In the rapidly evolving landscape of healthcare,the integration of Artificial Intelligence(AI)and Natural Language Processing(NLP)holds immense promise for revolutionizing data analytics and decision-making processes....In the rapidly evolving landscape of healthcare,the integration of Artificial Intelligence(AI)and Natural Language Processing(NLP)holds immense promise for revolutionizing data analytics and decision-making processes.Current techniques for personalized medicine,disease diagnosis,treatment recommendations,and resource optimization in the Internet of Medical Things(IoMT)vary widely,including methods such as rule-based systems,machine learning algorithms,and data-driven approaches.However,many of these techniques face limitations in accuracy,scalability,and adaptability to complex clinical scenarios.This study investigates the synergistic potential of AI-driven optimization techniques and NLP applications in the context of the IoMT.Through the integration of advanced data analytics methodologies with NLP capabilities,we propose a comprehensive framework designed to enhance personalized medicine,streamline disease diagnosis,provide treatment recommendations,and optimize resource allocation.Using a systematic methodology data was collected from open data repositories,then preprocessed using data cleaning,missing value imputation,feature engineering,and data normalization and scaling.Optimization algorithms,such as Gradient Descent,Adam Optimization,and Stochastic Gradient Descent,were employed in the framework to enhance model performance.These were integrated with NLP processes,including Text Preprocessing,Tokenization,and Sentiment Analysis to facilitate comprehensive analysis of the data to provide actionable insights from the vast streams of data generated by IoMT devices.Lastly,through a synthesis of existing research and real-world case studies,we demonstrated the impact of AI-NLP fusion on healthcare outcomes and operational efficiency.The simulation produced compelling results,achieving an average diagnostic accuracy of 93.5%for the given scenarios,and excelled even further in instances involving rare diseases,achieving an accuracy rate of 98%.With regard to patient-specific treatment plans it generated them with an average precision of 96.7%.Improvements in early risk stratification and enhanced documentation were also noted.Furthermore,the study addresses ethical considerations and challenges associated with deploying AI and NLP in healthcare decision-making processes,offering insights into risk-mitigating strategies.This research contributes to advancing the understanding of AI-driven optimization algorithms in healthcare data analytics,with implications for healthcare practitioners,researchers,and policymakers.By leveraging AI and NLP technologies in IoMT environments,this study paves the way for innovative strategies to enhance patient care and operational effectiveness.Ultimately,this work underscores the transformative potential of AI-NLP fusion in shaping the future of healthcare.展开更多
The prediction of human diseases,particularly COVID-19,is an extremely challenging task not only for medical experts but also for the technologists supporting them in diagnosis and treatment.To deal with the predictio...The prediction of human diseases,particularly COVID-19,is an extremely challenging task not only for medical experts but also for the technologists supporting them in diagnosis and treatment.To deal with the prediction and diagnosis of COVID-19,we propose an Internet of Medical Things-based Smart Monitoring Hierarchical Mamdani Fuzzy Inference System(IoMTSM-HMFIS).The proposed system determines the various factors like fever,cough,complete blood count,respiratory rate,Ct-chest,Erythrocyte sedimentation rate and C-reactive protein,family history,and antibody detection(lgG)that are directly involved in COVID-19.The expert system has two input variables in layer 1,and seven input variables in layer 2.In layer 1,the initial identification for COVID-19 is considered,whereas in layer 2,the different factors involved are studied.Finally,advanced lab tests are conducted to identify the actual current status of the disease.The major focus of this study is to build an IoMT-based smart monitoring system that can be used by anyone exposed to COVID-19;the system would evaluate the user’s health condition and inform them if they need consultation with a specialist for quarantining.MATLAB-2019a tool is used to conduct the simulation.The COVID-19 IoMTSM-HMFIS system has an overall accuracy of approximately 83%.Finally,to achieve improved performance,the analysis results of the system were shared with experts of the Lahore General Hospital,Lahore,Pakistan.展开更多
Sleep apnea syndrome(SAS)is a breathing disorder while a person is asleep.The traditional method for examining SAS is Polysomnography(PSG).The standard procedure of PSG requires complete overnight observation in a lab...Sleep apnea syndrome(SAS)is a breathing disorder while a person is asleep.The traditional method for examining SAS is Polysomnography(PSG).The standard procedure of PSG requires complete overnight observation in a laboratory.PSG typically provides accurate results,but it is expensive and time consuming.However,for people with Sleep apnea(SA),available beds and laboratories are limited.Resultantly,it may produce inaccurate diagnosis.Thus,this paper proposes the Internet of Medical Things(IoMT)framework with a machine learning concept of fully connected neural network(FCNN)with k-near-est neighbor(k-NN)classifier.This paper describes smart monitoring of a patient’s sleeping habit and diagnosis of SA using FCNN-KNN+average square error(ASE).For diagnosing SA,the Oxygen saturation(SpO2)sensor device is popularly used for monitoring the heart rate and blood oxygen level.This diagnosis information is securely stored in the IoMT fog computing network.Doctors can care-fully monitor the SA patient remotely on the basis of sensor values,which are efficiently stored in the fog computing network.The proposed technique takes less than 0.2 s with an accuracy of 95%,which is higher than existing models.展开更多
The internet of medical things(IoMT)empowers patients to get adaptable,and virtualized gear over the internet.Task scheduling is the most fundamental problem in the IoMT-cloud since cloud execution commonly relies on ...The internet of medical things(IoMT)empowers patients to get adaptable,and virtualized gear over the internet.Task scheduling is the most fundamental problem in the IoMT-cloud since cloud execution commonly relies on it.Thus,a proposition is being made for a distinct scheduling technique to suitably meet these solicitations.To manage the scheduling issue,an artificial intelligence(AI)method known as a hybrid genetic algorithm(HGA)is proposed.The proposed AI method will be justified by contrasting it with other traditional optimization and AI scheduling approaches.The CloudSim is utilized to quantify its effect on various parameters like time,resource utilization,cost,and throughput.The proposed AI technique enhanced the viability of task scheduling with a better execution rate of 32.47ms and a reduced time of 40.16ms.Thus,the experimented outcomes show that the HGA reduces cost as well as time profoundly.展开更多
The Internet ofMedical Things(IoMT)and cloud-based healthcare applications,services are beneficial for better decision-making in recent years.Melanoma is a deadly cancer with a highermortality rate than other skin can...The Internet ofMedical Things(IoMT)and cloud-based healthcare applications,services are beneficial for better decision-making in recent years.Melanoma is a deadly cancer with a highermortality rate than other skin cancer types such as basal cell,squamous cell,andMerkel cell.However,detection and treatment at an early stage can result in a higher chance of survival.The classical methods of detection are expensive and labor-intensive.Also,they rely on a trained practitioner’s level,and the availability of the needed equipment is essential for the early detection of Melanoma.The current improvement in computer-aided systems is providing very encouraging results in terms of precision and effectiveness.In this article,we propose an improved region growing technique for efficient extraction of the lesion boundary.This analysis and detection ofMelanoma are helpful for the expert dermatologist.The CNN features are extracted using the pre-trained VGG-19 deep learning model.In the end,the selected features are classified by SVM.The proposed technique is gauged on openly accessible two datasets ISIC 2017 and PH2.For the evaluation of our proposed framework,qualitative and quantitative experiments are performed.The suggested segmentation method has provided encouraging statistical results of Jaccard index 0.94,accuracy 95.7%on ISIC 2017,and Jaccard index 0.91,accuracy 93.3%on the PH2 dataset.These results are notably better than the results of prevalent methods available on the same datasets.The machine learning SVMclassifier executes significantly well on the suggested feature vector,and the comparative analysis is carried out with existing methods in terms of accuracy.The proposed method detects and classifies melanoma far better than other methods.Besides,our framework gained promising results in both segmentation and classification phases.展开更多
Emerging telemedicine trends,such as the Internet of Medical Things(IoMT),facilitate regular and efficient interactions between medical devices and computing devices.The importance of IoMT comes from the need to conti...Emerging telemedicine trends,such as the Internet of Medical Things(IoMT),facilitate regular and efficient interactions between medical devices and computing devices.The importance of IoMT comes from the need to continuously monitor patients’health conditions in real-time during normal daily activities,which is realized with the help of various wearable devices and sensors.One major health problem is workplace stress,which can lead to cardiovascular disease or psychiatric disorders.Therefore,real-time monitoring of employees’stress in the workplace is essential.Stress levels and the source of stress could be detected early in the fog layer so that the negative consequences can be mitigated sooner.However,overwhelming the fog layer with extensive data will increase the load on fog nodes,leading to computational challenges.This study aims to reduce fog computation by proposing machine learning(ML)models with two phases.The first phase of theMLmodel assesses the priority of the situation based on the stress level.In the second phase,a classifier determines the cause of stress,which was either interruptions or time pressure while completing a task.This approach reduced the computation cost for the fog node,as only high-priority records were transferred to the fog.Low-priority records were forwarded to the cloud.Four MLapproaches were compared in terms of accuracy and prediction speed:Knearest neighbors(KNN),a support vector machine(SVM),a bagged tree(BT),and an artificial neural network(ANN).In our experiments,ANN performed best in both phases because it scored an F1 score of 99.97% and had the highest prediction speed compared with KNN,SVM,and BT.展开更多
Health care visualization through Internet of Things(IoT)over wireless sensor network(WSN)becomes a current research attention due to medical sensor evolution of devices.The digital technology-based communication syst...Health care visualization through Internet of Things(IoT)over wireless sensor network(WSN)becomes a current research attention due to medical sensor evolution of devices.The digital technology-based communication system is widely used in all application.Internet of medical thing(IoMT)assisted health-care application ensures the continuous health monitoring of a patient and provides the early awareness of the one who is suffered without human participation.These smart medical devices may consume with limited resources and also the data generated by these devices are large in size.These IoMT based applications suffer from the issues such as security,anonymity,privacy,and interoper-ability.To overcome these issues,data aggregation methods are the solution that can concatenate the data generated by the sensors and forward it into the base station through fog node with efficient encryption and decryption.This article proposed a well-organized data aggregation and secured transmission approach.The data generated by the sensor are collected and compressed.Aggregator nodes(AN)received the compressed data and concatenate it.The concatenated and encrypted data is forward to fog node using the enhanced Paillier cryptogra-phy-based encryption with Message Authentication code(MAC).Fog node extracts the forwarded data from AN using Fog message extractor method(FME)with decryption.The proposed system ensures data integrity,security and also protects from security threats.This proposed model is simulated in Net-work Simulator 2.35 and the evaluated simulation results proves that the aggregation with MAC code will ensures the security,privacy and also reduces the communication cost.Fog node usages in between Aggregator and base station,will reduce the cloud server/base station computational overhead and storage cost.The proposed ideology is compared with existing data aggregation schemes in terms of computational cost,storage cost,communication cost and energy cost.Cost of communication takes 18.7 ms which is much lesser than existing schemes.展开更多
Rapid technological advancement has enabled modern healthcare systems to provide more sophisticated and real-time services on the Internet of Medical Things(IoMT).The existing cloud-based,centralized IoMT architecture...Rapid technological advancement has enabled modern healthcare systems to provide more sophisticated and real-time services on the Internet of Medical Things(IoMT).The existing cloud-based,centralized IoMT architectures are vulnerable to multiple security and privacy problems.The blockchain-enabled IoMT is an emerging paradigm that can ensure the security and trustworthiness of medical data sharing in the IoMT networks.This article presents a private and easily expandable blockchain-based framework for the IoMT.The proposed framework contains several participants,including private blockchain,hospitalmanagement systems,cloud service providers,doctors,and patients.Data security is ensured by incorporating an attributebased encryption scheme.Furthermore,an IoT-friendly consensus algorithm is deployed to ensure fast block validation and high scalability in the IoMT network.The proposed framework can perform multiple healthcare-related services in a secure and trustworthy manner.The performance of blockchain read/write operations is evaluated in terms of transaction throughput and latency.Experimental outcomes indicate that the proposed scheme achieved an average throughput of 857 TPS and 151 TPS for read and write operations.The average latency is 61 ms and 16 ms for read and write operations,respectively.展开更多
Reliable transmission is vital to the success of the next generation ofcommunications technologies and Fifth Generation (5G) networks. Many sensitive applications, such as eHealth and medical services, can benefit fro...Reliable transmission is vital to the success of the next generation ofcommunications technologies and Fifth Generation (5G) networks. Many sensitive applications, such as eHealth and medical services, can benefit from a 5G network. The Internet of Medical Things (IoMT) is a new field that fosters themaintenance of trust among various IoMT Device to Device (D2D) modern technologies. In IoMT the medical devices have to be connected through a wirelessnetwork and constantly needs to be self-configured to provide consistent and effi-cient data transmission. The medical devices need to be connected with sophisticated protocols and architecture to handle the synergy of the monitoring devices.Today, one of the commonly used algorithms in D2D communication is the Optimized Link State Routing protocol (OLSR). The OLSR is considerably good ateffectively utilizing the bandwidth and reserving the paths. One of the majorattack against the OLSR is the Node isolation attack, also known as the Gray holedenial of service attack. The Gray hole attack exploits the vulnerabilities presentwith sharing the topological information of the network. The attackers may usethis topological information to maliciously disconnect the target nodes from theexisting network and stops rendering the communication services to the victimnode. Hence, considering the sensitivity and security concerns of the data usedin e-Health applications, these types of attacks must be detected and disabledproactively. In this work, a novel Node Authentication (NA) with OLSR is proposed. The simulation experiments illustrated that the proposed protocol has anexcellent Packet Delivery Ratio, minimal End-End delay, and minimal Packet losswhen compared to the Ad-hoc On-Demand Distance Victor (AODV) protocol andthe proposed authentication scheme was able to protect the OLSR protocol from anode isolation attack.展开更多
Data management becomes essential component of patient healthcare.Internet of Medical Things(IoMT)performs a wireless communication between E-medical applications and human being.Instead of consulting a doctor in the ...Data management becomes essential component of patient healthcare.Internet of Medical Things(IoMT)performs a wireless communication between E-medical applications and human being.Instead of consulting a doctor in the hospital,patients get health related information remotely from the physician.The main issues in the E-Medical application are lack of safety,security and priv-acy preservation of patient’s health care data.To overcome these issues,this work proposes block chain based IoMT Processed with Hybrid consensus protocol for secured storage.Patients health data is collected from physician,smart devices etc.The main goal is to store this highly valuable health related data in a secure,safety,easy access and less cost-effective manner.In this research we combine two smart contracts such as Practical Byzantine Fault Tolerance with proof of work(PBFT-PoW).The implementation is done using cloud technology setup with smart contracts(PBFT-PoW).The accuracy rate of PBFT is 90.15%,for PoW is 92.75%and our proposed work PBFT-PoW is 99.88%.展开更多
The Internet of Medical Things(IoMT)is an online device that senses and transmits medical data from users to physicians within a time interval.In,recent years,IoMT has rapidly grown in the medicalfield to provide heal...The Internet of Medical Things(IoMT)is an online device that senses and transmits medical data from users to physicians within a time interval.In,recent years,IoMT has rapidly grown in the medicalfield to provide healthcare services without physical appearance.With the use of sensors,IoMT applications are used in healthcare management.In such applications,one of the most important factors is data security,given that its transmission over the network may cause obtrusion.For data security in IoMT systems,blockchain is used due to its numerous blocks for secure data storage.In this study,Blockchain-assisted secure data management framework(BSDMF)and Proof of Activity(PoA)protocol using malicious code detection algorithm is used in the proposed data security for the healthcare system.The main aim is to enhance the data security over the networks.The PoA protocol enhances high security of data from the literature review.By replacing the malicious node from the block,the PoA can provide high security for medical data in the blockchain.Comparison with existing systems shows that the proposed simulation with BSD-Malicious code detection algorithm achieves higher accuracy ratio,precision ratio,security,and efficiency and less response time for Blockchain-enabled healthcare systems.展开更多
Patient privacy and data protection have been crucial concerns in Ehealthcare systems for many years.In modern-day applications,patient data usually holds clinical imagery,records,and other medical details.Lately,the ...Patient privacy and data protection have been crucial concerns in Ehealthcare systems for many years.In modern-day applications,patient data usually holds clinical imagery,records,and other medical details.Lately,the Internet of Medical Things(IoMT),equipped with cloud computing,has come out to be a beneficial paradigm in the healthcare field.However,the openness of networks and systems leads to security threats and illegal access.Therefore,reliable,fast,and robust security methods need to be developed to ensure the safe exchange of healthcare data generated from various image sensing and other IoMT-driven devices in the IoMT network.This paper presents an image protection scheme for healthcare applications to protect patients’medical image data exchanged in IoMT networks.The proposed security scheme depends on an enhanced 2D discrete chaotic map and allows dynamic substitution based on an optimized highly-nonlinear S-box and diffusion to gain an excellent security performance.The optimized S-box has an excellent nonlinearity score of 112.The new image protection scheme is efficient enough to exhibit correlation values less than 0.0022,entropy values higher than 7.999,and NPCR values around 99.6%.To reveal the efficacy of the scheme,several comparison studies are presented.These comparison studies reveal that the novel protection scheme is robust,efficient,and capable of securing healthcare imagery in IoMT systems.展开更多
Recent research efforts have created rapid advances in the field of the Internet of Things (IoT) in terms of communication protocols, sensing technologies, computing capabilities, next-generation wireless technologies...Recent research efforts have created rapid advances in the field of the Internet of Things (IoT) in terms of communication protocols, sensing technologies, computing capabilities, next-generation wireless technologies, big data and AI techniques, and on-device, edge, cloud processing. These advances created a paradigm shift and generated a wide range of potential opportunities for a new major field known as the Internet of Medical Things (IoMT), as well as subfields like mobile Health (mHeatlh) and digital or electronic Health (eHealth). This paper provides an overview of the Internet of Medical Things and presents a classification to define the primary users and their roles and involvement in smart healthcare systems. The paper then presents taxonomy on the deployment scales of different healthcare environments, from personal healthcare to widescale connected healthcare systems. The overview also discusses the n-tier architecture of IoMT, then presents a set of taxonomies and classifications on the different medical devices used in healthcare systems as well as non-medical devices used to provide context-aware information about the surrounding environment. The paper then concludes the overview by presenting the different healthcare-related applications and services, a comparison between traditional and smart healthcare systems, and the different obstacles and challenges in the field of IoMT to guide the development of new services and devices. Many survey papers in the literature focused on similar points;however, up to our knowledge, this is the first paper to present taxonomies and classifications for these IoMT essential topics. The presented taxonomies and classifications provide the manufacturers of medical devices and the developers of healthcare services a deeper understanding of the healthcare systems’ landscape to address different requirements and demands.展开更多
With the rapid progress in data-driven approaches,artificial intelligence,and big data analytics technologies,utilizing electroencephalogram(EEG)signals for emotion analysis in the field of the Internet of Medical Thi...With the rapid progress in data-driven approaches,artificial intelligence,and big data analytics technologies,utilizing electroencephalogram(EEG)signals for emotion analysis in the field of the Internet of Medical Things can effectively assist in the diagnosis of specific diseases.While existing emotion analysis methods focus on the utilization of effective deep models for data-driven and big data analytics technology,they often struggle to extract long-range dependencies and accurately model local relationships within multi-channel EEG signals.In addition,the subjective scores of the subjects may not match the predefined emotional labels.To overcome these limitations,this paper proposes a new data-driven dynamic graph-embedded Transformer network(DGETN)that has emerged in different tasks of graph data mining for emotion analysis of EEG signals in the scene of IoMT.Firstly,we extract the frequency features differential entropy(DE)and use the linear dynamic system(LDS)method to alleviate the redundancy and noise information.Secondly,to effectively explore the long-range information and local modeling ability,a novel feature extraction module is designed by embedding the dynamic graph convolution operations in the Transformer encoder for mining the discriminant features of data.Moreover,the graph convolution operations can effectively exploit the spatial information between different channels.At last,we introduce the minimum category confusion(MCC)loss to alleviate the fuzziness of classification.We take two commonly used EEG sentiment analysis datasets as a study.The DGETN has achieved state-of-the-art accuracies of 99.38%on the SEED dataset,and accuracies of 99.24%and 98.85%for valence and arousal prediction on the DEAP dataset,respectively.展开更多
云辅助医疗物联网系统是智慧医疗领域发展的新趋势,患者隐私数据通常以密态的形式外包存储于云端,这将导致数据拥有者失去对自身数据的控制权限,并带来数据检索不便.针对上述问题,本文提出了一种支持策略隐藏的可搜索属性加密方案,结合...云辅助医疗物联网系统是智慧医疗领域发展的新趋势,患者隐私数据通常以密态的形式外包存储于云端,这将导致数据拥有者失去对自身数据的控制权限,并带来数据检索不便.针对上述问题,本文提出了一种支持策略隐藏的可搜索属性加密方案,结合密文策略属性加密与公钥可搜索加密的优势,确保云辅助(cloud-assisted Internet of Medical Things,IoMT)系统中共享数据的机密性,实现了敏感数据的细粒度访问控制并支持关键字搜索.并且,利用在线/离线加密和外包解密等方法降低了资源受限设备的计算开销,使得密文策略的属性加密方案可以在云辅助IoMT系统中实施.同时,引入策略隐藏技术,将属性加密访问策略中的属性值隐藏于密文中,防止数据拥有者的隐私泄露.在安全性方面,证明本方案的密文信息在选定访问结构和选择明文攻击下具有不可区分性,以及陷门信息在选择关键字攻击下具有不可区分性.最后,利用JPBC(Javapairing-based cryptography)密码库对本方案与其他相关方案在功能特性、通信开销和计算开销等方面进行对比,结果表明本方案在密钥生成和加密阶段计算效率更高且存储开销更低.展开更多
The Internet of Medical Things(IoMT)is transforming healthcare by enabling real-time data collection,analysis,and personalized treatment through interconnected devices such as sensors and wearables.The integration of ...The Internet of Medical Things(IoMT)is transforming healthcare by enabling real-time data collection,analysis,and personalized treatment through interconnected devices such as sensors and wearables.The integration of Digital Twins(DTs),the virtual replicas of physical components and processes,has also been found to be a game changer for the ever-evolving IoMT.However,these advancements in the healthcare domain come with significant cybersecurity challenges,exposing it to malicious attacks and several security threats.Intrusion Detection Systems(IDSs)serve as a critical defense mechanism,yet traditional IDS approaches often struggle with the complexity and scale of IoMT networks.With this context,this paper follows a systematic approach to analyze the existing literature and highlight the current trends and challenges related to IDS in the IoMT domain.We leveraged techniques like bibliographic and keyword analysis to collect 832 research works published from 2007 to 2025,aligned with the theme“Digital Twins and IDS in IoMT.”It was found that by simulating device behaviours and network interactions in IoMT,DTs not only provide a proactive platform for early threat detection,but also offer a scalable and adaptive approach to mitigating evolving security threats in IoMT.Overall,this review provides a closer look into the role of IDS and DT in securing IoMT systems and sheds light on the possible research directions for developers and the research community.展开更多
This study investigates the different aspects of multimedia computing in Video Synthetic Aperture Radar(Video-SAR)as a new mode of radar imaging for real-time remote sensing and surveillance.This research also conside...This study investigates the different aspects of multimedia computing in Video Synthetic Aperture Radar(Video-SAR)as a new mode of radar imaging for real-time remote sensing and surveillance.This research also considers new suggestions in the systematic design,research taxonomy,and future trends of radar data processing.Despite the conventional modes of SAR imaging,Video-SAR can generate video sequences to obtain online monitoring and green surveillance throughout the day and night(regardless of light sources)in all weathers.First,an introduction to Video-SAR is presented.Then,some specific properties of this imaging mode are reviewed.Particularly,this research covers one of the most important aspects of the Video-SAR systems,namely,the systematic design requirements,and also some new types of visual distortions which are different from the distortions,artifacts and noises observed in the conventional imaging radar.In addition,some topics on the general features and high-performance computing of Video-SAR towards radar communications through Unmanned Aerial Vehicle(UAV)platforms,Internet of Multimedia Things(IoMT),Video-SAR data processing issues,and real-world applications are investigated.展开更多
The Internet of Medical Things(Io MT) is regarded as a critical technology for intelligent healthcare in the foreseeable 6G era. Nevertheless, due to the limited computing power capability of edge devices and task-rel...The Internet of Medical Things(Io MT) is regarded as a critical technology for intelligent healthcare in the foreseeable 6G era. Nevertheless, due to the limited computing power capability of edge devices and task-related coupling relationships, Io MT faces unprecedented challenges. Considering the associative connections among tasks, this paper proposes a computing offloading policy for multiple-user devices(UDs) considering device-to-device(D2D) communication and a multi-access edge computing(MEC)technique under the scenario of Io MT. Specifically,to minimize the total delay and energy consumption concerning the requirement of Io MT, we first analyze and model the detailed local execution, MEC execution, D2D execution, and associated tasks offloading exchange model. Consequently, the associated tasks’ offloading scheme of multi-UDs is formulated as a mixed-integer nonconvex optimization problem. Considering the advantages of deep reinforcement learning(DRL) in processing tasks related to coupling relationships, a Double DQN based associative tasks computing offloading(DDATO) algorithm is then proposed to obtain the optimal solution, which can make the best offloading decision under the condition that tasks of UDs are associative. Furthermore, to reduce the complexity of the DDATO algorithm, the cacheaided procedure is intentionally introduced before the data training process. This avoids redundant offloading and computing procedures concerning tasks that previously have already been cached by other UDs. In addition, we use a dynamic ε-greedy strategy in the action selection section of the algorithm, thus preventing the algorithm from falling into a locally optimal solution. Simulation results demonstrate that compared with other existing methods for associative task models concerning different structures in the Io MT network, the proposed algorithm can lower the total cost more effectively and efficiently while also providing a tradeoff between delay and energy consumption tolerance.展开更多
The Internet of Medical Things(IoMT)is an application of the Internet of Things(IoT)in the medical field.It is a cutting-edge technique that connects medical sensors and their applications to healthcare systems,which ...The Internet of Medical Things(IoMT)is an application of the Internet of Things(IoT)in the medical field.It is a cutting-edge technique that connects medical sensors and their applications to healthcare systems,which is essential in smart healthcare.However,Personal Health Records(PHRs)are normally kept in public cloud servers controlled by IoMT service providers,so privacy and security incidents may be frequent.Fortunately,Searchable Encryption(SE),which can be used to execute queries on encrypted data,can address the issue above.Nevertheless,most existing SE schemes cannot solve the vector dominance threshold problem.In response to this,we present a SE scheme called Vector Dominance with Threshold Searchable Encryption(VDTSE)in this study.We use a Lagrangian polynomial technique and convert the vector dominance threshold problem into a constraint that the number of two equal-length vectors’corresponding bits excluding wildcards is not less than a threshold t.Then,we solve the problem using the proposed technique modified in Hidden Vector Encryption(HVE).This technique makes the trapdoor size linear to the number of attributes and thus much smaller than that of other similar SE schemes.A rigorous experimental analysis of a specific application for privacy-preserving diabetes demonstrates the feasibility of the proposed VDTSE scheme.展开更多
基金the Researchers Supporting Project number(RSP2024R281),King Saud University,Riyadh,Saudi Arabia.
文摘In the rapidly evolving landscape of healthcare,the integration of Artificial Intelligence(AI)and Natural Language Processing(NLP)holds immense promise for revolutionizing data analytics and decision-making processes.Current techniques for personalized medicine,disease diagnosis,treatment recommendations,and resource optimization in the Internet of Medical Things(IoMT)vary widely,including methods such as rule-based systems,machine learning algorithms,and data-driven approaches.However,many of these techniques face limitations in accuracy,scalability,and adaptability to complex clinical scenarios.This study investigates the synergistic potential of AI-driven optimization techniques and NLP applications in the context of the IoMT.Through the integration of advanced data analytics methodologies with NLP capabilities,we propose a comprehensive framework designed to enhance personalized medicine,streamline disease diagnosis,provide treatment recommendations,and optimize resource allocation.Using a systematic methodology data was collected from open data repositories,then preprocessed using data cleaning,missing value imputation,feature engineering,and data normalization and scaling.Optimization algorithms,such as Gradient Descent,Adam Optimization,and Stochastic Gradient Descent,were employed in the framework to enhance model performance.These were integrated with NLP processes,including Text Preprocessing,Tokenization,and Sentiment Analysis to facilitate comprehensive analysis of the data to provide actionable insights from the vast streams of data generated by IoMT devices.Lastly,through a synthesis of existing research and real-world case studies,we demonstrated the impact of AI-NLP fusion on healthcare outcomes and operational efficiency.The simulation produced compelling results,achieving an average diagnostic accuracy of 93.5%for the given scenarios,and excelled even further in instances involving rare diseases,achieving an accuracy rate of 98%.With regard to patient-specific treatment plans it generated them with an average precision of 96.7%.Improvements in early risk stratification and enhanced documentation were also noted.Furthermore,the study addresses ethical considerations and challenges associated with deploying AI and NLP in healthcare decision-making processes,offering insights into risk-mitigating strategies.This research contributes to advancing the understanding of AI-driven optimization algorithms in healthcare data analytics,with implications for healthcare practitioners,researchers,and policymakers.By leveraging AI and NLP technologies in IoMT environments,this study paves the way for innovative strategies to enhance patient care and operational effectiveness.Ultimately,this work underscores the transformative potential of AI-NLP fusion in shaping the future of healthcare.
文摘The prediction of human diseases,particularly COVID-19,is an extremely challenging task not only for medical experts but also for the technologists supporting them in diagnosis and treatment.To deal with the prediction and diagnosis of COVID-19,we propose an Internet of Medical Things-based Smart Monitoring Hierarchical Mamdani Fuzzy Inference System(IoMTSM-HMFIS).The proposed system determines the various factors like fever,cough,complete blood count,respiratory rate,Ct-chest,Erythrocyte sedimentation rate and C-reactive protein,family history,and antibody detection(lgG)that are directly involved in COVID-19.The expert system has two input variables in layer 1,and seven input variables in layer 2.In layer 1,the initial identification for COVID-19 is considered,whereas in layer 2,the different factors involved are studied.Finally,advanced lab tests are conducted to identify the actual current status of the disease.The major focus of this study is to build an IoMT-based smart monitoring system that can be used by anyone exposed to COVID-19;the system would evaluate the user’s health condition and inform them if they need consultation with a specialist for quarantining.MATLAB-2019a tool is used to conduct the simulation.The COVID-19 IoMTSM-HMFIS system has an overall accuracy of approximately 83%.Finally,to achieve improved performance,the analysis results of the system were shared with experts of the Lahore General Hospital,Lahore,Pakistan.
基金Taif University Researchers Supporting Project Number(TURSP-2020/98),Taif University,Taif,Saudi Arabia.
文摘Sleep apnea syndrome(SAS)is a breathing disorder while a person is asleep.The traditional method for examining SAS is Polysomnography(PSG).The standard procedure of PSG requires complete overnight observation in a laboratory.PSG typically provides accurate results,but it is expensive and time consuming.However,for people with Sleep apnea(SA),available beds and laboratories are limited.Resultantly,it may produce inaccurate diagnosis.Thus,this paper proposes the Internet of Medical Things(IoMT)framework with a machine learning concept of fully connected neural network(FCNN)with k-near-est neighbor(k-NN)classifier.This paper describes smart monitoring of a patient’s sleeping habit and diagnosis of SA using FCNN-KNN+average square error(ASE).For diagnosing SA,the Oxygen saturation(SpO2)sensor device is popularly used for monitoring the heart rate and blood oxygen level.This diagnosis information is securely stored in the IoMT fog computing network.Doctors can care-fully monitor the SA patient remotely on the basis of sensor values,which are efficiently stored in the fog computing network.The proposed technique takes less than 0.2 s with an accuracy of 95%,which is higher than existing models.
文摘The internet of medical things(IoMT)empowers patients to get adaptable,and virtualized gear over the internet.Task scheduling is the most fundamental problem in the IoMT-cloud since cloud execution commonly relies on it.Thus,a proposition is being made for a distinct scheduling technique to suitably meet these solicitations.To manage the scheduling issue,an artificial intelligence(AI)method known as a hybrid genetic algorithm(HGA)is proposed.The proposed AI method will be justified by contrasting it with other traditional optimization and AI scheduling approaches.The CloudSim is utilized to quantify its effect on various parameters like time,resource utilization,cost,and throughput.The proposed AI technique enhanced the viability of task scheduling with a better execution rate of 32.47ms and a reduced time of 40.16ms.Thus,the experimented outcomes show that the HGA reduces cost as well as time profoundly.
文摘The Internet ofMedical Things(IoMT)and cloud-based healthcare applications,services are beneficial for better decision-making in recent years.Melanoma is a deadly cancer with a highermortality rate than other skin cancer types such as basal cell,squamous cell,andMerkel cell.However,detection and treatment at an early stage can result in a higher chance of survival.The classical methods of detection are expensive and labor-intensive.Also,they rely on a trained practitioner’s level,and the availability of the needed equipment is essential for the early detection of Melanoma.The current improvement in computer-aided systems is providing very encouraging results in terms of precision and effectiveness.In this article,we propose an improved region growing technique for efficient extraction of the lesion boundary.This analysis and detection ofMelanoma are helpful for the expert dermatologist.The CNN features are extracted using the pre-trained VGG-19 deep learning model.In the end,the selected features are classified by SVM.The proposed technique is gauged on openly accessible two datasets ISIC 2017 and PH2.For the evaluation of our proposed framework,qualitative and quantitative experiments are performed.The suggested segmentation method has provided encouraging statistical results of Jaccard index 0.94,accuracy 95.7%on ISIC 2017,and Jaccard index 0.91,accuracy 93.3%on the PH2 dataset.These results are notably better than the results of prevalent methods available on the same datasets.The machine learning SVMclassifier executes significantly well on the suggested feature vector,and the comparative analysis is carried out with existing methods in terms of accuracy.The proposed method detects and classifies melanoma far better than other methods.Besides,our framework gained promising results in both segmentation and classification phases.
基金funded by the Deanship of Scientific Research(DSR)at King Abdulaziz University,Jeddah,under Grant No.IFPIP:1181-611-1443.
文摘Emerging telemedicine trends,such as the Internet of Medical Things(IoMT),facilitate regular and efficient interactions between medical devices and computing devices.The importance of IoMT comes from the need to continuously monitor patients’health conditions in real-time during normal daily activities,which is realized with the help of various wearable devices and sensors.One major health problem is workplace stress,which can lead to cardiovascular disease or psychiatric disorders.Therefore,real-time monitoring of employees’stress in the workplace is essential.Stress levels and the source of stress could be detected early in the fog layer so that the negative consequences can be mitigated sooner.However,overwhelming the fog layer with extensive data will increase the load on fog nodes,leading to computational challenges.This study aims to reduce fog computation by proposing machine learning(ML)models with two phases.The first phase of theMLmodel assesses the priority of the situation based on the stress level.In the second phase,a classifier determines the cause of stress,which was either interruptions or time pressure while completing a task.This approach reduced the computation cost for the fog node,as only high-priority records were transferred to the fog.Low-priority records were forwarded to the cloud.Four MLapproaches were compared in terms of accuracy and prediction speed:Knearest neighbors(KNN),a support vector machine(SVM),a bagged tree(BT),and an artificial neural network(ANN).In our experiments,ANN performed best in both phases because it scored an F1 score of 99.97% and had the highest prediction speed compared with KNN,SVM,and BT.
基金This research was supported by Korea Institute for Advancement of Technology(KIAT)grant funded by the Korea Government(MOTIE)(P0012724,The Competency Development Program for Industry Specialist)the Soonchunhyang University Research Fund+2 种基金This research was supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute(KHIDI)funded by the Ministry of Health&Welfare,Republic of Korea(grant number:HI21C1831)the Soonchunhyang University Research Fund.
文摘Health care visualization through Internet of Things(IoT)over wireless sensor network(WSN)becomes a current research attention due to medical sensor evolution of devices.The digital technology-based communication system is widely used in all application.Internet of medical thing(IoMT)assisted health-care application ensures the continuous health monitoring of a patient and provides the early awareness of the one who is suffered without human participation.These smart medical devices may consume with limited resources and also the data generated by these devices are large in size.These IoMT based applications suffer from the issues such as security,anonymity,privacy,and interoper-ability.To overcome these issues,data aggregation methods are the solution that can concatenate the data generated by the sensors and forward it into the base station through fog node with efficient encryption and decryption.This article proposed a well-organized data aggregation and secured transmission approach.The data generated by the sensor are collected and compressed.Aggregator nodes(AN)received the compressed data and concatenate it.The concatenated and encrypted data is forward to fog node using the enhanced Paillier cryptogra-phy-based encryption with Message Authentication code(MAC).Fog node extracts the forwarded data from AN using Fog message extractor method(FME)with decryption.The proposed system ensures data integrity,security and also protects from security threats.This proposed model is simulated in Net-work Simulator 2.35 and the evaluated simulation results proves that the aggregation with MAC code will ensures the security,privacy and also reduces the communication cost.Fog node usages in between Aggregator and base station,will reduce the cloud server/base station computational overhead and storage cost.The proposed ideology is compared with existing data aggregation schemes in terms of computational cost,storage cost,communication cost and energy cost.Cost of communication takes 18.7 ms which is much lesser than existing schemes.
基金The Deanship of Scientific Research(DSR)at King Abdulaziz University(KAU),Jeddah,Saudi Arabia has funded this project,under grant no.(RG-91-611-42).
文摘Rapid technological advancement has enabled modern healthcare systems to provide more sophisticated and real-time services on the Internet of Medical Things(IoMT).The existing cloud-based,centralized IoMT architectures are vulnerable to multiple security and privacy problems.The blockchain-enabled IoMT is an emerging paradigm that can ensure the security and trustworthiness of medical data sharing in the IoMT networks.This article presents a private and easily expandable blockchain-based framework for the IoMT.The proposed framework contains several participants,including private blockchain,hospitalmanagement systems,cloud service providers,doctors,and patients.Data security is ensured by incorporating an attributebased encryption scheme.Furthermore,an IoT-friendly consensus algorithm is deployed to ensure fast block validation and high scalability in the IoMT network.The proposed framework can perform multiple healthcare-related services in a secure and trustworthy manner.The performance of blockchain read/write operations is evaluated in terms of transaction throughput and latency.Experimental outcomes indicate that the proposed scheme achieved an average throughput of 857 TPS and 151 TPS for read and write operations.The average latency is 61 ms and 16 ms for read and write operations,respectively.
文摘Reliable transmission is vital to the success of the next generation ofcommunications technologies and Fifth Generation (5G) networks. Many sensitive applications, such as eHealth and medical services, can benefit from a 5G network. The Internet of Medical Things (IoMT) is a new field that fosters themaintenance of trust among various IoMT Device to Device (D2D) modern technologies. In IoMT the medical devices have to be connected through a wirelessnetwork and constantly needs to be self-configured to provide consistent and effi-cient data transmission. The medical devices need to be connected with sophisticated protocols and architecture to handle the synergy of the monitoring devices.Today, one of the commonly used algorithms in D2D communication is the Optimized Link State Routing protocol (OLSR). The OLSR is considerably good ateffectively utilizing the bandwidth and reserving the paths. One of the majorattack against the OLSR is the Node isolation attack, also known as the Gray holedenial of service attack. The Gray hole attack exploits the vulnerabilities presentwith sharing the topological information of the network. The attackers may usethis topological information to maliciously disconnect the target nodes from theexisting network and stops rendering the communication services to the victimnode. Hence, considering the sensitivity and security concerns of the data usedin e-Health applications, these types of attacks must be detected and disabledproactively. In this work, a novel Node Authentication (NA) with OLSR is proposed. The simulation experiments illustrated that the proposed protocol has anexcellent Packet Delivery Ratio, minimal End-End delay, and minimal Packet losswhen compared to the Ad-hoc On-Demand Distance Victor (AODV) protocol andthe proposed authentication scheme was able to protect the OLSR protocol from anode isolation attack.
基金Research Supporting Project number(RSP-2021/323)King Saud University,Riyadh,Saudi Arabia.
文摘Data management becomes essential component of patient healthcare.Internet of Medical Things(IoMT)performs a wireless communication between E-medical applications and human being.Instead of consulting a doctor in the hospital,patients get health related information remotely from the physician.The main issues in the E-Medical application are lack of safety,security and priv-acy preservation of patient’s health care data.To overcome these issues,this work proposes block chain based IoMT Processed with Hybrid consensus protocol for secured storage.Patients health data is collected from physician,smart devices etc.The main goal is to store this highly valuable health related data in a secure,safety,easy access and less cost-effective manner.In this research we combine two smart contracts such as Practical Byzantine Fault Tolerance with proof of work(PBFT-PoW).The implementation is done using cloud technology setup with smart contracts(PBFT-PoW).The accuracy rate of PBFT is 90.15%,for PoW is 92.75%and our proposed work PBFT-PoW is 99.88%.
基金Taif University Researchers Supporting Project Number(TURSP-2020/98),Taif University,Taif,Saudi Arabia.
文摘The Internet of Medical Things(IoMT)is an online device that senses and transmits medical data from users to physicians within a time interval.In,recent years,IoMT has rapidly grown in the medicalfield to provide healthcare services without physical appearance.With the use of sensors,IoMT applications are used in healthcare management.In such applications,one of the most important factors is data security,given that its transmission over the network may cause obtrusion.For data security in IoMT systems,blockchain is used due to its numerous blocks for secure data storage.In this study,Blockchain-assisted secure data management framework(BSDMF)and Proof of Activity(PoA)protocol using malicious code detection algorithm is used in the proposed data security for the healthcare system.The main aim is to enhance the data security over the networks.The PoA protocol enhances high security of data from the literature review.By replacing the malicious node from the block,the PoA can provide high security for medical data in the blockchain.Comparison with existing systems shows that the proposed simulation with BSD-Malicious code detection algorithm achieves higher accuracy ratio,precision ratio,security,and efficiency and less response time for Blockchain-enabled healthcare systems.
基金funded by the Deanship of Scientific Research at Princess Nourah bint Abdulrahman University,through the Research Funding Program,Grant No.(FRP-1443-11).
文摘Patient privacy and data protection have been crucial concerns in Ehealthcare systems for many years.In modern-day applications,patient data usually holds clinical imagery,records,and other medical details.Lately,the Internet of Medical Things(IoMT),equipped with cloud computing,has come out to be a beneficial paradigm in the healthcare field.However,the openness of networks and systems leads to security threats and illegal access.Therefore,reliable,fast,and robust security methods need to be developed to ensure the safe exchange of healthcare data generated from various image sensing and other IoMT-driven devices in the IoMT network.This paper presents an image protection scheme for healthcare applications to protect patients’medical image data exchanged in IoMT networks.The proposed security scheme depends on an enhanced 2D discrete chaotic map and allows dynamic substitution based on an optimized highly-nonlinear S-box and diffusion to gain an excellent security performance.The optimized S-box has an excellent nonlinearity score of 112.The new image protection scheme is efficient enough to exhibit correlation values less than 0.0022,entropy values higher than 7.999,and NPCR values around 99.6%.To reveal the efficacy of the scheme,several comparison studies are presented.These comparison studies reveal that the novel protection scheme is robust,efficient,and capable of securing healthcare imagery in IoMT systems.
文摘Recent research efforts have created rapid advances in the field of the Internet of Things (IoT) in terms of communication protocols, sensing technologies, computing capabilities, next-generation wireless technologies, big data and AI techniques, and on-device, edge, cloud processing. These advances created a paradigm shift and generated a wide range of potential opportunities for a new major field known as the Internet of Medical Things (IoMT), as well as subfields like mobile Health (mHeatlh) and digital or electronic Health (eHealth). This paper provides an overview of the Internet of Medical Things and presents a classification to define the primary users and their roles and involvement in smart healthcare systems. The paper then presents taxonomy on the deployment scales of different healthcare environments, from personal healthcare to widescale connected healthcare systems. The overview also discusses the n-tier architecture of IoMT, then presents a set of taxonomies and classifications on the different medical devices used in healthcare systems as well as non-medical devices used to provide context-aware information about the surrounding environment. The paper then concludes the overview by presenting the different healthcare-related applications and services, a comparison between traditional and smart healthcare systems, and the different obstacles and challenges in the field of IoMT to guide the development of new services and devices. Many survey papers in the literature focused on similar points;however, up to our knowledge, this is the first paper to present taxonomies and classifications for these IoMT essential topics. The presented taxonomies and classifications provide the manufacturers of medical devices and the developers of healthcare services a deeper understanding of the healthcare systems’ landscape to address different requirements and demands.
文摘With the rapid progress in data-driven approaches,artificial intelligence,and big data analytics technologies,utilizing electroencephalogram(EEG)signals for emotion analysis in the field of the Internet of Medical Things can effectively assist in the diagnosis of specific diseases.While existing emotion analysis methods focus on the utilization of effective deep models for data-driven and big data analytics technology,they often struggle to extract long-range dependencies and accurately model local relationships within multi-channel EEG signals.In addition,the subjective scores of the subjects may not match the predefined emotional labels.To overcome these limitations,this paper proposes a new data-driven dynamic graph-embedded Transformer network(DGETN)that has emerged in different tasks of graph data mining for emotion analysis of EEG signals in the scene of IoMT.Firstly,we extract the frequency features differential entropy(DE)and use the linear dynamic system(LDS)method to alleviate the redundancy and noise information.Secondly,to effectively explore the long-range information and local modeling ability,a novel feature extraction module is designed by embedding the dynamic graph convolution operations in the Transformer encoder for mining the discriminant features of data.Moreover,the graph convolution operations can effectively exploit the spatial information between different channels.At last,we introduce the minimum category confusion(MCC)loss to alleviate the fuzziness of classification.We take two commonly used EEG sentiment analysis datasets as a study.The DGETN has achieved state-of-the-art accuracies of 99.38%on the SEED dataset,and accuracies of 99.24%and 98.85%for valence and arousal prediction on the DEAP dataset,respectively.
文摘云辅助医疗物联网系统是智慧医疗领域发展的新趋势,患者隐私数据通常以密态的形式外包存储于云端,这将导致数据拥有者失去对自身数据的控制权限,并带来数据检索不便.针对上述问题,本文提出了一种支持策略隐藏的可搜索属性加密方案,结合密文策略属性加密与公钥可搜索加密的优势,确保云辅助(cloud-assisted Internet of Medical Things,IoMT)系统中共享数据的机密性,实现了敏感数据的细粒度访问控制并支持关键字搜索.并且,利用在线/离线加密和外包解密等方法降低了资源受限设备的计算开销,使得密文策略的属性加密方案可以在云辅助IoMT系统中实施.同时,引入策略隐藏技术,将属性加密访问策略中的属性值隐藏于密文中,防止数据拥有者的隐私泄露.在安全性方面,证明本方案的密文信息在选定访问结构和选择明文攻击下具有不可区分性,以及陷门信息在选择关键字攻击下具有不可区分性.最后,利用JPBC(Javapairing-based cryptography)密码库对本方案与其他相关方案在功能特性、通信开销和计算开销等方面进行对比,结果表明本方案在密钥生成和加密阶段计算效率更高且存储开销更低.
基金This research is conducted as part of the project titled“Digital Twin-based Intrusion Detection System Using Federated Learning for IoMT”(2024-2027),supported by C3iHub,IIT Kanpur,India,under Sanction Order No.:IHUB-NTIHAC/2024/01/3.
文摘The Internet of Medical Things(IoMT)is transforming healthcare by enabling real-time data collection,analysis,and personalized treatment through interconnected devices such as sensors and wearables.The integration of Digital Twins(DTs),the virtual replicas of physical components and processes,has also been found to be a game changer for the ever-evolving IoMT.However,these advancements in the healthcare domain come with significant cybersecurity challenges,exposing it to malicious attacks and several security threats.Intrusion Detection Systems(IDSs)serve as a critical defense mechanism,yet traditional IDS approaches often struggle with the complexity and scale of IoMT networks.With this context,this paper follows a systematic approach to analyze the existing literature and highlight the current trends and challenges related to IDS in the IoMT domain.We leveraged techniques like bibliographic and keyword analysis to collect 832 research works published from 2007 to 2025,aligned with the theme“Digital Twins and IDS in IoMT.”It was found that by simulating device behaviours and network interactions in IoMT,DTs not only provide a proactive platform for early threat detection,but also offer a scalable and adaptive approach to mitigating evolving security threats in IoMT.Overall,this review provides a closer look into the role of IDS and DT in securing IoMT systems and sheds light on the possible research directions for developers and the research community.
文摘This study investigates the different aspects of multimedia computing in Video Synthetic Aperture Radar(Video-SAR)as a new mode of radar imaging for real-time remote sensing and surveillance.This research also considers new suggestions in the systematic design,research taxonomy,and future trends of radar data processing.Despite the conventional modes of SAR imaging,Video-SAR can generate video sequences to obtain online monitoring and green surveillance throughout the day and night(regardless of light sources)in all weathers.First,an introduction to Video-SAR is presented.Then,some specific properties of this imaging mode are reviewed.Particularly,this research covers one of the most important aspects of the Video-SAR systems,namely,the systematic design requirements,and also some new types of visual distortions which are different from the distortions,artifacts and noises observed in the conventional imaging radar.In addition,some topics on the general features and high-performance computing of Video-SAR towards radar communications through Unmanned Aerial Vehicle(UAV)platforms,Internet of Multimedia Things(IoMT),Video-SAR data processing issues,and real-world applications are investigated.
基金supported by National Natural Science Foundation of China(Grant No.62071377,62101442,62201456)Natural Science Foundation of Shaanxi Province(Grant No.2023-YBGY-036,2022JQ-687)The Graduate Student Innovation Foundation Project of Xi’an University of Posts and Telecommunications under Grant CXJJDL2022003.
文摘The Internet of Medical Things(Io MT) is regarded as a critical technology for intelligent healthcare in the foreseeable 6G era. Nevertheless, due to the limited computing power capability of edge devices and task-related coupling relationships, Io MT faces unprecedented challenges. Considering the associative connections among tasks, this paper proposes a computing offloading policy for multiple-user devices(UDs) considering device-to-device(D2D) communication and a multi-access edge computing(MEC)technique under the scenario of Io MT. Specifically,to minimize the total delay and energy consumption concerning the requirement of Io MT, we first analyze and model the detailed local execution, MEC execution, D2D execution, and associated tasks offloading exchange model. Consequently, the associated tasks’ offloading scheme of multi-UDs is formulated as a mixed-integer nonconvex optimization problem. Considering the advantages of deep reinforcement learning(DRL) in processing tasks related to coupling relationships, a Double DQN based associative tasks computing offloading(DDATO) algorithm is then proposed to obtain the optimal solution, which can make the best offloading decision under the condition that tasks of UDs are associative. Furthermore, to reduce the complexity of the DDATO algorithm, the cacheaided procedure is intentionally introduced before the data training process. This avoids redundant offloading and computing procedures concerning tasks that previously have already been cached by other UDs. In addition, we use a dynamic ε-greedy strategy in the action selection section of the algorithm, thus preventing the algorithm from falling into a locally optimal solution. Simulation results demonstrate that compared with other existing methods for associative task models concerning different structures in the Io MT network, the proposed algorithm can lower the total cost more effectively and efficiently while also providing a tradeoff between delay and energy consumption tolerance.
基金supported in part by the National Natural Science Foundation of China under Grant Nos.61872289 and 62172266in part by the Henan Key Laboratory of Network Cryptography Technology LNCT2020-A07the Guangxi Key Laboratory of Trusted Software under Grant No.KX202308.
文摘The Internet of Medical Things(IoMT)is an application of the Internet of Things(IoT)in the medical field.It is a cutting-edge technique that connects medical sensors and their applications to healthcare systems,which is essential in smart healthcare.However,Personal Health Records(PHRs)are normally kept in public cloud servers controlled by IoMT service providers,so privacy and security incidents may be frequent.Fortunately,Searchable Encryption(SE),which can be used to execute queries on encrypted data,can address the issue above.Nevertheless,most existing SE schemes cannot solve the vector dominance threshold problem.In response to this,we present a SE scheme called Vector Dominance with Threshold Searchable Encryption(VDTSE)in this study.We use a Lagrangian polynomial technique and convert the vector dominance threshold problem into a constraint that the number of two equal-length vectors’corresponding bits excluding wildcards is not less than a threshold t.Then,we solve the problem using the proposed technique modified in Hidden Vector Encryption(HVE).This technique makes the trapdoor size linear to the number of attributes and thus much smaller than that of other similar SE schemes.A rigorous experimental analysis of a specific application for privacy-preserving diabetes demonstrates the feasibility of the proposed VDTSE scheme.
