The use of Long Range (LoRa) technology in Internet of Things (IoT) deployments is exponentially increasing, as it allows to form one-hop networks linking tiny nodes to one (or more) gateways and ensuring a low power consumption. In dense networks, predicting the number of supported nodes in relation to their position and the propagation environment is essential to ensure a reliable and stable communication and limit costs. In this paper, after comparing different path loss models based on a field measurement campaign of LoRa Received Signal Strength Indicator (RSSI) values within our University campus, we implement two main modifications to the LoRa Simulator tool, in order to improve its accuracy in the prediction of the number of sustainable nodes, according to the target Data Extraction Rate. By an improved path loss evaluation, and using three gateways, the number of nodes could increase theoretically from about 100 to about 6000. Future work includes the possibility to validate the accuracy of the tool, by designing a dense network operating in real conditions (i.e. large industrial plant, small/medium size city area) and testing its performances.

A novel experimental-based tool for the design of LoRa networks / Spinsante, S.; Gioacchini, L.; Scalise, L.. - (2019), pp. 317-322. ((Intervento presentato al convegno 2nd IEEE International Workshop on Metrology for Industry 4.0 and IoT, MetroInd 4.0 and IoT 2019 tenutosi a Napoli (Italia) nel 4-6 Giugno 2019 [10.1109/METROI4.2019.8792833].

A novel experimental-based tool for the design of LoRa networks

Gioacchini L.;
2019

Abstract

The use of Long Range (LoRa) technology in Internet of Things (IoT) deployments is exponentially increasing, as it allows to form one-hop networks linking tiny nodes to one (or more) gateways and ensuring a low power consumption. In dense networks, predicting the number of supported nodes in relation to their position and the propagation environment is essential to ensure a reliable and stable communication and limit costs. In this paper, after comparing different path loss models based on a field measurement campaign of LoRa Received Signal Strength Indicator (RSSI) values within our University campus, we implement two main modifications to the LoRa Simulator tool, in order to improve its accuracy in the prediction of the number of sustainable nodes, according to the target Data Extraction Rate. By an improved path loss evaluation, and using three gateways, the number of nodes could increase theoretically from about 100 to about 6000. Future work includes the possibility to validate the accuracy of the tool, by designing a dense network operating in real conditions (i.e. large industrial plant, small/medium size city area) and testing its performances.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2945112