724 research outputs found

    A Sensor Network with Embedded Data Processing and Data-to-Cloud Capabilities for Vibration-Based Real-Time SHM

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    This work describes a network of low power/low-cost microelectromechanical- (MEMS-) based three-axial acceleration sensors with local data processing and data-to-cloud capabilities. In particular, the developed sensor nodes are capable to acquire acceleration time series and extract their frequency spectrum peaks, which are autonomously sent through an ad hoc developed gateway device to an online database using a dedicated transfer protocol. The developed network minimizes the power consumption to monitor remotely and in real time the acceleration spectra peaks at each sensor node. An experimental setup in which a network of 5 sensor nodes is used to monitor a simply supported steel beam in free vibration conditions is considered to test the performance of the implemented circuitry. The total weight and energy consumption of the entire network are, respectively, less than 50 g and 300 mW in continuous monitoring conditions. Results show a very good agreement between the measured natural vibration frequencies of the beam and the theoretical values estimated according to the classical closed formula. As such, the proposed monitoring network can be considered ideal for the SHM of civil structures like long-span bridges

    Energy efficient data collection and dissemination protocols in self-organised wireless sensor networks

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    Wireless sensor networks (WSNs) are used for event detection and data collection in a plethora of environmental monitoring applications. However a critical factor limits the extension of WSNs into new application areas: energy constraints. This thesis develops self-organising energy efficient data collection and dissemination protocols in order to support WSNs in event detection and data collection and thus extend the use of sensor-based networks to many new application areas. Firstly, a Dual Prediction and Probabilistic Scheduler (DPPS) is developed. DPPS uses a Dual Prediction Scheme combining compression and load balancing techniques in order to manage sensor usage more efficiently. DPPS was tested and evaluated through computer simulations and empirical experiments. Results showed that DPPS reduces energy consumption in WSNs by up to 35% while simultaneously maintaining data quality and satisfying a user specified accuracy constraint. Secondly, an Adaptive Detection-driven Ad hoc Medium Access Control (ADAMAC) protocol is developed. ADAMAC limits the Data Forwarding Interruption problem which causes increased end-to-end delay and energy consumption in multi-hop sensor networks. ADAMAC uses early warning alarms to dynamically adapt the sensing intervals and communication periods of a sensor according to the likelihood of any new events occurring. Results demonstrated that compared to previous protocols such as SMAC, ADAMAC dramatically reduces end-to-end delay while still limiting energy consumption during data collection and dissemination. The protocols developed in this thesis, DPPS and ADAMAC, effectively alleviate the energy constraints associated with WSNs and will support the extension of sensorbased networks to many more application areas than had hitherto been readily possible

    Smart levee in Poland. Full-scale monitoring experimental study of levees by different methods

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    This paper presents two types of control and measurement networks used in the levee built as part of the ISMOP project. The first control and measurement network based on pore pressure and temperature sensors. Additionally, it contains the fiber-optic technology. The second network include design experimental sensors, constructed for the development of solutions that can be used in existing flood embankments

    A Social Internet of Things Smart City Solution for Traffic and Pollution Monitoring in Cagliari

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    In the last years, the smart city paradigm has been deeply studied to support sustainable mobility and to improve human living conditions. In this context, a new smart city based on Social Internet of Things paradigm is presented in this article. Starting from the tracking of all vehicles (that is, private and public) and pedestrians, integrated with air quality measurements (that is, in real time by mobile and fixed sensors), the system aims to improve the viability of the city, both for pedestrian and vehicular users. A monitoring network based on sensors and devices hosted on board in local public transport allows real time monitoring of the most sensitive areas both from traffic congestion and from an environmental point of view. The proposed solution is equipped with an appropriate intelligence that takes into account instantaneous speed, type of traffic, and instantaneous pollution data, allowing to evaluate the congestion and pollution condition in a specific moment. Moreover, specific tools support the decisions of public administration facilitating the identification of the most appropriate actions for the implementation of effective policies relating to mobility. All collected data are elaborated in real time to improve traffic viability suggesting new directions and information to citizens to better organize how to live in the city

    A Social Internet of Things Smart City Solution for Traffic and Pollution Monitoring in Cagliari

    Get PDF
    In the last years, the smart city (SC) paradigm has been deeply studied to support sustainable mobility and to improve human living conditions. In this context, a new SC based on the Social Internet of Things paradigm is presented in this article. Starting from the tracking of all vehicles (that is, private and public) and pedestrians, integrated with air quality measurements (that is, in real time by mobile and fixed sensors), the system aims to improve the viability of the city, both for pedestrian and vehicular users. A monitoring network based on sensors and devices hosted on board in local public transport allows real-time monitoring of the most sensitive areas both from traffic congestion and from an environmental point of view. The proposed solution is equipped with an appropriate intelligence that takes into account instantaneous speed, type of traffic, and instantaneous pollution data, allowing to evaluate the congestion and pollution condition in a specific moment. Moreover, specific tools support the decisions of public administration facilitating the identification of the most appropriate actions for the implementation of effective policies relating to mobility. All collected data are elaborated in real time to improve traffic viability suggesting new directions and information to citizens to better organize how to live in the city

    A review of tidal energy - Resource, feedbacks, and environmental interactions

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    International audienceThe ocean contains a variety of renewable energy resources, little of which has been exploited. Here, we review both tidal range and tidal stream energy, with a focus on the resource, feedbacks, and environmental interactions. The review covers a wide range of timescales of relevance to tidal energy, from fortnightly (spring-neap) and semi-diurnal variability, down to array, and device-scale turbulence. When simulating the regional tidal energy resource, and to assess environmental impacts, it is necessary to account for feedbacks between the tidal array and the resource itself. We critically review various methods for simulating energy extraction, from insights gained through theoretical studies of “tidal fences” in idealized channels, to realistic three-dimensional model studies with complex geometry and arrays of turbines represented by momentum sinks and additional turbulence due to the presence of rotors and support structures. We discuss how variability can be reduced by developing multiple (aggregated) sites with a consideration of the enhanced phase diversity offered by exploiting less energetic tidal currents. This leads to future research questions that have not yet been explored in depth at first-generation tidal sites in relatively sheltered channels (e.g., the interaction of waves with currents). Such enhanced understanding of real sea conditions, including the effects of wind and waves, leads to our other identified primary future research direction—reduced uncertainties in turbulence predictions, including the development of realistic models that simulate the interaction between ambient turbulence and the turbulence resulting from multiple wakes, and changes to system-wide hydrodynamics, water quality, and sedimentation
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