Abstract
The growth of the Internet of Things (IoT) over the past few years enabled a lot of application domains. Due to the increasing number of IoT connected devices, the amount of generated data is increasing too. Processing huge amounts of data is complex due to the continuously running situation recognition algorithms. To overcome these problems, this paper proposes an approach for optimizing the usage of situation recognition algorithms in Internet of Things domains. The key idea of our approach is to select important data, based on situation recognition purposes, and to execute the situation recognition algorithms after all relevant data have been collected. The main advantage of our approach is that situation recognition algorithms will not be executed each time new data is received, thus allowing the reduction of the situation recognition algorithms execution frequency and saving computational resources.
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Mammadova, C., Ben Hmida, H., Braun, A., Kuijper, A. (2017). New Approach for Optimizing the Usage of Situation Recognition Algorithms Within IoT Domains. In: Braun, A., Wichert, R., Maña, A. (eds) Ambient Intelligence. AmI 2017. Lecture Notes in Computer Science(), vol 10217. Springer, Cham. https://doi.org/10.1007/978-3-319-56997-0_15
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DOI: https://doi.org/10.1007/978-3-319-56997-0_15
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