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Sustainable smart city initiatives significantly improve the living standards of citizens and bring significant changes in economic, environmental, and social well-being. Sustainable and remote medical services are vital attributes of modern smart health infrastructure. Smart remote health infrastructure uses interconnected devices to gather and exchange health data. In smart health infrastructure, massive quantities of health data are gathered as Personal Health Records (PHR). Clinical images form a major part of the accumulated PHR. As medical images are confidential and sensitive, secure storage and transmission are significant. Hence, different cryptographic techniques are used to enhance medical image security. The conventional cryptographic schemes are complex and generate a single cipher image corresponding to a plain image. The cipher images are stored in a single database. Such storage makes the cryptosystem weak against single-point attacks. This paper discusses a simple, keyless, and distributed secure storage mechanism for medical images called SecMISS. SecMISS uses Random Grid-based Visual Secret Sharing (RGVSS) along with super-resolution for secure encryption with distributed storage and better reconstruction. The security parameters such as SSIM (≈ 0), correlation of adjacent pixels (≈ 0), PSNR (≤ 8 dB), and entropy (= 1) between share and initial image show that SecMISS provides highest level of security. Similarly, different Human Visual System (HVS) parameters between the initial and reconstructed images show an improvement in the reconstruction. Experimental results show that SecMISS outperforms the existing technique in terms of security and reconstruction. Thereby SecMISS can be efficiently used for securing medical image in sustainable smart hospitals and health infrastructures. [ABSTRACT FROM AUTHOR]

The present spreading out in healthcare initiated with the appreciation of e-healthcare by interconnecting the medical sensors. IoT played a dynamic part in the healthcare industry, which founded the realization of the Industrial Internet of Things (IIoT). The IIoT is becoming a recognized measure of healthcare universally and is shifting the aptitude to treat patients. IIoT-enabled smart health can easily authenticate the patients and expedites efficient tracking. It supports critical information sharing for effective diagnosis and treatments. IIoT-enabled health systems are the combination of sensors, cameras, devices, and objects that produce a giant quantity of data called big data. The spreading out of Big Data founded the realization of Machine Learning (ML) to predict diseases for better treatment. The major goal of IIoT-enabled smart health systems is to investigate big data and provide valued insights for improved QoS. This research suggests an IIoT-enabled system for healthcare to process the Big Data generated by IIoT sensors using machine learning. The proposed scheme is the optimized parallel and distributed framework for real-time efficient processing. The proposed framework is comprised of three modules that are big data preparation, optimized model building, and big data computation. The data preparation is performed for accurate prediction and to get faster real-time big data processing. The current proposals lack effective prediction and resourceful parallel data processing. The proposed framework is simulated through an experiment with the parallel and distributed framework. The efficiency of the proposed scheme has been verified using authentic datasets with experimentations and simulations. [ABSTRACT FROM AUTHOR]

This research puts forward novel deep hybrid frameworks for assessment of mental health indicators viz. depression and sleep quality using IoT-based motor activity recordings. The study employs long short-term memory (LSTM) to extract high-level features from sequential motor activity data, which are then combined with statistical features of the raw data to form a hybrid feature extraction model. The combined feature vector obtained via the hybrid feature extractor is fed into four regression models, namely linear regression (LR), sequential minimal optimization regression (SMOR), random forest regression (RFR) and adaptive neuro-fuzzy inference system (ANFIS) forming four deep hybrid regression models, namely LSTM-LR, LSTM-SMOR, LSTM-RFR, and LSTM-ANFIS for prediction of depression and sleep quality. The proposed deep hybrid frameworks are validated on benchmark datasets, namely the Depresjon dataset and the MESA actigraphy dataset, and the best performance is observed by LSTM-RFR with the adjusted R2 value of 74.19% on the MESA dataset. On validating the significance of statistical features, it has been observed that all the models show significant improvement in performance on combining the statistical features with high-level features with highest reduction in mean absolute error as 4.0436 and highest increase in R2 statistic as 0.1651. Model building time of the four regression methods is also compared for which LR gave the best results as only 0.01 s for training and testing. [ABSTRACT FROM AUTHOR]

Social Internet of Things (SIoT) is one of the emerging research areas which integrates the Internet of Things (IoT) with social networking concepts. It envisages better information sharing, by overcoming the scalability issues in IoT. It is supposed to overhaul the smart health industry, as it is a flexible compared to conventional IoT based solutions. It however poses many challenges with data authentication and privacy, being a couple of such issues that need to be addressed. In this paper, we present an efficient watermarking method for tamper detection, localization, and self-recovery of medical images for SIoT-based smart health networks. The cover image is initially divided into four equal parts, with each part progressively divided into 4 × 4 non-overlapping blocks. A watermark is prepared from a group of randomly chosen four, 4 × 4 blocks, determined by chaotic mapping. The information is embedded into 4 sub-blocks mapped utilizing chaotic sequence such that the restoration process can be carried out even if three sub-blocks out of four containing the information, get tampered with. The proposed technique is found to restore almost 1/3rd of the tampered image and produces a high-quality image with the average Peak Signal-to-Noise Ratio value of above 33 dB after the restoration process. The performance of the proposed method for various collage attacks shows its efficacy over other state-of-the-art techniques making it a potential candidate for SIoT driven smart health systems. [ABSTRACT FROM AUTHOR]

The terrible cost of injuries and sudden illnesses does have fatal consequences that exposes the limitations of the current prehospital processes in terms of time for emergency staff to arrive on scene and lack of first aid skills among the available incident witnesses. In this paper we aim at developing a smart pervasive chatbot for emergency case assistance based on cloud computing called SPeCECA that assists victims or incident witnesses to help avoiding deterioration of the subject's condition and maintaining his/her physical integrity until the aid arrives, which could dramatically increase the victim's survivability chances. Therefore, even a person with no first aid skills, can help the victim to survive by performing first aid support as suggested by the virtual assistant. Furthermore, thanks to its connectivity with the emergency medical service, trusted person(s), and the access to social media, SPeCECA has its own way of alarming the emergency case, in parallel, after having released the degree of the emergency situation's severity. The proposed method is a mobile pervasive healthcare service in the form of a connected mobile application as a virtual assistant for the benefit of anyone facing an emergency case. The proposed chatbot allows an online human-bot interaction that supports different scenarios for every single emergency case. The design of the system is introduced by its six interdependent components: information pre-processing component (IPPC), natural language processing component (NLPC), context component (CC), information post-processing component (IPoPC), response generator component (RGC), and alert message constructor component (AMCC). [ABSTRACT FROM AUTHOR]

Today's medical industry can be represented by a human-centered u-healthcare paradigm that is available and accessible anywhere, where high-tech IT can serve as the basis at any time and any place. In addition, in the medical industry, studies of many of the developments and applications are actively conducted based on the development of information communication technology. The aim of medical-information systems is the construction of an advanced IT and integrated u-healthcare system that evolves in the direction of integrated medical-based IT-convergence systems. Accordingly, to resolve the problems of telemedicine, in terms of the remote access to medical data, some public and private initiatives have been proposed, ranging from patient-mobility approaches to medical data. In addition, regarding GENICloud, which provides links with the existing future Internet testbeds and the Eucalyptus Cloud, two out of the seven GENIAM APIs have been announced as the common APIs of the future Internet testbeds in GENI, and they have been implemented and are provided. In the present GENI Cloud system, due to the provision of limited APIs, restrictions may occur in the future Internet testbeds and the Eucalyptus Cloud system management. Therefore, this study proposes a cloud-based mobile health service for the enhancement of the quality of service (QoS) including factors such as reliability and response time to resolve the problems of the broadband-communication infrastructure in the existing mobile health service and the delay problem on the wireless body area network. In this paper, we propose the cloud-based u-healthcare network with a QoS-guaranteed mobile health service. For this method, the TMO-distribution object model that was used in the existing research to implement a reliable and efficient cloud system for users was not used, and instead, a cloud-platform environment was built up through the construction of a distributed system based on a cluster-based mobile object. For this purpose, this study considered the characteristics of the wireless-communication environments between the terminals and the cloud servers in the mobile cloud environment and the proposed cloud mobility services and the specialized mobile cloud-control software. Later, for linkages with cloud computing environments and testbeds was proposed. In addition, this study carried out a cloud mobility-control design to provide a service in the mobile cloud environment that is based on the actual future Internet testbeds. Lastly, based on the structured cloud-platform environment, this study designed access interfaces to provide a mobile healthcare service in consideration of the user convenience. For the mobile-service access interfaces, since the same service interfaces can be used to access the characteristics and functions of all of the applications from browsers and device clients, the model-view-controller structure of the platform was designed, including the components for the further improvement of the requirements, reuse, and maintenance of the codes in medium and large distributed systems. [ABSTRACT FROM AUTHOR]

To introduce smart health in Wuhan, and provide some references for other cities. As the largest mega-city in central China, Wuhan is investing large amounts of resources to push forward the development of Smart Wuhan and Health Wuhan, and it has unique features. It is one of the centerpieces of China's New Healthcare Reform, and great hope is put on it to help solve the conflict between limited healthcare resources and the large population of patients. How to plan and design smart health is important. The construction of Wuhan Smart Health includes some aspects as follows, like requirement analysis, the establishment of objectives and blueprint, the architecture design of regional health information platform, evaluation and implementation, problems and solutions, and so on. Wuhan Smart Health has obtained some achievements in health network, information systems, resident's health records, information standard, and the first phase of municipal health information platform. The focus of this article is the whole construction process of smart health in Wuhan. Although there are some difficulties during this period, some smart health services and management have been reflected. Compared with other cities or countries, Wuhan Smart Health has its own advantages and disadvantages. This study aims to provide a reference for other cities. Because smart health of Wuhan is characteristic in construction mode. Though still in the initial stage, it has great potentials in the future. [ABSTRACT FROM AUTHOR]