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LoRa-Based Sensor Networks for the New Frontier of the IoT

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: closed (28 February 2023) | Viewed by 8025

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Dr. Alessandro Pozzebon

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Guest Editor

Department of Information Engineering, University of Padova, 35131 Padova, Italy
Interests: Internet of Things; low power wide area networks; augmented and virtual sensing techniques; embedded ML; distributed measurement systems; wireless sensor networks
Special Issues, Collections and Topics in MDPI journals

Dr. Giacomo Peruzzi

E-Mail Website
Guest Editor

Department of Information Engineering and Mathematics, University of Siena, Siena, Italy
Interests: Internet of Things; Low Power Wide Area Networks; LoRaWAN; Distributed Measurement Systems; Wireless Sensor Networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Long-range (LoRa) modulation and LoRa wide-area network (LoRaWAN) protocol are de-facto key enabling technologies for wireless sensor networks (WSN) and the Internet of Things (IoT). Their robustness, reliability, and pervasive coverage capabilities have been extensively proven and studied for years since their launch on the market. At the same time, the feasibility of setting up network infrastructures, which rely on such facilities, has been successfully shown up in a variety of application scenarios, from industries to Smart Cities, from precision agriculture to vehicular networks, and from harsh environments to satellite contexts.

It is now time to project LoRa-based sensor networks on the new frontier of the IoT by exploiting their pivotal features for setting up brand new paradigms, and for tackling problems from a different perspective as well. This goal can be achieved by pushing the technologies beyond their limits, thus creating new challenges, so as to foster disruptive alternatives to the standard ideas of connectivity within the big picture of the IoT.

This Special Issue aims at accomplishing the aforementioned commitments, so to provide strong advancements in the state-of-the-art, inviting original contributions and survey related, but not limited to, the following topics:

LoRa physical layer;
LoRaWAN protocol;
LoRa-based network protocols enabling edge/fog computing paradigms;
LoRa-based networks for machine learning and artificial intelligence;
LoRa/LoRaWAN architectures for the Internet of Things;
LoRa/LoRaWAN-based sensor networks;
Low Power LoRa/LoRaWAN sensor nodes;
Energy Harvesting techniques for LoRa/LoRaWAN sensor nodes;
Power saving strategies for LoRa/LoRaWAN networks;
LoRa/LoRaWAN architectures for Smart Industries;
LoRa/LoRaWAN architectures for Smart Cities;
LoRa/LoRaWAN architectures for harsh environments;
LoRa/LoRaWAN architectures relying on satellite links;
Environmental monitoring with LoRa/LoRaWAN.

Dr. Alessandro Pozzebon
Dr. Giacomo Peruzzi
Guest Editors

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Published Papers (3 papers)

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Research

22 pages, 2456 KiB

Open AccessArticle

Estimating Volumetric Water Content in Soil for IoUT Contexts by Exploiting RSSI-Based Augmented Sensors via Machine Learning

by Matteo Bertocco, Stefano Parrino, Giacomo Peruzzi and Alessandro Pozzebon

Sensors 2023, 23(4), 2033; https://0-doi-org.brum.beds.ac.uk/10.3390/s23042033 - 10 Feb 2023

Cited by 10 | Viewed by 1850

Abstract

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. Full article

(This article belongs to the Special Issue LoRa-Based Sensor Networks for the New Frontier of the IoT)

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27 pages, 3055 KiB

Open AccessArticle

SF-Partition-Based Clustering and Relaying Scheme for Resolving Near–Far Unfairness in IoT Multihop LoRa Networks

by Dick Mugerwa, Youngju Nam, Hyunseok Choi, Yongje Shin and Euisin Lee

Sensors 2022, 22(23), 9332; https://0-doi-org.brum.beds.ac.uk/10.3390/s22239332 - 30 Nov 2022

Cited by 6 | Viewed by 1509

Abstract

Long range (LoRa) is one of the most successful low-power wide-area networking technologies because it is ideally suited for long-distance, low-bit rate, and low-power communications in the unlicensed sub-GHz spectrum utilized for Internet of things (IoT) networks. The effectiveness of LoRa depends on the link budget (i.e., spreading factor (SF), bandwidth (BW), and transmission power (TX)). Due to the near–far effect, the allocation of a link budget to LoRa devices (LDs) in large coverage regions is unfair between them depending on their distance to the GW. Thus, more transmission opportunities are given to some LDs to the detriment of other LD’s opportunities. Numerous studies have been conducted to address the prevalent near–far fairness problem. Due to the absence of a tractable analytical model for fairness in the LoRa network, however, it is still difficult to solve this problem completely. Thus, we propose an SF-partition-based clustering and relaying (SFPCR) scheme to achieve enormous LD connectivity with fairness in IoT multihop LoRa networks. For the SF partition, the SFPCR scheme determines the suitable partitioning threshold point for bridging packet delivery success probability gaps between SF regions, namely, the lower SF zone (LSFZ) and the higher SF zone (HSFZ). To avoid long-distance transmissions to the GW, the HSFZ constructs a density-based subspace clustering that generates clusters of arbitrary shape for adjacent LDs and selects cluster headers by using a binary score representation. To support reliable data transmissions to the GW by multihop communications, the LSFZ offers a relay LD selection that ideally chooses the best relay LD to extend uplink transmissions from LDs in the HSFZ. Through simulations, we show that the proposed SFPCR scheme exhibits the highest success probability of 65.7%, followed by the FSRC scheme at 44.6%, the mesh scheme at 34.2%, and lastly the cluster-based scheme at 29.4%, and it conserves the energy of LDs compared with the existing schemes. Full article

(This article belongs to the Special Issue LoRa-Based Sensor Networks for the New Frontier of the IoT)

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24 pages, 5933 KiB

Open AccessArticle

LoRa Network-Based System for Monitoring the Agricultural Sector in Andean Areas: Case Study Ecuador

by Edgar Fabián Rivera Guzmán, Edison David Mañay Chochos, Mauricio Danilo Chiliquinga Malliquinga, Paúl Francisco Baldeón Egas and Renato Mauricio Toasa Guachi

Sensors 2022, 22(18), 6743; https://0-doi-org.brum.beds.ac.uk/10.3390/s22186743 - 07 Sep 2022

Cited by 4 | Viewed by 3527

Abstract

This article focuses on the development of a system based on the long-range network (LoRa), which is used for monitoring the agricultural sector and is implemented in areas of the Andean region of Ecuador. The LoRa network is applied for the analysis of climatic parameters by monitoring temperature, relative humidity, soil moisture and ultraviolet radiation. It consists of two transmitter nodes and one receiver node, a LoRa Gateway with two communication channels for data reception and one for data transmission, and an IoT server. In addition, a graphical user interface has been developed in Thinger.io to monitor the crops and remotely control the actuators. The research conducted contains useful information for the deployment of a LoRa network in agricultural crops located in mountainous areas above 2910 m.a.s.l., where there are terrains with irregular orography, reaching a coverage of 50 hectares and a range distance of 875 m to the farthest point in the community of Chirinche Bajo, Ecuador. An average RSSI of the radio link of −122 dBm was obtained in areas with a 15% slope and 130 m difference in height according to the Gateway, where the presence of vegetation, eucalyptus trees and no line-of-sight generated interference to the radio signal. The success rate of PDR packet delivery with an SF of nine, had a better performance, with values of no less than 76% and 92% in uplink and downlink respectively. Finally, the technological gap is reduced, since the network reaches places where traditional technologies do not exist, allowing farmers to make timely decisions in the production process in the face of adverse weather events. Full article

(This article belongs to the Special Issue LoRa-Based Sensor Networks for the New Frontier of the IoT)

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