Air quality dataset from an indoor airport travelers transit area.

The experimental dataset (organized in a semicolon-separated text format) is composed by air quality records collected over a 1-year period (October 2022-October 2023) in an indoor travelers' transit area in the Brindisi airport, Italy. In detail, the dataset consists of three CSV files (ranging from 7M records to 11M records) resulting from the on-field data collection performed by three prototypical Internet of Things (IoT) sensing nodes, designed and implemented at the IoTLab of the University of Parma, Italy, featuring a Raspberry Pi 4 (as processing unit) which three low-cost commercial sensors (namely: Adafruit MiCS5524, Sensirion SCD30, Sensirion SPS30) are connected to. The sensors sample the air in the monitored static indoor environment every 2 s. Each collected record composing the experimental dataset contains (i) the identifier of the IoT node that sampled the air parameters; (ii) the presence of gases (as a unified value concentration); (iii) the concentration of carbon dioxide (CO2) in the travelers' transit area, together with air temperature and humidity; and (iv) the concentration of particulate matter (PM) in the indoor monitored environment - in terms of particles' mass concentration (µg/m3), number of particles (#/cm3), and typical particle size (µm) - for particles with a diameter up to 0.5 µm (PM0.5), 1 µm (PM1), 2.5 µm (PM2.5), 4 µm (PM4), and 10 µm (PM10). Therefore, on the basis of the monitored air parameters in the indoor travelers' transit area, the experimental dataset might be expedient for further analyses - e.g., for calculating Air Quality Indexes (AQIs) taking into account the collected information - and for comparison with information sampled in different contexts and scenarios - examples could be indoor domestic environments, as well as outdoor monitoring in smart cities or public transports.

Forecasting Air Temperature on Edge Devices with Embedded AI.

With the advent of the Smart Agriculture, the joint utilization of Internet of Things (IoT) and Machine Learning (ML) holds the promise to significantly improve agricultural production and sustainability. In this paper, the design of a Neural Network (NN)-based prediction model of a greenhouse's internal air temperature, to be deployed and run on an edge device with constrained capabilities, is investigated. The model relies on a time series-oriented approach, taking as input variables the past and present values of the air temperature to forecast the future ones. In detail, we evaluate three different NN architecture types-namely, Long Short-Term Memory (LSTM) networks, Recurrent NNs (RNNs) and Artificial NNs (ANNs)-with various values of the sliding window associated with input data. Experimental results show that the three best-performing models have a Root Mean Squared Error (RMSE) value in the range 0.289÷0.402∘C, a Mean Absolute Percentage Error (MAPE) in the range of 0.87÷1.04%, and a coefficient of determination (R2) not smaller than 0.997. The overall best performing model, based on an ANN, has a good prediction performance together with low computational and architectural complexities (evaluated on the basis of the NetScore metric), making its deployment on an edge device feasible.

Ultrasonic-Based Environmental Perception for Mobile 5G-Oriented XR Applications.

One of the sectors that is expected to significantly benefit from 5G network deployment is eXtended Reality (XR). Besides the very high bandwidth, reliability, and Quality of Service (QoS) to be delivered to end users, XR also requires accurate environmental perception for safety reasons: this is fundamental when a user, wearing XR equipment, is immersed in a "virtual" world, but moves in a "real" environment. To overcome this limitation (especially when using low-cost XR equipments, such as cardboards worn by the end user), it is possible to exploit the potentialities offered by Internet of Things (IoT) nodes with sensing/actuating capabilities. In this paper, we rely on ultrasonic sensor-based IoT systems to perceive the surrounding environment and to provide "side information" to XR systems, then performing a preliminary experimental characterization campaign with different ultrasonic IoT system configurations worn by the end user. The combination of the information flows associated with XR and IoT components is enabled by 5G technology. An illustrative experimental scenario, relative to a "Tourism 4.0" IoT-aided VR application deployed by Vodafone in Milan, Italy, is presented.

LoRaFarM: A LoRaWAN-Based Smart Farming Modular IoT Architecture.

Presently, the adoption of Internet of Things (IoT)-related technologies in the Smart Farming domain is rapidly emerging. The ultimate goal is to collect, monitor, and effectively employ relevant data for agricultural processes, with the purpose of achieving an optimized and more environmentally sustainable agriculture. In this paper, a low-cost, modular, and Long-Range Wide-Area Network (LoRaWAN)-based IoT platform, denoted as "LoRaWAN-based Smart Farming Modular IoT Architecture" (LoRaFarM), and aimed at improving the management of generic farms in a highly customizable way, is presented. The platform, built around a core middleware, is easily extensible with ad-hoc low-level modules (feeding the middleware with data coming from the sensors deployed in the farm) or high-level modules (providing advanced functionalities to the farmer). The proposed platform has been evaluated in a real farm in Italy, collecting environmental data (air/soil temperature and humidity) related to the growth of farm products (namely grapes and greenhouse vegetables) over a period of three months. A web-based visualization tool for the collected data is also presented, to validate the LoRaFarM architecture.