The transition from LTE to 5G, and therefore from a monolithic network architecture to a service based architecture, introduces a myriad of new challenges. Most significantly, the decomposition of core components into individual services introduces an extensive signalling traffic overhead within the 5G core network. Additionally, there is a general lack of observability for the 5G core, hindering advancements in modelling and optimization for the system as a whole as well as the individual network functions. To address this, we present a measurement methodology to increase observability within the 5G core system and extract crucial information for the signalling traffic for specific user equipments within the individual network functions. Furthermore, we use our proposed methodology to conduct a case study on the performance of the attachment procedure in Open5GS, a commonly used open source implementation, under different loads. Lastly, we investigate the stability of the Access and Mobility Management Function under heavy load.

5G New Radio (NR) is designed to support diverse services, shifting from a fixed smartphone-centric infrastructure to flexible deployment options tailored for verticals like Ultra-Reliable Low-Latency Communications (URLLC) and Machine-Type Communications (MTC). To optimize the QoS of 5G NR to the service needs, understanding the impact of the introduced configuration parameters is critical. This paper investigates the configuration space of 5G NR using open-source 5G standalone deployments based on OpenAirInterface (OAI) and srsRAN (SRS). We conduct a detailed study on the impact of Next Generation NodeB (gNB) configurations on uplink and downlink throughput and latency, we compare the 5G NR implementations of OAI and SRS, and we investigate reproducibility across different testbeds at the University of Wuerzburg and NTNU. Our datasets are made publicly available.

The growing popularity of LoRa-based solutions, such as weather, environmental, and climate monitoring, has resulted in larger LoRaWAN deployments and increased network load. However, the random channel access method used in LoRaWAN leads to higher message collision probabilities and potential data loss, diminishing the networks energy efficiency. This paper addresses this issue by proposing a hardware-independent model that assesses energy consumption and efficiency in different LoRaWAN channel access approaches. Through simulation and analysis of real energy values, we provide insights and recommendations for optimizing the network performance and energy usage. This study fills a research gap and contributes to the development of more robust and energy-efficient LoRaWAN systems for various application areas.

The steady growth of IoT networks and the rise of LoRa and LoRaWAN as a wireless communication protocol in smart city deployments invoke the need for energy efficient and performance focused channel access mechanisms. Since LoRa makes use of the random access protocol ALOHA, the expected collision probability is directly related to the density of deployed IoT devices. In order to alleviate this scalability issue of LoRa deployments, and the energy losses caused by rising packet collisions, this work proposes a novel channel access mechanism that aims to mitigate high collision probabilities as a result of dense, bursty periods of transmissions, without the need for centralized control instances or dedicated control messages. Based on simulation results, we show that the proposed mechanism is able to mitigate between 30% and 100% of collisions compared to pure ALOHA, depending on factors such as system load and device density.

The recent rise of the Internet of Things (IoT) has given way to numerous challenges and research questions. One of the most critical issues in the area of low powered devices is the question of energy efficiency. Here, technologies like LoRa or Zigbee emerged, promising low power consumption while maintaining adequate performance. However, even when using these tailor made technologies, several configuration aspects need to be taken into account to provide high performance, energy efficient operation. To this end, we propose a generic model to compute the energy efficiency of wireless sensors under the assumption of perfect CSMA/CA channel access. We present numerical results for a typical LoRa device and highlight extensions towards other channel access mechanisms. Finally, we apply Kleinrock’s power metric to obtain ideal system configurations for varying load parameters.

Internet of Things (IoT) and Machine-to-Machine (M2M) devices have seen a significant growth in usage and deployment over the last years. Gaining insight into device and data usage behavior exhibited by the participants of mobile networks is elementary for Mobile Network Operator (MNO) and Mobile Virtual Network Operator (MVNO) to scale their networks and provide a reliable service. This work aims to make use of its first of a kind dataset, spanning multiple countries and MNOs, to provide a detailed characterization of GPRS Tunneling Protocol (GTP) tunnels and devices. To this end, general statistics are used to describe the observed data traffic focusing on the distribution of the tunnel duration and volume as well as periodic device behavior. An approximate metric is introduced to analyze the periodicity and synchronicity of IoT devices. Lastly, we publish the data investigated in this work and provide a large scale dataset on the data usage behavior of IoT devices to interested researchers.

LoRaWAN is one of the most promising Internet of Things technologies with regard to low energy consumption. However, the currently used random channel access has much potential for improvement. Thus, current literature studies alter-native channel access approaches, but the energy consumption is often not taken into consideration. For that reason, we present a generic model to quantify energy consumption in LoRaWAN for different channel access mechanisms based on a state machine. With our model, we can describe the energy consumption for specific access mechanisms or for the complete network. Our model shows that random access only performs best if no additional receive windows are opened. A simple improvement is Listen before Talk. For networks with high load, improvements are achieved by a more complex scheduled MAC. Our model serves as a basis for future energy consumption studies, conducted through measurements or simulations.

The adaptation of Internet of Things in our everyday life shows different new demands and challenges. One of the fastest growing technologies in this context are Low Power Wide Area Networks with LoRaWAN as one of their most prominent representatives. The promise of this technology is to transmit sensor data over large distances with very little energy consumption. But transmission behavior, and especially overhead and collision probability, must be studied in detail to improve transmission quality and energy consumption due to the random access nature of LoRaWAN. Because of that, this work investigates the LoRaWAN channel capacity by analyzing the transmission overhead, the collision probability, and the data loss. At the end, an energy consumption comparison is done. The contribution is a novel approach based on aggregation and retransmission that shows a decreased data loss of up to 20% compared to the currently used random channel access.