Optimization of the Energy Consumption of Connected Objects

Moutaib, Mohammed; Ahajjam, Tarik; Fattah, Mohammed; Farhaoui, Youssef; Aghoutane, Badraddine; Bekkali, Moulhime El

doi:10.3991/ijim.v15i24.26985

Cited by 6 publications

(3 citation statements)

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“…This MAC protocol was selected for the QSCL design. By doing this, the EC during data transmission is significantly decreased [13]. • We proposed a CH selection procedure based on the nodes' RE and maximum probable distance at the network layer [7].…”

Section: • Residual Energy (Re)mentioning

confidence: 99%

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Quality of Service-Based Cross-Layer Protocol for Wireless Sensor Networks

Arockiaraj

Makkithaya

2022

Int. J. Interact. Mob. Technol.

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Sensor nodes in wireless sensor networks (WSN) are used for perceiving, monitoring, and controlling a wide range of applications. Owing to the small size of sensor nodes and limited power sources, energy saving is critical for ensuring network longevity. Protocols in different layers consume energy for their function. It is possible to significantly reduce energy usage by including energy-efficiency measures in the protocol design. Most protocols in the literature focus on the energy efficiency in individual layers. Recent studies have shown that cross-layer designs are more energy efficient than individual layer designs. Therefore, this study presents a cross-layer protocol design that combines network and data link layers to minimize energy consumption. This article proposes a novel "Quality of Service Based Cross-layer (QSCL) Protocol" by combining the IEEE 802.15.4-based MAC protocol and the LEACH-based routing protocol. The dynamic duty cycle of the IEEE 802.15.4 protocol was modified based on the amount of data present in the node, which minimized the energy consumption of the data-transfer mechanism. The cluster head (CH) selection of the LEACH-based protocol was modified to consider the average residual energy (RE) of the nodes and their distance from the sink. This helps preserve the energy in the CH, thereby extending the network lifetime. Simulation studies demonstrated that the proposed QSCL outperformed the existing protocols by prolonging the network lifetime.

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Section: • Residual Energy (Re)mentioning

confidence: 99%

“…Figure 3 shows the setup and steady-state phases of the complete round. In the steady-state phase, CHs collect data from their CM nodes and deliver them to the sink for further processing [13][14] [15]. Depending on the quantity of data to be delivered, the duration of each round may vary.…”

Section: Introductionmentioning

confidence: 99%

Quality of Service-Based Cross-Layer Protocol for Wireless Sensor Networks

Arockiaraj

Makkithaya

2022

Int. J. Interact. Mob. Technol.

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“…Although the fetal ECG is a powerful diagnostic tool, its interpretation often requires significant expertise and is subject to human error [2]. In a world where healthcare systems are under increasing pressure, reliable and automated analysis of this data is not only desirable but almost essential [3].…”

Section: Introductionmentioning

confidence: 99%

Fetal electrocardiogram prediction using machine learning: a random forest-based approach

Moutaib,

Fattah,

Farhaoui

et al. 2024

IJEECS

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<p class="IOPText">Monitoring fetal health during pregnancy ensures safe delivery and the newborn’s well-being. The fetal electrocardiogram (fetal ECG) is a valuable tool for assessing fetal cardiac health, but interpretation of ECG data can be challenging due to its complexity and variability. In this work, we explore the application of machine learning, particularly random forest, to predict and analyze fetal ECGs. With its ability to manage large datasets and provide precise insights, random forest is a promising solution for this challenge. By comparing our random forest-based approach with other standard machine learning techniques such as artificial neural network (ANN), support vector machines (SVM), and recurrent neural networks (RNN), we observed that our solution outperformed these methods in accuracy, robustness, and reliability. This article details the methodology used, the implementation of the algorithm, as well as the comparative results obtained. Emphasis is placed on the benefits of random forest in this specific medical context, highlighting its potential as a future tool for fetal ECG prediction. Ultimately, our research suggests a shift toward random forest-based solutions for more efficient and accurate analysis of fetal ECGs, with direct implications for clinical practice and fetal well-being.</p>

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