This paper aims at proposing an augmented sensing method for estimating volumetric water content (VWC) in soil for Internet of Underground Things (IoUT) applications. The system exploits an IoUT sensor node embedding a low-cost, low-precision soil moisture sensor and a long-range wide-area network (LoRaWAN) transceiver sending relative measurements within LoRaWAN packets. The VWC estimation is achieved by means of machine learning (ML) algorithms combining the readings provided by the soil moisture sensor with the received signal strength indicator (RSSI) values measured at the LoRaWAN gateway side during broadcasting. A dataset containing such measurements was especially collected in the laboratory by burying the IoUT sensor node within a plastic case filled with sand, while several VWCs were artificially created by progressively adding water. The adopted ML algorithms are trained and tested using three different techniques for estimating VWC. Firstly, the low-cost, low-precision soil moisture sensor is calibrated by resorting to an ML model exploiting only its raw readings to estimate VWC. Secondly, a virtual VWC sensor is shown, where no real sensor readings are used because only LoRaWAN RSSIs are exploited. Lastly, an augmented VWC sensing method relying on the combination of RSSIs and soil moisture sensor readings is presented. The findings of this paper demonstrate that the augmented sensor outperforms both the virtual sensor and the calibrated real soil moisture sensor. The latter provides a root mean square error (RMSE) of $3.33\%$, a virtual sensor of $8.67\%$, and an augmented sensor of $1.84\%$, which improves down to $1.53\%$ if filtered in post-processing.

In this paper, the WirelessHART communication scheme is investigated. The protocol is at first described, in order to underline the key aspects of its design; then, a simulation tool is presented: it enables analysis that accounts for both protocol and physical layers issues. In particular, interference effects on protocol choices can be easily analyzed. The experimental results, obtained from simulations, highlight improvements of WirelessHART with respect to IEEE 802.15.4 standard, concerning the Packet Error Ratio (PER) degradation of interfering networks.

The performance of modern wireless communication systems are strictly related to the adopted medium access mechanism. An effective one, based on the carrier sense multiple access with collision avoidance technique, is hereinafter investigated. In particular, the performance of a CSMA/CA-based wireless sensor network, designed for industrial monitoring and performing both cyclic polling and acyclic alarm management, are analyzed. Effects of in-channel interference are also studied. The aim of the paper is to gain, from practical experiments, results and guidelines useful for the measurement and optimization of CSMA/CA-based sensor network performance, when employed for industrial monitoring. In such direction, a suitable IEEE 802.15.4 wireless sensors' testbed has been deployed and an high layer protocol for industrial monitoring has been implemented. The results obtained from the experiments carried out are shown.

Coexistence issues between IEEE 802.11b (Wi-Fi) wireless communication networks and IEEE 802.15.4 wireless sensor networks (WSN), operating within the same 2.4 GHz ISM-band, are hereinafter assessed. In particular, meaningful experiments performed through a suitable testbed and a cross-layer measurement approach are presented. Such experiments are executed both at physical layer, through measurements of channel power and signal-to-interference ratio (SIR), and at network/transport layer, by means of packet loss ratio estimations. Different testbed configurations are analyzed, varying characteristics like packet rate, packet size, SIR, network topology and number of wireless nodes. The purpose of the work is to deduce from experiments practical information and guidelines showing how optimizing networks setup and mitigating coexistence problems in some real-life configurations. Practical details about testbed, test environment and performed experiments are provided in the paper.

While commercial drones are nowadays effectively used across many application sectors, they can still pose several security risks and, if tampered, may serve as a tool for illegitimate activities. Therefore, it is important to design methods that facilitate the recognition of malicious drones. This paper shows a contactless vibration measurement method that fulfils this task. Indeed, it is demonstrated that the vibration fingerprint of a specific type of propeller can be extracted with a radar sensor. Building upon such a result, a measurement methodology for decoding the vibration signatures of drones is presented as well. In the second part of the work, by controlling variations of the Pulse Width Modulation (PWM) signal driving the drone motors, it is proved that the proposed radar technique can extract a simple PWM codification. Experimental results validate both the methods showing that, by setting up a fixed rotational speed of the propellers, the vibration of the drone chassis measured by the radar allows to discriminate the type of mounted propellers. On the other hand, the radar is also capable of performing such a discrimination of the propeller type, even in the presence of the encoded PWM signal. As the process requires contactless measurements, it will have minimum impact on the operation of the drone while flying or hovering.

The impact of out-of-channel interference on IEEE 802.15.4 wireless sensor networks (WSNs) performance is here dealt with. In particular, the effect on networks based on the carrier sense multiple access with collision avoidance (CSMA/CA) protocol is investigated. To this aim, a theoretical analysis of the problem is outlined, and results from both simulations and experiments are provided. Measurements are performed both at physical layer, through parameters like in-channel power, and at network layer, through packet error ratio estimates. The purpose is to provide from simulations and experiments a set of helpful information and hints to be used for an efficient measurement and troubleshoot of interference problems caused on IEEE 802.15.4 WSNs by out-of-band signals.