EERP-DPM: Energy Efficient Routing Protocol Using Dual Prediction Model for Healthcare Using IoT

Almalki, Faris A.; Soufiene, Ben Othman; Almalki, Fahad; Sakli, Hèdi

doi:10.1155/2021/9988038

Cited by 37 publications

(13 citation statements)

References 35 publications

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“…High loss rates, poor data rates, and inconsistency result from the interconnection of asset-obsessed devices [5]. Industrial automation, smart buildings, smart education, medical assistance, health monitoring, and senior care are just a few of the applications for these Low-power and Lossy networks [6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

BE-RPL: Balanced-load and Energy-efficient RPL

Hussain¹,

Roopa²

2023

Computer Systems Science and Engineering

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Internet of Things (IoT) empowers imaginative applications and permits new services when mobile nodes are included. For IoT-enabled low-power and lossy networks (LLN), the Routing Protocol for Low-power and Lossy Networks (RPL) has become an established standard routing protocol. Mobility under standard RPL remains a difficult issue as it causes continuous path disturbance, energy loss, and increases the end-to-end delay in the network. In this unique circumstance, a Balanced-load and Energy-efficient RPL (BE-RPL) is proposed. It is a routing technique that is both energy-efficient and mobility-aware. It responds quicker to link breakage through received signal strength-based mobility monitoring and selecting a new preferred parent reactively. The proposed system also implements load balancing among stationary nodes for leaf node allocation. Static nodes with more leaf nodes are restricted from participating in the election for a new preferred parent. The performance of BE-RPL is assessed using the COOJA simulator. It improves the energy use, network control overhead, frame acknowledgment ratio, and packet delivery ratio of the network.

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Section: Introductionmentioning

confidence: 99%

BE-RPL: Balanced-load and Energy-efficient RPL

Hussain¹,

Roopa²

2023

Computer Systems Science and Engineering

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“…These systems should be easy to set up, upgradeable, accurate, durable, and dependable. The authors of [ 6 ] emphasised the importance of using optimization techniques to enhance efficiency for prediction in healthcare applications.…”

Section: Introductionmentioning

confidence: 99%

ResNet-50 for 12-Lead Electrocardiogram Automated Diagnosis

Sakli

Ghabri

Soufiene

et al. 2022

Computational Intelligence and Neuroscience

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Nowadays, the implementation of Artificial Intelligence (AI) in medical diagnosis has attracted major attention within both the academic literature and industrial sector. AI would include deep learning (DL) models, where these models have been achieving a spectacular performance in healthcare applications. According to the World Health Organization (WHO), in 2020 there were around 25.6 million people who died from cardiovascular diseases (CVD). Thus, this paper aims to shad the light on cardiology since it is widely considered as one of the most important in medicine field. The paper develops an efficient DL model for automatic diagnosis of 12-lead electrocardiogram (ECG) signals with 27 classes, including 26 types of CVD and a normal sinus rhythm. The proposed model consists of Residual Neural Network (ResNet-50). An experimental work has been conducted using combined public databases from the USA, China, and Germany as a proof-of-concept. Simulation results of the proposed model have achieved an accuracy of 97.63% and a precision of 89.67%. The achieved results are validated against the actual values in the recent literature.

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“…Accordingly, it occurs through unique addressing models that help the node to communicate with others and to cooperate with neighbors for achieving their goals 1 . The IoT appliances in various fields are industrial automation, mobile health, smart buildings, medical aids, assistance to elderly, intelligent energy management, agriculture, 2–4 and smart education 5,6 . IoT is one of the most innovative concepts in the modern era, with its effects being seen in every aspect of human endeavor and offering more potential for smart life.…”

Section: Introductionmentioning

confidence: 99%

Deep maxout network for lung cancer detection using optimization algorithm in smart Internet of Things

Ramkumar¹,

Paul

Maram

et al. 2022

Concurrency and Computation

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Summary The Internet of Things (IoT) has appreciably influenced the technology world in the context of interconnectivity, interoperability, and connectivity using smart objects, connected sensors, devices, data, and appliances. The IoT technology has mainly impacted the global economy, and it extends from industry to different application scenarios, like the healthcare system. This research designed anti‐corona virus‐Henry gas solubility optimization‐based deep maxout network (ACV‐HGSO based deep maxout network) for lung cancer detection with medical data in a smart IoT environment. The proposed algorithm ACV‐HGSO is designed by incorporating anti‐corona virus optimization (ACVO) and Henry gas solubility optimization (HGSO). The nodes simulated in the smart IoT framework can transfer the patient medical information to sink through optimal routing in such a way that the best path is selected using a multi‐objective fractional artificial bee colony algorithm with the help of fitness measure. The routing process is deployed for transferring the medical data collected from the nodes to the sink, where detection of disease is done using the proposed method. The noise exists in medical data is removed and processed effectively for increasing the detection performance. The dimension‐reduced features are more probable in reducing the complexity issues. The created approach achieves improved testing accuracy, sensitivity, and specificity as 0.910, 0.914, and 0.912, respectively.

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EERP-DPM: Energy Efficient Routing Protocol Using Dual Prediction Model for Healthcare Using IoT

Cited by 37 publications

References 35 publications

BE-RPL: Balanced-load and Energy-efficient RPL

BE-RPL: Balanced-load and Energy-efficient RPL

ResNet-50 for 12-Lead Electrocardiogram Automated Diagnosis

Deep maxout network for lung cancer detection using optimization algorithm in smart Internet of Things

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