Analysis of the Reliability of LoRa

qadir, qahhar muhammad

doi:10.1109/lcomm.2020.3034865

Cited by 21 publications

(6 citation statements)

References 15 publications

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“…In fact, as LoRaWAN operates in licence-free frequency bands (as further detailed in Subsection 2.1), coexistence challenges with other well-known radio standards (e.g., Zigbee, Bluetooth, etc.) in these frequency bands might emerge, leading to interference problems [17,18]. More in detail, a "background noise" may appear, due to the presence of devices exchanging data in parallel via heterogeneous radio communication protocols, as well as vehicles emitting electromagnetic waves through their electronic boards (e.g., in urban and sub-urban contexts).…”

Section: Overview On Lorawan and Data Representationmentioning

confidence: 99%

LoRa meets IP: a Container-based Architecture to Virtualize LoRaWAN End Nodes

Cilfone,

Davoli,

Ferrari

2024

IEEE Trans. on Mobile Comput.

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In this work, a container-based architecture for the integration of Long Range Wide Area Network (LoRaWAN) end nodes-e.g., used to monitor industrial machines or mobile entities in specific environments-with Internet Protocol (IP)-based networks is proposed and its performance is investigated. To this end, we exploit the native service and resource discovery support of the Constrained Application Protocol (CoAP), as well as its light traffic requirements, owing to its use of User Datagram Protocol (UDP) rather than Transmission Control Protocol (TCP). This approach (i) adapts transparently (with no impact) to both private and public LoRaWAN networks, (ii) enables seamless interaction between LoRaWAN-based and CoAP-based nodes, through a logical "virtualization" of LoRaWAN nodes at server side, and (iii) enables routing among LoRaWAN end nodes, overcoming LoRaWAN's absence of inter-node communication and lack of compliance (at the end nodes' side) with IP. Two virtualization approaches are proposed: (i) virtualization of a single end node (represented as a CoAP server) per container and (ii) virtualization of multiple end nodes (as CoAP servers) per container. Finally, deployments of the proposed virtualization architectures, using both a laptop and an Internet of Things (IoT) device (e.g., a Raspberry Pi), are considered, highlighting how the best solution relies on the use of several containers, with more than one CoAP server per container.

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Section: Overview On Lorawan and Data Representationmentioning

confidence: 99%

LoRa meets IP: a Container-based Architecture to Virtualize LoRaWAN End Nodes

Cilfone,

Davoli,

Ferrari

2024

IEEE Trans. on Mobile Comput.

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“…Following the limitation of the Okumura-Hata model, which is applicable to land mobile services with a frequency range of 100-1500 MHz, distances of 1-20 km, and base station height of 30-200 m, Petajajarvi et al obtained the maximum range for different transmitted powers with a constant variable height from the Okumura-Hata model [22]. Furthermore, the received power obtained was as low as -120 dBm for the two transceivers, resulting in a lower signal translation.…”

Section: Lora and Radio Modulationmentioning

confidence: 99%

“…Where 𝐿 , the path loss, which is a combination of 𝑃 , 𝐺 , 𝐺 , and 𝑃 , which are the antenna transmission power, receiver power gain, transmitter power gain, and RSSI, respectively. The path loss with wavelength or frequency is denoted by (22).…”

Section: Evaluation Of Lora Performancementioning

confidence: 99%

Evaluation of LoRa Network Performance for Water Quality Monitoring Systems

Syed Taha,

Abu Talip,

Mohamad

et al. 2024

Preprint

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A smart water quality monitoring system based on the Internet of Things (IoT) paradigm was designed to protect water resources from pollution in real-time. Long-range (LoRa) application of the low-power, wide-area networking (LPWAN) concept has become a phenomenon in IoT smart monitoring applications. This study proposes the implementation of a LoRa network in a water quality monitoring system-based IoT approach. The LoRa nodes were embedded with measuring sensors pH, turbidity, temperature, total dissolved solids (TDS), and dissolved oxygen (DO), in the designated water station. It operates at a transmission power of 14 dB and a bandwidth of 125 kHz. The network properties were tested with two different antenna gains of 2.1 dBi and 3 dBi with three different spread factors (SF) of 7, 9, and 12. The water stations were located at Sungai Pantai and Sungai Anak Air Batu, both rivers on the Universiti Malaya campus. Following a dashboard display and K-means analysis of the water quality data received by the LoRa gateway, it was determined that both rivers are Class II B rivers. The results from the evaluation of LoRa performance on the Received Strength Signal Indicator (RSSI), Signal Noise Ratio (SNR), loss packet, and path loss at best were -83 dBm, 7 dB, &lt; 0%, and 64.41 dB, respectively, with a minimum received sensitivity of -129.1 dBm. LoRa has demonstrated its efficiency in an urban environment for smart river monitoring purposes.

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“…Thus, denoising techniques are worth investigating to extract useful information from the noisy signal. Besides, the ALOHA protocol used in LoRaWAN generates collisions and packet losses when two or more devices transmit using the same spreading factor (SF) and frequency band at the same time [1].…”

Section: Introductionmentioning

confidence: 99%

“…M. Tesfay 1,2 , S. Kharbech 2,3 , E.-P. Simon 1 , and L. Clavier 1,2 are with 1 Univ. Lille, CNRS, Centrale Lille, UMR 8520 -IEMN, 59000 Lille, France (e-mail: 1 firstname.name@univ-lille.fr), 2 IMT Nord Europe, Centre for Digital Systems, 59000 Lille, France (email: 2 firstname.name@imt-nordeurope.fr), and 3 Univ.…”

Section: Introductionmentioning

confidence: 99%

Signal Denoising and Detection for Uplink in LoRa Networks Based on Bayesian-Optimized Deep Neural Networks

Tesfay

Kharbech²,

Simon

et al. 2023

IEEE Commun. Lett.

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Long-range and low-power communications are suitable technologies for the Internet of things networks. The long-range implies a very low signal-to-noise ratio at the receiver. In addition, low power consumption requires reduced signaling, hence the use of less complex protocols, such as ALOHA, so reduced communication coordination. Therefore, the increase of objects using this technology will automatically lead to an increase in interference. In this paper, we propose a detector for Long Range (LoRa) networks based on an autoencoder for denoising and dealing with the interference, followed by a convolutional neural network for symbol detection. Simulation results demonstrate that the proposed approach outperforms both the convolutional neural network-based detector and the classical LoRa detector in the presence of interference from other LoRa users. The proposed detector shows around 3 dB gain for a target Symbol Error Rate (SER) of 10 −4 .

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Analysis of the Reliability of LoRa

Cited by 21 publications

References 15 publications

LoRa meets IP: a Container-based Architecture to Virtualize LoRaWAN End Nodes

LoRa meets IP: a Container-based Architecture to Virtualize LoRaWAN End Nodes

Evaluation of LoRa Network Performance for Water Quality Monitoring Systems

Signal Denoising and Detection for Uplink in LoRa Networks Based on Bayesian-Optimized Deep Neural Networks

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