Improving the Reliability of Long-Range Communication against Interference for Non-Line-of-Sight Conditions in Industrial Internet of Things Applications
Abstract
:1. Introduction
- Development and analysis of an open-source prototype of a standard-compliant LoRa Physical Layer (PHY) Software-Defined Radio (SDR) based on GNU Radio.
- Simulation and evaluation of LoRa’s performance in NLoS conditions, in a noisy mobile environment, using key metrics such as BER, SINR, and data rate, demonstrating its high communication reliability for IIoT.
- Exploration and integration of the LoRa protocol into the Universal Software Radio Peripheral (USRP) for IIoT applications, leveraging the versatility of the USRP for communication system development and prototyping to provide a reliable and energy-efficient communication system capable of transmitting and receiving data over varying distances, making it suitable for industrial applications.
- A comprehensive evaluation of LoRa technology combining an open-source LoRa PHY SDR prototype, mobility, and performance metrics analysis to contribute to the knowledge base and help operators, system designers, and researchers make informed decisions about deploying wireless communication systems in industrial applications.
2. Related Works
3. Research Methodology and Procedures
3.1. Technical Specifications and Core Concepts of LoRa Technology
3.2. Building the LoRa Research Environment: GNU Radio Software and Hardware Setup (SDR)
3.2.1. GNU Radio Interface
3.2.2. Hardware Setup (SDR)
4. Experimental and Real-World Implementation
5. Results and Discussion
5.1. SINR Analysis
5.2. BLER Analysis
5.3. Data Rate Analysis
5.4. Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
ADR | Adaptive Data Rate |
BER | Bit Error Rate |
BLER | Block Error Rate |
BW | Bandwidth |
CF | Carrier Frequency |
CR | Coding Rate |
CSMA | Carrier Sense Multiple Access |
GNU | GNU’s Not Unix! |
IoT | Internet of Things |
IIoT | Industrial Internet of Things |
ISM | Industrial, Scientific and Medical |
LoRa | Long Range |
LoS | Line-of-Sight |
LPWAN | Low-Power Wide-Area Network |
NB-IoT | Narrowband IoT |
NLoS | Non-Line-of-sight |
PHY | Physical Layer |
RX | Receiver |
SDR | Software-Defined-Radio |
SF | Spreading Factor |
SINR | Signal-to-Noise-Interference-plus-Noise Ratio |
TP | Transmit Power |
TX | Transmitter |
UAV | Unmanned Aerial Vehicle |
USRP | Universal Software Radio Peripheral |
V2V | Vehicle-to-Vehicle |
WSN | Wireless Sensor Network |
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Study | Focus | Experimental Setup | Major Findings |
---|---|---|---|
[17] | LPWAN performance in urban scenarios | Simulation | Scalability of LPWAN networks with high success rates |
[18] | Alternative frequency bands for LPWAN | Not specified | Path-loss model for extended-range LPWAN communication |
[19] | Wireless tracking system for public transport | Wireless communication between bus stops and central base station | Cost-effective real-time monitoring with minimal power consumption |
[20] | LPWAN communications in vehicular ecosystem | Application of LPWAN technology (LoRa) to vehicular communications | Unprecedented coverage ranges in vehicular ecosystem |
[21] | Experimental Study of LPWAN for mobile IoT applications | Not specified | Impact of mobility on LPWAN performance, need for mobility-aware protocols |
[22] | Coverage of LoRa LPWAN through real-life measurements | Real-life measurements | Impressive communication ranges of over 15 km on land, nearly 30 km on water |
[23] | UAV-based LPWAN system in remote rural environments | Two-tier LPWAN system with UAV base stations | Connectivity augmentation in remote rural areas |
[24] | LoRa-based lane-change decision aid | Vehicle-to-vehicle communication using LoRa | Enhanced driver decision-making during lane changes |
[25] | LPWAN-Based agricultural vehicle tracking | LoRa and NB-IoT technologies for agricultural tracking | Analytical expressions for maximum transmission range based on data path-loss model |
[26] | Carrier Sense mechanism in LoRa networks | Low-cost IoT LoRa framework | Reduction of collisions in both short and long LoRa messages |
[27] | Exploration of LoRa waveform theory | Quantification of orthogonality in LoRa waveforms | Nonorthogonality across various LoRa spreading factors |
[28] | Bounding technique for individual node propagation | Reduction of energy and time for individual node propagation | 73% reduction in energy and time, leading to optimal network configurations |
[29] | Machine Learning for disseminating global configuration | Reinforcement learning for disseminating policies | 147% increase in per-node throughput compared to alternatives |
[30] | Open-source LoRa physical layer prototype on GNU Radio | Real testbed implementation to evaluate the error rate of LoRa for uncoded and coded cases | Testbed error rate performance is within 1 dB of MATLAB simulations |
Our Work | Reliability of LoRa in NLoS conditions | Real-world settings with mobility, LoRa PHY SDR using GNU Radio | Comprehensive assessment of BLER, SINR, and data rate, demonstrating approximately 90.23% reliability |
Parameters | Values | Description |
---|---|---|
Transmission Power (TP) | 4 dBm to 20 dBm | Power levels greater than 17 dBm Can only be used at 0.1 duty cycle due to hardware restrictions. |
Carrier Frequency (CF) | 137 MHz to 1020 MHz | Relays the messages received from LoRa sensor nodes to the central server. |
Spreading Factor (SF) | [SF6..SF12] | lengthens the airtime of the packet. Enhancing sensitivity and range by increasing the SNR. |
Bandwidth (BW) | 7.8 kHz to 500 kHz | Is the transmission band’s frequency width. A lower BW provides higher sensitivity but a lesser data rate. |
Coding Rate (CR) | 4/5, 4/6, 4/7, or 4/8 | Protects against interference bursts. |
Parameter | Value |
---|---|
CR | 4/9 |
SF | 10 |
BW | 200 kHz |
Center Frequency | 169.7 MHz |
Tx Power | 10 mw |
Sample Rate | 240 kHz |
Distance (m) | SINR (dB) | BLER | Data Rate (bps) |
---|---|---|---|
37 | ]40, 35[ | ]0, 0.1[ | ≥1500 |
65 | ]40, 30[ | ]0, 0.1[ | 1500 |
93 | ]30, 25[ | ]0, 0.1[ | ≥1500 |
124 | ]30, 20[ | ]0.1, 0.5[ | 1200 |
173 | ]20, 15[ | ]0.1, 0.5[ | ≤1500 |
204 | ≤15 | ]0.4, 0.5[ | 1000 |
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Share and Cite
Abdallah, B.; Khriji, S.; Chéour, R.; Lahoud, C.; Moessner, K.; Kanoun, O. Improving the Reliability of Long-Range Communication against Interference for Non-Line-of-Sight Conditions in Industrial Internet of Things Applications. Appl. Sci. 2024, 14, 868. https://doi.org/10.3390/app14020868
Abdallah B, Khriji S, Chéour R, Lahoud C, Moessner K, Kanoun O. Improving the Reliability of Long-Range Communication against Interference for Non-Line-of-Sight Conditions in Industrial Internet of Things Applications. Applied Sciences. 2024; 14(2):868. https://doi.org/10.3390/app14020868
Chicago/Turabian StyleAbdallah, Boubaker, Sabrine Khriji, Rym Chéour, Charbel Lahoud, Klaus Moessner, and Olfa Kanoun. 2024. "Improving the Reliability of Long-Range Communication against Interference for Non-Line-of-Sight Conditions in Industrial Internet of Things Applications" Applied Sciences 14, no. 2: 868. https://doi.org/10.3390/app14020868
APA StyleAbdallah, B., Khriji, S., Chéour, R., Lahoud, C., Moessner, K., & Kanoun, O. (2024). Improving the Reliability of Long-Range Communication against Interference for Non-Line-of-Sight Conditions in Industrial Internet of Things Applications. Applied Sciences, 14(2), 868. https://doi.org/10.3390/app14020868