Radio maps are expected to be an essential tool for the resource optimization and management of 5G automotive. In this paper, we consider the problem of radio map reconstruction using a wireless sensor network formed by sensor nodes in vehicles, access nodes from a smart city infrastructure, etc. Due to limited resource constraints in sensor networks, it is crucial to select a small number of sensor measurements for field reconstruction. In this context, we present a novel distributed incremental clustering algorithm based on the regression Kriging method for radio map reconstruction in terms of average received power at locations where no sensor measurements are available. The path-loss and shadowing components of the wireless channel are separately estimated. For shadowing estimation, clusters of sensors are adaptively formed and their size is optimized in terms of the least number of sensors by minimizing the ordinary Kriging variance. The complexity of the proposed algorithm is analyzed and simulation results are presented to showcase the algorithm efficacy to field reconstruction.

The Strategic Implementation Plan for speeding up the transformation of European Cities into "Smart cities" establishes 6 Actions Clusters and 11 Priority Areas. Some of them are directly related to the transport and sustainable urban mobility. The main goal is the application of new technologies to minimize the loss of time and energy, and to improve the satisfaction of citizens. The use and reuse of information as open data, already collected from the Public Administration and private entities is a source of possibilities adding extra value. Technologies such as Bluetooth sensors, Open Data platforms etc., become essential elements to achieve this goal. This paper highlights the opportunity to take advantage of emerging technologies, like the open source data platforms, or to use services for data linked queries. As an example of application of these technologies to the transport domain, several use cases with some results are presented.