Ali Al-Ataby

Mobile health platform for diabetes management based on the Internet-of-Things

This paper presents a new Internet of Thing (IoT)-based platform to support self-management of diabetes. This mobile health (mHealth) approach allows for multiple care dimensions of diabetes by means of remote collection and monitoring of patient data and provision of personalized and customized feedback on a smartphone platform. Such support to self-management of diabetes enables real-time clinical interaction and feedback tailored to the personal needs of the patient, utilizing current and historical patient data. The platform understands to what extent the patients activities comply with their individual treatment plans, deriving rule-based health indicators, and generating appropriate warnings and support in terms of feedback advices. The physical layer of the platform incorporates wireless nodes; each of which encompasses a set of medical sensors linked wirelessly to a mobile device. The physical layer nodes are linked to a Web-based application layer through an existing telecommunication infrastructure. A fully functional prototype system is designed, implemented, and its end-to-end functionality is tested successfully, with high patient acceptability levels demonstrated by means of a clinician-led pilot study.

Smart card with iris recognition for high security access environment

Smart cards are increasingly being used as a form of identification and authentication. One inherent problem with smart cards, however, is the possibility of loss or theft. Current options for securing smart cards against unauthorized use are primarily restricted to passwords. Passwords are easy enough for others to steal so that they do not offer sufficient protection. This has promoted interest in biometric identification methods, including iris recognition. The iris is, due to its unique biological properties, exceptionally suited for identification. It is protected from the environment, stable over time, unique in shape and contains a high amount of discriminating information. This paper proposes a method to integrate iris recognition with the smart card to develop a high security access environment. An iris recognition system and smart card programming circuit with its software have been designed. Template on card (TOC) category has been employed. Hence, the extracted iris features stored in smart card are compared against the data acquired from a camera or database for authentication. The proposed algorithm has superior performance in terms of security, accuracy and consistency compared with other published technology.

High Capacity Image Steganography Based on Curvelet Transform

Steganography is the art and science of concealing information in unremarkable cover media so as not to arouse an eavesdroppers suspicion. It is an application under information security field. Being classified under information security, steganography is characterized by having set of measures that rely on strengths and counter-measures (attacks) that are driven by weaknesses and vulnerabilities. Today, computer and network technologies provide easy-to-use communication channels for steganography. The aim of this paper is to propose a modified high-capacity image steganography technique based on curve let transform with acceptable levels of imperceptibility and distortion in the cover image and high level of overall security.

Robot Assistant in Management of Diabetes in Children Based on the Internet of Things

This paper presents a new eHealth platform incorporating humanoid robots to support an emerging multidimensional care approach for the treatment of diabetes. The architecture of the platform extends the Internet of Things to a Web-centric paradigm through utilizing existing Web standards to access and control objects of the physical layer. This incorporates capillary networks, each of which encompasses a set of medical sensors linked wirelessly to a humanoid robot linked (via the Internet) to a Web-centric disease management hub. This provides a set of services for both patients and their caregivers that support the full continuum of the multidimensional care approach of diabetes. The platform’s software architecture pattern enables the development of various applications without knowing low-level details of the platform. This is achieved through unifying the access interface and mechanism of handling service requests through a layered approach based on object virtualization and automatic service delivery. A fully functional prototype is developed, and its end-to-end functionality and acceptability are tested successfully through a clinician-led pilot study, providing evidence that both patients and caregivers are receptive to the introduction of the proposed platform.

Prediction of driver fatigue: Approaches and open challenges

Fatigue is a mental process that grows gradually and affects human reaction time and the consciousness. It is one of the causes of road fatal accidents around the globe. Although it is now generally accepted that fatigue plays an important role in road safety, it is still largely left to individual drivers to manage. The recent research in this area focuses on fatigue detection and the existing systems alert the drivers in severe fatigued stage. These systems use either physiological signs of the fatigue or the behavioural reaction to generate alerts. This research investigates the feasibility of using a group of fatigue symptoms (such as pupil response, gaze patterns, steering reaction and EEG) to build a robust fatigue detection algorithm that can be used in a real-life system for the early prediction and avoidance of fatigue development. Intensive testing and validation stages are required to ensure the reliability and the suitability of the system that should be able to detect fatigue levels at different degrees of tiredness. Moreover, the proposed system predicts subsequent stages of fatigue and generates an approximate behavioural model for each individual driver to enable more personalised and effective intervention.

HRV-based operator fatigue analysis and classification using wearable sensors

Fatigue assessment and quantification are essential requirements to reduce the risks that occur as a consequence of a fatigued operator. The new wearable device technology offers an accurate measuring ability to one or more of fatigue-related biological data, which helps in quantifying fatigue levels in real-life environments. This paper presents a new heart rate variability (HRV) based operator-fatigue analysis and classification method using low-cost wearable devices. HRV that is considered a robust fatigue metric is measured by several wearable devices including a chest-strap heart monitor and a wrist watch that measures heart rate, skin temperature and skin conductivity. The data collected from real subjects are used to create a training dataset for fatigue analysis and classification. Two supervised machine-learning algorithms based on multi-layer neural network and support vector machine are developed and implemented to identify the alertness/fatigue states of the operator. Performance of the developed classifiers demonstrated high alertness/fatigue prediction accuracy. Such findings proved that the proposed analysis and classification method is valid and practically applicable.

Estimation of driver alertness using low-cost wearable devices

Operator tiredness and fatigue pose significant safety risks in industries such as mining, trucking, aviation and air traffic control. Fatigue is a major factor behind on-the-job accidents, absenteeism and lowered productivity. The detection and prediction of fatigue is a technical challenge, confounded by the practical constraints of the working environment. This paper presents a mathematical fatigue model using low cost wearable biosensors, customized for each individual participant. The circadian rhythm mathematical model is the major part of fatigue model core. Heart rate and skin temperature, which are known circadian pacemakers, are used to build the proposed mathematical model. The customization and adaptation of the circadian is expected to improve the fatigue model and make it more accurate in the context of individuality and adaptation to the time of day and the activity at hand. Using low-cost, non-intrusive and portable biosensors allows the model to be tested in real working conditions with minimal impact on the operators. In addition to presenting a mathematical model for the customized circadian, this paper presents some preliminary results from a limited experiment, with some promising results.

High-capacity image steganography based on Haar DWT for hiding miscellaneous data

Steganography can be defined as the art and science of concealing private data within a carrier that could be a video, an image, an audio or a text that acts as a cover medium. Many steganography techniques exist; each has its own advantages and limitations. Capacity, robustness and the overall level of security are among the main factors to assess the performance of steganography algorithms. Also, the level of the stego-image distortion should be acceptable This paper aims at proposing a high capacity and efficient steganography technique, where binary images, color images, and large text files can be all concealed within a single cover image at the same time using Haar Wavelet transform. A high capacity of about 99% has been achieved using the proposed algorithm, with low mean square error (MSE) and high power signal-to-noise ratio (PSNR). This algorithm is developed with the aid of MATLAB environment. The obtained results from the proposed algorithm have been promising in terms of efficiency, performance, and capacity.

Mobile workflow management system based on the Internet of Things

The Internet of Things (IoT) is an emerging computing concept, which fuses the digital world and the physical world by bringing together different concepts and technical components. In this paper, we present a new mobile workflow management system (MWfMS) that handles the service of computer and peripherals repair and maintenance. It allows for (i) submission of service requests, (ii) provision of real-time notifications on work progress, (iii) automatic assignment and distribution of workload among the technical-support staff, and (iv) provision of quick solutions to known problems taken from database, without having to register for a new service request. The proposed MWfMS comprises a physical layer linked by the Internet to a remote workflow management hub (WfMH). The real objects (i.e. system stakeholders and their mobile devices represent the physical layer. The WfMH that hosts the main business logic and data storage is responsible for virtualization of physical objects, and creation and delivery of various management services to the end users. A fully functional prototype of the proposed system is designed and developed and its end-to-end functionality is tested successfully.

Building IPv6 based tunneling mechanisms for VoIP security

Internet protocol version 6 (IPv6) was developed to resolve the IPv4 address exhaustion problem and support new features. However, IPv6 still comprises some defectiveness of IPv4 protocol such as multimedia security. This paper presents IPv6-based tunneling mechanisms for securing Voice over Internet Protocol (VoIP) network traffic using OpenSwan IPSec (site-to-site). IPSec with Triple Data Encryption Algorithm (3DES) is used to create a Virtual Private Network (VPN) on top of existing physical networks. Secure communication mechanisms can therefore be provided for data and control information transmitted between networks. Secure VoIP-oriented mechanisms on VPN IPv6 have been designed, implemented and tested successfully using open source approaches. The performance of the IPv6 VoIP network is assessed experimentally in terms of several performance metrics including jitter, throughput and packet loss rate. The obtained results revealed that the proposed IPv6-based tunneling mechanisms for VoIP have negligible impact on network performance when compared to the previously reported work in literature, with a slight increase in the price of CPU and memory resources.