On the feasibility of attribute-based encryption on Internet of Things devices

Attribute-based encryption (ABE) could be an effective cryptographic tool for the secure management of Internet of Things (IoT) devices, but its feasibility in the IoT has been under-investigated thus far. This article explores such feasibility for well-known IoT platforms, namely, Intel Galileo Gen 2, Intel Edison, Raspberry pi 1 model B, and Raspberry pi zero, and concludes that adopting ABE in the IoT is indeed feasible

LISA 2.0: lightweight internet of things service bus architecture using node centric networking

Internet of things (IoT) technologies are advancing rapidly and a wide range of physical networking alternatives, communication standards and platforms are introduced. However, due to differences in system requirements and resource constraints in devices, there exist variations in these technologies, standards, and platforms. Consequently, application silos are formed. In contrast to the freedom of choice attained by a range of options, the heterogeneity of the technologies is a critical interoperability challenge faced by IoT systems. Moreover, IoT is also limited to address new requirements that arise due to the nature of the majority of smart devices. These requirements, such as mobility and intermittent availability, are hardly satisfied by the current IoT technologies following the end-to-end model inherited from the Internet. This paper introduces a lightweight, distributed, and embedded service bus called LISA which follows a Node Centric Networking architecture. LISA is designed to provide interoperability for resource-constrained devices in IoT. It also enables a natural way of embracing the new IoT requirements, such as mobility and intermittent availability, through node centric networking. LISA provides a simple application programming interface for developers, hiding the variations in platform, protocol or physical network, thus facilitating interoperability in IoT systems. LISA is inspired by network on terminal architecture (NoTA), a service centric open architecture originated by Nokia Research Center. Our extensive experimental results show the efficiency and scalability of LISA in providing a lightweight interoperability for IoT systems

Missing data resilient decision-making for healthcare IoT through personalization: A case study on maternal health

Remote health monitoring is an effective method to enable tracking of at-risk patients outside of conventional clinical settings, providing early-detection of diseases and preventive care as well as diminishing healthcare costs. Internet-of-Things (IoT) technology facilitates developments of such monitoring systems although significant challenges need to be addressed in the real-world trials. Missing data is a prevalent issue in these systems, as data acquisition may be interrupted from time to time in long-term monitoring scenarios. This issue causes inconsistent and incomplete data and subsequently could lead to failure in decision making. Analysis of missing data has been tackled in several studies. However, these techniques are inadequate for real-time health monitoring as they neglect the variability of the missing data. This issue is significant when the vital signs are being missed since they depend on different factors such as physical activities and surrounding environment. Therefore, a holistic approach to customize missing data in real-time health monitoring systems is required, considering a wide range of parameters while minimizing the bias of estimates. In this paper, we propose a personalized missing data resilient decision-making approach to deliver health decisions 24/7 despite missing values. The approach leverages various data resources in IoT-based systems to impute missing values and provide an acceptable result. We validate our approach via a real human subject trial on maternity health, in which 20 pregnant women were remotely monitored for 7 months. In this setup, a real-time health application is considered, where maternal health status is estimated utilizing maternal heart rate. The accuracy of the proposed approach is evaluated, in comparison to existing methods. The proposed approach results in more accurate estimates especially when the missing window is large.</p

HiCH: Hierarchical Fog-Assisted Computing Architecture for Healthcare IoT

The Internet of Things (IoT) paradigm holds significant promises for remote health monitoring systems. Due to their life-or mission-critical nature, these systems need to provide a high level of availability and accuracy. On the one hand, centralized cloud-based IoT systems lack reliability, punctuality and availability (e.g., in case of slow or unreliable Internet connection), and on the other hand, fully outsourcing data analytics to the edge of the network can result in diminished level of accuracy and adaptability due to the limited computational capacity in edge nodes. In this paper, we tackle these issues by proposing a hierarchical computing architecture, HiCH, for IoT-based health monitoring systems. The core components of the proposed system are 1) a novel computing architecture suitable for hierarchical partitioning and execution of machine learning based data analytics, 2) a closed-loop management technique capable of autonomous system adjustments with respect to patient's condition. HiCH benefits from the features offered by both fog and cloud computing and introduces a tailored management methodology for healthcare IoT systems. We demonstrate the efficacy of HiCH via a comprehensive performance assessment and evaluation on a continuous remote health monitoring case study focusing on arrhythmia detection for patients suffering from CardioVascular Diseases (CVDs)

RoSA: A Framework for Modeling Self-Awareness in Cyber-Physical Systems

The role of smart and autonomous systems is becoming vital in many areas of industry and society. Expectations from such systems continuously rise and become more ambitious: long lifetime, high reliability, high performance, energy efficiency, and adaptability, particularly in the presence of changing environments. Computational self-awareness promises a comprehensive assessment of the system state for sensible and well-informed actions and resource management. Computational self-awareness concepts can be used in many applications such as automated manufacturing plants, telecommunication systems, autonomous driving, traffic control, smart grids, and wearable health monitoring systems. Developing self-aware systems from scratch for each application is the most common practice currently, but this is highly redundant, inefficient, and uneconomic. Hence, we propose a framework that supports modeling and evaluation of various self-aware concepts in hierarchical agent systems, where agents are made up of self-aware functionalities. This paper presents the Research on Self-Awareness (RoSA) framework and its design principles. In addition, self-aware functionalities abstraction, data reliability, and confidence, which are currently provided by RoSA, are described. Potential use cases of RoSA are discussed. Capabilities of the proposed framework are showcased by case studies from the fields of healthcare and industrial monitoring. We believe that RoSA is capable of serving as a common framework for self-aware modeling and applications and thus helps researchers and engineers in exploring the vast design space of hierarchical agent-based systems with computational self-awareness

The botanical integrity of wheat products influences the gastric distention and satiety in healthy subjects

<p>Abstract</p> <p>Background</p> <p>Maintenance of the botanical integrity of cereal kernels and the addition of acetic acid (as vinegar) in the product or meal has been shown to lower the postprandial blood glucose and insulin response and to increase satiety. However, the mechanism behind the benefits of acetic acid on blood glucose and satiety is not clear. We hypothesized that the gastric emptying rate could be involved. Thus, the aim of this study was to evaluate the possible influence of maintained botanical integrity of cereals and the presence of acetic acid (vinegar) on gastric emptying rate (GER), postprandial blood glucose and satiety.</p> <p>Methods</p> <p>Fifteen healthy subjects were included in a blinded crossover trial, and thirteen of the subjects completed the study. Equicarbohydrate amounts of the following wheat-based meals were studied: white wheat bread, whole-kernel wheat bread or wholemeal wheat bread served with white wine vinegar. The results were compared with a reference meal consisting of white wheat bread without vinegar. The GER was measured with standardized real-time ultrasonography using normal fasting blood glucose <6.1 mmol/l or plasma glucose <7.0 mmol/l as an inclusion criterion. The GER was calculated as the percentage change in the antral cross-sectional area 15 and 90 minutes after ingestion of the various meals. Satiety scores were estimated and blood glucose was measured before and 15, 30, 45, 60, 90 and 120 min after the start of the meal.</p> <p>Results</p> <p>The whole-kernel wheat bread with vinegar resulted in significantly higher (<0.05) satiety than the wholemeal wheat bread and white wheat bread with vinegar and the reference bread. Wheat fiber present in the wholemeal wheat bread, or the presence of wheat kernels per se, did not affect the postprandial blood glucose or GER significantly compared with white wheat bread, neither did the addition of vinegar to white bread affect these variables. There was no correlation found between the satiety with antral areas or GER</p> <p>Conclusion</p> <p>The present study shows higher satiety after a whole-kernel wheat bread meal with vinegar. This may be explained by increased antral distension after ingestion of intact cereal kernels but, in this study, not by a lower gastric emptying rate or higher postprandial blood glucose response.</p> <p>Trial registration</p> <p>NTR1116</p

Continuous 7-month Internet of Things -based monitoring of health parameters of pregnant and Postpartum Women: prospective observational feasibility study

Background: Monitoring during pregnancy is vital to ensure the mother's and infant's health. Remote continuous monitoring provides health care professionals with significant opportunities to observe health-related parameters in their patients and to detect any pathological signs at an early stage of pregnancy, and may thus partially replace traditional appointments.Objective: This study aimed to evaluate the feasibility of continuously monitoring the health parameters (physical activity, sleep, and heart rate) of nulliparous women throughout pregnancy and until 1 month postpartum, with a smart wristband and an Internet of Things (IoT)-based monitoring system.Methods: This prospective observational feasibility study used a convenience sample of 20 nulliparous women from the Hospital District of Southwest Finland. Continuous monitoring of physical activity/step counts, sleep, and heart rate was performed with a smart wristband for 24 hours a day, 7 days a week over 7 months (6 months during pregnancy and 1 month postpartum). The smart wristband was connected to a cloud server. The total number of possible monitoring days during pregnancy weeks 13 to 42 was 203 days and 28 days in the postpartum period.Results: Valid physical activity data were available for a median of 144 (range 13-188) days (75% of possible monitoring days), and valid sleep data were available for a median of 137 (range 0-184) days (72% of possible monitoring days) per participant during pregnancy. During the postpartum period, a median of 15 (range 0-25) days (54% of possible monitoring days) of valid physical activity data and 16 (range 0-27) days (57% of possible monitoring days) of valid sleep data were available. Physical activity decreased from the second trimester to the third trimester by a mean of 1793 (95% CI 1039-2548) steps per day (PConclusions: The smart wristband with IoT technology was a feasible system for collecting representative data on continuous variables of health parameters during pregnancy. Continuous monitoring provides real-time information between scheduled appointments and thus may help target and tailor pregnancy follow-up.</p

Sleep tracking of a commercially available smart ring and smartwatch against medical-grade actigraphy in everyday settings: instrument validation study

Background: Assessment of sleep quality is essential to address poor sleep quality and understand changes. Owing to the advances in the Internet of Things and wearable technologies, sleep monitoring under free-living conditions has become feasible and practicable. Smart rings and smartwatches can be employed to perform mid- or long-term home-based sleep monitoring. However, the validity of such wearables should be investigated in terms of sleep parameters. Sleep validation studies are mostly limited to short-term laboratory tests; there is a need for a study to assess the sleep attributes of wearables in everyday settings, where users engage in their daily routines.Objective: This study aims to evaluate the sleep parameters of the Oura ring along with the Samsung Gear Sport watch in comparison with a medically approved actigraphy device in a midterm everyday setting, where users engage in their daily routines.Methods: We conducted home-based sleep monitoring in which the sleep parameters of 45 healthy individuals (23 women and 22 men) were tracked for 7 days. Total sleep time (TST), sleep efficiency (SE), and wake after sleep onset (WASO) of the ring and watch were assessed using paired t tests, Bland-Altman plots, and Pearson correlation. The parameters were also investigated considering the gender of the participants as a dependent variable.Results: We found significant correlations between the ring's and actigraphy's TST (r=0.86; PConclusions: In a sample population of healthy adults, the sleep parameters of both the Oura ring and Samsung watch have acceptable mean differences and indicate significant correlations with actigraphy, but the ring outperforms the watch in terms of the nonstaging sleep parameters.</p

An IoT-Enabled Stroke Rehabilitation System Based on Smart Wearable Armband and Machine Learning

Surface electromyography signal plays an important role in hand function recovery training. In this paper, an IoT-enabled stroke rehabilitation system was introduced which was based on a smart wearable armband (SWA), machine learning (ML) algorithms, and a 3-D printed dexterous robot hand. User comfort is one of the key issues which should be addressed for wearable devices. The SWA was developed by integrating a low-power and tiny-sized IoT sensing device with textile electrodes, which can measure, pre-process, and wirelessly transmit bio-potential signals. By evenly distributing surface electrodes over user's forearm, drawbacks of classification accuracy poor performance can be mitigated. A new method was put forward to find the optimal feature set. ML algorithms were leveraged to analyze and discriminate features of different hand movements, and their performances were appraised by classification complexity estimating algorithms and principal components analysis. According to the verification results, all nine gestures can be successfully identified with an average accuracy up to 96.20%. In addition, a 3-D printed five-finger robot hand was implemented for hand rehabilitation training purpose. Correspondingly, user's hand movement intentions were extracted and converted into a series of commands which were used to drive motors assembled inside the dexterous robot hand. As a result, the dexterous robot hand can mimic the user's gesture in a real-time manner, which shows the proposed system can be used as a training tool to facilitate rehabilitation process for the patients after stroke