Abstract
Smart cities leverage large amounts of data acquired in the urban environment in the context of decision support tools. These tools enable monitoring the environment to improve the quality of services offered to citizens. The increasing diffusion of personal Internet of things devices capable of sensing the physical environment allows for low-cost solutions to acquire a large amount of information within the urban environment. On the one hand, the use of mobile and intermittent sensors implies new scenarios of large-scale data analysis; on the other hand, it involves different challenges such as intermittent sensors and integrity of acquired data. To this effect, edge computing emerges as a methodology to distribute computation among different IoT devices to analyze data locally. We present here a new methodology for imputing environmental information during the acquisition step, due to missing or otherwise out of order sensors, by distributing the computation among a variety of fixed and mobile devices. Numerous experiments have been carried out on real data to confirm the validity of the proposed method.
Highlights
IntroductionThe spreading of the Internet of things (IoT) enabled cities to become urban data sensing platforms where a huge quantity of devices, connected to the Internet, are capable of providing various information about the environment [1]
We present a new approach for estimating missing environmental information by considering both mobile devices and custom sensors
To evaluate the correctness of the proposed technique we considered a real dataset of environmental information provided by Arpae-SIMC, the weather service of the EmiliaRomagna region in Italy which provides weather warnings to the Italian Civil Protection
Summary
The spreading of the Internet of things (IoT) enabled cities to become urban data sensing platforms where a huge quantity of devices, connected to the Internet, are capable of providing various information about the environment [1]. Data collected from IoT devices can be used for different purposes. Data analysis is important in the context of decision support systems, where the information is used to provide optimized services to citizens and to predict their behavior and to act opportunely to ensure both security and a better quality of life. The increasing quantity of data produced by sensing devices leads to challenges regarding large-scale data analysis, privacy issues, autonomous learning and integration of noisy and heterogeneous info [3]
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