Loading...
2 results
Search Results
Now showing 1 - 2 of 2
- " In search of light": enhancing touristic recommender services with local weather dataPublication . Dionisio, Mara; Paulino, Teresa; Suri, Trisha; Autzen, Nicolas; Schöning, JohannesMany destinations’ economies strongly rely on tourism. Therefore, it is crucial to meet tourists’ expectations, so they will return to the destination. The geographical formation of certain touristic islands often leads to local climates where it can be rainy and windy on one side of the island, whereas the other part is sunny. In this paper, we present a novel use for a network of sensors, LightBeam, a mobile location-based application aiming to improve the tourists’ experience. The application focuses on providing real-time guidance for tourists seeking sunlight to maximize their holiday experience by suggesting the closest points of interest (POIs) to the user with the “best sunlight”. To achieve this, we implemented and installed a network of geospatial sensors. The data from the sensor network is combined with the current location of the users to provide recommendations. We report on the initial design and prototype of LightBeam.
- Passive Wi-Fi monitoring in the wild: a long-term study across multiple location typologiesPublication . Ribeiro, Miguel; Nunes, Nuno; Nisi, Valentina; Schöning, JohannesIn this paper, we present a systematic analysis of large-scale human mobility patterns obtained from a passive Wi-Fi tracking system, deployed across different location typologies. We have deployed a system to cover urban areas served by public transportation systems as well as very isolated and rural areas. Over 4 years, we collected 572 million data points from a total of 82 routers covering an area of 2.8 km2. In this paper we provide a systematic analysis of the data and discuss how our low-cost approach can be used to help communities and policymakers to make decisions to improve people’s mobility at high temporal and spatial resolution by inferring presence characteristics against several sources of ground truth. Also, we present an automatic classification technique that can identify location types based on collected data.