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In the automotive field, the introduction of keyless access systems is revolutionizing car entry techniques currently dominated by a physical key. In this context, this paper investigates the possible use of smartphones to create a PEPS (Passive Entry Passive Start) system using the BLE (Bluetooth Low-Energy) Fingerprinting technique that allows, along with a connection to a low-cost BLE micro-controllers network, determining the driver’s position, either inside or outside the vehicle. Several issues have been taken into account to assure the reliability of the proposal; in particular, (i) spatial orientation of each microcontroller-based BLE node which ensures the best performance at 180° and 90° referred to as the BLE scanner and the advertiser, respectively; (ii) data filtering techniques based on Kalman Filter; and (iii) definition of new network topology, resulting from the merger of two standard network topologies. Particular attention has been paid to the selection of the appropriate measurement method capable of assuring the most reliable positioning results by means of the adoption of only six embedded BLE devices. This way, the global accuracy of the system reaches 98.5%, while minimum and maximum accuracy values relative to the individual zones equal, respectively, to 97.3% and 99.4% have been observed, thus confirming the capability of the proposed method of recognizing whether the driver is inside or outside the vehicle.

This Special Issue is focused on recent advances in integrated monitoring and modelling technologies for agriculture and forestry. The selected contributions cover a wide range of topics, including wireless field sensing systems, satellite and UAV remote sensing, ICT and IoT applications for smart farming.

The paper deals with the problem of improving the maximum sample rate of analog-to-digital converters (ADCs) included in low cost wireless sensing nodes. To this aim, the authors propose an efficient acquisition strategy based on the combined use of high-resolution time-basis and compressive sampling. In particular, the high-resolution time-basis is adopted to provide a proper sequence of random sampling instants, and a suitable software procedure, based on compressive sampling approach, is exploited to reconstruct the signal of interest from the acquired samples. Thanks to the proposed strategy, the effective sample rate of the reconstructed signal can be as high as the frequency of the considered time-basis, thus significantly improving the inherent ADC sample rate. Several tests are carried out in simulated and real conditions to assess the performance of the proposed acquisition strategy in terms of reconstruction error. In particular, the results obtained in experimental tests with ADC included in actual 8- and 32-bits microcontrollers highlight the possibility of achieving effective sample rate up to 50 times higher than that of the original ADC sample rate.

Maintenance strategies based on structural health monitoring can provide effective support in the optimization of scheduled repair of existing structures, thus enabling their lifetime to be extended. With specific regard to reinforced concrete (RC) structures, the state of the art seems to still be lacking an efficient and cost-effective technique capable of monitoring material properties continuously over the lifetime of a structure. Current solutions can typically only measure the required mechanical variables in an indirect, but economic, manner, or directly, but expensively. Moreover, most of the proposed solutions can only be implemented by means of manual activation, making the monitoring very inefficient and then poorly supported. This paper proposes a structural health monitoring system based on a wireless sensor network (WSN) that enables the automatic monitoring of a complete structure. The network includes wireless distributed sensors embedded in the structure itself, and follows the monitoring-based maintenance (MBM) approach, with its ABCDE paradigm, namely: accuracy, benefit, compactness, durability, and easiness of operations. The system is structured in a node level and has a network architecture that enables all the node data to converge in a central unit. Human control is completely unnecessary until the periodic evaluation of the collected data. Several tests are conducted in order to characterize the system from a metrological point of view and assess its performance and effectiveness in real RC conditions.