Large-scale Internet of Things (IoT) deployments demand long-range wireless communications, especially in urban and metropolitan areas. LoRa is one of the most promising technologies in this context due to its simplicity and flexibility. Indeed, deploying LoRa networks in dense IoT scenarios must achieve two main goals: efficient communications among a large number of devices and resilience against dynamic channel conditions due to demanding environmental settings (e.g., the presence of many buildings). This work investigates adaptive mechanisms to configure the communication parameters of LoRa networks in dense IoT scenarios. To this end, we develop FLoRa, an open-source framework for end-to-end LoRa simulations in OMNeT++. We then implement and evaluate the Adaptive Data Rate (ADR) mechanism built into LoRa to dynamically manage link parameters for scalable and efficient network operations. Extensive simulations show that ADR is effective in increasing the network delivery ratio under stable channel conditions, while keeping the energy consumption low. Our results also show that the performance of ADR is severely affected by a highly-varying wireless channel. We thereby propose an improved version of the original ADR mechanism to cope with variable channel conditions. Our proposed solution significantly increases both the reliability and the energy efficiency of communications over a noisy channel, almost irrespective of the network size. Finally, we show that the delivery ratio of very dense networks can be further improved by using a network-aware approach, wherein the link parameters are configured based on the global knowledge of the network.

Long Range (LoRa) is a wireless communication standard specifically targeted for resource-constrained Internet of Things (IoT) devices. LoRa is a promising solution for smart city applications as it can provide long-range connectivity with a low energy consumption. The number of LoRa-based networks is growing due to its operation in the unlicensed radio bands and the ease of network deployments. However, the scalability of such networks suffers as the number of deployed devices increases. In particular, the network performance drops due to increased contention and interference in the unlicensed LoRa radio bands. This results in an increased number of dropped messages and, therefore, unreliable network communications. Nevertheless, network performance can be improved by appropriately configuring the radio parameters of each node. To this end, we formulate integer linear programming models to configure LoRa nodes with the optimal parameters that allow all devices to reliably send data with a low energy consumption. We evaluate the performance of our solutions through extensive network simulations considering different types of realistic deployments. We find that our solution consistently achieves a higher delivery ratio (up to 8% higher) than the state of the art with minimal energy consumption. Moreover, the higher delivery ratio is achieved by a large percentage of nodes in each network, thereby resulting in a fair allocation of radio resources. Finally, the optimal network configurations are obtained within a short time, usually much faster than the state of the art. Thus, our solution can be readily used by network operators to determine optimal configurations for their IoT deployments, resulting in improved network reliability.

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