboard Cite

show abstract

Advancements in Metal‐Organic, Enzymatic, and Nanocomposite Platforms for Wireless Sensors of the Next Generation

Mohan,

Virender,

Gupta

et al. 2024

Adv Funct Materials

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Advanced wireless sensors, incorporating metal‐organic frameworks (MOFs), enzymatic systems, and nanocomposites, offer unparalleled solutions for monitoring analytes and human physiological signals. These cutting‐edge sensors, when used with external devices, enable real‐time monitoring of analytes and physicochemical processes within the human body, thereby enhancing the understanding of complex biological systems. This study presents advancements in sensor development, fabrication techniques, and user‐friendly protocols. The performance of these sensors is evaluated based on their selectivity, sensitivity, and detection limits. Moreover, this article explores limitations, challenges, and key strategies to enhance analyte recognition from onsite environmental and biological species, ensuring human point‐of‐care safety.

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Secure data transmission in wireless networking through node deployment and Artificial Bird optimized deep learning network

Luqman,

Faridi

2024

Telecommun Syst

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Maximizing energy efficiency in wireless sensor networks for data transmission: A Deep Learning-Based Grouping Model approach

Cited by 19 publications

References 35 publications

A comprehensive review of energy efficient routing protocols for query driven wireless sensor networks

A comprehensive review of energy efficient routing protocols for query driven wireless sensor networks

Advancements in Metal‐Organic, Enzymatic, and Nanocomposite Platforms for Wireless Sensors of the Next Generation

Secure data transmission in wireless networking through node deployment and Artificial Bird optimized deep learning network

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