Noise pollution reduction in the environment is a major challenge from a societal and health point of view. To implement strategies to improve sound environments, experts need information on existing noise. The first source of information is based on the elaboration of noise maps using software, but with limitations on the realism of the maps obtained, due to numerous calculation assumptions. The second is based on the use of measured data, in particular through professional measurement observatories, but in limited numbers for practical and financial reasons. More recently, numerous technical developments, such as the miniaturization of electronic components, the accessibility of low-cost computing processors and the improved performance of electric batteries, have opened up new prospects for the deployment of low-cost sensor networks for the assessment of sound environments. Over the past fifteen years, the literature has presented numerous experiments in this field, ranging from proof of concept to operational implementation. The purpose of this article is firstly to review the literature, and secondly, to identify the expected technical characteristics of the sensors to address the problem of noise pollution assessment. Lastly, the article will also put forward the challenges that are needed to respond to a massive deployment of low-cost noise sensors.

International audienceWireless sensor networks are constrained by their energy supply. In order to relief this constraint, scavenging ambient energy from the environment has been considered. However, most existing energy harvesting devices rely on a single energy source, potentially reducing the sensor reliability. In this paper, we present an architecture for multi-source energy harvesting, aimed at low cost and easy integration with existing wireless sensors. Unlike existing architectures, our solution relies on a single power conditioning block. This block is powered by multiple sources, selected through a switch matrix by a dedicated controller. A prototype has been developed, validated and compared with alternative architectures. First results show our architecture benefits for systems using many heterogeneous sources, and highlights improvement possibilities through the addition of MPPT (Maximum Power Point Tracking) circuitry

Urban noise reduction is a societal priority. In this context, the European Directive 2002/49/EC aims at producing strategic noise maps for large cities. However, nowadays the relevance of such maps is questionable, due to considerable uncertainties, which are rarely quantified. Conversely, the development of noise observatories can provide useful information for a more realistic description of the sound environment, but at the expense of insufficient spatial resolution and high costs. Thus, the CENSE project aims at proposing a new methodology for the production of more realistic noise maps, based on an assimilation of simulated and measured data, collected through a dense network of low-cost sensors that rely on new technologies. In addition, the proposed approach tries to take into account the various sources of uncertainty, either from measurements and modeling. Beyond the production of physical indicators, the project also includes advanced sound environments characterization, through sound recognition and perceptual assessments. CENSE is resolutely a multidisciplinary project, bringing together experts from environmental acoustics, data assimilation, statistics, GIS, sensor networks, signal processing, and noise perception. As the project is in launch state, the present communication will focus on a global overview, emphasizing the innovative and key points of the project.

Many connected devices are expected to be deployed during the next few years. Energy harvesting appears to be a good solution to power these devices but is not a reliable power source due to the time-varying nature of most energy sources. It is possible to harvest energy from multiple energy sources to tackle this problem, thus increasing the amount and the consistency of harvested energy. Additionally, a power management system can be implemented to compute how much energy can be consumed and to allocate this energy to multiple tasks, thus adapting the device quality of service to its energy capabilities. The goal is to maximize the amount of measured and transmitted data while avoiding power failures as much as possible. For this purpose, an industrial sensor node platform was extended with a multi-source energy-harvesting circuit and programmed with a novel energy-allocation system for multi-task devices. In this paper, a multi-source energy-harvesting LoRaWAN node is proposed and optimal energy allocation is proposed when the node runs different sensing tasks. The presented hardware platform was built with off-the-shelf components, and the proposed power management system was implemented on this platform. An experimental validation on a real LoRaWAN network shows that a gain of 51% transmitted messages and 62% executed sensing tasks can be achieved with the multi-source energy-harvesting and power-management system, compared to a single-source system.

In order to enable IoT nodes to efficiently use their energy harvesting capabilities, algorithms are used to determine a reasonable energy budget and allocate it to the node tasks, enabling energy neutral operation. However, most of these algorithms have been implemented and evaluated in simulation frameworks. In this paper, we evaluate the implementation of these algorithms to manage the energy of real-world LoRaWAN IoT nodes. We measure and compare the performance of the different energy budget estimation methods on a commercial LoRaWAN IoT platform. Results show that in this use-case, the choice of algorithm impacts the system Quality of Service by less than 15 %. This enables much simpler energy budget estimation methods to be used.