A novel autonomous wireless sensor node for  IoT applications

Nguyen, Ngan; Nguyen, Quoc Cuong; Le, Minh Thuy

doi:10.12928/telkomnika.v17i5.12811

Cited by 11 publications

(12 citation statements)

References 15 publications

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“…Wireless networks technology-based internet of things (IoT) [1][2][3] has increased significantly [4,5] and interesting to study, especially smart health services [6,7] such as monitoring systems for blood pressure, heartbeat, and body temperature. IoT-based medical applications and health checks will provide benefits for medical personnel in reducing costs, improving quality of life, strengthening medical diagnoses [8], and subsequent treatment processes [9].…”

Section: Introductionmentioning

confidence: 99%

Internet of things–based vital sign monitoring system

Alamsyah

Subito

Ikhlayel

et al. 2020

IJECE

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Wireless network technology-based internet of things (IoT) has increased significantly and exciting to study, especially vital sign monitoring (body temperature, heart rate, and blood pressure). Vital sign monitoring is crucial to carry out to strengthen medical diagnoses and the continuity of patient health. Vital sign monitoring conducted by medical personnel to diagnose the patient's health condition is still manual. Medical staff must visit patients in each room, and the equipment used is still cable-based. Vital sign examination like this is certainly not practical because it requires a long time in the process of diagnosis. The proposed vital sign monitoring system design aims to assist medical personnel in diagnosing the patient's illness. Vital sign monitoring system uses HRM-2511E sensor for heart detection, DS18b20 sensor for body temperature detection, and MPX5050DP sensor for blood pressure detection. Vital sign data processing uses a raspberry pi as a data delivery media-based internet of things (IoT). Based on the results of the vital sign data retrieval shows that the tool designed functioning correctly. The accuracy of the proposed device for body temperature is 99.51%, heart rate is 97.90%, and blood pressure is 97.69%.

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Section: Introductionmentioning

confidence: 99%

Internet of things–based vital sign monitoring system

Alamsyah

Subito

Ikhlayel

et al. 2020

IJECE

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“…The result in (14) has also been shown in [9]. Note for a square of side a, ( ) 0.521405a, a result in (7) can be used to show that…”

Section: Stationary Distribution (With Pausing)mentioning

confidence: 55%

“…These chose multipoint relays are demonstrated in the HELLO messages with the link status MPR. On the gathering of HELLO messages, every node can develop its MPR selector table with the nodes who have chosen it as a multipoint relay [14] Performance Metrics So as to conduct this study of OLSR, its performance has been analyzed under four mobility models (Random Waypoint, Random Direction, Random Walk, and Steady-State Random Waypoint) with respect to tree performance metrics based on CBR traffic. Simulations have been carried out by Network Simulator 3 (NS-3).…”

Section: Multipoint Relaysmentioning

confidence: 99%

Link-state QoS routing protocol under various mobility models

Laqtib¹,

Yassini²,

Hasnaoui

2019

IJEECS

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<p>Mobile Ad Hoc Network (MANET) consists of a group of mobile or wireless nodes that are placed randomly and dynamically that causes the continual change between nodes. A mobility model attempts to mimic the movement of real mobile nodes that change the speed and direction with time. The mobility model that accurately represents the characteristics of the mobile nodes in an ad hoc network is the key to examine whether a given protocol. The aim of this paper is to compare the performance of four different mobility models (i.e. Random Waypoint, Random Direction, Random walk, and Steady-State Random Waypoint) in MANET. These models were configured with Optimized Link State Routing (OLSR) protocol under three QoS (Quality of Service) <a title="Learn more about Metrics" href="https://www.sciencedirect.com/topics/engineering/metrics">metrics</a> such as the Packet Delivery Ratio (PDR), Throughput, End-to-End delay. The simulation results show the effectiveness of Steady-State Random Waypoint Mobility Models and encourage further investigations to extend it in order to guarantee other QoS requirements.</p>

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“…A smart wearable device based on internet of things for the safety of… (Elsyea Adia Tunggadewi) 709 technology internet of things, the concept by which certain objects have the ability to transfer data [10]- [20] and is equipped with GPS for sharing location [21], as well as a camera for capturing images around [22]. In previous research [2], the smart shoe uses a GPS and GSM module, which can work together to send locations to the family, and the raspberry Pi-3 whose main control is the Arduino Uno, equipped with a camera to record what happened.…”

Section: Introductionmentioning

confidence: 99%

A smart wearable device based on internet of things for the safety of children in online transportation

Tunggadewi

Agustin

Yunardi

2021

IJEECS

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The world needs to pay attention to children who often become victims of violence and cannot escape social problems. Various safety devices that are commonly known as smart wearable devices have been created, but they still have many shortcomings. Thus, in this research a safety device that can be held by children is designed and is equipped with a button that can be pressed, then it will automatically send the location and photo of the scene to the parent's cellphone via the telegram application. It uses the Raspberry Pi Zero W controller, the GNSS HMC5983 SAW LNA GPS Module to determine the location, and the 5MP Raspberry Pi Zero Camera Module to capture the incident. Based on the results, the average time needed to share locations is 0.91 seconds, and the average time needed to capture is 11.57 seconds, if the device and receiving cellphone use the same network. Additionally, the average time needed to share locations is 0.96 seconds, and the average time needed to capture is 12.09 seconds, if the device and receiving cellphone use a different network. Both conditions have 97.5% location accuracy rate and 100% photo accuracy rate.

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A novel autonomous wireless sensor node for IoT applications

Cited by 11 publications

References 15 publications

Internet of things–based vital sign monitoring system

Internet of things–based vital sign monitoring system

Link-state QoS routing protocol under various mobility models

A smart wearable device based on internet of things for the safety of children in online transportation

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