Future challenges and recommendations

Rapid advances in information technology and telecommunications, and in particular mobile and wireless communications, converge towards the emergence of a new type of “infostructure” that has the potential of supporting a large spectrum of advanced services for healthcare and health. Currently the ICT community produces a great effort to drill down from the vision and the promises of wireless and mobile technologies and provide practical application solutions. Research and development include data gathering and omni-directional transfer of vital information, integration of human machine interface technology into handheld devices and personal applications, security and interoperability of date and integration with hospital legacy systems and electronic patient record. The ongoing evolution of wireless technology and mobile device capabilities is changing the way healthcare providers interact with information technologies. The growth and acceptance of mobile information technology at the point of care, coupled with the promise and convenience of data on demand, creates opportunities for enhanced patient care and safety. The developments presented in this section demonstrate clearly the innovation aspects and trends towards user oriented applications

Modelling a portable personal health record

Active and responsible involvement of patients in their own health is accepted as an important contribution towards an increased quality of health services in general. Management of Personal Health Information by the patient can play an important role in the improvement in quality of the information available to health care professionals and as a means of patient involvement. Electronic Health Records are a means of storing this kind of information but their management usually falls under the responsibility of an institution and not on the patient himself. A Personal Health Record under the direct control and management of the patient is the natural solution for the problem. When implemented in a storage hardware portable device, a PHR, allows for total mobility. Personal Health Information is very sensitive in nature so any implementation has to address security and privacy issues. With this in mind we propose a structure for a secure Patient Health Record stored in a USB pen device under the patient’s direct management and responsibility

Understanding security risks and users perception towards adopting wearable Internet of Medical Things

This thesis examines users’ perception of trust within the context of security and privacy of Wearable Internet of Medical Things (WIoMT). WIoMT is a collective term for all medical devices connected to internet to facilitate collection and sharing of health-related data such as blood pressure, heart rate, oxygen level and more. Common wearable devices include smart watches and fitness bands. WIoMT, a phenomenon due to Internet of Things (IoT) has become prevalent in managing the day-to-day activities and health of individuals. This increased growth and adoption poses severe security and privacy concerns. Similar to IoT, there is a need to analyse WIoMT security risks as they are used by individuals and organisations on regular basis, risking personal and confidential information. Additionally, for better implementation, performance, adoption, and secured wearable medical devices, it is crucial to observe users’ perception. Users’ perspectives towards trust are critical for adopting WIoMT. This research aimed to understand users’ perception of trust in the adoption of WIoMT, while also exploring the security risks associated with adopting wearable IoMT. Employing a quantitative method approach, 189 participants from Western Sydney University completed an online survey. The results of the study and research model indicated more than half of the variance (R2 = 0.553) in the Intention to Use WIoMT devices, which was determined by the significant predictors (95% Confidence Interval; p < 0.05), Perceived Usefulness, Perceived Ease of Use and Perceived Security and Privacy. Among these two, the domain Perceived Security and Privacy was found to have significant outcomes. Hence, this study reinforced that a WIoMT user intends to use the device only if he/she trusts the device; trust here has been defined in terms of its usefulness, easy to use and security and privacy features. This finding will be a steppingstone for equipment vendors and manufacturers to have a good grasp on the health industry, since the proper utilisation of WIoMT devices results in the effective and efficient management of health and wellbeing of users. The expected outcome from this research also aims to identify how users’ security and perception matters while adopting WIoMT, which in future can benefit security professionals to examine trust factors when implementing new and advanced WIoMT devices. Moreover, the expected result will help consumers as well as different healthcare industry to create a device which can be easily adopted and used securely by consumers

Evaluating Machine Learning Techniques for Smart Home Device Classification

Smart devices in the Internet of Things (IoT) have transformed the management of personal and industrial spaces. Leveraging inexpensive computing, smart devices enable remote sensing and automated control over a diverse range of processes. Even as IoT devices provide numerous benefits, it is vital that their emerging security implications are studied. IoT device design typically focuses on cost efficiency and time to market, leading to limited built-in encryption, questionable supply chains, and poor data security. In a 2017 report, the United States Government Accountability Office recommended that the Department of Defense investigate the risks IoT devices pose to operations security, information leakage, and endangerment of senior leaders [1]. Recent research has shown that it is possible to model a subject’s pattern-of-life through data leakage from Bluetooth Low Energy (BLE) and Wi-Fi smart home devices [2]. A key step in establishing pattern-of-life is the identification of the device types within the smart home. Device type is defined as the functional purpose of the IoT device, e.g., camera, lock, and plug. This research hypothesizes that machine learning algorithms can be used to accurately perform classification of smart home devices. To test this hypothesis, a Smart Home Environment (SHE) is built using a variety of commercially-available BLE and Wi-Fi devices. SHE produces actual smart device traffic that is used to create a dataset for machine learning classification. Six device types are included in SHE: door sensors, locks, and temperature sensors using BLE, and smart bulbs, cameras, and smart plugs using Wi-Fi. In addition, a device classification pipeline (DCP) is designed to collect and preprocess the wireless traffic, extract features, and produce tuned models for testing. K-nearest neighbors (KNN), linear discriminant analysis (LDA), and random forests (RF) classifiers are built and tuned for experimental testing. During this experiment, the classifiers are tested on their ability to distinguish device types in a multiclass classification scheme. Classifier performance is evaluated using the Matthews correlation coefficient (MCC), mean recall, and mean precision metrics. Using all available features, the classifier with the best overall performance is the KNN classifier. The KNN classifier was able to identify BLE device types with an MCC of 0.55, a mean precision of 54%, and a mean recall of 64%, and Wi-Fi device types with an MCC of 0.71, a mean precision of 81%, and a mean recall of 81%. Experimental results provide support towards the hypothesis that machine learning can classify IoT device types to a high level of performance, but more work is necessary to build a more robust classifier

A security architecture for personal networks

Abstract Personal Network (PN) is a new concept utilizing pervasive computing to meet the needs of the user. As PNs edge closer towards reality, security becomes an important concern since any vulnerability in the system will limit its practical use. In this paper we introduce a security architecture designed for PNs. Our aim is to use secure but lightweight mechanisms suitable for resource constrained devices and wireless communication. We support pair-wise keys for secure cluster formation and use group keys for securing intra-cluster communication. In order to analyze the performance of our proposed mechanisms, we carry out simulations using ns-2. The results show that our mechanisms have a low overhead in terms of delay and energy consumption

On the Deployment of Healthcare Applications over Fog Computing Infrastructure

Fog computing is considered as the most promising enhancement of the traditional cloud computing paradigm in order to handle potential issues introduced by the emerging Interned of Things (IoT) framework at the network edge. The heterogeneous nature, the extensive distribution and the hefty number of deployed IoT nodes will disrupt existing functional models, creating confusion. However, IoT will facilitate the rise of new applications, with automated healthcare monitoring platforms being amongst them. This paper presents the pillars of design for such applications, along with the evaluation of a working prototype that collects ECG traces from a tailor-made device and utilizes the patient's smartphone as a Fog gateway for securely sharing them to other authorized entities. This prototype will allow patients to share information to their physicians, monitor their health status independently and notify the authorities rapidly in emergency situations. Historical data will also be available for further analysis, towards identifying patterns that may improve medical diagnoses in the foreseeable future