Today, LoRaWAN networks have reached a major milestone on the Internet of Things application domains thanks to their fascinating characteristics, especially in terms of large coverage and low energy consumption. Several studies have focused on the performance optimization of these networks. Indeed, gateway-centric allocation strategies look for the optimal configuration of the transmission parameters and send them to thousands of end-devices through MAC commands on the downlink. However, most of these allocation strategies are validated either by simulation or mathematical models. In this paper, we discuss the fundamentals and the requirements of the MAC commands transmission on the downlink. Our design allows IoT applications to transmit frames on the downlink hitherto reserved for MAC configuration commands, makes easier the implementation of new spreading factor assignment strategies, and provides an experimental framework for Downlink performance analysis. We validated our proposal through extensive experimental study, demonstrated the limitation of downlink over uplink due to the sensitivity of the end-device receiver, and finally we studied the impact of traffic load on the downlink performance.

LoRaWAN is a promising enabling technology for connected things. Nowadays, it is largely used in many application areas thanks to its relevant features such as long-range, low-cost, and low power consumption. However, as with all wireless technologies, mobility remains a major concern bringing end-devices out of their home operator coverage. In this paper, we investigate the inter-operator roaming capability in mobile scenarios. We proposed a novel LoRaWAN roaming scheme to enable inter-operator roaming based on DNS resolution and end-device context migration between networks. Moreover, we extended the LoRaWAN architecture while maintaining the integrity of the existing mechanisms and with a minimum prior configuration requirements. In order to validate our solution, we designed and implemented a test-bed platform integrating our extensions to Chirpstack.

LoRaWAN Enabled networks are expected to have a dizzying growth. Thus, an efficient allocation of wireless resources so as to support a large number of nodes is a major concern. In this paper we propose an SF assignment approach paying attention on the traffic load both per Spreading Factor and over the channels. Indeed, our strategy consists in finding a better distribution of the nodes on the SF by orchestrating an effective load balancing. Moreover, the performance of our solution is evaluated under diverse network configurations taking into account the capture effect and the non-orthogonality of SFs. In addition, we validated some assumptions by full-scale experiments like for the 3GPP path loss model which is used for the first time in LoRa simulations. Our results suggest that Load Shifting leads to better performance in terms of DER (Date Extraction Rate) while guaranteeing good scalability on the network size and density.