As the world becomes increasingly interconnected, emerging and innovative sensing technologies are shaping the future of agriculture, with a special focus on sustainability-related issues. In this context, we envision the possibility to exploit Social Internet of Things for sensing of environmental conditions (solar radiation, humidity, air temperature, and soil moisture) and communications, deep learning for plant disease detection, and crowdsourcing for images collection and classification, engaging farmers and community garden owners and experts. Through, data fusion and deep learning, the designed system can exploit the collected data and predict when a plant would (or not) get a disease, with a specific degree of precision, with the final purpose to render agriculture more sustainable. We here present the architecture, the deep learning model, and the responsive Web app. Finally, some experimental evaluations and usability/engagement tests are reported and discussed, together with final remarks, limitations, and future work.

This paper presents mPASS (mobile Pervasive Accessibility Social Sensing), a system designed to collect data about urban and architectural accessibility and to provide users with personalized paths, computed on the basis of their preferences and needs. The system combines data obtained by sensing, crowdsourcing and mashing-up with main geo-referenced social systems, with the aim of offering services based on a detailed and valid data set.

This work presents a novel geospatial mapping service, based on OpenStreetMap, which has been designed and developed in order to provide personalized path to users with special needs. This system gathers data related to barriers and facilities of the urban environment via crowd sourcing and sensing done by users. It also considers open data provided by bus operating companies to identify the actual accessibility feature and the real time of arrival at the stops of the buses. The resulting service supports citizens with reduced mobility (users with disabilities and/or elderly people) suggesting urban paths accessible to them and providing information related to travelling time, which are tailored to their abilities to move and to the bus arrival time. The manuscript demonstrates the effectiveness of the approach by means of a case study focusing on the differences between the solutions provided by our system and the ones computed by main stream geospatial mapping services.

Urban environments, university campuses, and public and private buildings often present architectural barriers that prevent people with disabilities and special needs to move freely and independently. This paper presents a systematic mapping study of the scientific literature proposing devices, and software applications aimed at fostering accessible wayfinding and navigation in indoor and outdoor environments. We selected 111 out of 806 papers published in the period 2009–2020, and we analyzed them according to different dimensions: at first, we surveyed which solutions have been proposed to address the considered problem; then, we analyzed the selected papers according to five dimensions: context of use, target users, hardware/software technologies, type of data sources, and user role in system design and evaluation. Our findings highlight trends and gaps related to these dimensions. The paper finally presents a reflection on challenges and open issues that must be taken into consideration for the design of future accessible places and of related technologies and applications aimed at facilitating wayfinding and navigation.