Human-centric Computing and Information Sciences volume 13, Article number: 19 (2023)
Cite this article 1 Accesses
Smart cities are composed of intelligent industrial things that enhance people’s lives and save lives. Intelligent remote patient monitoring helps predict the patient's condition. Internet of Things (IoT), artificial intelligence (AI), and cloud computing have improved the healthcare industry. Edge computing speeds up patient data transmission and ensures latency, reliability, and response time. Nonetheless, the transmission of massive amounts of patient data may lead to IoT data security vulnerabilities, which is both a concern and a challenge. This research proposed a secure, scalable, and responsive patient monitoring system. This model used the lightweight attribute-based encryption (LABE), which encrypts and decrypts IoT patient data to protect cloud-based IoT patient data. Edge servers are positioned between the IoT and cloud to increase QoS and diagnose patient impairment. The deep belief network (DBN) predicts and monitors patient health. The bat optimization algorithm (BOA) optimizes the hyperparameters. This study used deep belief to identify hyper parameters and BOA for optimization. Swarm intelligence improves the prediction results and edge–cloud reaction time. The simulation environment assessed the secure patient health monitoring system to ensure its efficiency, security, and efficacy. The proposed model offers effective patient remote health monitoring through a secure edge–cloud–IoT environment with improved accuracy (97.9%), precision (95.6%), recall (94.6%), F1-score (94.9%), and FDR (0.06).
Cloud Computing, Edge Computing, Internet of Things (IoT), Patient Health Monitoring, Smart Cities, Cryptography, Bat Algorithm, Deep Belief Network
Internet of Things (IoT) uses medical sensors to sense patient data and interpret, and process, and respond to them in a timely manner over the network. Data from the sensing devices are transformed into natural scenarios, producing a smart environment. The innovation of IoT creates a smart environment that integrates the smart devices in the network to share, communicate, and perform a particular process. The smart modules in the network can perform these tasks with coordination between them. Smart cities include various applications such as healthcare, smart electricity, transportation, management, smart buildings, and sewerages. Through smart services, the smart data element is generated and used for smart city applications [1–5]. Cloud and edge computing provide the best services to smart cities due to the characteristics of smart node collaboration. Since the smart devices are located far away from the cloud server, edge computing is introduced to reduce the network traffic of the transmission, which in turn may lead to increased response time and traffic. Due to various reasons such as smart devices cooperation, 5G/6G network exploitation, and base station service providers, edge computing is also incorporated.
In smart cities, the real environment is changed into an automation environment based on the smart devices that provide reliable management of the data. It has various components including weather monitoring, smart homes, waste management, energy management, buildings, medical services, sewerage, air pollution control, monitoring of forest fire, traffic control, health monitoring, radiation level monitoring, intelligent shopping, smart maps, smart lighting, and vehicle auto diagnosis [1–3]. The integration of IoT–edge and cloud requires the healthcare system to be available everywhere to secure fast response for the patients and doctors. On the other hand, accessing medical hospitals is still a challenging issue in smart healthcare using smart devices. Patients with serious vital signs need instant solutions in the fastest way. Thus, the analyzed results must be accurate based on the previous analysis, and the response time should be minimized with reduced network latency. Therefore, smart healthcare is integrated with an edge–cloud environment to overcome the issues with the utilization of available resources and technologies in the smart city environment. The revolution of smart devices in healthcare systems serves various significant purposes such as measuring the patient’s blood sugar, body temperature, weight, blood pressure, and stress through wearable devices. Thus, the integration of edge–cloud–IoT is of utmost importance, both with regard to the real world and research. The smart remote patient healthcare monitoring system in the cloud IoT scenario consists of various kinds of patient biological data that are transmitted over the network and stored in the cloud anywhere . Through the IoT network, the transmitted patient data gives rise to confidentiality and security issues that have to be addressed carefully . Therefore, lightweight cryptography approaches are applied to provide security of the medical data that is necessary for the secure and safe management of patient medical information [3, 4].
Akhbarifar et al.  developed a remote health monitoring method using the lightweight block encryption approach to provide security of the patient data and applied various machine learning algorithms for the prediction of patient heart diseases. They concluded that the R-star approach performs better with accuracy of 95% than support vector machine (SVM), random forest (RF), J48, and multilayer perceptron (MLP). Jayaram and Prabakaran  proposed remote patient monitoring and rehabilitation with a privacy-preserving secure healthcare system using edge-level privacy-preserving additive homomorphic encryption. With the implementation of filtering and offloading decisions, this work reduces the network traffic and response time. The proposed adaptive weighted probabilistic classifier performs better with accuracy of 96.9% compared to other classifiers such as neural network, linear SVM, polynomial SVM, radial basis SVM, and sigmoid SVM. With this motivation, this paper proposes a secure edge–cloud–IoT-based smart city healthcare system to provide optimal on-time care to the patients. This proposed approach also ensures reduced latency and response time. The contributions of this work are as follows:
Patients’ sensitive health-related data are collected through IoT sensors using the wearable devices of the patients.
Patients’ sensitive data are encrypted using the lightweight attribute-based encryption (LABE) approach in the edge network to ensure the security and confidentiality of the patient medical data.
The encrypted sensitive data are decrypted by the cloud processor for prediction. The decrypted data are preprocessed using normalization and scaling approaches to make the data balanced.
The preprocessed data are used for the prediction process using the deep belief network (DBN) optimized with the bat optimization algorithm (BOA).
This section discusses the most relevant existing research works related to edge–cloud–IoT-based smart healthcare systems. Alrazgan  developed an edge–cloud-based healthcare system for smart cities, studying the offloading approaches for mobile edge computing using PSO, ACO, and DPSO to improve the quality of service of the network and concluding that DPSO performs better in terms of reducing latency and energy. Suryandari et al.  developed a remote patient monitoring system to manage the resources of the hospital effectively using patient monitoring at home through IoT. The systems provide data access and monitoring through the user-friendly gateway.
Liu et al.  developed a cloud-based system using a digital twin healthcare system to observe, analyze, and predict the elderly’s health status through wearable medical devices for monitoring patients’ health status and suggested innovations in the digital twin healthcare system. Ganesan and Sivakumar  and Nguyen et al.  developed a deep learning approach based on heart disease prediction in an IoT environment. Abdelaziz et al.  proposed a cloud–IoT-based medical monitoring system and analyzed various classification methods for the prediction of diabetes mellitus, hypertension, renal disorder, and heart disease. A lightweight selective encryption algorithm was proposed by Qui et al.  using a machine learning method to protect data security. Zhou et al.  developed a Fibonacci Q matrix-based logarithmic encryption for cyber systems. This method has been extended with fuzzy and guaranteed data security in a model proposed by Ma et al. . Sun  studied cyber security approaches such as multi-authority-based encryption, key policy attribute-based encryption, fine-grain, trust, revocation, multi-tenant, trace approach, and hierarchical and proxy re-encryption to ensure security in the cloud. Abd El-Latif et al.  proposed a quantum walk-based encryption method with permutation phases in healthcare systems to protect patient data confidentiality without compromising the image encryption efficiency and robustness. Hassan et al.  developed a certificate-less public key encryption approach to protecting the authorized cloud server with an equality test scheme for smart healthcare systems. Hameed et al.  proposed a cipher blockchain advanced encryption model with Huffman coding and wavelet transform to improve data safety and efficiency of data storage between the stakeholders.
Ben Dhaou et al.  reviewed various wearable device techniques, algorithms, and technologies in terms of the Internet of medical things. They also surveyed the transformation methods used for fog computing with IoT devices. Cao et al.  proposed a medical health monitoring system with IoT and cloud computing using three terminals: sensor, gateway, and service. Based on the community and region, patients are efficiently monitored through GSM and the website. Zhang et al.  proposed the real-time health monitoring of patients through 5G mobile edge computing with IoT using an artificial intelligence algorithm for diagnosis. Table 1 compares the healthcare systems based on their merits and demerits [8, 23–28].
Security poses a major challenge in the medical industry. Medical data have to be personalized by hospital. There are many deep learning techniques using wireless sensor network (WSN) to improve the performance of IoT. Convolution neural network (CNN) for malware classification  and cognitive architecture for cyber security  require more time to identify the intrusions. Likewise, some research [31–35] studies used machine learning-based feature extraction and classification for malware prediction. Bio-inspired robots are used with Internet of Medical Things (IoMT) for securing the data in cloud . Blockchain-assisted cloud network for the security system  in cloud is implemented with less cost.
Table 1. Comparison of existing healthcare systems
|Adebiyi et al. ||Optimized genetic algorithm-based feature selection is performed on the Gambia dataset using SVM kernel approaches||Model is complicated||Improved classification accuracy|
|Suryandari et al. ||Remote patient monitoring with efficient management of hospital resources||Lack of accuracy in disease diagnosis and high error in classification||Improved classification speed and outlier removal in IoT|
|Gupta et al. ||IoT-based remote patient monitoring system||The model is complicated, with increased computation time||Improved classification accuracy|
|Tan and Halim ||IoT-based healthcare monitoring and diagnosis system||A complicated model with maximum error and reduced computational speed||Optimal classification accuracy|
|Hiriyannaiah et al. ||LSTM-based patient health monitoring system||Increased computational speed||Improved classification accuracy|
|Arowolo et al. ||Optimized genetic algorithm with PCA and ICA for patient disease diagnosis and monitoring in IoT||Model is complicated||Optimal accuracy in classification|
|Vahidi Farashah et al. ||Clustering and deep learning-based patient disease analysis||Increased computation time||Improved classification accuracy|
The proposed patient health monitoring system integrates the cloud platform with an edge network to reduce latency, cost, and traffic. This section discusses the system model, encryption-decryption method, and disease prediction approaches.
The proposed secure edge–cloud–IoT-based patient remote health monitoring system is shown in Fig 1, which consists of four components: IoT network, edge network, cloud platform, and healthcare providers. The patient’s medical data such as heart rate, body temperature, blood pressure, blood sugar, stress level, pulse counter, consciousness level, etc., are collected from IoT devices. The collected sensitive medical data are shared to the edge network through gateways. This edge network also provides computing and storage capability that is integrated with the cloud computing platform. Such collected data are processed in the edge layer to ensure security using a LABE algorithm that will protect the patient data from attackers. The encrypted data are sent to the cloud server through the cloud gateway. Next, the cloud platform is responsible for secure data processing, and it also provides central storage for the healthcare system. All the virtual machines in the cloud–edge platform ensure data security and integrity before processing. The cloud layer decrypts the sensitive medical data and preprocesses the information using normalization approaches to make the data balanced for improved prediction accuracy. The cloud layer then employs a deep learning model called DBN for the prediction of patients’ vulnerabilities. The hyperparameters of the neural network are optimized using the BOA approach to avoid overfitting issues. Additionally, the optimization algorithm reduces the load of an edge–cloud environment with its heuristic searching behavior. Next, the healthcare providers can analyze the predicted data severity through continuous monitoring and assessments. Based on the observed reports, the medical professionals are updated on the patient health status. In case of any vulnerabilities in inpatient health conditions, the emergency alert is given to doctors and patients’ caretakers, with doctors providing online prescriptions remotely.
IoT device data such as patient identification data and patients’ previous medical data are entered.
Patients’ current medical data are collected from medical sensors.
The collected medical data are sent to the next component for the encryption process.
Read the collected medical data presented in Section 3.2.
Encrypt the data using LABE and send the encrypted data to the cloud storage.
|Patient identification data||Patients’ previous clinical data||Patient medical data gathered from sensors|
|Patient’s national id||Height and weight||Oxygen saturation|
|Name||Smoker/alcohol drinker/drug user||Body temperature|
|Gender||Blood sugar||Blood pressure|
|Occupation||Hypertension history||Blood sugar|
|Mobile number||Blood cholesterol||Heart rate|
|Address||Blood pressure||Isolated systolic and diastolic blood pressure
HDL (high-density lipoprotein) and LDL (low-density lipoprotein) cholesterol
Read the encrypted data from the LABE algorithm encryption process.
Decrypt the data using the secret key and access policy by the cloud services.
Transfer the decrypted data to store in the cloud for the authorized processing of disease prediction.
(17)Step 5: If rd2>$r_i$then
The performance evaluation of the proposed remote patient health monitoring system was carried out using the medical data of healthy and unhealthy patients. The IoT environment provides an efficient platform to collect the patients’ vital signs for better health monitoring, and the experiment was simulated with 300 samples. The implementation was executed in Python using scikit-learn library, with Sage math providing the execution of LABE. Evaluation metrics such as accuracy, precision, recall, and F-score were used to compare the efficiency of the proposed model with existing works using the WEKA tool by comparing the results with other classification algorithms. The samples were selected using k-fold cross validation, which randomly divides the data into k distinct fold with identical sizes. Classification was trained and tested for k number of times using the values 5, 10, 15, and 20 fold.
(25)The dataset is distributed in k folds randomly with the same number of instances. In training data, the classifier identifies the patient diseases with the normalized data. In testing data, k-th fold data were tested with the trained model.
Proposed DBN-BOA: accuracy = 97.9%, precision = 95.6 %, recall = 94.6%, F1-score = 94.98%, and false detection rate (FDR) = 0.06.
SVM: accuracy = 80%, precision = 81%, recall = 80.4%, F1-score = 82.7%, and FDR = 2.1.
MLP: accuracy = 89%, precision = 83 %, recall = 83.2%, F1-score = 83.7%, and FDR = 1.4.
CNN: accuracy = 91%, precision = 89.2 %, recall = 88.4%, F1-score = 88.8%, and FDR = 0.9.
|Healthcare systems||Network capacity (kbps)||Response time (second)|
|Security scheme type||Types of attack|
In this study, the layered components of secure edge–cloud–IoT-based remote patient healthcare monitoring were proposed for the early diagnosis of patient disease and prediction. This proposed work incorporated a secure lightweight cryptography algorithm to ensure the security of the medical data at the edge network component. The usage of edge node before the cloud component is expected to reduce network traffic and response time. From various geographic locations, the requested data from the patients are securely processed in the cloud component. The cloud layer decrypts the data and processes it for early diagnosis. The gathered secure data are preprocessed in the cloud component prior to being used for prediction. Preprocessed medical data are classified using the proposed deep learning model called DBN-BOA optimization approach. The optimization algorithm significantly increases the accuracy of the prediction. In order to validate the performance of the proposed edge–cloud–IoT-based secure patient monitoring system, a comparative analysis of the existing classifiers and healthcare systems was performed in terms of evaluation metrics such as accuracy, precision, recall, F1-score, FDR, network capacity, and response time, based on k-fold validation on different k values. The training data are executed with k folds such as 5, 10, 15 and 20. The proposed algorithm secured improved accuracy (97.9%), precision (95.6%), recall (94.6%), F1-score (94.9%), and FDR (0.06) compared to other approaches. Compared to existing healthcare systems, the proposed approach required minimum network capacity (120 kbps) and response time (70 seconds). The statistical analysis proved the efficiency of the proposed healthcare system, which defends against plaintext attack, collusion attack, replay attack, and eavesdropping attacks. Since deep learning algorithms perform much better on larger datasets, in the future, the proposed secure patient health monitoring system will be tested with a larger amount of patient data. Additionally, blockchain-based security features are planned to be executed in cloud layers for the enhanced protection of the confidentiality of patient data.
Conceptualization: SB, NB. Funding acquisition: WP. Investigation and methodology: MA, CS. Project administration: MZ. Resources: NB. Supervision: WP. Writing of the original draft: MA. Writing of the review and editing: SB, MA, CS. Software and validation: MA, MZ. Formal analysis: WP, MZ. Data curation and visualization: MZ, NB.
This work was partially supported by the Centre for Innovation and Transfer of Natural Sciences and Engineering Knowledge of the University of Rzeszow, Poland.
The authors declare that they have no competing interests.
Dr. Saravana Balaji B is an Assistant Professor at the Department of Information Technology, College of Engineering & Computer Science, Lebanese French University, Erbil, Kurdistan, Iraq. He has 16 years of teaching and research experience. He has strong knowledge of cloud computing, computer networks, and the semantic web. He has acted as a resource person for various workshops and faculty development programs organized by different institutions. He has completed his undergraduate and postgraduate computer science and engineering degrees with first-class qualifications. He earned a Ph.D. degree from Anna University Chennai in the Information and Communication Engineering faculty. So far, he has published more than 40 research articles in various reputed international journals and conferences. He has been acting as a reviewer for many SCI and Scopus Indexed Journals.
Dr. WIESŁAW PAJA is an Assistant Professor at the University of Rzeszów, Institute of Computer Science, Poland. He holds a PhD in Computer Science in 2008 at AGH University of Science and Technology in Poland. He has experience in developing and implementing machine learning methods in practical applications. In particular, in applications in the field of medical diagnosis support. His research interests are in the development of significant feature selection methods, their extensions, and applications to practical problems. In particular, the application of these methods in supporting medical diagnosis. He has authored or co-authored numerous publications in prestigious journals. He has also presented his research at numerous international scientific conferences. He has also conducted research projects funded by national programs.
Dr. Milos Antonijevic has PhD in Computer sciences (study program Advanced security systems) from Singidunum University and Master degree of Engineer of Organizational Sciences from Faculty of Organizational sciences, University of Belgrade (study program E-business, average grade 10,00). He started his career in education 14 years ago at High School of Graphics and media in Belgrade. He currently works as an Assistant professor at Singidunum University, Belgrade, Serbia and as certified ISO 27001 Auditor for various accreditation authorities. He is involved in scientific research in the field of computer science with focus on implementation of AI and optimization algorithms in various security systems. His other area of interest includes human-computer interaction, machine learning and cloud and distributed computing.
Dr. Catalin Stoean has a research background in evolutionary computation and intelligent systems. His research interests involve finding appropriate machine learning means to solve real-world tasks from various fields like medicine, economy and even cultural heritage. The applications he works on refer to classification of data of various types (numerical, images, text), clustering, time series modelling, image segmentation. Deep learning represents another research topic where he has activated in recent years. Catalin is a Fulbright and DAAD alumni, he received the habilitation title in Romania in 2021, he published numerous articles in prestigious journals and presented the research findings at many international conferences and universities.
Dr. Nebojsa Bacanin received his Ph.D. degree from Faculty of Mathematics, University of Belgrade in 2015 (study program Computer Science, average grade 10,00). He started University career in Serbia 13 years ago at Graduate School of Computer Science in Belgrade. He currently works as an associate professor and as a vice-dean at Faculty of Informatics and Computing, Singidunum University, Belgrade, Serbia. He is involved in scientific research in the field of computer science and his specialty includes stochastic optimization algorithms, swarm intelligence, soft-computing and optimization and modeling, as well as artificial intelligence algorithms, swarm intelligence, machine learning, image processing and cloud and distributed computing. He has published more than 120 scientific papers in high quality journals and international conferences indexed in Clarivate Analytics JCR, Scopus, WoS, IEEExplore, and other scientific databases, as well as in Springer Lecture Notes in Computer Science and Procedia Computer Science book chapters. He has also published 2 books in domains of Cloud Computing and Advanced Java Spring Programming. He is a member of numerous editorial boards, scientific and advisory committees of international conferences and journals. He is a regular reviewer for international journals with high Clarivate Analytics and WoS impact factor such as Journal of Ambient Intelligence & Humanized Computing, Soft Computing, Applied Soft Computing, Information Sciences, Journal of Cloud Computing, IEEE Transactions on Computers, IEEE Review, Swarm and Evolutionary Computation, Journal of King Saud University ¨C Computer and Information Sciences, SoftwareX, Neurocomputing, Operations Research Perspectives, etc. He actively participates in 1 national and 1 international projects from the domain of computer science. He has also been included in the prestigious Stanford University list with 2% best world researchers for the years 2020 and 2022.
Saravana Balaji B1,*, Wiesław Paja2, Milos Antonijevic3, Catalin Stoean4, Nebojsa Bacanin3, and Miodrag Zivkovic3, IoT Integrated Edge Platform for Secure Industrial Application with Deep Learning, Article number: 13:19 (2023) Cite this article 1 AccessesDownload citation
Anyone you share the following link with will be able to read this content:
Provided by the Springer Nature SharedIt content-sharing initiative