Adaptive Parameters for LoRa-Based Networks Physical-Layer
Abstract
:1. Introduction
- Verify the possibility of changing the configuration ’parameters’ in LoRa on the fly to increase the network performance in terms of SNR and BER;
- Propose and validate a novel algorithm with adaptive parameters considering the history of configuration shifts, creating a smarter cognitive-radio for LoRa;
- Compare the state of the art with the proposal using a real dataset.
2. Related Work
3. Proposal
3.1. Network and System Model
3.2. InstantChange: A Simple Instant Automatic Parameter Changing
Algorithm 1: Cognitive LoRa for two (or more) frequencies. Proposed by [14]. |
/*Range of frequencies evaluated. */
//Range of spread spectrum evaluated.
//Range of bandwidth evaluated. 125/250 kHZ for EU433 or 125/500 kHz for AU915 [31]. |
3.3. SlidingChange: A Proposal of a Sliding Window for Automatic Parameter Changing
Algorithm 2: Moving average in cognitive LoRa. |
Algorithm 3: Sliding window of length equals to . |
Algorithm 4: Moving average values for windows’ length equals to . |
4. Evaluation
4.1. Prototype
4.2. Mobile App for Command and Control
4.3. Results
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Acknowledgments
Conflicts of Interest
References
- Pasolini, G.; Buratti, C.; Feltrin, L.; Zabini, F.; De Castro, C.; Verdone, R.; Andrisano, O. Smart city pilot projects using LoRa and IEEE802.15.4 technologies. Sensors 2018, 18, 1118. [Google Scholar] [CrossRef] [PubMed]
- Farooq, M.U.; Waseem, M.; Mazhar, S.; Khairi, A.; Kamal, T. A Review on Internet of Things (IoT). Int. J. Comput. Appl. 2015, 113, 1–7. [Google Scholar]
- Varsier, N.; Schwoerer, J. Capacity limits of LoRaWAN technology for smart metering applications. In Proceedings of the 2017 IEEE International Conference on Communications (ICC), Paris, France, 21–25 May 2017; IEEE: New York, NY, USA, 2017; pp. 1–6. [Google Scholar] [CrossRef]
- Centenaro, M.; Vangelista, L.; Zanella, A.; Zorzi, M. Long-range communications in unlicensed bands: The rising stars in the IoT and smart city scenarios. IEEE Wirel. Commun. 2016, 23, 60–67. [Google Scholar] [CrossRef]
- Bianchi, G.; Cuomo, F.; Garlisi, D.; Tinnirello, I. Sequential Waterfilling for Adaptive Data Rate allocation in LoraWAN. arXiv 2019, arXiv:1907.12360. [Google Scholar]
- Codeluppi, G.; Cilfone, A.; Davoli, L.; Ferrari, G. LoRaFarM: A LoRaWAN-Based Smart Farming Modular IoT Architecture. Sensors 2020, 20, 2028. [Google Scholar] [CrossRef]
- Miles, B.; Bourennane, E.B.; Boucherkha, S.; Chikhi, S. A study of LoRaWAN protocol performance for IoT applications in smart agriculture. Comput. Commun. 2020, 164, 148–157. [Google Scholar] [CrossRef]
- Ramli, M.R.; Daely, P.T.; Kim, D.S.; Lee, J.M. IoT-based adaptive network mechanism for reliable smart farm system. Comput. Electron. Agric. 2020, 170, 105287. [Google Scholar] [CrossRef]
- O Sales, F.; Marante, Y.; Vieira, A.B.; Silva, E.F. Energy Consumption Evaluation of a Routing Protocol for Low-Power and Lossy Networks in Mesh Scenarios for Precision Agriculture. Sensors 2020, 20, 3814. [Google Scholar] [CrossRef] [PubMed]
- Wang, H.; Fapojuwo, A.O. A Survey of Enabling Technologies of Low Power and Long Range Machine-to-Machine Communications. IEEE Commun. Surv. Tutor. 2017, 19, 2621–2639. [Google Scholar] [CrossRef]
- Lima, E.; Moraes, J.; Oliveira, H.; Cerqueira, E.; Zeadally, S.; Rosário, D. Adaptive priority-aware LoRaWAN resource allocation for Internet of Things applications. Ad Hoc Netw. 2021, 122, 102598. [Google Scholar] [CrossRef]
- Silva, F.S.D.; Neto, E.P.; Oliveira, H.; Rosário, D.; Cerqueira, E.; Both, C.; Zeadally, S.; Neto, A.V. A Survey on Long-Range Wide-Area Network Technology Optimizations. IEEE Access 2021, 9, 106079–106106. [Google Scholar] [CrossRef]
- Alliance, L. White Paper: A Technical Overview of LoRa and LoRaWAN; The LoRa Alliance: San Ramon, CA, USA, 2015; pp. 7–11. [Google Scholar]
- Figueiredo, L.M.; Franco Silva, E. Cognitive-LoRa: Adaptation-aware of the physical layer in LoRa-based networks. In Proceedings of the 2020 IEEE Symposium on Computers and Communications (ISCC), Rennes, France, 7–10 July 2020; pp. 1–6. [Google Scholar] [CrossRef]
- Mitola, J.; Maguire, G.Q. Cognitive radio: Making software radios more personal. IEEE Pers. Commun. 1999, 6, 13–18. [Google Scholar] [CrossRef]
- Yucek, T.; Arslan, H. A survey of spectrum sensing algorithms for cognitive radio applications. IEEE Commun. Surv. Tutor. 2009, 11, 116–130. [Google Scholar] [CrossRef]
- Abdelfadeel, K.Q.; Cionca, V.; Pesch, D. Fair Adaptive Data Rate Allocation and Power Control in LoRaWAN. In Proceedings of the 2018 IEEE 19th International Symposium on “A World of Wireless, Mobile and Multimedia Networks” (WoWMoM), Chania, Greece, 12–15 June 2018; pp. 14–15. [Google Scholar] [CrossRef]
- Farhad, A.; Kim, D.H.; Pyun, J.Y. R-arm: Retransmission-assisted resource management in lorawan for the internet of things. IEEE Internet Things J. 2021, 9, 7347–7361. [Google Scholar] [CrossRef]
- Bor, M.C.; Roedig, U.; Voigt, T.; Alonso, J.M. Do LoRa low-power wide-area networks scale? In Proceedings of the 19th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems, 13–17 November 2016; pp. 59–67. [Google Scholar] [CrossRef]
- Goldoni, E.; Savazzi, P.; Favalli, L.; Vizziello, A. Correlation between weather and signal strength in LoRaWAN networks: An extensive dataset. Comput. Netw. 2022, 202, 108627. [Google Scholar] [CrossRef]
- Reynders, B.; Meert, W.; Pollin, S. Power and spreading factor control in low power wide area networks. In Proceedings of the 2017 IEEE International Conference on Communications (ICC), Paris, France, 21–25 May 2017; pp. 1–6. [Google Scholar] [CrossRef]
- Valach, A.; Macko, D. Improvement of LoRa Communication Scalability using Machine Learning Based Adaptiveness. 2021. [Google Scholar] [CrossRef]
- Moraes, J.; Oliveira, H.; Cerqueira, E.; Both, C.; Zeadally, S.; Rosário, D. Evaluation of an Adaptive Resource Allocation for LoRaWAN. J. Signal Process. Syst. 2021, 1–15. [Google Scholar] [CrossRef]
- Slabicki, M.; Premsankar, G.; Di Francesco, M. Adaptive configuration of LoRa networks for dense IoT deployments. In Proceedings of the NOMS 2018-2018 IEEE/IFIP Network Operations and Management Symposium, Taipei, Taiwan, 23–27 April 2018; IEEE: New York, NY, USA, 2018; pp. 1–9. [Google Scholar]
- Jeon, W.S.; Jeong, D.G. Adaptive Uplink Rate Control for Confirmed Class A Transmission in LoRa Networks. IEEE Internet Things J. 2020, 7, 10361–10374. [Google Scholar] [CrossRef]
- Farhad, A.; Kim, D.H.; Subedi, S.; Pyun, J.Y. Enhanced lorawan adaptive data rate for mobile internet of things devices. Sensors 2020, 20, 6466. [Google Scholar] [CrossRef]
- Anedda, M.; Desogus, C.; Murroni, M.; Giusto, D.D.; Muntean, G.M. An energy-efficient solution for multi-hop communications in low power wide area networks. In Proceedings of the 2018 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB), Valencia, Spain, 6–8 June 2018; IEEE: New York, NY, USA, 2018; pp. 1–5. [Google Scholar]
- Moysiadis, V.; Lagkas, T.; Argyriou, V.; Sarigiannidis, A.; Moscholios, I.D.; Sarigiannidis, P. Extending ADR mechanism for LoRa enabled mobile end-devices. Simul. Model. Pract. Theory 2021, 113, 102388. [Google Scholar] [CrossRef]
- Perahia, E.; Stacey, R. Next Generation Wireless LANs: 802.11 n and 802.11 ac; Cambridge University Press: Cambridge, UK, 2013. [Google Scholar]
- Semtech. LoRa® Modulation Basics. Semtech Corporation, 2nd ed.; AN1200.22; 2015. [Google Scholar]
- Alliance, L. RP002–1.0. 1 LoRaWAN Regional Parameters. 2020. [Google Scholar]
- Faber, M.J.; van der Zwaag, K.M.; dos Santos, W.G.V.; Rocha, H.R.D.O.; Segatto, M.E.V.; Silva, J.A.L. A Theoretical and Experimental Evaluation on the Performance of LoRa Technology. IEEE Sens. J. 2020, 20, 9480–9489. [Google Scholar] [CrossRef]
- Afroz, F.; Subramanian, R.; Heidary, R.; Sandrasegaran, K.; Ahmed, S. SINR, RSRP, RSSI and RSRQ measurements in long term evolution networks. Int. J. Wirel. Mob. Netw. 2015, 7, 113–123. [Google Scholar] [CrossRef]
- Abuarqoub, A.; Abusaimeh, H.; Hammoudeh, M.; Uliyan, M.; Abu-Hashem, M.; Murad, S.; Al-Jarrah, M.; Alfayez, F. A Survey on Internet of Things Enabled Smart Campus Applications. 2017; 1–7. [Google Scholar] [CrossRef]
- Al-Sarawi, S.; Anbar, M.; Abdullah, R.; Al Hawari, A.B. Internet of Things Market Analysis Forecasts, 2020–2030. In Proceedings of the 2020 Fourth World Conference on Smart Trends in Systems, Security and Sustainability (WorldS4), London, UK, 27–28 July 2020; pp. 449–453. [Google Scholar] [CrossRef]
LoRa Improvement Proposal | Year | Optimization Goal | Cognitive | LoRa Modulation Parameters | |
---|---|---|---|---|---|
Real-Time Settings | SF | BW | |||
Bor et al. [19] | 2016 | Maximize the transmission range and energy-saving | X | X | |
Bianchi et al. [5] | 2018 | Address the unfair LoRaWAN characteristic | X | ||
Valach and Macko [22] | 2019 | Reduce collisions | |||
Lima et al. [11] | 2017 | Reduce collisions | |||
Moraes et al. [23] | 2019 | Maximize QoS | X | ||
Reynders et al. [21] | 2018 | PDR and throughput | X | ||
Abdelfadeel et al. [17] | 2018 | Improve LoRaWAN capacity and reduce collisions | X | X | |
Jeon and Jeong [25] | 2020 | Maximize the channel utilization and reduce collisions | X | ||
Ramli et al. [8] | 2020 | Improve the noise resilience and PDR | X | ||
Slabicki et al. [24] | 2018 | SNR smoothing | X | ||
Moysiadis et al. [28] | 2021 | Improve packet-delivered | X | ||
Farhad et al. [26] | 2020 | Improve scalability, maximize SNR | X | ||
Figueiredo and Franco Silva [14] | 2020 | Improve scalability, maximize SNR and BER | X | X | X |
Proposed mechanism | 2023 | Improve scalability, maximize SNR and BER | X | X | X |
Bandwidth (kHz) | ||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
125 | 250 | 500 | ||||||||||||
CR | ||||||||||||||
4/5 | 4/6 | 4/7 | 4/8 | 4/5 | 4/6 | 4/7 | 4/8 | 4/5 | 4/6 | 4/7 | 4/8 | |||
SF | 7 | 5.5 | 4.6 | 3.9 | 3.4 | 10.9 | 9.1 | 7.8 | 6.8 | 21.9 | 18.2 | 15.6 | 13.7 | kbps |
8 | 3.1 | 2.6 | 2.2 | 2.0 | 6.3 | 5.2 | 4.5 | 3.9 | 12.5 | 10.4 | 8.9 | 7.8 | ||
9 | 1.8 | 1.5 | 1.3 | 1.1 | 3.5 | 2.9 | 2.5 | 2.2 | 7.0 | 5.9 | 5.0 | 4.4 | ||
10 | 1.0 | 0.8 | 0.7 | 0.6 | 2.0 | 1.6 | 1.4 | 1.2 | 3.9 | 3.3 | 2.8 | 2.4 | ||
11 | 0.5 | 0.4 | 0.4 | 0.3 | 1.1 | 0.9 | 0.8 | 0.7 | 2.1 | 1.8 | 1.5 | 1.3 | ||
12 | 0.3 | 0.2 | 0.2 | 0.2 | 0.6 | 0.5 | 0.4 | 0.4 | 1.2 | 1.0 | 0.8 | 0.7 |
Window | Experiment | BW | Average | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
125 kHz | 250 kHz | 500 kHz | |||||||||
SF | SF | SF | |||||||||
10 | 11 | 12 | 10 | 11 | 12 | 10 | 11 | 12 | |||
LR-ADR [28] | 1 | 4.01% | 3.22% | 4.14% | 3.10% | 4.37% | 2.92% | 2.98% | 2.21% | 3.78% | 3.05% |
2 | 3.92% | 2.75% | 3.33% | 3.34% | 2.54% | 2.88% | 2.34% | 2.02% | 2.09% | ||
3 | 4.03% | 4.04% | 3.17% | 3.16% | 2.43% | 2.88% | 2.03% | 2.45% | 2.13% | ||
Windowless | 1 | 4.03% | 2.91% | 4.74% | 4.54% | 4.77% | 3.72% | 2.98% | 2.31% | 3.88% | 3.17% |
2 | 4.01% | 2.50% | 3.60% | 3.80% | 2.56% | 2.98% | 2.44% | 2.19% | 2.49% | ||
3 | 3.81% | 2.45% | 3.53% | 3.76% | 2.23% | 2.92% | 2.03% | 2.10% | 2.37% | ||
w = 10 | 1 | 5.88% | 9.79% | 7.85% | 5.90% | 3.33% | 5.29% | 9.33% | 3.31% | 5.17% | 4.60% |
2 | 4.48% | 5.11% | 4.49% | 3.73% | 2.65% | 4.91% | 4.50% | 2.81% | 3.98% | ||
3 | 4.03% | 4.64% | 3.90% | 3.36% | 1.97% | 4.90% | 2.59% | 2.40% | 3.80% | ||
w = 20 | 1 | 6.42% | 4.09% | 11.89% | 3.00% | 3.32% | 5.04% | 8.14% | 3.33% | 8.20% | 4.34% |
2 | 4.66% | 4.04% | 5.50% | 2.50% | 2.89% | 4.81% | 2.54% | 2.67% | 5.06% | ||
3 | 3.92% | 3.09% | 4.07% | 2.02% | 2.68% | 4.35% | 2.30% | 2.22% | 4.36% | ||
w = 30 | 1 | 4.10% | 4.32% | 5.09% | 5.90% | 4.74% | 4.83% | 4.26% | 5.39% | 4.36% | 3.61% |
2 | 3.21% | 3.70% | 4.67% | 2.84% | 1.47% | 4.51% | 2.99% | 5.33% | 4.17% | ||
3 | 1.92% | 2.48% | 3.83% | 1.96% | 1.44% | 3.64% | 2.30% | 2.80% | 1.19% | ||
w = 40 | 1 | 3.21% | 5.91% | 7.39% | 4.50% | 2.56% | 3.53% | 2.97% | 4.49% | 2.60% | 2.89% |
2 | 2.06% | 3.12% | 4.01% | 2.17% | 1.46% | 3.21% | 2.96% | 2.31% | 2.29% | ||
3 | 1.29% | 2.58% | 1.72% | 1.31% | 1.39% | 3.02% | 1.45% | 2.26% | 2.24% |
Window/Technique | BW | Average | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
125 kHz | 250 kHz | 500 kHz | ||||||||
SF | SF | SF | ||||||||
10 | 11 | 12 | 10 | 11 | 12 | 10 | 11 | 12 | ||
LR-ADR [28] | 46 | 52 | 55 | 58 | 57 | 42 | 43 | 39 | 41 | 48.111 |
Windowless | 33 | 32 | 32 | 38 | 37 | 24 | 15 | 13 | 22 | 27.333 |
w = 10 | 32 | 52 | 38 | 46 | 46 | 39 | 30 | 28 | 28 | 37.667 |
w = 20 | 24 | 29 | 22 | 23 | 32 | 22 | 15 | 21 | 18 | 22.889 |
w = 30 | 19 | 19 | 17 | 19 | 18 | 17 | 12 | 13 | 15 | 16.556 |
w = 40 | 14 | 17 | 11 | 13 | 14 | 15 | 9 | 9 | 13 | 12.778 |
Window | SNR Gain | Number of Changes |
---|---|---|
10 | 44.89% | 37.80% |
20 | 36.73% | −16.26% |
30 | 13.77% | −39.43% |
40 | −8.92% | −53.25% |
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Silva, E.F.; Figueiredo, L.M.; de Oliveira, L.A.; Chaves, L.J.; de Oliveira, A.L.; Rosário, D.; Cerqueira, E. Adaptive Parameters for LoRa-Based Networks Physical-Layer. Sensors 2023, 23, 4597. https://doi.org/10.3390/s23104597
Silva EF, Figueiredo LM, de Oliveira LA, Chaves LJ, de Oliveira AL, Rosário D, Cerqueira E. Adaptive Parameters for LoRa-Based Networks Physical-Layer. Sensors. 2023; 23(10):4597. https://doi.org/10.3390/s23104597
Chicago/Turabian StyleSilva, Edelberto Franco, Lucas M. Figueiredo, Leonardo A. de Oliveira, Luciano J. Chaves, André L. de Oliveira, Denis Rosário, and Eduardo Cerqueira. 2023. "Adaptive Parameters for LoRa-Based Networks Physical-Layer" Sensors 23, no. 10: 4597. https://doi.org/10.3390/s23104597
APA StyleSilva, E. F., Figueiredo, L. M., de Oliveira, L. A., Chaves, L. J., de Oliveira, A. L., Rosário, D., & Cerqueira, E. (2023). Adaptive Parameters for LoRa-Based Networks Physical-Layer. Sensors, 23(10), 4597. https://doi.org/10.3390/s23104597