State-of-the-art Earthquake Early Warning systems rely on a network of sensors connected to a fusion center in a client–server paradigm. The fusion center runs different algorithms on the whole data set to detect earthquakes. Instead, we propose moving computation to the edge, with detector nodes that probe the environment and process information from nearby probes to detect earthquakes locally. Our approach tolerates multiple node faults and partial network disruption and keeps all data locally, enhancing privacy. This paper describes our proposal’s rationale and explains its architecture. We then present an implementation that uses Raspberry, NodeMCU, and the Crowdquake machine learning model.

Earthquake Early Warning state of the art systems rely on a network of sensors connected to a fusion center in a client-server paradigm. Instead, we propose moving computation to the edge, with detector nodes that probe the environment and process information from nearby probes to detect earthquakes locally. Our approach tolerates multiple node faults and partial network disruption and keeps all data locally, enhancing privacy. This paper describes our proposal's rationale and explains its architecture. We then present an implementation using Raspberry, NodeMCU, and the Crowdquake machine learning model.

Smartphone sensors can collect data in many different contexts. They make it feasible to obtain large amounts of data at little or no cost because most people own mobile phones. In this work, we focus on collecting motion data in the car using a smartphone. Motion sensors, such as accelerometers and gyroscopes, can help obtain information about the vehicle’s dynamics. However, the different positioning of the smartphone in the car leads to difficulty interpreting the sensed data due to an unknown orientation, making the collection useless. Thus, we propose an approach to automatically re-orient smartphone data collected in the car to a standardized orientation (i.e., with zero yaw, roll, and pitch angles with respect to the vehicle). We use a combination of a least-square plane approximation and a Machine Learning model to infer the relative orientation angles. Then we populate rotation matrices and perform the data rotation. We trained the model by collecting data using a vehicle physics simulator.

On-street parking places parallel to the curb have variable lengths, depending on the size of the car that emptied the place. Thus, a Smart Parking system should publish such places only to drivers whose car is shorter than the available parking length. We developed a crowdsourced Smart Parking app, based on implicit interaction, intending to publish an available parking spot only to drivers whose car can fit the existing place. This app detects the type of parking (parallel vs angle or perpendicular) using machine learning on the driver's smartphone and considers the length of the cars involved.

Cruising-for-parking in an urban area is a time-consuming and frustrating activity. We present four machine learning-based models to predict the parking availability of street segments in an urban area on a three-level scale, which navigator and smart-parking apps can exploit to ease and reduce the cruising phase. The models were trained with data generated by a cruising-for-parking simulator that we developed, replicating four parking behavior types (workers, residents, buyers, and visitors). The generated data is comparable to that collectible with smartphones’ sensors. We simulated 40 users moving for 200 weeks in the city area of San Giovanni in Rome. We collected information about users’ parking, unparking, and cruising actions over considered road segments at different time slots. Once a significant amount of trips were collected, we extracted ten features for each road segment at a given time slot. With the obtained dataset, which contained 761 samples, we trained and compared four supervised machine learning models that receive the history of a segment and, in return, classify the Parking Availability Level of the segment as Green, Yellow or Red. The four models were further evaluated in a different city area, San Lorenzo, and obtained very accurate results. We can predict parking availability with an accuracy above 97% for all the street segments where we collected 30 or more user actions, confirming the robustness of the simulator in generating synthetic cruising-for-parking data and the suitability of designing a Parking Availability Classifier (PAC) based on data collectible by smartphones.