Abstract
The main aim of this study was to use Empirical mapping to test the efficiency of local low cost wireless network sensors (LPWAN - Low-Power Wide Area Network) before being applied in real wine-growing conditions. The second aim was to obtain information on the communication distances to be expected from a LPWAN, taking into account the specific needs and real conditions of a vineyard. A hand-held autonomous end-device was specifically built to simulate short messages sent by sensors via a locally designed LPWAN. This device was used to test the quality of the network from different locations within an entire vineyard and also inside the cellar. Two parameters were used to test the quality of reception of the messages: i) The Received Signal Strength Indication (RSSI), which is the received signal power measured in decibels (dB or dBm), and ii) the Signal-to-Noise Ratio (SNR), which is the ratio of the received signal power to the ambient noise power. Maps of signal reception and errors between the observed and the theoretical signal highlighted how vineyard environment (e.g., hedges, topography, and buildings) affects the signal. The results show that the maximum communication distance differed considerably from distances published in the literature. In the open field, the signal, although attenuated by the distance, was received up to 600 meters away, or even more in favourable conditions. Meanwhile, in urban areas the signal was attenuated by buildings and the electro-magnetic environment and therefore communication distances were very short (< 50 m). Empirical mapping has great potential for determining how the local environment affects signal quality and as a decision support tool for identifying the optimal location for the sensors and gateway. With a single well-positioned gateway, such low cost wireless sensor networks (LPWAN-LoRa) could be used by small to medium-sized vineyards to collect information from sensors either outside in the fields or indoors in the vineyard cellar. This paper proposes a very cheap method (< 40 €) for testing and spatialising the quality of a low cost wireless sensor network before its implementation, and it also provides information on zones with low quality reception.
Highlights
Many papers have shown the importance of wireless sensor networks (WSN) in agriculture (Subashini and Mathiyalagan, 2016; Jawad et al, 2017; Liu, 2018; Thakur et al, 2019; Farooq et al, 2020)
This study aims at verifying whether the proposed empirical mapping method can reveal: i) the locations with the highest signal reception, ii) the locations where the maximum distance for reception can be expected, iii) the locations where environmental features like hedges, topography and buildings could affect signal propagation, and iv) how the components of a cellar, in conjunction with their relative location, could affect the spatial distribution of signal reception
In order to install a WSN within a whole vineyard, a practitioner would need to overcome the difficulty of predicting the quality of reception in the conditions of the area in question
Summary
Many papers have shown the importance of wireless sensor networks (WSN) in agriculture (Subashini and Mathiyalagan, 2016; Jawad et al, 2017; Liu, 2018; Thakur et al, 2019; Farooq et al, 2020). It can be used in both research projects and as decision support for commercial services. There is potential for using WSNs in cellars, for monitoring winemaking processes (Costa et al, 2007; Anastasi et al, 2009; Chinchamalatpure and Sakhare, 2012). The advantage of installing WSN in pre-existing buildings such as cellars is that it would not entail significant additional costs due to minor renovation work being required and to wireless technologies
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