Secure and Scalable Healthcare Data Transmission in IoT Based on Optimized Routing Protocols for Mobile Computing Applications

Refaee, Eshrag; Parveen, Shabana; Begum, Khan Mohamed Jarina; Parveen, Fatima; Raja, M. Chithik; Gupta, Shashi Kant; Santhosh, K.

doi:10.1155/2022/5665408

Cited by 39 publications

(14 citation statements)

References 31 publications

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“…To evaluate the efficiency of our proposed SBAODV scheme, we compared it with several other routing protocols, including low-power and noisy routing protocol (LPNRP) [32], tree protocol for healthcare (TPH) [41], a routing protocol for healthcare (RPH) [42], fuzzy dynamic trust-based routing protocol (FDTRP) [43], and dual attack detection routing protocol (DADRP) [44]. We assumed a reactive gateway as the IoMT gateway in the network and simulated the network for 600 s using a constant bit rate (CBR) traffic model and UDP application.…”

Section: Methodsmentioning

confidence: 99%

A UAV-Assisted Stackelberg Game Model for Securing loMT Healthcare Networks

Shaikh

Wang

Khan

et al. 2023

Drones

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On the one hand, the Internet of Medical Things (IoMT) in healthcare systems has emerged as a promising technology to monitor patients’ health and provide reliable medical services, especially in remote and underserved areas. On the other hand, in disaster scenarios, the loss of communication infrastructure can make it challenging to establish reliable communication and to provide timely first aid services. To address this challenge, unmanned aerial vehicles (UAVs) have been adopted to assist hospital centers in delivering medical care to hard-to-reach areas. Despite the potential of UAVs to improve medical services in emergency scenarios, their limited resources make their security critical. Therefore, developing secure and efficient communication protocols for IoMT networks using UAVs is a vital research area that can help ensure reliable and timely medical services. In this paper, we introduce a novel Stackelberg security-based game theory algorithm, named Stackelberg ad hoc on-demand distance vector (SBAODV), to detect and recover data affected by black hole attacks in IoMT networks using UAVs. Our proposed scheme utilizes the substantial Stackelberg equilibrium (SSE) to formulate strategies that protect the system against attacks. We evaluate the performance of our proposed SBAODV scheme and compare it with existing routing schemes. Our results demonstrate that our proposed scheme outperforms existing schemes regarding packet delivery ratio (PDR), networking load, throughput, detection ratio, and end-to-end delay. Specifically, our proposed SBAODV protocol achieves a PDR of 97%, throughput ranging from 77.7 kbps to 87.3 kbps, and up to 95% malicious detection rate at the highest number of nodes. Furthermore, our proposed SBADOV scheme offers significantly lower networking load (7% to 30%) and end-to-end delay (up to 30%) compared to existing routing schemes. These results demonstrate the efficiency and effectiveness of our proposed scheme in ensuring reliable and secure communication in IoMT emergency scenarios using UAVs.

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Section: Methodsmentioning

confidence: 99%

A UAV-Assisted Stackelberg Game Model for Securing loMT Healthcare Networks

Shaikh

Wang

Khan

et al. 2023

Drones

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“…M Al-Amin et al [26] proposed a framework for sentimental analysis using some reviews on the internet. Refaee et al [27]suggested a system for IoT-based secure and scalable healthcare data transmission that is based on an improved routing protocol.Rajagopal et al [28] investigated the potential effects of arti cial intelligence systems on business results using a mix of consumer choice and the matching principle. In terms of entrepreneurship, the study showed that arti cial intelligence systems can aid in improved decision-making.…”

Section: Literature Reviewmentioning

confidence: 99%

Prediction of Public Opinion through Real-Time Monitoring using Multimodal Recurrent Neural Network

Gupta¹,

Limka²,

Hossain³

et al. 2023

Preprint

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There is growing use of technology and presence of people online globally. Electronic platforms have also become the avenue for expression of opinions on emerging issues by people. However, some of the posts or comments posted online could sometimes be negative with far reaching effects on the community. The use of machine learning algorithms through sentiment analysis offers means that could be used to mine data and analyse opinions that emanate from online to reach decisions or monitor ethical compliance. This study presents an approach that uses a Multimodal fusion with Recurrent Neural Networks (M-RNN) to predict opinions through decision making and real time monitoring. The dataset was trained using standard methods like a decision tree classifier, and the M-RNN model achieved an accuracy of roughly 82.80%. The training model's relative average error was close to 0.503% when using the M-RNN methodology, and the training cycle only needs to be repeated 250 times to achieve satisfactory results, a figure that is low when compared to other conventional methods.

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“…In [15] proposed a secure and scalable framework for healthcare data transfer in the Internet of Things based on optimized routing protocols. First, health data from various IoT devices such as wearable and sensors is collected.…”

Section: Literature Reviewmentioning

confidence: 99%

Energy Efficient Clustering and Routing Using Hybrid Fuzzy with Modified Rider Optimization Algorithm in IoT - Enabled Wireless Body Area Network

Dinesh

2023

JMC

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Wireless sensor networks are widely used in various Internet of Things applications, including healthcare, underwater sensor networks, body area networks, and multiple offices. Wireless Body Area Network (WBAN) simplifies medical department tasks and provides a solution that reduces the possibility of errors in the medical diagnostic process. The growing demand for real-time applications in such networks will stimulate significant research activity. Designing scenarios for such critical events while maintaining energy efficiency is difficult due to dynamic changes in network topology, strict power constraints, and limited computing power. The routing protocol design becomes crucial to WBAN and significantly impacts the communication stack and network performance. High node mobility in WBAN results in quick topology changes, affecting network scalability. Node clustering is one of many other mechanisms used in WBANs to address this issue. We consider optimization factors like distance, latency, and power consumption of IoT devices to achieve the desired CH selection. This paper proposes a high-level CH selection and routing approach using a hybrid fuzzy with a modified Rider Optimization Algorithm (MROA). This research work is implemented using MATLAB software. The simulations are carried out under a range of conditions. In terms of energy consumption and network life time, the proposed scheme outperforms current state-of-the-art techniques like Low Energy Adaptive Clustering Hierarchy (LEACH), Energy Control Routing Algorithm (ECCRA), Energy Efficient Routing Protocol (EERP), and Simplified Energy Balancing Alternative Aware Routing Algorithm (SEAR).

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Secure and Scalable Healthcare Data Transmission in IoT Based on Optimized Routing Protocols for Mobile Computing Applications

Cited by 39 publications

References 31 publications

A UAV-Assisted Stackelberg Game Model for Securing loMT Healthcare Networks

A UAV-Assisted Stackelberg Game Model for Securing loMT Healthcare Networks

Prediction of Public Opinion through Real-Time Monitoring using Multimodal Recurrent Neural Network

Energy Efficient Clustering and Routing Using Hybrid Fuzzy with Modified Rider Optimization Algorithm in IoT - Enabled Wireless Body Area Network

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