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    IoT-DMCP: An IoT data management and control platform for smart cities
    (SCITEPRESS – Science and Technology Publications, 2019) Boulkaboul, Sahar; Djenouri, Djamel; Bouhafs, Sadmi; Belaid, Mohand
    This paper presents a design and implementation of a data management platform to monitor and control smart objects in the Internet of Things (IoT). This is through IPv4/IPv6, and by combining IoT specific features and protocols such as CoAP, HTTP and WebSocket. The platform allows anomaly detection in IoT devices and real-time error reporting mechanisms. Moreover, the platform is designed as a standalone application, which targets at extending cloud connectivity to the edge of the network with fog computing. It extensively uses the features and entities provided by the Capillary Networks with a micro-services based architecture linked via a large set of REST APIs, which allows developing applications independently of the heterogeneous devices. The platform addresses the challenges in terms of connectivity, reliability, security and mobility of the Internet of Things through IPv6. The implementation of the platform is evaluated in a smart home scenario and tested via numeric results. The results show low latency, at the order of few ten of milliseconds, for building control over the implemented mobile application, which confirm realtime feature of the proposed solution.
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    New GPU-based Swarm Intelligence Approach For Reducing Big Association Rules Space
    (CERIST, 2017-06-14) Djenouri, Youcef; Bendjoudi, Ahcène; Djenouri, Djamel; Belhadi, Asma; Nouali-Taboudjemat, Nadia
    This paper deals with exploration and mining of association rules in big data, with the big challenge of increasing computation time. We propose a new approach based on meta-rules discovery that gives to the user the summary of the rules’ space through a meta-rules representation. This allows the user to decide about the rules to take and prune. We also adapt a pruning strategy of our previous work to keep only the representatives rules. As the meta-rules space is much larger than the rules space, two approaches are proposed for efficient exploitation. The first one uses a bees swarm optimization method in the meta-rules discovery process, which is extended using GPU-based parallel programming to form the second one. The sequential version has been first tested using medium rules set, and the results show clear improvement in terms of the number of returned meta-rules. The two versions have then been compared on large scale rules sets, and the results illustrate the acceleration on the summarization process by the parallel approach without reducing the quality of resulted meta-rules. Further experiments on Webdocs big data instances reveal that the proposed method of pruning rules by summarizing meta-rules considerably reduces the association rules space compared to state-of-the-art association rules mining-based approaches.