SUMMARYRecent trends in information and communications technologies are oriented toward the design of the Future Internet and the Internet of Things. While IPv6-based mobile ad hoc networks (MANETs) are emerging as an important building block of these new technologies, it is necessary to come up with adequate self-configuration capabilities allowing for seamless and automated configuration of addresses in mobile environment. The mechanisms of stateless address autoconfiguration proposed for IPv6 networks are supposed to automate some configuration steps; however, they would need to be aligned with the requirements imposed by MANET networks. Therefore, in this article, we present Neighbor Discovery ++ -an extended IPv6 Neighbor Discovery protocol for enhanced duplicate address detection in MANETs, which provides increased coverage of network nodes, while minimizing protocol overhead. It exploits efficient flooding mechanism on the basis of the multipoint relay concept, which makes it an interesting approach also for large-scale networks. Trials performed on the designated real-world testbed platform indicate that ND++ is a promising solution to support efficient address autoconfiguration in MANETs.
The emergence of self-managing networks can be seen as an enabler for increased dependability, reliability and robustness of the network layer. All these features are significant for the services and applications relying on the network infrastructure. This paper explores the links between traditional Fault-Management functions belonging to the management plane and the fundamental network functions for Resilience and Survivability embedded inside the protocol modules of a node/device. This results in an architectural framework that allows nodes/devices to implement the converging aspects of Fault-Management (now becoming autonomic), Resilience and Survivability in a self-managing network. The components and adaptation mechanisms of the proposed framework will make the network layer more robust and application/service aware. Thus, the dependability, reliability, and adaptability of the upper layer services and applications are expected to increase.
eHealth services category has a diversified set of traffic patterns and demands in terms of QoS assurances. Existing QoS solutions were designed to support only aggregated classes of service and cannot differentiate traffic based on an application's behavioral pattern. In order to improve the performance of eHealth applications for home and mobile users there is a need to develop new traffic identification techniques, which would work at the edge of the network. This paper addresses the above problem by proposing machine learning-based approach for eHealth traffic identification. We investigate different techniques which combine the results from multiple machine learning classifiers and show which combination of techniques is best suited for identifying diverse eHealth traffic. Our approach is validated in a mobile e-health application context and the results prove that multi-classification techniques can be used in practice to provide application-based service differentiation.
<b><i>Introduction:</i></b> The use of commercially available automatic speech recognition (ASR) software is challenged when dysarthria accompanies a physical disability. To overcome this issue, a mobile and personal speech assistant (mPASS) platform was developed, using a speaker-dependent ASR software. <b><i>Objective:</i></b> The aim of this study was to evaluate the performance of the proposed platform and to compare mPASS recognition accuracy to a commercial speaker-independent ASR software. In addition, secondary aims were to investigate the relationship between severity of dysarthria and accuracy and to explore people with dysarthria perceptions on the proposed platform. <b><i>Methods:</i></b> Fifteen individuals with dysarthric speech and 20 individuals with nondysarthric speech recorded 24 words and 5 sentences in a clinical environment. Differences in recognition accuracy between the two systems were evaluated. In addition, mPASS usability was assessed with a technology acceptance model (TAM) questionnaire. <b><i>Results:</i></b> In both groups, mean accuracy rates were significantly higher with mPASS compared to the commercial ASR for words and for sentences. mPASS reached good levels of usefulness and ease of use according to the TAM questionnaire. <b><i>Conclusions:</i></b> Practical applicability of this technology is realistic: the mPASS platform is accurate, and it could be easily used by individuals with dysarthria.
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