14,619 research outputs found

    TechNews digests: Autumn 2004

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    TechNews is a technology, news and analysis service aimed at anyone in the education sector keen to stay informed about technology developments, trends and issues. TechNews focuses on emerging technologies and other technology news. TechNews service : digests september 2004 till May 2010 Analysis pieces and News combined publish every 2 to 3 month

    MobiHealth-Innovative 2.5/3G mobile services and applications for health care

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    MobiHealth aims at introducing new mobile value added services in the area of healthcare, based on 2.5 (GPRS) and 3G (UMTS) technologies, thus promoting the use and deployment of GPRS and UMTS. This will be achieved by the integration of sensors and actuators to a Wireless Body Area Network (BAN). These sensors and actuators will continuously measure and transmit vital constants along with audio and video to health service providers and brokers, improving on one side the life of patients and allowing on the other side the introduction of new value-added services in the areas of disease prevention and diagnostic, remote assistance, para-health services, physical state monitoring (sports) and even clinical research. Furthermore, the MobiHealth BAN system will support the fast and reliable application of remote assistance in case of accidents by allowing the paramedics to send reliable vital constants data as well as audio and video directly from the accident site

    Behavior-Based Outlier Detection for Network Access Control Systems

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    Network Access Control (NAC) systems manage the access of new devices into enterprise networks to prevent unauthorised devices from attacking network services. The main difficulty with this approach is that NAC cannot detect abnormal behaviour of devices connected to an enterprise network. These abnormal devices can be detected using outlier detection techniques. Existing outlier detection techniques focus on specific application domains such as fraud, event or system health monitoring. In this paper, we review attacks on Bring Your Own Device (BYOD) enterprise networks as well as existing clustering-based outlier detection algorithms along with their limitations. Importantly, existing techniques can detect outliers, but cannot detect where or which device is causing the abnormal behaviour. We develop a novel behaviour-based outlier detection technique which detects abnormal behaviour according to a device type profile. Based on data analysis with K-means clustering, we build device type profiles using Clustering-based Multivariate Gaussian Outlier Score (CMGOS) and filter out abnormal devices from the device type profile. The experimental results show the applicability of our approach as we can obtain a device type profile for five dell-netbooks, three iPads, two iPhone 3G, two iPhones 4G and Nokia Phones and detect outlying devices within the device type profile

    The Prospects of M-Voting Implementation in Nigeria

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    Since independence, an average of 50% of registered voters participates in voting [1]. Similarly, an increasing rate of apathy was observed between the electorate and the elect, which was not unconnected with lack of transparency, accountability, and probity on the part of government [2]. Thus the electorate did not see the need to subject itself to any stress. Consequently, government is very committed to implementing the forth coming elections through e-voting. This paper proposes the prospects of m-voting implementation in Nigeria through the use of mobile phones, PDAs, etc. with guaranteed security, secrecy, and convenience in a democratization process. It also reviews the level of adoption of GSM in Nigeria, the implication of voting through the GSM, and finally introducing m-voting innovation in the voting process to increase voters’ access and participation rate in election

    Context-aware QoS provisioning for an M-health service platform

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    Inevitably, healthcare goes mobile. Recently developed mobile healthcare (i.e., m-health) services allow healthcare professionals to monitor mobile patient's vital signs and provide feedback to this patient anywhere at any time. Due to the nature of current supporting mobile service platforms, m-health services are delivered with a best-effort, i.e., there are no guarantees on the delivered Quality of Service (QoS). In this paper, we argue that the use of context information in an m-health service platform improves the delivered QoS. We give a first attempt to merge context information with a QoS-aware mobile service platform in the m-health services domain. We illustrate this with an epilepsy tele-monitoring scenario
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