An efficient routing protocol for wireless body sensor networks using reinforced learning algorithm in clusters

Jayabalan, E.; Pugazendi, R.

doi:10.1016/j.measen.2023.100730

Cited by 6 publications

(4 citation statements)

References 16 publications

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“…The body sensor network model for remote controls in the workplace is presented in part 5–1. In part 5–2, the effectiveness of the suggested approach is assessed, and evaluation parameters, including the package delivery rate, the average end-to-end delay, the network throughput and energy consumption, are evaluated as the main factors to compare with other similar routing methods, i.e., QoS-Based Multi-Path Routing (QMPR) [ 29 ], QLearning [ 28 ] and EPRS [ 22 ], with the mentioned parameters.…”

Section: The Results Of the Simulationmentioning

confidence: 99%

“…Over time, the proposed method exhibits a higher average end-to-end delay in comparison to the QMPR [ 29 ] and QLearning [ 28 ] algorithms because in contrast to the above algorithms, the throughput of the introduced method experiences a reduction. However, compared with the proposed method, these algorithms consume more energy.…”

Section: The Results Of the Simulationmentioning

confidence: 99%

“…This algorithm effectively identified the nearest, most efficient, and shortest node for transmitting pharmaceutical data. The method proposed by[ 28 ], utilized the Q-Learning to discover the best next step node to transfer the data. To receive the patients’ vital signs and send them to the medical centers, the introduced technique used a number of wearable sensor nodes.…”

Section: Review Of Literaturementioning

confidence: 99%

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Enhancing healthcare IoT systems for diabetic patient monitoring: Integration of Harris Hawks and grasshopper optimization algorithms

Hosseinzadeh,

Arabi,

Ali

et al. 2024

PLoS ONE

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The integration of the Internet of Things (IoT) in healthcare, especially for people with diabetes, allows for constant health monitoring. This means that doctors can watch over patients’ health more closely, making sure they catch any issues early on. With this technology, healthcare workers can be more accurate and effective when keeping an eye on how patients are doing. This not only helps in keeping track of patients’ health in real-time but also makes the whole process more reliable and efficient.By implementing appropriate routing techniques, the transmission of diabetic patients’ data to medical centers will facilitate real-time and timely responses from healthcare professionals. The grasshopper optimization algorithm is employed in the proposed approach to cluster network nodes, resulting in the formation of a network tree that facilitates the establishment of connections between the cluster head and the base station. After identifying the cluster head and establishing the clusters, the second stage of routing is implemented by employing the Harris Hawks optimization algorithm. This algorithm ensures that the data pertaining to diabetic patients is transmitted to the treatment centers and hospitals with minimal delay. For node routing, the optimal next step is selected based on the parameters such as the residual energy of the node, the ratio of delivered data packages, and the number of the neighbors of the node. To continue, first, the MATLAB software is utilized to simulate the proposed method, and then, it is compared with other similar methods. This comparison is conducted based on various parameters, including delay, energy consumption, network throughput, and network lifespan. Compared to other methods, the proposed method demonstrates a significant 33% improvement in the average point-to-point delay parameter in the subsequent iterations or rounds.

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Section: The Results Of the Simulationmentioning

confidence: 99%

Section: The Results Of the Simulationmentioning

confidence: 99%

Section: Review Of Literaturementioning

confidence: 99%

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Enhancing healthcare IoT systems for diabetic patient monitoring: Integration of Harris Hawks and grasshopper optimization algorithms

Hosseinzadeh,

Arabi,

Ali

et al. 2024

PLoS ONE

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“…The RF-EH converts the received RF signals into electricity and provides a power solution for energyconstrained wireless networks. It has a sustainable power supply from a radio environment and is used in various applications, such as wireless sensor networks [90], wireless body networks [91], and wireless charging systems [92].…”

Section: Energy Harvesting Uav Networkmentioning

confidence: 99%

A Survey on Non-Orthogonal Multiple Access for Unmanned Aerial Vehicle Networks: Machine Learning Approach

Mayarakaca,

Lee

2024

IEEE Access

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The rapid evolution of wireless communication has affected unmanned aerial vehicles (UAV), which are expected to be used in diverse applications in smart cities, military operations, and cellular networks. To address the significant impacts of rapid wireless communication advancements, along with the escalating demand for user equipment (UE), multiple access technique approaches, such as non-orthogonal multiple access (NOMA), have been proposed. NOMA has the key distinguishing feature of supporting more UE, particularly UAV-enabled communication networks. In these networks, to support more UE, multiple UE share the same frequency resources, which can be beneficial for UAV communication networks. To achieve optimal results, UAVs need to adaptively adjust to their environment, and this can be achieved through employing machine learning (ML). ML is a subfield of artificial intelligence (AI) that enables computers to learn from data and make predictions or decisions without being explicitly programmed. Employing ML can aid NOMA for UAV networks to analyze various parameters and rely on accurate predictions regarding various aspects of communication, such as channel conditions, user mobility, and traffic demands. The use of ML in NOMA-UAV networks can lead to significant improvements in wireless communication systems. In this paper, we present a survey on the potential of NOMA techniques applied to UAVs using ML methods to enhance UAVs in wireless communication networks. Specifically, a basic overview of UAV and NOMA will first be introduced. The role of NOMA in UAV networks is then divided into two categories: the principles and applications of NOMA in UAV networks. Finally, implement ML on NOMA for UAVs by representing the diverse applications of ML systems. In addition, we highlight several open research problems as possible directions for future research.INDEX TERMS Aerial networks, machine learning (ML), non-orthogonal multiple Access (NOMA), unmanned aerial vehicles (UAV).

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Thermal-energy-efficient-secured-link reliable and delay-aware routing protocol (TESLDAR) for wireless body area networks

Mohanty,

Sahoo,

Kabat

2024

International Journal of Computers and Applications

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An efficient routing protocol for wireless body sensor networks using reinforced learning algorithm in clusters

Cited by 6 publications

References 16 publications

Enhancing healthcare IoT systems for diabetic patient monitoring: Integration of Harris Hawks and grasshopper optimization algorithms

Enhancing healthcare IoT systems for diabetic patient monitoring: Integration of Harris Hawks and grasshopper optimization algorithms

A Survey on Non-Orthogonal Multiple Access for Unmanned Aerial Vehicle Networks: Machine Learning Approach

Thermal-energy-efficient-secured-link reliable and delay-aware routing protocol (TESLDAR) for wireless body area networks

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