A Novel Architecture for Efficient Fog to Cloud Data Management in Smart Cities
Amir Sinaeepourfard, Jordi Garcia, Xavier Masip-Bruin and Eva Marin-Tordera

Traditional smart city resources management rely on cloud based solutions to provide a centralized and rich set of open data. The advantages of cloud based frameworks are their ubiquity, (almost) unlimited resources capacity, cost efficiency, as well as elasticity. However, accessing data from the cloud implies large network traffic, high data latencies, and higher security risks. Alternatively, fog computing emerges as a promising technology to absorb these inconveniences. The use of devices at the edge provides closer computing facilities, reduces network traffic and latencies, and improves security. We have defined a new framework for data management in the context of smart city through a global fog to cloud management architecture; in this paper we present the data acquisition block. As a first experiment we estimate the network traffic during data collection, and compare it with a traditional real system. We also show the effectiveness of some basic data aggregation techniques in the model, such as redundant data elimination and data compression.