1,720,987 research outputs found
IoT Transmission Technologies for Distributed Measurement Systems in Critical Environments
Full text linkDistributed measurement systems are spread in the most diverse application scenarios, and Internet of Things (IoT) transmission equipment is usually the enabling technologies for such measurement systems that need to feature wireless connectivity to ensure pervasiveness. Because wireless measurement systems have been deployed for the last years even in critical environments, assessing transmission technologies performances in such contexts is fundamental. Indeed, they are the most challenging ones for wireless data transmission due to their intrinsic attenuation capabilities.
Several scenarios in which measurement systems can be deployed are analysed. Firstly, marine contexts are treated by considering above-the-sea wireless links. Such setting can be experienced in whichever application requiring remote monitoring of facilities and assets that are offshore installed. Some instances are offshore sea farming plants, or remote video monitoring systems installed on seamark buoys. Secondly, wireless communications taking place from the underground to the aboveground are covered. This scenario is typical of precision agriculture applications, where the accurate measurement of underground physical parameters is needed to be remotely sent to optimise crops reducing the wastefulness of fundamental resources (e.g., irrigation water). Thirdly, wireless communications occurring from the underwater to the abovewater are addressed. Such situation is inevitable for all those infrastructures monitoring conservation status of underwater species like algae, seaweeds and reef. Then, wireless links happening traversing metal surfaces and structures are tackled. Such context is commonly encountered in asset tracking and monitoring (e.g., containers), or in smart metering applications (e.g., utility meters). Lastly, sundry harsh environments that are typical of industrial monitoring (e.g., vibrating machineries, harsh temperature and humidity rooms, corrosive atmospheres) are tested to validate pervasive measurement infrastructures even in such contexts that are usually experienced in Industrial Internet of Things (IIoT) applications. The performances of wireless measurement systems in such scenarios are tested by sorting out ad-hoc measurement campaigns. Finally, IoT measurement infrastructures respectively deployed in above-the-sea and underground-to-aboveground settings are described to provide real applications in which such facilities can be effectively installed. Nonetheless, the aforementioned application scenarios are only some amid their sundry variety. Indeed, nowadays distributed pervasive measurement systems have to be thought in a broad way, resulting in countless instances: predictive maintenance, smart healthcare, smart cities, industrial monitoring, or smart agriculture, etc.
This Thesis aims at showing distributed measurement systems in critical environments to set up pervasive monitoring infrastructures that are enabled by IoT transmission technologies. At first, they are presented, and then the harsh environments are introduced, along with the relative theoretical analysis modelling path loss in such conditions. It must be underlined that this Thesis aims neither at finding better path loss models with respect to the existing ones, nor at improving them. Indeed, path loss models are exploited as they are, in order to derive estimates of losses to understand the effectiveness of the deployed infrastructure. In fact, some transmission tests in those contexts are described, along with providing examples of these types of applications in the field, showing the measurement infrastructures and the relative critical environments serving as deployment sites. The scientific relevance of this Thesis is evident since, at the moment, the literature lacks a comparative study like this, showing both transmission performances in critical environments, and the deployment of real IoT distributed wireless measurement systems in such contexts
A Novel Methodology to Remotely and Early Diagnose Sleep Bruxism by Leveraging on Audio Signals and Embedded Machine Learning
No full textMonitoring Workers Safety in Restricted Working Areas: A Case-Study with LoRaWAN Technology
No full textPerformance Measurements of LoRaWAN Connectivity for Links Through Metallic Shields: Preliminary and Feasibility Tests
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A Sensor Fusion Paradigm for Particulate Matter Monitoring Exploiting an Embedded Sound Level Meter for Virtual Sensing Techniques
No full textA Machine Learning Model for Microcontrollers Enabling Low Power Indoor Positioning Systems via Visible Light Communication
No full textThis paper presents a low-power Visible Light Localisation (VLL) Artificial Intelligence (AI)-enabled system for Indoor Positioning (IP) purposes. Compared to other IP techniques, VLL offers a similar positioning accuracy, but with the extremely desirable feature of low energy consumption, an aspect of primary relevance in the framework of Wireless Sensor Networks (WSN), self-sufficient sensing systems, Industry 4.0 and Internet of Things (IoT). The proposed system is composed of three modulated optical sources (i.e. LEDs) and a photodiode receiver mounted on the target to be localised. The localisation task is performed by processing the received light intensities through Machine Learning (ML) regression models trained with a set of data gathered during a calibration phase. The regressors are designed to be executed on a low-power microcontroller present in the target, hence establishing an embedded ML paradigm also preserving reduced power consumption features. The proposed models are trained exploiting datasets with different sizes, searching for a trade-off between the training set size, i.e. the duration and complexity of the calibration phase, and the maximum tolerable root mean square error (RMSE). In both cases, some localisation tests show that a satisfactory accuracy can be reached even with a limited complexity of the calibration procedure and that the obtained results fulfil the error constraint used for model design
Offshore LoRaWAN Networking: Transmission Performances Analysis Under Different Environmental Conditions
Full text linkThis paper presents the architecture and the performances of a Long Range Wide Area Network (LoRaWAN) infrastructure used for data transmission from a floating sensor node placed in the middle of the sea to a multi-Gateway structure positioned on the coast, whose purpose is the monitoring of offshore breeding cages within a fish farming plant. In particular, the sensor node is installed on a seamark buoy and it is interfaced with ad hoc sensors for the measurement of marine parameters. All sampled data are conveyed ashore to the Gateways by successfully covering the distance of 8.33 km. The paper presents the results concerning the performances of the data transmission for a 70 days operating period, analyzing the radio parameters (Signal-to-Noise Ratio - SNR and Received Signal Strength Indicator - RSSI) in relation with the variations of environmental parameters like temperature, relative humidity and atmospheric pressure as well as weather conditions. The proposed system demonstrates the usability of the LoRaWAN in all those cases where data collection from offshore monitoring structures is required: while this infrastructure focuses on the monitoring of fish farming plants, it may find application in several contexts, from navigational buoys to offshore oil plants
Architecture of a hydroelectrically powered wireless sensor node for underground environmental monitoring
Full text linkThis study describes a sensor node powered by an energy harvesting method based on the watermill principle. This method is suitable whenever a sensor node has to be deployed in the nearby of an underground water line, such as a drainage system or an aqueduct. The operating scenario for whom this solution has been developed and employed is a wireless sensor network for the monitoring of the environmental conditions of the so-called 'Bottini' in Siena, Italy. The 'Bottini' is a network of medieval aqueducts dug in the underground of the historic centre of the city, in which water still flows nowadays. Using the proposed energy harvesting system the sensor nodes are able to operate independently, minimising the maintenance and allowing the real-time monitoring of environmental parameters, thanks, in order to manage the preservation of this ancient site. The entire system is composed of three parts: the power generation system, the data acquisition system and the wireless transmission system. The whole architecture has been tested in the operating scenario, precisely in 'Fontebranda', one of the biggest fountains in the 'Bottini' network
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