Priority Schemes for Life Extension and Data Delivery in Body Area Wireless Sensor Networks with Cognitive Radio Capabilities

Chávez, Sergio M. Martínez; Rivero-Ángeles, Mario E.; Jiménez, Laura Ivoone Garay; Romero‐Ibarra, Issis C.

doi:10.1155/2019/2637830

Cited by 3 publications

(2 citation statements)

References 32 publications

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“…e interference of the base station concerns the priority frame selection-based CDMA [10]; from that, optimal selection of sensing groups has been given. In [11], the author has proposed the two priority schemes such as energy efficiency and data gathering. It mainly focuses on event-related data to be transmitted in the system.…”

Section: Related Workmentioning

confidence: 99%

[Retracted] Effects of Novel Material Field Effect Transistor for Heterogeneous Energy and Traffic‐Aware Secure Applications

Bhuvaneswari

Ruby

Manjunathan³

et al. 2021

Advances in Materials Science and Engineering

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The advent of the automated technological revolution has enabled the Internet of Things to rejuvenate, revolutionize, and redeem the services of sensors. The recent development of microsensor devices is distributed in a real-world terrestrial environment to sense various environmental changes. The energy consumption of the remotely deployed microsystems depends on its utilization efficiency. Improper utilization of sensor nodes’ heterogeneity could lead to uneven energy consumption and load imbalance across the network, which will degrade the performance of the network. The proposed heterogeneous energy and traffic aware (HETA) considers the key parameters such as delay, throughput, traffic load, energy consumption, and life span. The residual energy and a minimum distance between the base station and cluster members are taken into consideration for the cluster head selection. The probability of hitting data traffic has been utilized to analyse energy and traffic towards the base station. The role of the sensor node has been realized and priority-based data forwarding are also proposed. As a result, the heterogeneous energy and traffic aware perform well in balancing traffic towards the base station, which is analysed in terms of maximum throughput and increase in a lifetime of heterogeneous energy networks more than 5000 rounds, and the algorithm outperforms 34.5% of nodes are alive with transmissible energy. The proposed research also endorses unequal clustering and minimum energy consumption. We have modeled our proposed research using various p-type junctionless nanowire FET without doping injunctions. The materials used in this analysis were silicon (Si), germanium (Ge), indium phosphide (InP), gallium arsenide (GaAs), and Al(x)Ga(1−x)As. The dimensions of the p-type cylindrical nanowire channel were 25 nm long and 10 nm in diameter.

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Section: Related Workmentioning

confidence: 99%

[Retracted] Effects of Novel Material Field Effect Transistor for Heterogeneous Energy and Traffic‐Aware Secure Applications

Bhuvaneswari

Ruby

Manjunathan³

et al. 2021

Advances in Materials Science and Engineering

View full text Add to dashboard Cite

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“…Some works in sensing-based healthcare are focused on reducing big data collection using aggregation, compression, and prediction methods [6][7][8][9][10][11][12]. The authors of [6] proposed the Priority-based Compressed Data Aggregation (PCDA) technique to reduce the amount of heath data transmitted.…”

Section: Related Workmentioning

confidence: 99%

A Hadoop-Based Platform for Patient Classification and Disease Diagnosis in Healthcare Applications

Harb

Mroue

Mansour

et al. 2020

Sensors

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Nowadays, the increasing number of patients accompanied with the emergence of new symptoms and diseases makes heath monitoring and assessment a complicated task for medical staff and hospitals. Indeed, the processing of big and heterogeneous data collected by biomedical sensors along with the need of patients' classification and disease diagnosis become major challenges for several health-based sensing applications. Thus, the combination between remote sensing devices and the big data technologies have been proven as an efficient and low cost solution for healthcare applications. In this paper, we propose a robust big data analytics platform for real time patient monitoring and decision making to help both hospital and medical staff. The proposed platform relies on big data technologies and data analysis techniques and consists of four layers: real time patient monitoring, real time decision and data storage, patient classification and disease diagnosis, and data retrieval and visualization. To evaluate the performance of our platform, we implemented our platform based on the Hadoop ecosystem and we applied the proposed algorithms over real health data. The obtained results show the effectiveness of our platform in terms of efficiently performing patient classification and disease diagnosis in healthcare applications.

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A Sensor-Based Data Analytics for Patient Monitoring in Connected Healthcare Applications

et al. 2021

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Nowadays, keeping a strong and good health is one of the main concern of the general public or governments. The Internet of Things (IoT) has been emerged as an efficient solution to build smart healthcare systems deployed either at hospitals or in-home. Such networks rely on biomedical sensors which are used in electronics-based medical equipment to remotely collect vital signs of patients (pressure, temperature, hart rate, oxygen saturation etc.). Generally, these biosensors are implemented on or inside the patient's body and take three types of record data such as numerical, images and videos. However, the big data collected by various biomedical sensors along with the need of emergency detection, the limited sensor energies, and the prediction of the progress of patient situation are the major challenges for heath-based IoT applications. In order to overcome these challenges, we propose, in this paper, an efficient sensor-based data analytics for real-time patient monitoring and assessment to help both hospital and medical staff. The proposed mechanism consists in three phases: Emergency detection, adapting sensing frequency and real time prediction of patient situation. Through simulations on real health data, we show the effectiveness of our mechanism compared to other exiting techniques.

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Priority Schemes for Life Extension and Data Delivery in Body Area Wireless Sensor Networks with Cognitive Radio Capabilities

Cited by 3 publications

References 32 publications

[Retracted] Effects of Novel Material Field Effect Transistor for Heterogeneous Energy and Traffic‐Aware Secure Applications

[Retracted] Effects of Novel Material Field Effect Transistor for Heterogeneous Energy and Traffic‐Aware Secure Applications

A Hadoop-Based Platform for Patient Classification and Disease Diagnosis in Healthcare Applications

A Sensor-Based Data Analytics for Patient Monitoring in Connected Healthcare Applications

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