Section: New Results
Smart cities are a key factor in the consumption of materials and resources. As populations grow and resources become scarcer, the efficient usage of these limited goods becomes more important. Building on and integrating with a huge amount of data, the cities of the future are becoming a realization today. There are millions of sensors in place already, monitoring various things in metropolises. In the near future, these sensors will multiply until they can monitor everything from streetlights and trashcans to road conditions and energy consumption. In this context, effective strategies or solutions for refining data sets can play a key role. In , we propose a scheme in which passive RFID is shown as an interesting alternative and complement to WSN to alleviate the cost of some Smart City applications.
Also, in Smart Cities, crowd sensing may help to identify the current speed for each street, the congested areas, etc. In this context, map matching techniques are required to map a sequence of GPS waypoints into a set of streets on a common map. Unfortunately, most map matching approaches are probabilistic. In , we propose rather an unambiguous algorithm, able to identify all the possible paths that match a given sequence of waypoints. We need an unambiguous identification for each waypoints set. For instance, the actual speed should be assigned to the correct set of streets, without error. To identify all the possible streets, we construct the set of candidates iteratively. We identify all the edge candidates around each waypoint, and reconstruct all the possible sub-routes that connect them. We then verify a set of constraints, to eliminate impossible routes. The road segments common to all computed routes form an unambiguous match. We evaluate the matching ratio of our technique on real city maps (London, Paris and Luxembourg). We also validate our approach with a real GPS trace in Seattle.