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High-definition (HD) maps are one of the key technologies supporting autonomous-driving vehicles (ADV). Especially in urban scenarios, the field of view of sensors is often limited, and HD map provides critical information about upcoming road environmental data. Maps used for ADV are high resolution with centimeter-level accuracy and their correctness is fundamental when analyzing safety of upcoming maneuvers.

This paper proposes an approach for online map validation (OMV) based on spatial and temporal correlation of smart- sensors. Smart sensors are capable of analyzing the validity of regions of the map independently from one another. Results from the sensors are then fused together over multiple regions and time samples for providing a unified view to software components deciding on upcoming maneuvers which areas of the maps are consistent with sensor data and which not.

Bike-sharing has outgrown its first failures in the ’60s and ’70s and become ubiquitous around the world. This rapid growth is strongly intertwined with the rise of Smart Cities: the use of connected bikes makes the service more practical for users, avoids thefts and provides a large amount of data for system planners. Over the past few years, the research on bike- sharing has bloomed, providing several innovative solutions to improve the service and encourage citizens to use environmentally friendly modes of transportation, reducing both traffic and commuting times. In this work, we present the most promising developments towards a tighter integration between Smart City data and techniques and the operation and planning of bike- sharing system, focusing on two model use-cases: New York City’s CitiBike service, a large system with hundreds of stations, and Padova’s GoodBike system, which has just 28 stations.

The analysis of data to monitor human-related activities plays a crucial role in the development of smart policies to improve well being and sustainability of our cities. For several applications in this context anomalies in time series can be associated to smaller timeframes such as days or weeks.

In this work we propose a consensus-based anomaly detection approach that exploits the power of the Symbolic Aggregate approXimation (SAX) and the specificity of such time series. In our approach, the normalization of the signal becomes a proper element of the modeling. In fact, we conjecture that different normalization horizons allow to include in the shape of the timeseries patterns an additional, variable, component from a longer period trend. To support the analysis phase, a calendar can be used as an additional source of information to discriminate between really unwanted anomalies and expected anomalies (e.g. weekends), or even to signal a possible anomaly whenever a “normal” behavior is not expected.

Preliminary experiments on temperature analysis in an indoor environment, with the scope of thermal energy saving, showed that our approch effectivly identified of all known anomalies, and also pointed out some unexpected, but clear, anomalies. 

“Smart City” is a powerful paradigm that applies the most advanced communication technologies to urban environments, with the final aim of enhancing the quality of life in cities and provide a wide set of value-added services to both citizens and administration. A fundamental step towards the practical realization of the Smart City concept consists in the development of a communication infrastructure capable of collecting data from a large variety of different devices in a mostly uniform and seamless manner, according to the Internet of Things (IoT) paradigm. While the scientific and commercial interest in IoT has been constantly growing in the last years, practical experimentation of IoT systems has just begun. In this paper, we present and discuss the Padova Smart City system, an experimental realization of an urban IoT system designed within the Smart City framework and deployed in the city of Padova, Italy. We describe the system architecture and discuss the fundamental technical choices at the base of the project. Then, we analyze the data collected by the system and show how simple data processing techniques can be used to gain insights on the functioning of the monitored system, public traffic lighting in our specific case, as well as other information concerning the urban environment.