This paper presents an intelligent streetlight management system based on LED lamps, designed to facilitate its deployment in existing facilities. The proposed approach, which is based on wireless communication technologies, will minimize the cost of investment of traditional wired systems, which always need civil engineering for burying of cable underground and consequently are more expensive than if the connection of the different nodes is made over the air. The deployed solution will be aware of their surrounding’s environmental conditions, a fact that will be approached for the system intelligence in order to learn, and later, apply dynamic rules. The knowledge of real time illumination needs, in terms of instant use of the street in which it is installed, will also feed our system, with the objective of providing tangible solutions to reduce energy consumption according to the contextual needs, an exact calculation of energy consumption and reliable mechanisms for preventive maintenance of facilities.

Recently, there has been growing attention to the power grid management due to the increasing concerns on global warming. With the advancement in electric vehicles (EV) industry and the evolution in batteries, EVs become an important contributor to the grid with capability of bidirectional power exchange with the grid. In this context, Vehicle-to-Grid (V2G) systems enable multiple functionalities between EVs and the corresponding aggregator. Thus, reliable, long-range communication capabilities between aggregator and EVs is compulsory. In this paper, wireless channel analysis for aggregator and electrical vehicle communication using Long-Range Wide Area Network (LoRaWAN) technology in V2G is presented, in order to test a low-cost solution with large coverage and reduced power consumption profile. Wireless channel and system-level measurements have been performed in a real urban scenario between EV’s charging station in Pamplona (Spain) and a vehicle in motion using LoRaWAN 868 MHz devices. Wireless channel characterization is performed by implementing a full 3D urban scenario model, including elements such as buildings, vehicles, users and urban infrastructure such as lamp posts and benches. By means of in-house developed 3D Ray Launching algorithm with hybrid simulation capabilities, estimations of received power levels, signal to noise ratio and time domain parameters have been obtained, for the complete volume of the scenario under test in dense urban conditions. V2G end to end communication has been validated by implementing an intra-vehicle Controller Area Network-BUS (CAN BUS) data gathering system connected to the vehicle LoRaWAN transceiver and subsequently, to a cloud-based web service. The results show that the accurate deterministic based radio channel analysis enables to optimize the network design of LoRaWAN networks in a vehicular environment, considering inter-vehicular and infrastructure links, enabling scalable, low cost end to end data exchange for the deployment of ancillary V2G services.

Transportation system is experiencing steady growth, with the aim of providing more efficient, reliable, and comfortable services, in the framework of intelligent transportation systems. Moreover, context aware environments are one of the main drivers in the achievement of smart cities and smart regions. In this paper, wireless sensor networks (WSNs) embedded in urban transportation systems will be analyzed in terms of impact on wireless channel behavior and system performance. An in-house developed 3-D ray launching tool, with the inclusion of in-house human body model as well as with the study of interference levels is employed. Wireless channel estimations indicate an initial infrastructure node density in the order of 1node/150m2 to 1node/500m2 as a function of the employed transceivers. A practical solution has been implemented, combining an Android-based application and a system level architecture over the WSN for urban train transportation environmental monitoring, providing interaction between users and the environment, with the aid of a combined WSN/WLAN platform as well as an implemented software architecture, scalable in terms of user density and future needs.

A practical experience on monitoring the data generated by electric buses is presented, focusing on energy consumption, charge and state of the batteries. The work is carried out in the framework of a global smart city strategy developed by the H2020 Smart City Lighthouse STARDUST project. The crucial role of the data collection and transmission from electric buses has become evident in this work, so the adopted solutions are covered in detail. A practical electric bus charging station configuration is considered, operating within the city of Pamplona, Spain, with an urban route setting in which electric charging is performed. Various key factors for the practical implementation of the necessary communication infrastructure, including wireless Low Power Wide Area connectivity challenges within the urban scenario settings, based in LoRa/LoRaWAN communication system nodes. The monitoring system architecture is also presented, in which specific machine learning modules in order to collect patterns and visualization of data to enhance planning, operation and maintenance procedures.

In the last decades, the cities' capacity for generating digital information has grown exponentially. In this context, the successful implementation of smart cities' concept depends on the current possibility of handling the significant volumes of sensed data. This is particularly notorious in the case of urban mobility. Researchers in the field of urban planning have shown a great interest in urban mobility problems, proposing different route recommendation services towards making it easier and safer to move around the city. This paper addresses the development of an urban data platform and how to obtain and integrate information from sensors and other data sources to provide aggregated and intelligent views of raw data to support urban mobility. With the aim of evaluating the efficiency of the developed platform, we present an intelligent urban mobility solution, where the context-awareness, user preferences, and environmental factors play a significant role in the process of route planning. Finally, our work provides an experiment to assess different long-range wireless communication technologies to enable its implementation within an urban environment.