Abstract
The diversity and sheer expanding in the number of Internet of Things (IoT) devices in a smart city context has raised substantial problems about storage and processing. Different sensors use different data formats. A situation is formed by combining data obtained from different sensors. This combination process needs a unified representation of sensor data. However, processing this massive amount of data and combining it to represent appropriate situations is a difficult task. To overcome this challenge, a data aggregation mechanism that is both efficient and light-weight is required. In this research, we developed a new data aggregation technique in cloud servers, where the processed data is transformed into a two-dimensional image-like spatial representation called Situation Image (S-image). We also developed a prototype that realizes the aforementioned aggregation model. In our experiment, multiple data mining techniques were chosen for processing various datasets in order to meet a variety of application goals. The experimental findings proved the viability of our data aggregation method.
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Hossain, S.A., Rahman, M.A., Hossain, M.A. (2023). S-Image (Situation Image): A New Technique for Data Aggregation in Cloud Server for IoT Based Smart City. In: Arai, K. (eds) Proceedings of the Future Technologies Conference (FTC) 2022, Volume 2. FTC 2022 2022. Lecture Notes in Networks and Systems, vol 560. Springer, Cham. https://doi.org/10.1007/978-3-031-18458-1_18
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