Simulation of Urban Mobility (SUMO) is a powerful traffic simulation program which can work at different scales, from sub-microscopic to macroscopic. Depending on the available input dataset, it is possible to build lots of different configurations, changing routing and car-follow algorithms, and many parameters. Building a basic SUMO scenario is a multi-step activity involving the followings: preparing the transportation network, traffic definition, setting a routing algorithm, and running the simulation. The aim of the present work is to show a detailed real case study explaining how to build a complete scenario and run simulations, starting from the preparation of the network from Open Street Map. The last part of the present paper is about how to use the SUMO output files with MongoDB in order to keep track of significant information resulting from each simulation.

In the past few years, the Smart City concept became one of the main driving forces for the transition towards sustainable economy and improved mobility. Tourism, as one of the fastest growing economies worldwide, is an integrated part of the Smart City paradigm. Taking into consideration recent studies performed by the United Nations, stating that almost one third of the population is directly affected by disability, the concept of Accessible Tourism needs also to be integrated in the future vision for tourism, especially in the context of Smart Cities, environments fully benefiting from the recent technological advances. Within the combined framework of Smart Cities and Accessible Tourism, the Internet-of-Things (IoT) concept is the key technological point for the development of smart urban environments. IoT and big data are both technology-driven developments, leading to scenarios such as the Smart Cities one that has the potential to make citizen live smarter, more sustainable and more accessible. This chapter analyses the key requirements for IoT applications in a Smart City context, the state-of-the-art for the use of IoT for Accessible Tourism applications and proposes an architecture together with its practical implementation, tailored for the use-case of accessible tourism for physically impaired persons.

This paper presents an exploratory activity with a drone inspection service for environmental control. The aim of the service is to provide technical support to decision-makers in environmental risk management. The proposed service uses IoT for the interaction between a mobile application, a Smart City platform, and an Unmanned Aircraft System (UAS). The mobile application allows the users to report risky situations, such as fire ignition, spills of pollutants in water, or illegal dumping; the user has only to specify the class of the event, while the geographical coordinates are automatically taken from device-integrated GPS. The message sent from the mobile application arrives to a Smart City platform, which shows all the received alerts on a 3D satellite map, to support decision-makers in choosing where a drone inspection is required. From the Smart City platform, the message is sent to the drone service operator; a CSV file defining the itinerary of the drone is automatically built and shown through the platform; the drone starts the mission providing a video, which is used by the decision-makers to understand whether the situation calls for immediate action. An experimental activity in an open field was carried out to validate the whole chain, from the alert to the drone mission, enriched by a Smart City platform to enable a decision-maker to better manage the situation.

A research analysis on item-based algorithms for collaborative filtering is presented. The aim of the presented activity was to find a configuration of an item-based algorithm capable of providing good results but also independent from the data set. Four data sets were used for the algorithm validation: Netflix, MovieLens, BookCrossing, and Jester. The experimentation involved the following aspects: similarity computation, size of the neighbourhood, prediction computation, minimum number of co-rated items. Results were evaluated in terms of root mean squared error (RMSE). The result of the activity is an independent domain configuration for an item-based algorithm which produced satisfactory results with most of the above mentioned data sets.

This paper describes the development activity that has been carried out for a living laboratory for the city of Cagliari aimed at functioning as a learning center for local SMEs willing to improve their skills in IoT and create applications that will be integrated in an open innovation ecosystem. The many users belonging to the various SMEs involved in the project required an ICT laboratory with a platform that could manage them and provide a multi-tenant environment for the development of IoT applications. The architecture also had to be scalable and interoperable, and the resulting platform had to collect many kinds of data from sensors or other data sources, elaborate them, and show georeferenced information on a 3D satellite interactive view along with statistics on side panels. This work was based on a platform already developed by CRS4 for a previous project. Preserving the concept of the decision-making tool for Smart Cities, almost every component was redesigned, and, in this paper, we describe the new solutions that have been implemented. Starting from the former structure, further features were added in a novel way in order to offer an enhanced framework that can deal with the activities of the laboratory, exploiting the scalability of the open-source systems involved, their robustness and flexibility, and leveraging domain standards. In this article, the main challenges involved in the development of the platform are described, as well as the solutions that have been implemented so far.