Literature DB >> 33572272

Collision Avoidance Resource Allocation for LoRaWAN.

Natalia Chinchilla-Romero1, Jorge Navarro-Ortiz1,2, Pablo Muñoz1,2, Pablo Ameigeiras1,2.   

Abstract

The number of connected IoT devices is significantly increasing and it is expected to reach more than two dozens of billions of IoT connections in the coming years. Low Power Wide Area Networks (LPWAN) have become very relevant for this new paradigm due to features such as large coverage and low power consumption. One of the most appealing technologies among these networks is LoRaWAN. Although it may be considered as one of the most mature LPWAN platforms, there are still open gaps such as its capacity limitations. For this reason, this work proposes a collision avoidance resource allocation algorithm named the Collision Avoidance Resource Allocation (CARA) algorithm with the objective of significantly increase system capacity. CARA leverages the multichannel structure and the orthogonality of spreading factors in LoRaWAN networks to avoid collisions among devices. Simulation results show that, assuming ideal radio link conditions, our proposal outperforms in 95.2% the capacity of a standard LoRaWAN network and increases the capacity by almost 40% assuming a realistic propagation model. In addition, it has been verified that CARA devices can coexist with LoRaWAN traditional devices, thus allowing the simultaneous transmissions of both types of devices. Moreover, a proof-of-concept has been implemented using commercial equipment in order to check the feasibility and the correct operation of our solution.

Entities:  

Keywords:  LoRa MAC; LoRaWAN; PER; capacity; path loss; throughput

Year:  2021        PMID: 33572272      PMCID: PMC7915080          DOI: 10.3390/s21041218

Source DB:  PubMed          Journal:  Sensors (Basel)        ISSN: 1424-8220            Impact factor:   3.576


  2 in total

1.  Slotted ALOHA on LoRaWAN-Design, Analysis, and Deployment.

Authors:  Tommaso Polonelli; Davide Brunelli; Achille Marzocchi; Luca Benini
Journal:  Sensors (Basel)       Date:  2019-02-18       Impact factor: 3.576

2.  On-Demand LoRa: Asynchronous TDMA for Energy Efficient and Low Latency Communication in IoT.

Authors:  Rajeev Piyare; Amy L Murphy; Michele Magno; Luca Benini
Journal:  Sensors (Basel)       Date:  2018-11-01       Impact factor: 3.576

  2 in total
  1 in total

1.  New Results for the Error Rate Performance of LoRa Systems over Fading Channels.

Authors:  Kostas Peppas; Spyridon K Chronopoulos; Dimitrios Loukatos; Konstantinos Arvanitis
Journal:  Sensors (Basel)       Date:  2022-04-27       Impact factor: 3.847

  1 in total

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