Hybrid Smart Contracts for Securing IoMT Data

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 privacy 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%.     

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.     

Proof of Activity Protocol for IoMT Data Security

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 medical field 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.     

移动环境下神经功能评价多通道交互模型

随着平板电脑、智能手机、智能手表等智能移动设备的普及,利用便携的智能移动设备随时随地进行健康评价受到了国内外学者的广泛关注.人机交互特有的多通道、交互式、人机协同的计算能够有效地提高移动环境下神经功能评价的准确度.然而,目前很少有研究对人机交互在这一应用场景上发挥的作用进行过充分讨论,更没有形成统一的多通道交互模型.为此,首先分析了目前移动设备上主流的神经功能评价方法,归纳总结出了一套适用该应用场景的交互原语和交互任务.然后,在此基础上提出了移动环境下神经功能评价多通道人机交互模型——MINA（multimodal human-computer interaction model for nerve function assessment in mobile environment),并对该模型的移动医学评价和多通道融合特点进行了分析.最后,依据此模型给出神经系统疾病检测的应用实例.实践证明,MINA能够较好地指导交互式神经功能评价应用的设计和开发,多通道融合的方式能够有效地提高医学评价的准确度.     

Fog Computing-inspired Smart Home Framework for Predictive Veterinary Healthcare

Domestic Pet Care has been an important domain in the healthcare industry. In the presented study, a comprehensive framework of the Smart VetCare system for the health monitoring of domestic pets has been presented. The work is focused on the remote surveillance of domestic animals’ health conditions inside the home environment using IoMT Technology. Specifically, pet health is analyzed for vulnerability in the ambient home environment and ubiquitous activities over a fog computing platform of FogBus. Furthermore, a temporal data granule is formulated and the Probability of Health Vulnerability (PoHV) is defined for determining the health severity of the animal. Additionally, the Temporal Sensitivity Measure (TSM) is defined for real-time pet healthcare analysis, which is visualized using the Self Organized Mapping (SOM) Technique. For validation purposes, the framework is deployed in the smart home environment using 12 IoMT WiSense Nodes and Health Sensor belt for monitoring a domestic dog of American Bully breed over the dynamic resource management platform of FogBus and iFogSim simulator. Based on the comparison with numerous state-of-the-art techniques, the proposed framework can register a better precision value (94.78%), accuracy value (95.38%), sensitivity value (93.71%), and f-measure value (94.41%).     

Paillier Cryptography Based Message Authentication Code for IoMT Security

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 healthcare 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 interoperability. 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 cryptography-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 Network 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.     

Green media-aware medical IoT system

Rapid proliferation in state-of-the art technologies has revolutionized the medical market for providing urgent, effective and economical health facilities to aging society. In this context media (i.e., video) transmission is considered as a quite significant step during first hour of the emergency for presenting a big and better picture of the event. However, the energy hungry media transmission process and slow progress in battery technologies have become a major and serious problem for the evolution of video technology in medical internet of things (MIoT) or internet of medical things (IoMT). So, promoting Green (i.e., energy-efficient) transmission during voluminous and variable bit rate (VBR) video in MIoT is a challenging and crucial problem for researchers and engineers. Therefore, the need arose to conduct research on Green media transmission techniques to cater the need of upcoming wearable healthcare devices. Thus, this research contributes in two distinct ways; first, a novel and sustainable Green Media Transmission Algorithm (GMTA) is proposed, second, a mathematical model and architecture of Green MIoT are designed by considering a 8-min medical media stream named, ‘Navigation to the Uterine Horn, transection of the horn and re-anastomosis’ to minimize transmission energy consumption in media-aware MIoT, and to develop feasible media transmission schedule for sensitive and urgent health information from physian to patients and vice vers through extremely power hungry natured wearable devices. The experimental results demonstrate that proposed GMTA saves energy up to 41%, to serve the community.

     

A Novel Internet of Medical Thing Cryptosystem Based on Jigsaw Transformation and Ikeda Chaotic Map

Image encryption has attracted much interest as a robust security solution for preventing unauthorized access to critical image data. Medical picture encryption is a crucial step in many cloud-based and healthcare applications. In this study, a strong cryptosystem based on a 2D chaotic map and Jigsaw transformation is presented for the encryption of medical photos in private Internet of Medical Things (IoMT) and cloud storage. A disorganized three-dimensional map is the foundation of the proposed cipher. The dispersion of pixel values and the permutation of their places in this map are accomplished using a nonlinear encoding process. The suggested cryptosystem enhances the security of the delivered medical images by performing many operations. To validate the efficiency of the recommended cryptosystem, various medical image kinds are used, each with its unique characteristics. Several measures are used to evaluate the proposed cryptosystem, which all support its robust security. The simulation results confirm the supplied cryptosystem’s secrecy. Furthermore, it provides strong robustness and suggested protection standards for cloud service applications, healthcare, and IoMT. It is seen that the proposed 3D chaotic cryptosystem obtains an average entropy of 7.9998, which is near its most excellent value of 8, and a typical NPCR value of 99.62%, which is also near its extreme value of 99.60%. Moreover, the recommended cryptosystem outperforms conventional security systems across the test assessment criteria.     

Extreme learning machine and bayesian optimization-driven intelligent framework for IoMT cyber-attack detection

The Journal of Supercomputing - The Internet of Medical Things (IoMT) is a bionetwork of allied medical devices, sensors, wearable biosensor devices, etc. It is gradually reforming the healthcare...     

Intelligent healthcare service based on context inference using smart device

In the field of “U-Healthcare Service”, many studies have been actively conducted to develop “smart device”-based healthcare applications that enable healthcare providers and patients to be better served through interoperations among various kinds of sensors and wireless network interfaces. In particular, contemporary intelligent healthcare services not only recognize users’ context information through smart devices, computers, and so forth, but also acquire information from heterogeneous sensors to achieve context-aware inference services. Among such information, the weather information is tightly related to diseases such as asthma and allergies. Therefore, there is a high demand for research to utilize the weather information for healthcare services. In this paper, we propose a context inference-based intelligent healthcare service that exploits both the weather conditions information and the diverse healthcare ontologies available on the Internet. The proposed service aims at modeling a context ontology in users’ healthcare service environment and defining the inference rules, thereby accomplishing a satisfactory real-time healthcare service.     

State of the art of smart homes

In this paper we present a review of the state of the art of smart homes. We will first look at the research work related to smart homes from various view points; first in the view point of specific techniques such as smart homes that utilize computer vision based techniques, smart homes that utilize audio-based techniques and then smart homes that utilize multimodal techniques. Then we look at it from the view point of specific applications of smart homes such as eldercare and childcare applications, energy efficiency applications and finally in the research directions of multimedia retrieval for ubiquitous environments. We will summarize the smart homes based research into these two categories. In the survey we found out that some well-known smart home applications like video based security applications has seen the maturity in terms of new research directions while some topics like smart homes for energy efficiency and video summarization are gaining momentum.     

Unobtrusive physiological monitoring in an airplane seat

Air travel has become the preferred mode of long-distance transportation for most of the world’s travelers. People of every age group and health status are traveling by airplane and thus the airplane has become part of our environment, in which people with health-related limitations need assistive support. Since the main interaction point between a passenger and the airplane is the seat, this work presents a smart airplane seat for measuring health-related signals of a passenger. We describe the design, implementation and testing of a multimodal sensor system integrated into the seat. The presented system is able to measure physiological signals, such as electrocardiogram, electrodermal activity, skin temperature, and respiration. We show how the design of the smart seat system is influenced by the trade-off between comfort and signal quality, i.e. incorporating unobtrusive sensors and dealing with erroneous signals. Artifact detection through sensor fusion is presented and the working principle is shown with a feasibility study, in which normal passenger activities were performed. Based on the presented method, we are able to identify signal regions in which the accuracies for detecting the heart- and respiration-rate are 88 and 82%, respectively, compared to 40 and 76% without any artifact removal.     

The effect of design on the usability and real world effectiveness of medical devices: A case study with adolescent users

Adolescents are currently overlooked in many fields of healthcare research and as a result are often required to use medical devices that have been designed for use by either children or adults. This can lead to poor adherence and a reduction in health outcomes.     

An Immutable Framework for Smart Healthcare Using Blockchain Technology

The advancements in sensing technologies, information processing, and communication schemes have revolutionized the healthcare sector. Electronic Healthcare Records (EHR) facilitate the patients, doctors, hospitals, and other stakeholders to maintain valuable data and medical records. The traditional EHRs are based on cloud-based architectures and are susceptible to multiple cyberattacks. A single attempt of a successful Denial of Service (DoS) attack can compromise the complete healthcare system. This article introduces a secure and immutable blockchain-based framework for the Internet of Medical Things (IoMT) to address the stated challenges. The proposed architecture is on the idea of a lightweight private blockchain-based network that facilitates the users and hospitals to perform multiple healthcare-related operations in a secure and trustworthy manner. The efficacy of the proposed framework is evaluated in the context of service execution time and throughput. The experimental outcomes indicate that the proposed design attained lower service execution time and higher throughput under different control parameters.     

A survey on semanticized and personalized health recommender systems

Health 3.0 is a health-related extension of the Web 3.0 concept. It is based on the semantic Web which provides for semantically organizing electronic health records of individuals. Health 3.0 is rapidly gaining ground as a new research topic in many academic and industrial disciplines. Due to the recent rapid spread of wearable sensors and smart devices with access to social media, migrating health services from the traditional centre-based health system to personal health care is inevitable. In this current era of greater personalization, treating patients' health problems according to their profile and medical data gathered is possible using the latest information technologies. Consequently, personalized health recommender systems have gained importance. Empowering the utility of advanced Web technology in personalized health systems is still challenging due to pressing issues, such as lack of low cost and accurate smart medical sensors and wearable devices, existing investment in legacy Web system architecture in health sector, heterogeneity of medical data gathered by myriad health care institutions and isolated health services, and interoperability issues as well as multi-dimensionality of medical data. By tracing recent developments, this paper offers a systematic review through recent research on semantic Web-enabled personalized health systems, namely, semanticized personalized health recommender systems with the key enabling technologies, major applications, and successful case studies. Critical questions derived from the research studies were discussed, and main directions of open issues were identified leading to recommendations for future study in the field of personalized health recommender systems.     

Emerging information technologies for enhanced healthcare

The appropriate collection and consumption of electronic health information about an individual patient or population is the bedrock of modern healthcare, where electronic medical records (EMR) serve as the main carrier. This paper first introduces the main goal of this special issue and gives a brief guideline. Then, the present situation of the adoption of EMRs is reviewed. After that, the emerging information technologies are presented which have a great impact on the healthcare provision. These include health sensing for medical data collection, medical data analysis and utilization for accurate detection and prediction. Next, cloud computing is discussed, as it may provide scalable and cost-effective delivery of healthcare services. Accordingly, the current state of academic research is documented on emerging information technologies for new paradigms of healthcare service. At last, conclusions are made.     

Applying Deep Learning to Individual and Community Health Monitoring Data: A Survey

In the recent years, deep learning models have addressed many problems in various fields. Meanwhile, technology development has spawned the big data in healthcare rapidly. Nowadays, application of deep learning to solve the problems in healthcare is a hot research direction. This paper introduces the application of deep learning in healthcare extensively. We focus on 7 application areas of deep learning, which are electronic health records (EHR), electrocardiography (ECG), electroencephalogram (EEG), community healthcare, data from wearable devices, drug analysis and genomics analysis. The scope of this paper does not cover medical image processing since other researchers have already substantially reviewed it. In addition, we analyze the merits and drawbacks of the existing works, analyze the existing challenges, and discuss future trends.     

A design method for an intelligent manufacturing and service system for rehabilitation assistive devices and special groups

The maturity of Industrial 4.0 technologies (smart wearable sensors, Internet of things [IoT], cloud computing, etc.) has facilitated the iteration and digitization of rehabilitation assistive devices (RADs) and the innovative development of intelligent manufacturing systems of RADs, expanding the value-added component of smart healthcare services. The intelligent manufacturing service mode, based on the concept of the product life cycle, completes the multi-source data production process analysis and the optimization of manufacturing, operation, and maintenance through intelligent industrial Internet of things and other means and improves the product life cycle management and operation mechanism. The smart product-service system (PSS) realizes the value-added of products by providing users with personalized products and value-added services, service efficiency, and sustainable development and gradually forms an Internet-product-service ecosystem. However, research on the PSS of RADs for special populations is relatively limited. Thus, this paper provides an overview of an IoT-based production model for RADs and a smart PSS-based development method of multimodal healthcare value-added services for special people. Taking the hand rehabilitation training devices for autistic children as a case, this paper verifies the effectiveness and availability of the proposed method. Compared with the traditional framework, the method used in this paper primarily helps evaluate rehabilitation efficacy, personalizes schemes for patients, provides auxiliary intelligent manufacturing service data and digital rehabilitation data for RAD manufacturers, and optimizes the product iteration development procedures by combining user-centered product interaction, multimodal evaluation, and value-added design. This study incorporates the iterative design of RADs into the process of smart PSS to provide some guidance to the RADs design manufacturers.     

A secure and efficient certificateless authenticated key agreement protocol for smart healthcare

As more and more important smart healthcare becomes in people’s daily life, its security issues draw increasing attention. It is stringent to design trusty communication protocol guaranteeing the data security and users privacy. Recently, Wang et al. proposed a certificateless authenticated key agreement (CL-AKA) protocol such that providing a resolution to transmit unforged data over open channel in smart healthcare. Claiming that their protocol could resist attacks from two types of adversaries, Wang et al. also compared their protocol with several related works. However, in this paper, we analyze their protocol and point out that the protocol lacks forward security. Moreover, aiming at remedying such defects, this paper proposes an improved CL-AKA protocol. Not only does the improved CL-AKA protocol satisfy the security requirements but also behaves efficient compared with other related works.