LoRaWAN Resource Allocation Optimization Model for IoT Applications
Abstract
LoRaWAN is the most widely used long-range wireless IoT application that works with high density, as LoRaWAN connects devices that require low-cost, long-range communication services. However, LoRaWAN densification poses a number of challenges due to simultaneous transmission interference on the same channel or higher power consumption by the device. In this context, it is crucial to understand LoRaWAN's resource allocation mechanisms to optimize the configuration of radio specific parameters, ie, Spreading Factor (SF) and Carrier Frequency (CF), where the optimization of transmission parameters via models Optimization is an open challenge. This paper presents MARCO, a resource allocation optimization model for minimazing LoRaWAN QoS for IoT applications. The MARCO model considers mixed integer linear programming to define optimal SF and CF parameter settings, as well as overall network traffic specifications. Simulation results demonstrate the efficiency in terms of data extraction rate, number of collisions and energy compared to existing LoRaWAN resource allocation models.
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