An Architecture Supporting Intelligent Mobile Healthcare Using Human-Computer Interaction HCI Principles

Recent advancements in the Internet of Things IoT and cloud computing have paved the way for mobile Healthcare (mHealthcare) services. A patient within the hospital is monitored by several devices. Moreover, upon leaving the hospital, the patient can be remotely monitored whether directly using body wearable sensors or using a smartphone equipped with sensors to monitor different user-health parameters. This raises potential challenges for intelligent monitoring of patient’s health. In this paper, an improved architecture for smart mHealthcare is proposed that is supported by HCI design principles. The HCI also provides the support for the User-Centric Design (UCD) for smart mHealthcare models. Furthermore, the HCI along with IoT`s (Internet of Things) 5-layered architecture has the potential of improving User Experience (UX) in mHealthcare design and help saving lives. The intelligent mHealthcare system is supported by the IoT sensing and communication layers and health care providers are supported by the application layer for the medical, behavioral, and health-related information. Health care providers and users are further supported by an intelligent layer performing critical situation assessment and performing a multi-modal communication using an intelligent assistant. The HCI design focuses on the ease-of-use, including user experience and safety, alarms, and error-resistant displays of the end-user, and improves user’s experience and user satisfaction.     

物联网情景感知技术研究

与互联网不同,物联网(Internet of Things, IoT)通过各类通信技术将具有标识、感知或者执行能力的物理实体互联,形成了“物物互连”的虚拟网络。随着计算机及通信技术的迅速发展,计算资源将通布人们周围的环境,情景感知技术应运而生。情景感知获得传感器采集的情景信息以后,对信息进行智能处理,自主地为用户提供服务。物联网具有海量信息的特性,传统的情景信息处理方法已不再适用。对物联网情景感知技术进行了详细的介绍,首先给出情景和情景感知的概念及其研究发展和应用。然后,结合物联网特性,以情景感知流程为主线,探讨了信息获取、建模和智能处理等内容。最后,系统结构是情景感知的关键,因此对现有的系统结构进行了分析和对比,结合物联网环境论述了当前情景感知系统的不足之处,并给出了情景感知系统的参考结构。     

A comprehensive health assessment framework to facilitate IoT-assisted smart workouts: A predictive healthcare perspective

Enormous potential of Internet of Things (IoT) Technology has made it feasible to perceive and analyze real time health conditions in ubiquitous manner. Moreover, incorporation of IoT in healthcare industry has led researchers around the world to develop smart applications like mobile healthcare, health-aware recommendations, and intelligent healthcare systems. Inspired from these aspects, this research presents an intelligent healthcare framework based on IoT Technology to provide ubiquitous healthcare to person during his/her workout sessions. The intelligence of the presented framework lies with its ability to analyze real time health conditions during workouts and predict probabilistic health state vulnerability. For predictive purpose, the proposed framework indulges the utilization of Artificial Neural Network (ANN) model, which is comprised of three phases namely, monitor, learn, and predict. In addition to this, the presented framework is supported by a mathematical foundation to predict probabilistic vulnerability, in terms of Probabilistic State of Vulnerability (PSoV). In order to determine the validity and applicability of the proposed framework, experiments were performed where 5 people with different attributes are monitored for 14 days using numerous smart sensors. Results, upon comparison with various state-of-the-art techniques, depict that the proposed system is superior in performance and is highly effective in delivering healthcare services during workouts.     

Improved Key Agreement Based Kerberos Protocol for M-Health Security

The development of wireless sensor network with Internet of Things (IoT) predicts various applications in the field of healthcare and cloud computing. This can give promising results on mobile health care (M-health) and Telecare medicine information systems. M-health system on cloud Internet of Things (IoT) through wireless sensor network (WSN) becomes the rising research for the need of modern society. Sensor devices attached to the patients’ body which is connected to the mobile device can ease the medical services. Security is the key connect for optimal performance of the m-health system that share the data of patients in wireless networks in order to maintain the anonymity of the patients. This paper proposed a secure transmission of M-health data in wireless networks using proposed key agreement based Kerberos protocol. The patients processed data are stored in cloud server and accessed by doctors and caregivers. The data transfer between the patients, server and the doctors are accessed with proposed protocol in order to maintain the confidentiality and integrity of authentication. The efficiency of the proposed algorithm is compared with the existing protocols. For computing 100 devices it consumes only 91milllisecond for computation.     

Advanced internet of things for personalised healthcare systems: A survey

As a new revolution of the Internet, Internet of Things (IoT) is rapidly gaining ground as a new research topic in many academic and industrial disciplines, especially in healthcare. Remarkably, due to the rapid proliferation of wearable devices and smartphone, the Internet of Things enabled technology is evolving healthcare from conventional hub based system to more personalised healthcare systems (PHS). However, empowering the utility of advanced IoT technology in PHS is still significantly challenging in the area considering many issues, like shortage of cost-effective and accurate smart medical sensors, unstandardised IoT system architectures, heterogeneity of connected wearable devices, multi-dimensionality of data generated and high demand for interoperability. In an effect to understand advance of IoT technologies in PHS, this paper will give a systematic review on advanced IoT enabled PHS. It will review the current research of IoT enabled PHS, and key enabling technologies, major IoT enabled applications and successful case studies in healthcare, and finally point out future research trends and challenges.     

Wearable IoT data stream traceability in a distributed health information system

With the soaring interest in the Internet of Things (IoT), some healthcare providers are facilitating remote care delivery through the use of wearable devices. These devices are employed for continuous streaming of personal medical data (e.g., vitals, medications, allergies, etc.) into healthcare information systems for the purposes of health monitoring and efficient diagnosis. However, a challenge from the perspective of the physicians is the inability to reliably determine which data belongs to who in real-time. This challenge emanates from the fact that healthcare facilities have numerous users who own multiple devices; thereby creating an N x M data source heterogeneity and complexities for the streaming process. As part of this research, we seek to streamline the process by proposing a wearable IoT data streaming architecture that offers traceability of data routes from the originating source to the health information system. To overcome the complexities of mapping and matching device data to users, we put forward an enhanced Petri Nets service model that aids with a transparent data trace route generation, tracking and the possible detection of medical data compromises. The results from several empirical evaluations conducted in a real-world wearable IoT ecosystem prove that: 1) the proposed system’s choice of Petri Net is best suited for linkability, unlinkability, and transparency of the medical IoT data traceability, 2) under peak load conditions, the IoT architecture exhibits high scalability, and 3) distributed health information system threats such as denial of service, man-in-the-middle, spoofing, and masking can be effectively detected.     

Internet of Things in Healthcare: Architecture,Applications, Challenges,and Solutions

Healthcare, the largest global industry, is undergoing significant transformations with the genesis of a new technology known as the Internet of Things (IoT). Many healthcare leaders are investing more money for transforming their services to harness the benefits provided by IoT, thereby paving the way for the Internet of Medical Things (IoMT), an extensive collection of medical sensors and associated infrastructure. IoMT has many benefits like providing remote healthcare by monitoring health vitals of patients at a distant place, providing healthcare services to elderly people, and monitoring a large group of people in a region or country for detection and prevention of epidemics. This paper provides a review of IoT in the healthcare domain by first describing the enabling technologies for delivering smart healthcare, followed by some of the key applications of IoT in healthcare. Next, a fog-based architecture consisting of three layers for IoT-based healthcare applications is proposed. Finally, we focus on some of the open challenges of IoT in healthcare, like fault tolerance, interoperability, latency, energy efficiency, and availability. Existing solutions for these challenges are also discussed.     

A design and implementation of a framework for games in IoT

Interconnection of the sensing and actuating devices providing the ability to share information across platform through a unified framework for enabling innovative applications. This is achieved by seamless ubiquitous sensing, data analytics and information representation as the unifying framework. Extending the current internet with interconnected objects and devices and their virtual representation has been a growing trend in recent years. Internet of Things (IoT) services are becoming a popular services. This will be supported challenges in a large of aspects such as smart health, green energy, smart home and personalized applications. So, the IoT plays more and more important issue in lifestyle through entertainment such as Games. As of yet, there has not been much research done on IoT environment games as a service. In this paper, we propose schemes of the design and implantation of IT convergence framework for games as a service of IoT. First of all, we discussed what to consider when design and implementation of IT convergence framework for games through contents using user’s mobile devices and various sensors in IoT environment and suggest related techniques. Then, we showed the possibility of games in the IoT environment by creating games and measuring the interactions of users in the IoT environment.     

Middleware for Internet of Things: Survey and Challenges

The Internet of things (IoT) applications span many potential fields. Furthermore, smart homes, smart cities, smart vehicular networks, and healthcare are very attractive and intelligent applications. In most of these applications, the system consists of smart objects that are equipped by sensors and Radio Frequency Identification (RFID) and may rely on other technological computing and paradigm solutions such as M2M (machine to machine) computing, Wifi, Wimax, LTE, cloud computing, etc. Thus, the IoT vision foresees that we can shift from traditional sensor networks to pervasive systems, which deliver intelligent automation by running services on objects. Actually, a significant attention has been given to designing a middleware that supports many features; heterogeneity, mobility, scalability, multiplicity, and security. This papers reviews the-state-of-the-art techniques for IoT middleware systems and reveals an interesting classification for these systems into service and agent-oriented systems. Therefore two visions have emerged to provide the IoT middleware systems: Via designing the middleware for IoT system as an eco-system of services or as an eco-system of agents. The most common feature of the two approaches is the ability to overcome heterogeneity issues. However, the agent approach provides context awareness and intelligent elements. The review presented in this paper includes a detailed comparison between the IoT middleware approaches. The paper also explores challenges that form directions for future research on IoT middleware systems. Some of the challenges arise, because some crucial features are not provided (or at most partially provided) by the existing middleware systems, while others have not been yet tackled by current research in IoT.     

面向医疗系统的隐私保护疾病预测研究

为了提高医疗数据的隐私性并有效对疾病进行预测,针对从物联网(IoT)设备收集的患者医疗数据,构建了面向医疗系统的隐私保护疾病预测系统框架,通过加密组合文本建立密钥提高了系统认证阶段的隐私性,加强系统和信息传输的安全性。利用基于对数循环值的椭圆曲线密码体制(LR-ECC)提高了数据传输阶段的安全性,从而授权的医护人员可以在医院侧安全地下载患者数据。运用基于象群遗传算法的的深度学习神经网络(EHGA-DLNN)分类技术在疾病预测系统(DPS)阶段实现了疾病数据的有效分类预测。实验结果表明,LR-ECC方法在加密时间和解密时间效率方面高于其他加密方法,并且能够达到98.87%的安全级别,EHGA-DLNN方法在疾病预测分类准确率达到98.35%。     

Dynamic framework to mining Internet of Things for multimedia services

The rapid and unprecedented technological advancements are currently dominated by two technologies. At one hand, we witness the rise of the Internet of Things (IoT) as the next evolution of the Internet. At the other hand, we witness a vast spread of social networks that connects people together socially and opens the door for people to share and express ideas, thoughts, and information. IoT is overpopulated by a vast number of objects, millions of multimedia services, and interactions. Therefore, the search of the right object that can provide the specific multimedia service is considered as an important issue. The merge of these two technologies resulted in new paradigm called Social IoT (SIoT). The main idea in SIoT is that every object can mine IoT in search for certain multimedia service. We investigate the issue of friends' management in SIoT and propose a framework to manage friends' requests. The proposed framework employs several mechanisms to better manage friends' relationships. The proposed framework consists of friend selection, friendship removal, and an update module. It proposes a weight-based algorithm and Naïve Bayes Classifier-based algorithm for the selection component. Moreover, a random service allocation model is proposed to construct service-specific network model. This model is then used in the simulation setup to examine the performance of different friends' management algorithms. The performance of the proposed framework is evaluated using simulation under different scenarios. The obtained simulation results show improvement over other strategies in terms of average degree of connections, average path length, local cluster coefficients, and throughput.     

Experience-Oriented Intelligence for Internet of Things

ABSTRACT

The Internet of Things (IoT) has gained significant attention from industry as well as academia during the past decade.The main reason behind this interest is the capabilities of the IoT for seamlessly integrating classical networks and networked objects, and hence allowing people to create an intelligent environment based on this powerful integration. However, how to extract useful information from data produced by IoT and facilitate standard knowledge sharing among different IoT systems are still open issues to be addressed. In this paper, we propose a novel approach, the Experience-Oriented Smart Things (EOST), that utilizes deep learning and knowledge representation concept called Decisional DNA to help IoT systems acquire, represent, and store knowledge, as well as share it amid various domains where it can be required to support decisions. Decisional DNA motivation stems from the role of deoxyribonucleic acid (DNA) in storing and sharing information and knowledge. We demonstrate our approach in a set of experiments, in which the IoT systems use knowledge gained from past experience to make decisions and predictions. The presented initial results show that the EOST is a very promising approach for knowledge capture, representation, sharing, and reusing in IoT systems.     

Network Learning-Enabled Sensor Association for Massive Internet of Things

The massive Internet of Things (IoT) comprises different gateways (GW) covering a given region of a massive number of connected devices with sensors. In IoT networks, transmission interference is observed when different sensor devices (SD) try to send information to a single GW. This is mitigated by allotting various channels to adjoining GWs. Furthermore, SDs are permitted to associate with any GW in a network, naturally choosing the one with a higher received signal strength indicator (RSSI), regardless of whether it is the ideal choice for network execution. Finding an appropriate GW to optimize the performance of IoT systems is a difficult task given the complicated conditions among GWs and SDs. Recently, in remote IoT networks, the utilization of machine learning (ML) strategies has arisen as a viable answer to determine the effect of various models in the system, and reinforcement learning (RL) is one of these ML techniques. Therefore, this paper proposes the use of an RL algorithm for GW determination and association in IoT networks. For this purpose, this study allows GWs and SDs with intelligence, through executing the multi-armed bandit (MAB) calculation, to investigate and determine the optimal GW with which to associate. In this paper, rigorous mathematical calculations are performed for this purpose and evaluate our proposed mechanism over randomly generated situations, which include different IoT network topologies. The evaluation results indicate that our intelligent MAB-based mechanism enhances the association as compared to state-of-the-art (RSSI-based) and related research approaches.     

一种高效隐私的区块链认知物联网框架

针对动态物联网隐私安全问题及低效推荐系统问题,提出一种高效隐私的区块链认知物联网框架。该框架分为区块链物联网管理层、认知过程层和需求层三层,区块链物联网管理层为认知层提供所需信息,然后对系统的可管理元素进行安全隐私的操作;在认知层中,认知引擎观察有关系统的信息,然后执行适当的算法来管理系统;在需求层中,通过认知规范语言（cognitive specification language,CSL）来描述网络的目标和行为。所提区块链物联网框架的认知推荐系统从过去发生的经验中学习,改进关于物联网推荐的决策,与其他物联网框架比较,所提框架和推荐系统具有隐私安全和高性能的推荐能力。     

Malicious insiders attack in IoT based Multi-Cloud e-Healthcare environment: <Emphasis Type="Italic">A Systematic Literature Review</Emphasis>

The emergence of Internet of Things (IoT) has introduced smart objects as the fundamental building blocks for developing a smart cyber-physical universal environment. The IoTs have innumerable daily life applications. The healthcare industry particularly has been benefited due to the provision of ubiquitous health monitoring, emergency response services, electronic medical billing, etc. Since IoT devices possess limited storage and processing power, therefore these intelligent objects are unable to efficiently provide the e-health facilities, or process and store enormous amount of collected data. IoTs are merged with Cloud Computing technology in Multi-Cloud form that basically helps cover the limitations of IoTs by offering a secure and on-demand shared pool of resources i.e., networks, servers, storage, applications, etc., to deliver effective and well-organized e-health amenities. Although the framework based on the integration of IoT and Multi-Cloud is contributing towards better patient care, yet on the contrary, it is challenging the privacy and reliability of the patients’ information. The purpose of this systematic literature review is to identify the top security threat and to evaluate the existing security techniques used to combat this attack and their applicability in IoT and Multi-Cloud based e-Healthcare environment.     

物联网架构和智能信息处理理论与关键技术

物联网(Internet of Things, IoT)是一种通过各种接入技术将海量电子设备与互联网进行互联的大规模虚拟网络,包括RFID、传感器以及执行器的电子设备通过互联网互联互通,将异构信息汇聚后共同完成某项特定的任务。为了解决物联网规模化运用中的关键技术,首先探讨了物联网与这些网络的区别,给出了物联网的定义、特征以及参考架构。同时,创新性地提出了区域服务器的概念,并通过本体论知识表达等方法的运用解决了物联网中的“信息孤岛”难题。智能信息处理是物联网的核心内容之一,而物联网的知识表达与情景感知等相关技术是物联网智能信息处理的核心内容。据此,着重分析研究了智能信息处理的信息空间定义、信息量化方法以及信息处理各阶段的主要问题及相应的解决办法。     

Data Security Storage Model of the Internet of Things Based on Blockchain

With the development of information technology, the Internet of Things (IoT) has gradually become the third wave of the worldwide information industry revolution after the computer and the Internet. The application of the IoT has brought great convenience to people’s production and life. However, the potential information security problems in various IoT applications are gradually exposed and people pay more attention to them. The traditional centralized data storage and management model of the IoT is easy to cause transmission delay, single point of failure, privacy disclosure and other problems, and eventually leads to unpredictable behavior of the system. Blockchain technology can effectively improve the operation and data security status of the IoT. Referring to the storage model of the Fabric blockchain project, this paper designs a data security storage model suitable for the IoT system. The simulation results show that the model is not only effective and extensible, but also can better protect the data security of the Internet of Things.     

Deep Forest-Based Fall Detection in Internet of Medical Things Environment

This article introduces a new medical internet of things (IoT) framework for intelligent fall detection system of senior people based on our proposed deep forest model. The cascade multi-layer structure of deep forest classifier allows to generate new features at each level with minimal hyperparameters compared to deep neural networks. Moreover, the optimal number of the deep forest layers is automatically estimated based on the early stopping criteria of validation accuracy value at each generated layer. The suggested forest classifier was successfully tested and evaluated using a public SmartFall dataset, which is acquired from three-axis accelerometer in a smartwatch. It includes 92781 training samples and 91025 testing samples with two labeled classes, namely non-fall and fall. Classification results of our deep forest classifier demonstrated a superior performance with the best accuracy score of 98.0% compared to three machine learning models, i.e., K-nearest neighbors, decision trees and traditional random forest, and two deep learning models, which are dense neural networks and convolutional neural networks. By considering security and privacy aspects in the future work, our proposed medical IoT framework for fall detection of old people is valid for real-time healthcare application deployment.     

ECCbAS: An ECC based authentication scheme for healthcare IoT systems

Smart technology is a concept for efficiently managing smart things such as vehicles, buildings, home appliances, healthcare systems and others, through the use of networks and the Internet. Smart architecture makes use of technologies such as the Internet of Things (IoT), fog computing, and cloud computing. The Smart Medical System (SMS), which is focused on communication networking and sensor devices, is one of the applications used in this architecture. In a smart medical system, a doctor uses cloud-based applications such as mobile devices, wireless body area networks, and other cloud-based apps to provide online therapy to patients. Consequently, with the advancement and growth of IoT and 6G wireless technology, privacy and security have emerged as two of the world’s most important issues. Recently, Sureshkumar et al. proposed an authentication scheme for medical wireless sensor networks (MWSN) by using an Elliptic Curve Cryptography (ECC) based lightweight authentication protocol and claimed that it provides better security for smart healthcare systems. This paper will demonstrate that this protocol is susceptible to attacks such as traceability, integrity contradiction, and de-synchronization with the complexity of one run of the protocol and a success probability of one. Furthermore, we also propose an ECC based authentication scheme called ECCbAS to address the Sureshkumar et al. protocol’s vulnerabilities and demonstrate its security using a variety of non-formal and formal methods.     

Application of iot medical image detection and prenatal genetic testing in obstetric clinic

The use of new techniques and methods can be significantly improved during prenatal examination and screening of fetal genetic abnormalities. Expanding the number of vectors in screening and screening for single-cell gene diseases in fetal DNA will increase the probability of success of the whole screening test.Recently, sub chromosomal abnormalities have been introduced into prenatal care. The lack of suitable tools makes it very difficult to obtain information about collection, management, and prenatal genetic testing. The purpose of this study is to improve the accessibility of nutritional information by using the advantages of advanced medical image detection and integrating the IoT (Internet of Things) and prenatal genetic testing platforms in obstetric outpatient clinics. Records are kept that the platform will allow IoT to interact with sensor practitioners and provide immediate access to medical images prior to delivery. This proposed system provides an IoT application for managing obstetric outpatient information and prenatal genetic testing requirements. The requirement for establishment is the realization of the development of an IoT platform for complete medical image detection information management with IoT input required for integration with existing medical information systems. In response, the purpose of the study is to use integrated IoT applications to help nutrition professionals, researchers, patients, and especially mothers, to enhance their collection of medical imaging and prenatal genetic testing IoT sensor based information transfer.