Publications Scientifiques

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    Data Caching in Edge Computing: A Survey
    (Institute of Electrical and Electronics Engineers Inc., 2024) Kara, Meliha Çağla; Benlakehal, Mohamed Elamine; Shayea, Ibraheem; Tussupov, Akhmet; Rzayeva, Leila
    As the Internet of Things (IoT) generates ever-increasing data streams, traditional cloud-centric architectures face crippling challenges in network latency, bandwidth consumption, and resource constraints. This paper explores how data caching in edge computing environments emerges as a potent solution, significantly impacting latency reduction, network efficiency, and overall system performance. We comprehensively review the landscape of edge IoT and data caching, analyze caching benefits and complexities, and delve into architectural integration, caching strategies, and algorithms tailored to address specific IoT challenges. Through case studies in chosen application domains, we quantify the performance improvements enabled by effective caching and pave the way for future research exploring novel caching methodologies and optimization techniques in the dynamic world of edge IoT.
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    An edge computing approach to explore indoor environmental sensor data for occupancy measurement in office spaces
    (IEEE, 2019) Zemouri, Sofiane; Magoni, Damien; Zemouri, Ayoub; Gkoufas, Yiannis; Katrinis, Kostas; Murphy, John
    Human occupancy measurement has become a topic of increasing interest in the past few years, due to the important role it plays in controlling a number of demand-driven applications like smart lighting and smart heating, as well as improving the energy efficiency of these applications in a broader sense. Office occupancy monitoring in commercial buildings can yield huge savings and improvements in terms of thermal, visual, and air quality. However, this is often impeded due to the lack of fine-grained occupancy information. This paper explores the use of low-priced environmental (temperature and humidity) sensor data for measuring occupancy in an office space. The idea behind this work is to leverage the variation divergence between humidity and temperature caused by human presence. We used a Raspberry Pi with a daughterboard called Sense Hat, which is equipped with the environmental sensors used in this study. The results are compared with occupancy data obtained from camera feeds in order to assess the effectiveness and the accuracy of the combined occupancy measurements, and show up to 87% accuracy