LoRaquatica: Studying Range and Location Estimation using LoRa and IoT in Aquatic Sensing

Radeta, Marko; Ribeiro, Miguel; Vasconcelos, Dinarte; Noronha, Hildegardo; Nunes, Nuno

doi:10.1109/percomworkshops48775.2020.9156088

Cited by 11 publications

(7 citation statements)

References 8 publications

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“…[16], the IoUT applications lie in three realms of scientiic, industrial, and security. Scientiic applications are like life observation life observation seabed [44] or in general tracking objects in sea [43] and monitoring of underwater ecosystem [8]. An interesting scientiic application is in aquariums where in ishes are PIT tagged and the visitors can learn about each specie in real-time when it is within a certain range from the visitor [31].…”

Section: Internet Of Underwater Thingsmentioning

confidence: 99%

“…The IoT has been already a hot topic of research for smart city [45,46,49], smart home [47,54], Internet of vehicles [9,14], agriculture vehicles [11ś13], car maintenance [6], transportation [18], automation in industries [37,41], energy management [29,30,35], healthcare [36], etc. Besides all the above applications which are mainly on land, as IoT approach underwater under the topic of IoUT, it has been being under research works for oil exploration [40], tracking objects in sea [43], underwater vehicle assistance [53,53], monitoring underwater environment [2], life observation on seabed [44], ecosystem monitoring [8], etc. Especially in the last mentioned case of ecological monitoring, IoUT can be used for continues observation of coral reefs which have been being afected the most by global warming for further studies.…”

Section: Introductionmentioning

confidence: 99%

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Underwater IoT Network by Blind MIMO OFDM Transceiver Based on Probabilistic Stone’s Blind Source Separation

Khosravy

Gupta

Dey

et al. 2022

ACM Trans. Sen. Netw.

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Telecommunications systems with Multi-Input Multi-Output (MIMO) structure using Orthogonal Frequency Division Modulation (OFDM) has a great potential of efficient application to a network of Internet of Things (IoT) of a high data rate. When the IoT network is amongst the underwater sensory devices known as the Internet of Underwater Things (IoUT), the electromagnetic wave can not play the role of baseband signal due to rapid fall off inside the water. Thus, Acoustic OFDM is a reliable replacement for conventional OFDM inside the water. A blind structure for MIMO acoustic OFDM using Independent Component Analysis (ICA) brings even further advantages in data rate and energy consumption by avoiding the required pilot and preamble data. This research work presents a blind MIMO Acoustic OFDM blind transceiver for IoUT based on Probabilistic Stone’s Blind Source Separation (PS-BSS). The proposed technique has multiple times lower complexity compared to the ICA-based technique while maintaining a comparable efficiency. As observed in the results carried out one hundred Monte Carlo runs of transmission random data bits, over a highly sparse channel that is the common case of an underwater environment the proposed PS-BSS-based technique dominates the ICA-based one, and as the sparseness of the channel decreases its efficiency is comparable to ICA-based technique. Thus in the case of a high sparse channel, the proposed technique is superior in both aspects of efficiency and complexity while over lower sparseness due to its comparative efficiency it can be hired as an optimum technique fulfilling a fair trade-off between efficiency and complexity.

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Section: Internet Of Underwater Thingsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Underwater IoT Network by Blind MIMO OFDM Transceiver Based on Probabilistic Stone’s Blind Source Separation

Khosravy

Gupta

Dey

et al. 2022

ACM Trans. Sen. Netw.

View full text Add to dashboard Cite

show abstract

“…Monitoring, tracking, and locating objects are applications of LoRaWAN technology in maritime environments. Whether monitoring natural disasters, water quality, or vessels, the technology delivers significant results in cost-effectiveness and real-time collection [ 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 ].…”

Section: Introductionmentioning

confidence: 99%

LoRa Technology Propagation Models for IoT Network Planning in the Amazon Regions

Lima,

Lopes,

Cardoso

et al. 2024

Sensors

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Designing and deploying telecommunications and broadcasting networks in the challenging terrain of the Amazon region pose significant obstacles due to its unique morphological characteristics. Within low-power wide-area networks (LPWANs), this research study introduces a comprehensive approach to modeling large-scale propagation loss channels specific to the LoRaWAN protocol operating at 915 MHz. The objective of this study is to facilitate the planning of Internet of Things (IoT) networks in riverside communities while accounting for the mobility of end nodes. We conducted extensive measurement campaigns along the banks of Universidade Federal do Pará, capturing received signal strength indication (RSSI), signal-to-noise ratio (SNR), and geolocated point data across various spreading factors. We fitted the empirical close-in (CI) and floating intercept (FI) propagation models for uplink path loss prediction and compared them with the Okumura–Hata model. We also present a new model for path loss with dense vegetation. Furthermore, we calculated received packet rate statistics between communication links to assess channel quality for the LoRa physical layer (PHY). Remarkably, both CI and FI models exhibited similar behaviors, with the newly proposed model demonstrating enhanced accuracy in estimating radio loss within densely vegetated scenarios, boasting lower root mean square error (RMSE) values than the Okumura–Hata model, particularly for spreading factor 9 (SF9). The radius coverage threshold, accounting for node mobility, was 945 m. This comprehensive analysis contributes valuable insights for the effective deployment and optimization of LoRa-based IoT networks in the intricate environmental conditions of the Amazon region.

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“…This also translates into a maximum receiver sensitivity of −137 dBm, depending on the exploited transmission parameters like the spreading factor (SF), the bandwidth, and the coding rate (CR). Such a feature resulted in the adoption of LoRaWAN in a myriad of application scenarios, and especially in the harsher ones: in non-line-of-sight (NLOS) conditions [ 1 , 2 ], in industrial contexts [ 3 , 4 ], in underground applications [ 5 , 6 ], in marine and offshore environments [ 7 , 8 ], and in search and rescue procedures in remote areas [ 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

LoRaWAN for Vehicular Networking: Field Tests for Vehicle-to-Roadside Communication

Di Renzone,

Parrino,

Peruzzi

et al. 2024

Sensors

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Vehicular wireless networks are one of the most valuable tools for monitoring platforms in the automotive domain. At the same time, Internet of Things (IoT) solutions are playing a crucial role in the same framework, allowing users to connect to vehicles in order to gather data related to their working cycle. Such tasks can be accomplished by resorting to either cellular or non-cellular wireless technologies. While the former can ensure low latency but require high running costs, the latter can be employed in quasi-real-time applications but definitely reduce costs. To this end, this paper proposes the results of two measurement campaigns aimed at assessing the performance of the long-range wide-area network (LoRaWAN) protocol when it is exploited as an enabling technology to provide vehicles with connectivity. Performances are evaluated in terms of packet loss (PL) and received signal strength indicator (RSSI) in wireless links. The two testing scenarios consisted of a transmitter installed on a motorbike running on an elliptical track and a receiver placed in the centre of the track, and a transmitter installed on the roof of a car and a receiver placed next to a straight road. Several speeds were tested, and all the spreading factors (SFs) foreseen by the protocol were examined, showing that the Doppler effect has a marginal influence on the receiving performance of the technology, and that, on the whole, performance is not significantly affected by the speed. Such results prove the feasibility of LoRaWAN links for vehicular network purposes.

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LoRaquatica: Studying Range and Location Estimation using LoRa and IoT in Aquatic Sensing

Cited by 11 publications

References 8 publications

Underwater IoT Network by Blind MIMO OFDM Transceiver Based on Probabilistic Stone’s Blind Source Separation

Underwater IoT Network by Blind MIMO OFDM Transceiver Based on Probabilistic Stone’s Blind Source Separation

LoRa Technology Propagation Models for IoT Network Planning in the Amazon Regions

LoRaWAN for Vehicular Networking: Field Tests for Vehicle-to-Roadside Communication

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