Considering the increase of urban population and traffic congestion, smart parking is always a strategic issue to work on, not only in the research field, but also from economic interests. Thanks to information and communication technology evolution, drivers can more efficiently find satisfying parking spaces with smart parking services. The existing and ongoing works on smart parking are complicated and transdisciplinary. While deploying a smart parking system, cities, as well as urban engineers, need to spend a very long time to survey and inspect all the possibilities. Moreover, many varied works involve multiple disciplines, which are closely linked and inseparable. To give a clear overview, we introduce a smart parking ecosystem and propose a comprehensive and thoughtful classification by identifying their functionalities and problematic focuses. We go through the literature over the period of 2000-2016 on parking solutions as they were applied to smart parking development and evolution, and propose three macro-themes: information collection, system deployment, and service dissemination. In each macro-theme, we explain and synthesize the main methodologies used in the existing works and summarize their common goals and visions to solve current parking difficulties. Finally, we give our engineering insights and show some challenges and open issues. Our survey gives an exhaustive study and a prospect in a multidisciplinary approach. Besides, the main findings of the current state-of-the-art throw out recommendations for future research on smart cities and the Internet architecture.

Air pollution has become a major issue in the modern megalopolis because of industrial emissions and increasing urbanization along with traffic jams and the heating/cooling of buildings. Monitoring urban air quality is therefore required by municipalities and the civil society. Current monitoring systems rely on reference sensing stations that are precise but massive, costly, and, therefore, seldom. In this paper, we focus on an alternative or complementary approach, with a network of low cost and autonomic wireless sensors, aiming at a finer spatiotemporal granularity of sensing. Generic deployment models in the literature are not adapted to the stochastic nature of pollution sensing. Our main contribution is to design integer linear programming models that compute sensor deployments capturing both the coverage of pollution under time-varying weather conditions and the connectivity of the infrastructure. We evaluate our deployment models on a real data set of Greater London. We analyze the performance of the proposed models and show that our joint coverage and connectivity formulation is tight and compact, with a reasonable enough execution time. We also conduct extensive simulations to derive engineering insights for effective deployments of air pollution sensors in an urban environment.

This article investigates the problem of the allocation of modulation and coding, subcarriers and power to users in LTE. The proposed model achieves inter-cell interference mitigation through the dynamic and distributed self-organization of cells. Therefore, there is no need for any a prior frequency planning. Moreover, a two-level decomposition method able to find near optimal solutions is proposed to solve the optimization problem. Finally, simulation results show that compared to classic reuse schemes the proposed approach is able to pack more users into the same bandwidth, decreasing the probability of user outage.

The recent developments in both nanotechnologies and wireless technologies have enabled the rise of small, low-cost and energy-efficient environmental sensing devices. Many projects involving dense sensor networks deployments have followed, in particular, within the Smart City trend. If such deployments are now within economical and technical reach, their maintenance and reliability remain, however, a challenge. In particular, reaching, then maintaining, the targeted quality of measurement throughout deployment duration is an important issue. Indeed, factory calibration is too expensive for systematic application to low-cost sensors, as these sensors are usually prone to drifting because of premature aging. In addition, there are concerns about the applicability of factory calibration to field conditions. These challenges have fostered many researches on in situ calibration. In situ means that the sensors are calibrated without removing them from their deployment location, preferably without physical intervention, often leveraging their communication capabilities. It is a critical challenge for the economical sustainability of networks with large-scale deployments. In this paper, we focus on in situ calibration methods for environmental sensor networks. We propose a taxonomy of the methodologies in the literature. Our classification relies on both the architecture of the network of sensors and the algorithmic principles of the calibration methods. This review allows us to identify and discuss two main challenges: how to improve the performance evaluation of such methods and how to enable a quantified comparison of these strategies?

The widespread use of low-cost environmental monitoring systems, together with recent developments in the design of Internet of Things architectures and protocols, has given new impetus to smart city applications. Such progress should, in particular, considerably improve the fine characterization of a wide range of physical quantities within our cities. Indeed, the cost-effectiveness of these emerging sensors combined with their reduced size allows for high density deployments resulting in a higher spatial granularity. In this paper, we briefly present the 3M’Air project that aims to explore the potential of participatory citizen measures using low-cost sensors in order to improve the local knowledge of air quality and temperature and then bridge the gap between individual exposure and regional measurements. We present then the design, implementation and evaluation of our low-cost, small-size WSN-based participatory monitoring system. This system is composed of mobile sensing nodes measuring temperature, humidity and a number of pollutants (NO2, PM1, PM2.5 and PM10). The collected data are sent to a server for analysis and building temperature and air quality maps. To validate our platform, we have carried out multiple tests to compare our sensor nodes to reference stations and to each other. We have also evaluated the energy consumption of our nodes under different configurations. The results are satisfactory and show that our nodes can be used in environmental participatory monitoring.