G. Greco

A solution for monitoring operations in harsh environment: A RFID reader for small UAV

RIFD technologies are applied in a various number of environmental monitoring applications. Efficient energy management is one of the most important prerequisites for the realization of such systems and the power consumption of the RFID tag during radio transmissions must be kept low. The proposed system is composed by the RFID tags distributed on the territory and a reader installed on an Unmanned Aerial Vehicle (UAV). The idea is to use the UAV to collect data from the RFID sensors scattered throughout the area by simply approaching them, flying above them, and downloading measured data. This solution can be adopted to implement a grid of independent RFID sensors covering a large area, or to query sensors located in dangerous scenarios for humans. RFID tags are equipped with measuring sensors and store locally the measured parameters; the reader is mounted on the UAV and through an appropriate communication protocols it identifies the tags, downloads the data and sends them to the Ground Control Station (GCS). At the GCS a technician can control the reader through a GUI console: it is possible to start the discovery, download the sensor data (manually or automatically) and clear the RFID tags memory. An ad-hoc mechanisms has also been implemented to join fast tag discovery procedure, fast data downloading and energy saving. The present paper describes the system, presents the testing methodology and analyses some achieved performances in a test scenario.

Localization of RFID tags for environmental monitoring using UAV

The paper presents the experimental implementation of a method to localize RFID tags in an outdoor environment using UAV. During the installation phase, it is possible to measure the coordinates of the installation point using a topographic GNSS receiver. The tags positions can evolve with time and after a specific desired period of time (e. g. 1 month or 1 year) it is necessary to relocate them. This can be done estimating the distance between the tags and a UAV, exploiting the measurements of the Received Signal Strength Indicator (RSSI). The tags are placed over an outdoor test area and a large amount of RSSI measurements are made in different position, well distributed in space, using a UAV equipped with a specific tag reader. On such data, a multilateration-based localization algorithm is applied achieving good results. The description of RFID tags is reported together with the localization algorithm, the test description and the preliminary results.

Extreme rain events analysis using X-band weather radar

The X-band radar installed in Turin was used to analyze extreme events. About 3 years of radar maps have been analyzed in comparisons with about 30 years of measurements made by rain gauges located in the same area. The entire monitored area was divided into 4 subareas considering the complex orography near Turin, namely the flatlands, mountains, hills and urban areas. For each subarea, the Generalized Extreme Values (GEV) distributions are estimated considering rain gauges data and X-band radar maps. Radar maps are properly processed to be comparable with rain gauges measurements considering reference areas of different size centered over each available gauge. It is shown that a limited temporal availability of X-band radar maps can be sufficient to obtain a good GEV distribution estimation, and that X-band weather radars are a good instrument to analyze extreme rain events where a dense rain gauge network is not available.

Real time outdoor localization of buried RFID tags through statistical methods

The problem that we have addressed with the presented study is the absolute localization of buried RFID tags in an outdoor environment where the tag is. This is the scenario that we have envisaged when the RFID sensors are installed inside a moving body like a glacier or a slow landslide. During the tags dislocation it is possible to measure the depth of the installation and to define the absolute coordinate of the installation points using a topographic GNSS receiver. Afterwards the body evolves with time, moves and after a while (e. g. 1 month, 1 year) it is necessary to locate the sensors in order to quantify the absolute movement. To find the new location we have decided to investigate a multilateration-based localization technique. In this paper we describe the approach to develop a low cost system for the reading of the tags and for their localization without the need of expensive radar systems (e. g. GPR) to locate them.

Identification of New Road Segments Using a Modified Version of the K-means Algorithm

The present work explains how to identify transformation and new road segments in existing electronic maps by using data coming from devices carried on board on different vehicles, by using a modified version of a standard clustering algorithm called k-means. The working dataset appears as sparse clouds of points with their centres over the road segments, including other different data. Because of the spatial distribution of the points and in order to allow the algorithm to converge in a short number of steps, a simple modification has been implemented. With respect to the standard k-means algorithm which works with a fixed number of clusters, the present modified version works with a large initial number of clusters, with sizes defined a priori on the basis of the smallest possible road segment. The number of clusters is progressively reduced considering, for each steps, only the clusters including a number of points above a specific thresholds. At the end of the algorithm, all the identified clusters are superimposed over a common map in order to validate if a new road segment is identified. The algorithm has been applied on different datasets acquired on the road network of Turin with good results allowing the identification of new road segments not present in the reference map and one-way roads (change in travel direction).