ArticleSensors (Basel, Switzerland)2020
Energy Constrained Optimization for Spreading Factor Allocation in LoRaWAN.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- ZBMG-LoRa: A Novel Zone-Based Multi-Gateway Approach Towards Scalable LoRaWANs for Internet of Things.Sensors (Basel, Switzerland) · 2025Article
- Improving Energy Efficiency in LoRaWAN Networks with Multiple Gateways.Sensors (Basel, Switzerland) · 2023Article
- Optimizing Resources and Increasing the Coverage of Internet-of-Things (IoT) Networks: An Approach Based on LoRaWAN.Sensors (Basel, Switzerland) · 2023Article
- A Reinforcement Learning Based Transmission Parameter Selection and Energy Management for Long Range Internet of Things.Sensors (Basel, Switzerland) · 2022Article
- A Survey on Adaptive Data Rate Optimization in LoRaWAN: Recent Solutions and Major Challenges.Sensors (Basel, Switzerland) · 2020Review
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Authors and funding
3 authors.
Funding
Abstract
This paper discusses a spreading factor allocation for Long Range Wide Area Network (LoRaWAN). Because Long Range (LoRa) is based on chirp spread spectrum that each spreading factor is approximately orthogonal to each other, the performance of LoRaWAN can be enhanced by allocating the spreading factor appropriately to end devices (EDs). Several spreading factor allocation techniques have been reported. Techniques shown in existing studies can improve some characteristics (e.g. throughput or packet reception probability (PRP)); however, there are a few studies that have focused on the energy consumption of the EDs. The LoRa communication offers a low power communication and this enables the improvement of the performance in exchange for the energy consumption. This paper presents a performance improvement technique via spreading factor allocations for LoRaWAN. We define the optimization problem for the spreading factor allocation to maximize the PRP under a constraint for the average energy consumption of all the EDs. It enables for the performance improvement under the constraint of the average energy consumption of all the EDs by solving the problem. This study further develops a method to solve the defined problem based on a distributed genetic algorithm, which is metaheuristics method. Although the techniques shown in the existing studies give the average energy consumption as a result of the performance improvement by the spreading factor allocation, the presented technique can enhance the LoRaWAN performance by allocating the spreading factor to EDs under the constraint for the average energy consumption of all the EDs. Numerical examples validate the effectiveness of the presented technique. The PRP performance of the presented technique is superior to that of the techniques shown in the existing studies despite that the average energy consumption of all the EDs of the presented technique is less than that of the techniques shown in the existing studies.
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