An Environment-Aware Adaptive Data-Gathering Method for Packet-Level Index Modulation in LPWA

TL;DR

This paper introduces a new method to reduce packet collisions in low-power wide-area networks by adapting to the environment.

Contribution

The novel contribution is an environment-aware adaptive data-gathering method that improves packet transmission efficiency in LPWA networks.

Findings

The proposed method reduces packet collisions by 15% in simulations.

Experimental results show a 30% improvement in packet collision rates.

The method adapts to factors affecting sensor information transmission probability.

Abstract

Low-power wide-area (LPWA) is a communication technology for the IoT that allows low power consumption and long-range communication. Additionally, packet-level index modulation (PLIM) can transmit additional information using multiple frequency channels and time slots. However, in a competitive radio access environment, where multiple sensors autonomously determine packet transmission, packet collisions occur when transmitting the same information. The packet collisions cause a reduction in the throughput. A method has been proposed to design a mapping table that shows the correspondence between indexes and information using a packet collision minimization criterion. However, the effectiveness of this method depends on how the probability of the occurrence of the information to be transmitted is modeled. We propose an environment-aware adaptive data-gathering method that identifies the location of factors affecting sensor information and constructs a model for the probability of the occurrence of sensor information. The packet collision rate of the environment-aware adaptive data-gathering method was clarified through computer simulations and actual experiments on a 429 MHz LPWA. We confirm that the proposed scheme improves the packet collision rate by 15% in the computer simulation and 30% in the experimental evaluation, respectively.

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