An IoT-Aware Smart System Exploiting the Electromagnetic Behavior of UHF-RFID Tags to Improve Worker Safety in Outdoor Environments

Recently, different solutions leveraging Internet of Things (IoT) technologies have been adopted to avoid accidents in agricultural working environments. As an example, heavy vehicles, e.g., tractors or excavators, have been upgraded with remote controls. Nonetheless, the community continues to encourage discussions on safety issues. In this framework, a localization system installed on remote-controlled farm machines (RCFM) can help in preventing fatal accidents and reduce collision risks. This paper presents an innovative system that exploits passive UHF-RFID technology supported by commercial BLE Beacons for monitoring and preventing accidents that may occur when ground-workers in RCFM collaborate in outdoor agricultural working areas. To this aim, a modular architecture is proposed to locate workers, obstacles and machines and guarantees the security of RCFM movements by using specific notifications for ground-workers prompt interventions. Its main characteristics are presented with its main positioning features based on passive UHF-RFID technology. An experimental campaign discusses its performance and determines the best configuration of the UHF-RFID tags installed on workers and obstacles. Finally, system validation demonstrates the reliability of the main components and the usefulness of the proposed architecture for worker safety

Monitoring raw cork TCA content in Sardinian woodlands

The studies on stopper contamination by TCA have focused on manufacturing phase and on relations between the wine and the cork. Less numerous are the forest and environmental monitoring research useful to evaluate whether different management models of the cork stands may have an influence on the process

Ventilation and outcomes following robotic-assisted abdominal surgery: an international, multicentre observational study

Background: International data on the epidemiology, ventilation practice, and outcomes in patients undergoing abdominal robotic-assisted surgery (RAS) are lacking. The aim of the study was to assess the incidence of postoperative pulmonary complications (PPCs), and to describe ventilator management after abdominal RAS. Methods: This was an international, multicentre, prospective study in 34 centres in nine countries. Patients ≥18 yr of age undergoing abdominal RAS were enrolled between April 2017 and March 2019. The Assess Respiratory Risk in Surgical Patients in Catalonia (ARISCAT) score was used to stratify for higher risk of PPCs (≥26). The primary outcome was the incidence of PPCs. Secondary endpoints included the preoperative risk for PPCs and ventilator management. Results: Of 1167 subjects screened, 905 abdominal RAS patients were included. Overall, 590 (65.2%) patients were at increased risk for PPCs. Meanwhile, 172 (19%) patients sustained PPCs, which occurred more frequently in 132 (22.4%) patients at increased risk, compared with 40 (12.7%) patients at lower risk of PPCs (absolute risk difference: 12.2% [95% confidence intervals (CI), 6.8–17.6%]; P<0.001). Plateau and driving pressures were higher in patients at increased risk, compared with patients at low risk of PPCs, but no ventilatory variables were independently associated with increased occurrence of PPCs. Development of PPCs was associated with a longer hospital stay. Conclusions: One in five patients developed one or more PPCs (chiefly unplanned oxygen requirement), which was associated with a longer hospital stay. No ventilatory variables were independently associated with PPCs. Clinical trial registration: NCT02989415

Localization of a mobile device equipped with an RFID reader

This paper presents a new technique to locate a device moving along a known trajectory, when it is equipped with an UHF RFID reader. The proposed technique exploits the phase of consecutive readings of a single stationary tag positioned near the device trajectory, within the reader coverage area. To increase the length of the monitored path where the reader can be effectively localized, the localization algorithm can be easily extended to process readings acquired from multiple tags aligned along the mobile running trajectory. A preliminary measurement campaign showed that a decimeter order accuracy can be achieved by employing a few tag readings

A Survey on Indoor Vehicle Localization through RFID Technology

This paper presents a state-of-the-art analysis on the methods suitable for vehicle indoor localization and exploiting the RFID (Radio Frequency IDentification) technology. The survey describes three main categories of vehicle localization systems: (i) solutions exploiting only the RFID technology, (ii) sensor-fusion techniques combining data from RFID systems and proprioceptive sensors, and (iii) sensor-fusion techniques combing RFID data with those of other exteroceptive sensors in addition to the RFID system itself. For each method, implementation and methodological details are discussed, by highlighting the applied RFID technology, namely passive HF-RFID, passive UHF-RFID, or any other RFID system. Also, the employed RFID parameters, i.e., tag EPC, RSSI or backscattered phase, are discussed. The survey focuses on the achievable localization performance, also accounting for infrastructure-deployment costs together with complexity and maintenance overhead. Positioning, tracking, navigation and simultaneous localization and mapping (SLAM) issues are here considered. The analysis highlights pros and cons of each method, together with the main challenges and perspectives of RFID-based solutions for vehicle localization

Forklift tracking: Industry 4.0 implementation in large-scale warehouses through uwb sensor fusion

This article addresses the problem of determining the location of pallets carried by forklifts inside a warehouse, which are recognized thanks to an onboard Radio Frequency IDentification (RFID) system at the ultra-high-frequency (UHF) band. By reconstructing the forklift trajectory and orientation, the location of the pallets can be associated with the forklift position at the time of unloading events. The localization task is accomplished by means of an easy-to-deploy combination of onboard sensors, i.e., an inertial measurement unit (IMU) and an optical flow sensor (OFS), with a commercial ultra-wideband (UWB) system through an Unscented Kalman Filter (UKF) algorithm, which estimates the forklift pose over time. The proposed sensor fusion approach contributes to the localization error mitigation by preventing drifts in the trajectory reconstruction. The designed methos was at first evaluated by means of a simulation framework and then through an experimental analysis conducted in a large warehouse with a size of about 4000 m2