Tuan Nguyen Gia

Smart e-Health Gateway: Bringing intelligence to Internet-of-Things based ubiquitous healthcare systems

There have been significant advances in the field of Internet of Things (IoT) recently. At the same time there exists an ever-growing demand for ubiquitous healthcare systems to improve human health and well-being. In most of IoT-based patient monitoring systems, especially at smart homes or hospitals, there exists a bridging point (i.e., gateway) between a sensor network and the Internet which often just performs basic functions such as translating between the protocols used in the Internet and sensor networks. These gateways have beneficial knowledge and constructive control over both the sensor network and the data to be transmitted through the Internet. In this paper, we exploit the strategic position of such gateways to offer several higher-level services such as local storage, real-time local data processing, embedded data mining, etc., proposing thus a Smart e-Health Gateway. By taking responsibility for handling some burdens of the sensor network and a remote healthcare center, a Smart e-Health Gateway can cope with many challenges in ubiquitous healthcare systems such as energy efficiency, scalability, and reliability issues. A successful implementation of Smart e-Health Gateways enables massive deployment of ubiquitous health monitoring systems especially in clinical environments. We also present a case study of a Smart e-Health Gateway called UTGATE where some of the discussed higher-level features have been implemented. Our proof-of-concept design demonstrates an IoT-based health monitoring system with enhanced overall system energy efficiency, performance, interoperability, security, and reliability.

Exploiting smart e-Health gateways at the edge of healthcare Internet-of-Things

Current developments in ICTs such as in Internet-of-Things (IoT) and CyberPhysical Systems (CPS) allow us to develop healthcare solutions with more intelligent and prediction capabilities both for daily life (home/office) and in-hospitals. In most of IoT-based healthcare systems, especially at smart homes or hospitals, a bridging point (i.e.,gateway) is needed between sensor infrastructure network and the Internet. The gateway at the edge of the network often just performs basic functions such as translating between the protocols used in the Internet and sensor networks. These gateways have beneficial knowledge and constructive control over both the sensor network and the data to be transmitted through the Internet. In this paper, we exploit the strategic position of such gateways at the edge of the network to offer several higher-level services such as local storage, real-time local data processing, embedded data mining, etc., presenting thus a Smart e-Health Gateway. We then propose to exploit the concept of Fog Computing in Healthcare IoT systems by forming a Geo-distributed intermediary layer of intelligence between sensor nodes and Cloud. By taking responsibility for handling some burdens of the sensor network and a remote healthcare center, our Fog-assisted system architecture can cope with many challenges in ubiquitous healthcare systems such as mobility, energy efficiency, scalability, and reliability issues. A successful implementation of Smart e-Health Gateways can enable massive deployment of ubiquitous health monitoring systems especially in clinical environments. We also present a prototype of a Smart e-Health Gateway called UT-GATE where some of the discussed higher-level features have been implemented. We also implement an IoT-based Early Warning Score (EWS) health monitoring to practically show the efficiency and relevance of our system on addressing a medical case study. Our proof-of-concept design demonstrates an IoT-based health monitoring system with enhanced overall system intelligence, energy efficiency, mobility, performance, interoperability, security, and reliability.

End-to-end security scheme for mobility enabled healthcare Internet of Things

We propose an end-to-end security scheme for mobility enabled healthcare Internet of Things (IoT). The proposed scheme consists of (i) a secure and efficient end-user authentication and authorization architecture based on the certificate based DTLS handshake, (ii) secure end-to-end communication based on session resumption, and (iii) robust mobility based on interconnected smart gateways. The smart gateways act as an intermediate processing layer (called fog layer) between IoT devices and sensors (device layer) and cloud services (cloud layer). In our scheme, the fog layer facilitates ubiquitous mobility without requiring any reconfiguration at the device layer. The scheme is demonstrated by simulation and a full hardware/software prototype. Based on our analysis, our scheme has the most extensive set of security features in comparison to related approaches found in literature. Energy-performance evaluation results show that compared to existing approaches, our scheme reduces the communication overhead by 26% and the communication latency between smart gateways and end users by 16%. In addition, our scheme is approximately 97% faster than certificate based and 10% faster than symmetric key based DTLS. Compared to our scheme, certificate based DTLS consumes about 2.2 times more RAM and 2.9 times more ROM resources. On the other hand, the RAM and ROM requirements of our scheme are almost as low as in symmetric key-based DTLS. Analysis of our implementation revealed that the handover latency caused by mobility is low and the handover process does not incur any processing or communication overhead on the sensors.

Fog Computing in Healthcare Internet of Things: A Case Study on ECG Feature Extraction

Internet of Things technology provides a competent and structured approach to improve health and wellbeing of mankind. One of the feasible ways to offer healthcare services based on IoT is to monitor humans health in real-time using ubiquitous health monitoring systems which have the ability to acquire bio-signals from sensor nodes and send the data to the gateway via a particular wireless communication protocol. The real-time data is then transmitted to a remote cloud server for real-time processing, visualization, and diagnosis. In this paper, we enhance such a health monitoring system by exploiting the concept of fog computing at smart gateways providing advanced techniques and services such as embedded data mining, distributed storage, and notification service at the edge of network. Particularly, we choose Electrocardiogram (ECG) feature extraction as the case study as it plays an important role in diagnosis of many cardiac diseases. ECG signals are analyzed in smart gateways with features extracted including heart rate, P wave and T wave via a flexible template based on a lightweight wavelet transform mechanism. Our experimental results reveal that fog computing helps achieving more than 90% bandwidth efficiency and offering low-latency real time response at the edge of the network.

SEA: A Secure and Efficient Authentication and Authorization Architecture for IoT-Based Healthcare Using Smart Gateways☆

In this paper, a secure and efficient authentication and authorization architecture for IoT-based healthcare is developed. Security and privacy of patients’ medical data are crucial for the accepta ...

Session Resumption-Based End-to-End Security for Healthcare Internet-of-Things

In this paper, a session resumption-based end-to-end security scheme for healthcare Internet of things (IoT) is pro-posed. The proposed scheme is realized by employing certificate-based DTLS handshake between end-users and smart gateways as well as utilizing DTLS session resumption technique. Smart gateways enable the sensors to no longer need to authenticate and authorize remote end-users by handing over the necessary security context. Session resumption technique enables end-users and medical sensors to directly communicate without the need for establishing the communication from the initial handshake. Session resumption technique has an abbreviated form of DTLS handshake and neither requires certificate-related nor public-key funtionalities. This alleviates some burden of medical sensors tono longer need to perform expensive operations. The energy-performance evaluations of the proposed scheme are evaluated by developing a remote patient monitoring prototype based on healthcare IoT. The energy-performance evaluation results show that our scheme is about 97% and 10% faster than certificate-based and symmetric key-based DTLS, respectively. Also, the certificate-based DTLS consumes about 2.2X more RAM and 2.9X more ROM resources required by our scheme. While, our scheme and symmetric key-based DTLS have almost similar RAM and ROM requirements. The security analysis reveals that the proposed scheme fulfills the requirements of end-to-end security and provides higher security level than related approaches found in the literature. Thus, the presented scheme is a well-suited solution to provide end-to-end security for healthcare IoT.

Customizing 6LoWPAN networks towards Internet-of-Things based ubiquitous healthcare systems

Embedded devices with enhanced communication capabilities, Internet of Things (IoT), are able to perform a wide variety of different tasks at present. One rapidly increasing application domain is healthcare. In this paper, we present an IoT-based architecture and system implementation for healthcare applications. The presented IoT-based system provides a cost-effective and easy way to analyze and monitor, either remotely or on the spot, real-time health data such as Electrocardiogram (ECG) and Electromyography (EMG) data. Health data is transmitted by utilizing IPv6 over low power wireless area networks (6LoWPAN). Our efficient customization of the 6LoWPAN network for health data provides energy efficient and reliable transmission in different scenarios that is required in several healthcare applications.

IoT-based fall detection system with energy efficient sensor nodes

Fall needs to be attentively considered due to its highly frequent occurrence especially with old people — up to one third of 65 and above year-old people around the world are risk of being injured due to falling. Furthermore, fall is a direct or indirect factor causing severe traumas such as brain injuries or bone fractures. However, timely medical attention might help to avoid serious consequences from a fall. A viable solution to solve this is an IoT-based system which takes advantage of wireless sensor networks, wearable devices, Fog and Cloud computing. To deliver sufficient degree of reliability, wearable devices working at the core of a fall detection system, are required to work for prolonged period of time. In this paper we investigate energy consumption of sensor nodes in an IoT-based fall detection system and present a design of a customized sensor node. In addition, we compare the customized sensor node with other sensor nodes, built on general purpose development boards. The results show that sensor nodes based on delicate customized devices are more energy efficient than the others based on general purpose devices while considering identical specification of micro-controller and memory capacity. Furthermore, our customized sensor node with energy efficiency selections can operate continuously up to 35 hours.

Fault tolerant and scalable IoT-based architecture for health monitoring

A novel Internet of Things based architecture supporting scalability and fault tolerance for healthcare is presented in this paper. The wireless system is constructed on top of 6LoWPAN energy efficient communication infrastructure to maximize the operation time. Fault tolerance is achieved via backup routing between nodes and advanced service mechanisms to maintain connectivity in case of failing connections between system nodes. The presented fault tolerance approach covers many fault situations such as malfunction of sink node hardware and traffic bottleneck at a node due to a high receiving data rate. A method for extending the number of medical sensing nodes at a single gateway is presented. A complete system architecture providing a quantity of features from bio-signal acquisition such as Electrocardiogram (ECG), Electroencephalography (EEG), and Electromyography (EMG) to the representation of graphical waveforms of these gathered bio-signals for remote real-time monitoring is proposed.

Leveraging Fog Computing for Healthcare IoT

Developments in technology have shifted the focus of medical practice from treating a disease to prevention. Currently, a significant enhancement in healthcare is expected to be achieved through the Internet of Things (IoT). There are various wearable IoT devices that track physiological signs and signals in the market already. These devices usually connect to the Internet directly or through a local smart phone or a gateway. Home-based and in hospital patients can be continuously monitored with wearable and implantable sensors and actuators. In most cases, these sensors and actuators are resource constrained to perform computing and operate for longer periods. The use of traditional gateways to connect to the Internet provides only connectivity and limited network services. With the introduction of the Fog computing layer, closer to the sensor network, data analytics and adaptive services can be realized in remote healthcare monitoring. This chapter focuses on a smart e-health gateway implementation for use in the Fog computing layer, connecting a network of such gateways, both in home and in hospital use. To show the application of the services, simple healthcare scenarios are presented. The features of the gateway in our Fog implementation are discussed and evaluated.