A Secured IoT Based Advanced Health Care System for Medical Field using Sensor Network

Nalajala, Paparao; Lakshmi, S Bhagya

doi:10.14419/ijet.v7i2.20.12184

Cited by 9 publications

(4 citation statements)

References 5 publications

(10 reference statements)

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“…Smart devices play a critical role in assessing a patient's health status by collecting data on parameters like temperature, blood pressure, heart rate, and saline bottle level [14]. Medical sensors now offer a solution for various medical applications, such as remote patient activity monitoring, patient information management, chronic illness diagnosis, and elderly healthcare [15,16].…”

Section: Related Workmentioning

confidence: 99%

Secure Healthcare Model Using Multi-Step Deep Q Learning Network in Internet of Things

Roy,

Teju,

Kandula

et al. 2024

Electronics

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Internet of Things (IoT) is an emerging networking technology that connects both living and non-living objects globally. In an era where IoT is increasingly integrated into various industries, including healthcare, it plays a pivotal role in simplifying the process of monitoring and identifying diseases for patients and healthcare professionals. In IoT-based systems, safeguarding healthcare data is of the utmost importance, to prevent unauthorized access and intermediary assaults. The motivation for this research lies in addressing the growing security concerns within healthcare IoT. In this proposed paper, we combine the Multi-Step Deep Q Learning Network (MSDQN) with the Deep Learning Network (DLN) to enhance the privacy and security of healthcare data. The DLN is employed in the authentication process to identify authenticated IoT devices and prevent intermediate attacks between them. The MSDQN, on the other hand, is harnessed to detect and counteract malware attacks and Distributed Denial of Service (DDoS) attacks during data transmission between various locations. Our proposed method’s performance is assessed based on such parameters as energy consumption, throughput, lifetime, accuracy, and Mean Square Error (MSE). Further, we have compared the effectiveness of our approach with an existing method, specifically, Learning-based Deep Q Network (LDQN).

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Section: Related Workmentioning

confidence: 99%

Secure Healthcare Model Using Multi-Step Deep Q Learning Network in Internet of Things

Roy,

Teju,

Kandula

et al. 2024

Electronics

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“…The data is processed by microcontroller. At last data from the sensors is viewed in the web server [10]. Ning [11] designed monitoring system for water quality.…”

Section: Literature Reviewmentioning

confidence: 99%

Internet of Things (Iot) Based Smart Water Quality Monitoring System

Spandana¹,

Rao²

2018

IJET

128

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The economical and effective system of water quality observation is the toughest implementation of impure water. Drinking water could be terribly precious for all people as water utilities face more challenges. These challenges arise due to high population, less water resources etc. So, different methods are used to monitor in the real time water quality. To make sure that safe distribution of water is done, it should be monitored in real time for new approach in IOT based water quality has been projected. Real time water quality observation is monitored by data acquisition, method and transmission with increase in the wireless device network technology in internet of things. The measured values from the sensors are interfaced by microcontroller and the processed values remotely to the core controller ARM with a WI-FI protocol. This projected water quality observation interfaces sensors with quality observation with IOT setting. WQM selects parameters of water like temperature, pH level, water level and CO2 by multiple different device nodes. This methodology sends the information to the web server. The data updated at intervals within the server may be retrieved or accessed from anyplace within the world. If the sensors do not work or get into abnormal conditions then a buzzer will be ON.

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“…In a DoS attack, significant traffic floods the networks to stop its services from reaching the intended users [25]. When this attack takes place on the software side, the attacker restricts a user from an application layer by rejecting services.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Stage Secure IoT Authentication Protocol

Alhusayni¹,

Wazirali²,

AlAkhras³

et al. 2023

Computer Systems Science and Engineering

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The Internet of Things (IoT) is a network of heterogeneous and smart devices that can make decisions without human intervention. It can connect millions of devices across the universe. Their ability to collect information, perform analysis, and even come to meaningful conclusions without human capital intervention matters. Such circumstances require stringent security measures and, in particular, the extent of authentication. Systems applied in the IoT paradigm point out high-interest levels since enormous damage will occur if a malicious, wrongly authenticated device finds its way into the IoT system. This research provides a clear and updated view of the trends in the IoT authentication area. Among the issues covered include a series of authentication protocols that have remained research gaps in various studies. This study applies a comparative evaluation of authentication protocols, including their strengths and weaknesses. Thus, it forms the foundation in the IoT authentication field of study. In that direction, a multi authentication architecture that involves secured means is proposed for protocol authentication. Informal analysis can affect the security of the protocols. Burrows-Abadi-Needham (BAN) logic provides proof of the attainment of mutual authentication. NS3 simulator tool is used to compare the performance of the proposed protocol to verify the formal security offered by the BAN logic.

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A Secured IoT Based Advanced Health Care System for Medical Field using Sensor Network

Cited by 9 publications

References 5 publications

Secure Healthcare Model Using Multi-Step Deep Q Learning Network in Internet of Things

Secure Healthcare Model Using Multi-Step Deep Q Learning Network in Internet of Things

Internet of Things (Iot) Based Smart Water Quality Monitoring System

A Multi-Stage Secure IoT Authentication Protocol

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