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20 pages, 1845 KiB

Open AccessArticle

Hybrid Multi-Label Classification Model for Medical Applications Based on Adaptive Synthetic Data and Ensemble Learning

by M. Priyadharshini, A. Faritha Banu, Bhisham Sharma, Subrata Chowdhury, Khaled Rabie and Thokozani Shongwe

Sensors 2023, 23(15), 6836; https://doi.org/10.3390/s23156836 - 31 Jul 2023

Cited by 4 | Viewed by 1456

Abstract

In recent years, both machine learning and computer vision have seen growth in the use of multi-label categorization. SMOTE is now being utilized in existing research for data balance, and SMOTE does not consider that nearby examples may be from different classes when [...] Read more.

In recent years, both machine learning and computer vision have seen growth in the use of multi-label categorization. SMOTE is now being utilized in existing research for data balance, and SMOTE does not consider that nearby examples may be from different classes when producing synthetic samples. As a result, there can be more class overlap and more noise. To avoid this problem, this work presented an innovative technique called Adaptive Synthetic Data-Based Multi-label Classification (ASDMLC). Adaptive Synthetic (ADASYN) sampling is a sampling strategy for learning from unbalanced data sets. ADASYN weights minority class instances by learning difficulty. For hard-to-learn minority class cases, synthetic data are created. Their numerical variables are normalized with the help of the Min-Max technique to standardize the magnitude of each variable’s impact on the outcomes. The values of the attribute in this work are changed to a new range, from 0 to 1, using the normalization approach. To raise the accuracy of multi-label classification, Velocity-Equalized Particle Swarm Optimization (VPSO) is utilized for feature selection. In the proposed approach, to overcome the premature convergence problem, standard PSO has been improved by equalizing the velocity with each dimension of the problem. To expose the inherent label dependencies, the multi-label classification ensemble of Adaptive Neuro-Fuzzy Inference System (ANFIS), Probabilistic Neural Network (PNN), and Clustering-Based Decision tree methods will be processed based on an averaging method. The following criteria, including precision, recall, accuracy, and error rate, are used to assess performance. The suggested model’s multi-label classification accuracy is 90.88%, better than previous techniques, which is PCT, HOMER, and ML-Forest is 65.57%, 70.66%, and 82.29%, respectively. Full article

(This article belongs to the Special Issue Emerging Trends and Challenges of IoT in Smart Healthcare Systems, Smart Cities and Education)

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19 pages, 4168 KiB

Open AccessArticle

Multimodal Emotion Detection via Attention-Based Fusion of Extracted Facial and Speech Features

by Dilnoza Mamieva, Akmalbek Bobomirzaevich Abdusalomov, Alpamis Kutlimuratov, Bahodir Muminov and Taeg Keun Whangbo

Sensors 2023, 23(12), 5475; https://doi.org/10.3390/s23125475 - 9 Jun 2023

Cited by 10 | Viewed by 3987

Abstract

Methods for detecting emotions that employ many modalities at the same time have been found to be more accurate and resilient than those that rely on a single sense. This is due to the fact that sentiments may be conveyed in a wide [...] Read more.

Methods for detecting emotions that employ many modalities at the same time have been found to be more accurate and resilient than those that rely on a single sense. This is due to the fact that sentiments may be conveyed in a wide range of modalities, each of which offers a different and complementary window into the thoughts and emotions of the speaker. In this way, a more complete picture of a person’s emotional state may emerge through the fusion and analysis of data from several modalities. The research suggests a new attention-based approach to multimodal emotion recognition. This technique integrates facial and speech features that have been extracted by independent encoders in order to pick the aspects that are the most informative. It increases the system’s accuracy by processing speech and facial features of various sizes and focuses on the most useful bits of input. A more comprehensive representation of facial expressions is extracted by the use of both low- and high-level facial features. These modalities are combined using a fusion network to create a multimodal feature vector which is then fed to a classification layer for emotion recognition. The developed system is evaluated on two datasets, IEMOCAP and CMU-MOSEI, and shows superior performance compared to existing models, achieving a weighted accuracy WA of 74.6% and an F1 score of 66.1% on the IEMOCAP dataset and a WA of 80.7% and F1 score of 73.7% on the CMU-MOSEI dataset. Full article

(This article belongs to the Special Issue Emerging Trends and Challenges of IoT in Smart Healthcare Systems, Smart Cities and Education)

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18 pages, 5171 KiB

Open AccessArticle

Analysis of the Improvement of Engineering Mechanics Experimental Methods Based on IoT and Machine Learning

by Yi Sun, Dongfa Sheng and Dewen Liu

Sensors 2023, 23(7), 3416; https://doi.org/10.3390/s23073416 - 24 Mar 2023

Viewed by 1160

Abstract

With the rapid development of sensor technology, machine learning, and the Internet of Things, wireless sensor networks have gradually become a research hotspot. In order to improve the data fusion performance of wireless sensor networks and ensure network security in the event of [...] Read more.

With the rapid development of sensor technology, machine learning, and the Internet of Things, wireless sensor networks have gradually become a research hotspot. In order to improve the data fusion performance of wireless sensor networks and ensure network security in the event of external attacks, this paper proposes a wireless sensor optimization algorithm model, involving wireless sensor networks, the Internet of Things, and other related fields. This paper first analyzes the role of the Internet of Things in wireless sensor networks, studies the localization mechanism and hierarchy of the Internet of Things based on wireless sensor networks, and improves the LE-RLPCCA (Position Estimation Robust Local Retention Criteria Correlation Analysis) localization algorithm model based on sensor grids. This paper discusses the problems of machine learning in wireless sensor networks, constructs a sensor-based machine learning model, and designs a data fusion algorithm for a wireless sensor networks’ machine learning model. The application of wireless sensors in engineering mechanics experiments is summarized, and the optimization algorithm model of the wireless sensor in engineering mechanics experiments is proposed. The analysis results show that the average accuracy of the DKFCM-FSVM (Density aware Kernel-based Fuzzy C-means Clustering algorithm Fuzzy Support Vector Machine) algorithm in detecting five behaviors is 0.997, 0.992, 0.904, 0.996, and 0.946, respectively, and the accuracy in detecting different behaviors is the best, 0.005, 0.01, 0.003, and 0.006 respectively. It achieves the lowest false positive rate in the detection of different behaviors, and the average false positive rate is 0.004, 0.003, 0.003, 0.008, and 0.005, respectively, which shows that the DKFCM-FSVM algorithm model of wireless sensor networks in engineering mechanics experiments is the optimal solution. The work of this paper has good reference value for the application of wireless sensor networks and the optimization of engineering mechanics experimental methods and is helpful for further research of sensor technology. Full article

(This article belongs to the Special Issue Emerging Trends and Challenges of IoT in Smart Healthcare Systems, Smart Cities and Education)

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18 pages, 1510 KiB

Open AccessArticle

Real-Time Estimation and Monitoring of COVID-19 Aerosol Transmission Risk in Office Buildings

by Jelle Vanhaeverbeke, Emiel Deprost, Pieter Bonte, Matthias Strobbe, Jelle Nelis, Bruno Volckaert, Femke Ongenae, Steven Verstockt and Sofie Van Hoecke

Sensors 2023, 23(5), 2459; https://doi.org/10.3390/s23052459 - 23 Feb 2023

Cited by 1 | Viewed by 1768

Abstract

A healthy and safe indoor environment is an important part of containing the coronavirus disease 2019 (COVID-19) pandemic. Therefore, this work presents a real-time Internet of things (IoT) software architecture to automatically calculate and visualize a COVID-19 aerosol transmission risk estimation. This risk [...] Read more.

A healthy and safe indoor environment is an important part of containing the coronavirus disease 2019 (COVID-19) pandemic. Therefore, this work presents a real-time Internet of things (IoT) software architecture to automatically calculate and visualize a COVID-19 aerosol transmission risk estimation. This risk estimation is based on indoor climate sensor data, such as carbon dioxide (CO₂) and temperature, which is fed into Streaming MASSIF, a semantic stream processing platform, to perform the computations. The results are visualized on a dynamic dashboard that automatically suggests appropriate visualizations based on the semantics of the data. To evaluate the complete architecture, the indoor climate during the student examination periods of January 2020 (pre-COVID) and January 2021 (mid-COVID) was analyzed. When compared to each other, we observe that the COVID-19 measures in 2021 resulted in a safer indoor environment. Full article

(This article belongs to the Special Issue Emerging Trends and Challenges of IoT in Smart Healthcare Systems, Smart Cities and Education)

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20 pages, 2596 KiB

Open AccessArticle

A Virtual Machine Consolidation Algorithm Based on Dynamic Load Mean and Multi-Objective Optimization in Cloud Computing

by Pingping Li and Jiuxin Cao

Sensors 2022, 22(23), 9154; https://doi.org/10.3390/s22239154 - 25 Nov 2022

Cited by 3 | Viewed by 1641

Abstract

High energy consumption and low resource utilization have become increasingly prominent problems in cloud data centers. Virtual machine (VM) consolidation is the key technology to solve the problems. However, excessive VM consolidation may lead to service level agreement violations (SLAv). Most studies have [...] Read more.

High energy consumption and low resource utilization have become increasingly prominent problems in cloud data centers. Virtual machine (VM) consolidation is the key technology to solve the problems. However, excessive VM consolidation may lead to service level agreement violations (SLAv). Most studies have focused on optimizing energy consumption and ignored other factors. An effective VM consolidation should comprehensively consider multiple factors, including the quality of service (QoS), energy consumption, resource utilization, migration overhead and network communication overhead, which is a multi-objective optimization problem. To solve the problems above, we propose a VM consolidation approach based on dynamic load mean and multi-objective optimization (DLMM-VMC), which aims to minimize power consumption, resources waste, migration overhead and network communication overhead while ensuring QoS. Fist, based on multi-dimensional resources consideration, the host load status is objectively evaluated by using the proposed host load detection algorithm based on the dynamic load mean to avoid an excessive VM consolidation. Then, the best solution is obtained based on the proposed multi-objective optimization model and optimized ant colony algorithm, so as to ensure the common interests of cloud service providers and users. Finally, the experimental results show that compared with the existing VM consolidation methods, our proposed algorithm has a significant improvement in the energy consumption, QoS, resources waste, SLAv, migration and network overhead. Full article

(This article belongs to the Special Issue Emerging Trends and Challenges of IoT in Smart Healthcare Systems, Smart Cities and Education)

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23 pages, 1735 KiB

Open AccessArticle

Cybersecurity Awareness and Training (CAT) Framework for Remote Working Employees

by Mohammad Hijji and Gulzar Alam

Sensors 2022, 22(22), 8663; https://doi.org/10.3390/s22228663 - 9 Nov 2022

Cited by 6 | Viewed by 7490

Abstract

Currently, cybersecurity plays an essential role in computing and information technology due to its direct effect on organizations’ critical assets and information. Cybersecurity is applied using integrity, availability, and confidentiality to protect organizational assets and information from various malicious attacks and vulnerabilities. The [...] Read more.

Currently, cybersecurity plays an essential role in computing and information technology due to its direct effect on organizations’ critical assets and information. Cybersecurity is applied using integrity, availability, and confidentiality to protect organizational assets and information from various malicious attacks and vulnerabilities. The COVID-19 pandemic has generated different cybersecurity issues and challenges for businesses as employees have become accustomed to working from home. Firms are speeding up their digital transformation, making cybersecurity the current main concern. For software and hardware systems protection, organizations tend to spend an excessive amount of money procuring intrusion detection systems, antivirus software, antispyware software, and encryption mechanisms. However, these solutions are not enough, and organizations continue to suffer security risks due to the escalating list of security vulnerabilities during the COVID-19 pandemic. There is a thriving need to provide a cybersecurity awareness and training framework for remote working employees. The main objective of this research is to propose a CAT framework for cybersecurity awareness and training that will help organizations to evaluate and measure their employees’ capability in the cybersecurity domain. The proposed CAT framework will assist different organizations in effectively and efficiently managing security-related issues and challenges to protect their assets and critical information. The developed CAT framework consists of three key levels and twenty-five core practices. Case studies are conducted to evaluate the usefulness of the CAT framework in cybersecurity-based organizational settings in a real-world environment. The case studies’ results showed that the proposed CAT framework can identify employees’ capability levels and help train them to effectively overcome the cybersecurity issues and challenges faced by the organizations. Full article

(This article belongs to the Special Issue Emerging Trends and Challenges of IoT in Smart Healthcare Systems, Smart Cities and Education)

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13 pages, 6290 KiB

Open AccessArticle

Cloud Servers: Resource Optimization Using Different Energy Saving Techniques

by Mohammad Hijji, Bilal Ahmad, Gulzar Alam, Ahmed Alwakeel, Mohammed Alwakeel, Lubna Abdulaziz Alharbi, Ahd Aljarf and Muhammad Umair Khan

Sensors 2022, 22(21), 8384; https://doi.org/10.3390/s22218384 - 1 Nov 2022

Cited by 3 | Viewed by 2295

Abstract

Currently, researchers are working to contribute to the emerging fields of cloud computing, edge computing, and distributed systems. The major area of interest is to examine and understand their performance. The major globally leading companies, such as Google, Amazon, ONLIVE, Giaki, and eBay, [...] Read more.

Currently, researchers are working to contribute to the emerging fields of cloud computing, edge computing, and distributed systems. The major area of interest is to examine and understand their performance. The major globally leading companies, such as Google, Amazon, ONLIVE, Giaki, and eBay, are truly concerned about the impact of energy consumption. These cloud computing companies use huge data centers, consisting of virtual computers that are positioned worldwide and necessitate exceptionally high-power costs to preserve. The increased requirement for energy consumption in IT firms has posed many challenges for cloud computing companies pertinent to power expenses. Energy utilization is reliant upon numerous aspects, for example, the service level agreement, techniques for choosing the virtual machine, the applied optimization strategies and policies, and kinds of workload. The present paper tries to provide an answer to challenges related to energy-saving through the assistance of both dynamic voltage and frequency scaling techniques for gaming data centers. Also, to evaluate both the dynamic voltage and frequency scaling techniques compared to non-power-aware and static threshold detection techniques. The findings will facilitate service suppliers in how to encounter the quality of service and experience limitations by fulfilling the service level agreements. For this purpose, the CloudSim platform is applied for the application of a situation in which game traces are employed as a workload for analyzing the procedure. The findings evidenced that an assortment of good quality techniques can benefit gaming servers to conserve energy expenditures and sustain the best quality of service for consumers located universally. The originality of this research presents a prospect to examine which procedure performs good (for example, dynamic, static, or non-power aware). The findings validate that less energy is utilized by applying a dynamic voltage and frequency method along with fewer service level agreement violations, and better quality of service and experience, in contrast with static threshold consolidation or non-power aware technique. Full article

(This article belongs to the Special Issue Emerging Trends and Challenges of IoT in Smart Healthcare Systems, Smart Cities and Education)

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17 pages, 1936 KiB

Open AccessArticle

Classification and Detection of COVID-19 and Other Chest-Related Diseases Using Transfer Learning

by Muhammad Tahir Naseem, Tajmal Hussain, Chan-Su Lee and Muhammad Adnan Khan

Sensors 2022, 22(20), 7977; https://doi.org/10.3390/s22207977 - 19 Oct 2022

Cited by 2 | Viewed by 2035

Abstract

COVID-19 has infected millions of people worldwide over the past few years. The main technique used for COVID-19 detection is reverse transcription, which is expensive, sensitive, and requires medical expertise. X-ray imaging is an alternative and more accessible technique. This study aimed to [...] Read more.

COVID-19 has infected millions of people worldwide over the past few years. The main technique used for COVID-19 detection is reverse transcription, which is expensive, sensitive, and requires medical expertise. X-ray imaging is an alternative and more accessible technique. This study aimed to improve detection accuracy to create a computer-aided diagnostic tool. Combining other artificial intelligence applications techniques with radiological imaging can help detect different diseases. This study proposes a technique for the automatic detection of COVID-19 and other chest-related diseases using digital chest X-ray images of suspected patients by applying transfer learning (TL) algorithms. For this purpose, two balanced datasets, Dataset-1 and Dataset-2, were created by combining four public databases and collecting images from recently published articles. Dataset-1 consisted of 6000 chest X-ray images with 1500 for each class. Dataset-2 consisted of 7200 images with 1200 for each class. To train and test the model, TL with nine pretrained convolutional neural networks (CNNs) was used with augmentation as a preprocessing method. The network was trained to classify using five classifiers: two-class classifier (normal and COVID-19); three-class classifier (normal, COVID-19, and viral pneumonia), four-class classifier (normal, viral pneumonia, COVID-19, and tuberculosis (Tb)), five-class classifier (normal, bacterial pneumonia, COVID-19, Tb, and pneumothorax), and six-class classifier (normal, bacterial pneumonia, COVID-19, viral pneumonia, Tb, and pneumothorax). For two, three, four, five, and six classes, our model achieved a maximum accuracy of 99.83, 98.11, 97.00, 94.66, and 87.29%, respectively. Full article

(This article belongs to the Special Issue Emerging Trends and Challenges of IoT in Smart Healthcare Systems, Smart Cities and Education)

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20 pages, 6304 KiB

Open AccessArticle

Towards Parallel Selective Attention Using Psychophysiological States as the Basis for Functional Cognition

by Asma Kanwal, Sagheer Abbas, Taher M. Ghazal, Allah Ditta, Hani Alquhayz and Muhammad Adnan Khan

Sensors 2022, 22(18), 7002; https://doi.org/10.3390/s22187002 - 15 Sep 2022

Cited by 5 | Viewed by 1985

Abstract

Attention is a complex cognitive process with innate resource management and information selection capabilities for maintaining a certain level of functional awareness in socio-cognitive service agents. The human-machine society depends on creating illusionary believable behaviors. These behaviors include processing sensory information based on [...] Read more.

Attention is a complex cognitive process with innate resource management and information selection capabilities for maintaining a certain level of functional awareness in socio-cognitive service agents. The human-machine society depends on creating illusionary believable behaviors. These behaviors include processing sensory information based on contextual adaptation and focusing on specific aspects. The cognitive processes based on selective attention help the agent to efficiently utilize its computational resources by scheduling its intellectual tasks, which are not limited to decision-making, goal planning, action selection, and execution of actions. This study reports ongoing work on developing a cognitive architectural framework, a Nature-inspired Humanoid Cognitive Computing Platform for Self-aware and Conscious Agents (NiHA). The NiHA comprises cognitive theories, frameworks, and applications within machine consciousness (MC) and artificial general intelligence (AGI). The paper is focused on top-down and bottom-up attention mechanisms for service agents as a step towards machine consciousness. This study evaluates the behavioral impact of psychophysical states on attention. The proposed agent attains almost 90% accuracy in attention generation. In social interaction, contextual-based working is important, and the agent attains 89% accuracy in its attention by adding and checking the effect of psychophysical states on parallel selective attention. The addition of the emotions to attention process produced more contextual-based responses. Full article

(This article belongs to the Special Issue Emerging Trends and Challenges of IoT in Smart Healthcare Systems, Smart Cities and Education)

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Review

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35 pages, 1650 KiB

Open AccessReview

A Survey on COVID-19 Data Analysis Using AI, IoT, and Social Media

by Muhammad Junaid Butt, Ahmad Kamran Malik, Nafees Qamar, Samad Yar, Arif Jamal Malik and Usman Rauf

Sensors 2023, 23(12), 5543; https://doi.org/10.3390/s23125543 - 13 Jun 2023

Cited by 4 | Viewed by 2385

Abstract

Coronaviruses are a well-established and deadly group of viruses that cause illness in both humans and animals. The novel type of this virus group, named COVID-19, was firstly reported in December 2019, and, with the passage of time, coronavirus has spread to almost [...] Read more.

Coronaviruses are a well-established and deadly group of viruses that cause illness in both humans and animals. The novel type of this virus group, named COVID-19, was firstly reported in December 2019, and, with the passage of time, coronavirus has spread to almost all parts of the world. Coronavirus has been the cause of millions of deaths around the world. Furthermore, many countries are struggling with COVID-19 and have experimented with various kinds of vaccines to eliminate the deadly virus and its variants. This survey deals with COVID-19 data analysis and its impact on human social life. Data analysis and information related to coronavirus can greatly help scientists and governments in controlling the spread and symptoms of the deadly coronavirus. In this survey, we cover many areas of discussion related to COVID-19 data analysis, such as how artificial intelligence, along with machine learning, deep learning, and IoT, have worked together to fight against COVID-19. We also discuss artificial intelligence and IoT techniques used to forecast, detect, and diagnose patients of the novel coronavirus. Moreover, this survey also describes how fake news, doctored results, and conspiracy theories were spread over social media sites, such as Twitter, by applying various social network analysis and sentimental analysis techniques. A comprehensive comparative analysis of existing techniques has also been conducted. In the end, the Discussion section presents different data analysis techniques, provides future directions for research, and suggests general guidelines for handling coronavirus, as well as changing work and life conditions. Full article

(This article belongs to the Special Issue Emerging Trends and Challenges of IoT in Smart Healthcare Systems, Smart Cities and Education)

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18 pages, 1840 KiB

Open AccessReview

The Potential of Blockchain Technology in Dental Healthcare: A Literature Review

by Takua Mokhamed, Manar Abu Talib, Mohammad Adel Moufti, Sohail Abbas and Faheem Khan

Sensors 2023, 23(6), 3277; https://doi.org/10.3390/s23063277 - 20 Mar 2023

Cited by 7 | Viewed by 2663

Abstract

Blockchain technology in the healthcare industry has potential to enable enhanced privacy, increased security, and an interoperable data record. Blockchain technology is being implemented in dental care systems to store and share medical information, improve insurance claims, and provide innovative dental data ledgers. [...] Read more.

Blockchain technology in the healthcare industry has potential to enable enhanced privacy, increased security, and an interoperable data record. Blockchain technology is being implemented in dental care systems to store and share medical information, improve insurance claims, and provide innovative dental data ledgers. Because the healthcare sector is a large and ever-growing industry, the use of blockchain technology would have many benefits. To improve dental care delivery, researchers advocate using blockchain technology and smart contracts due to their numerous advantages. In this research, we concentrate on blockchain-based dental care systems. In particular, we examine the current research literature, pinpoint issues with existing dental care systems, and consider how blockchain technology may be used to address these issues. Finally, the limitations of the proposed blockchain-based dental care systems are discussed which may be regarded as open issues. Full article

(This article belongs to the Special Issue Emerging Trends and Challenges of IoT in Smart Healthcare Systems, Smart Cities and Education)

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21 pages, 5204 KiB

Open AccessReview

Application and Challenges of IoT Healthcare System in COVID-19

by Abdullah A. Al-Atawi, Faheem Khan and Cheong Ghil Kim

Sensors 2022, 22(19), 7304; https://doi.org/10.3390/s22197304 - 26 Sep 2022

Cited by 10 | Viewed by 4842

Abstract

The importance of the IoT is increasing in every field of life, and it especially has a significant role in improving the efficiency of the healthcare system. Its demand further increased during COVID-19 to facilitate the patient remotely from their home digitally. Every [...] Read more.

The importance of the IoT is increasing in every field of life, and it especially has a significant role in improving the efficiency of the healthcare system. Its demand further increased during COVID-19 to facilitate the patient remotely from their home digitally. Every time the COVID-19 patient visited the doctor for minor complications, it increased the risk of spreading the virus and the cost for the patient. Another alarming situation arose when a patient was in a critical position and may not claim an emergency service from the nearby healthcare system, increasing the death rate. The IoT uses healthcare services to properly monitor COVID-19 patients by using the interconnected network to overcome these issues. Through the IoT, the patient is facilitated by the health care system without spreading the virus, decreasing the death ratio during COVID-19. This paper aims to discuss different applications, technologies, and challenges of the IoT healthcare system, related to COVID-19. Different databases were searched using keywords in PubMed, ResearchGate, Scopus, ACM, Springer, Elsevier, Google Scholar, etc. This paper is trying to discuss, identify, and highlight the useful applications of the IoT healthcare system to provide guidelines to the researchers, healthcare institutions, and scientists to overcomes the hazards of COVID-19 pandemics. Hence, IoT is beneficial by identifying the symptoms of COVID-19 patients and by providing better treatments that use the healthcare system efficiently. At the end of the paper, challenges and future work are discussed, along with useful suggestions through which scientists can benefit from the IoT healthcare system during COVID-19 and in a severe pandemic. The survey paper is not limited to the healthcare system and COVID-19, but it can be beneficial for future pandemics or in a worse situation. Full article

(This article belongs to the Special Issue Emerging Trends and Challenges of IoT in Smart Healthcare Systems, Smart Cities and Education)

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20 pages, 2470 KiB

Open AccessReview

Application of Internet of Things and Sensors in Healthcare

by Mohammad S. Al-kahtani, Faheem Khan and Whangbo Taekeun

Sensors 2022, 22(15), 5738; https://doi.org/10.3390/s22155738 - 31 Jul 2022

Cited by 35 | Viewed by 5741

Abstract

The Internet of Things (IoT) is an innovative technology with billions of sensors in various IoT applications. Important elements used in the IoT are sensors that collect data for desired analyses. The IoT and sensors are very important in smart cities, smart agriculture, [...] Read more.

The Internet of Things (IoT) is an innovative technology with billions of sensors in various IoT applications. Important elements used in the IoT are sensors that collect data for desired analyses. The IoT and sensors are very important in smart cities, smart agriculture, smart education, healthcare systems, and other applications. The healthcare system uses the IoT to meet global health challenges, and the newest example is COVID-19. Demand has increased during COVID-19 for healthcare to reach patients remotely and digitally at their homes. The IoT properly monitors patients using an interconnected network to overcome the issues of healthcare services. The aim of this paper is to discuss different applications, technologies, and challenges related to the healthcare system. Different databases were searched using keywords in Google Scholar, Elsevier, PubMed, ACM, ResearchGate, Scopus, Springer, etc. This paper discusses, highlights, and identifies the applications of IoT healthcare systems to provide research directions to healthcare, academia, and researchers to overcome healthcare system challenges. Hence, the IoT can be beneficial by providing better treatments using the healthcare system efficiently. In this paper, the integration of the IoT with smart technologies not only improves computation, but will also allow the IoT to be pervasive, profitable, and available anytime and anywhere. Finally, some future directions and challenges are discussed, along with useful suggestions that can assist the IoT healthcare system during COVID-19 and in a severe pandemic. Full article

(This article belongs to the Special Issue Emerging Trends and Challenges of IoT in Smart Healthcare Systems, Smart Cities and Education)

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