SF-Partition-Based Clustering and Relaying Scheme for Resolving Near-Far Unfairness in IoT Multihop LoRa Networks

Area of Science:

• Networking
• Wireless Communication
• Internet of Things (IoT)

Background:

• Long Range (LoRa) is a key technology for low-power, wide-area IoT networks, but suffers from the near-far effect causing unfairness in device communication.
• Existing solutions struggle to fully address the near-far fairness problem in LoRa networks due to the lack of a tractable analytical model.
• This unfairness leads to unequal transmission opportunities for LoRa devices (LDs) based on their proximity to the gateway (GW).

Purpose of the Study:

• To propose a novel scheme, SF-partition-based clustering and relaying (SFPCR), to achieve fair and extensive LoRa device connectivity in IoT multihop networks.
• To bridge packet delivery success probability gaps between different spreading factor (SF) regions.
• To enhance overall network performance and energy conservation for LoRa devices.

Main Methods:

• The SFPCR scheme partitions LoRa devices into lower SF zone (LSFZ) and higher SF zone (HSFZ) based on a determined threshold.
• HSFZ utilizes density-based subspace clustering for arbitrary-shaped clusters and binary score representation for cluster header selection to avoid long transmissions.
• LSFZ employs relay LD selection to facilitate multihop communications, extending uplink transmissions from HSFZ devices to the GW.

Main Results:

• The SFPCR scheme achieved the highest packet delivery success probability of 65.7% in simulations.
• Compared to other schemes, SFPCR outperformed FSRC (44.6%), mesh (34.2%), and cluster-based (29.4%) approaches.
• The proposed scheme also demonstrated significant energy conservation for LoRa devices.

Conclusions:

• The SFPCR scheme effectively addresses the near-far fairness problem in multihop LoRa IoT networks.
• It significantly improves connectivity and packet success probability while conserving device energy.
• SFPCR offers a viable solution for enhancing the performance of large-scale LoRa deployments.