With the proliferation of machine-to-machine (M2M) communication in the course of the last decade, the importance of low-power wide-area network (LPWAN) technologies intensifies. However, the abundance of accurate propagation models proposed for these systems by standardization bodies, vendors, and research community hampers the deployment planning. In this paper, we question the selection of accurate propagation models for Narrowband IoT (NB-IoT), LoRaWAN, and Sigfox LPWAN technologies, based on extensive measurement campaign in two mid-size European cities. Our results demonstrate that none of the state-of-the-art models can accurately describe the propagation of LPWAN radio signals in an urban environment. For this reason, we propose enhancements to the selected models based on our experimental measurements. Performing the fine-tuning of the propagation models for one of the cities, we select Ericsson Urban (NB-IoT, LoRaWAN) and 3GPP (Sigfox) models as the ones providing the closest match. Finally, we proceed to perform cross-validation of the propagation models using the data set for another city. The tuned models demonstrate an excellent match with the real data in the cross-validation phase. They outperform their competitors by at least 20 – 80% in terms of relative deviation from the measured signal levels presenting the accurate option for NB-IoT, LoRaWAN, and Sigfox deployments planning in mid-size cities.

This work is a data descriptor paper for measurements related to various operational aspects of LoRaWAN communication technology collected in Brno, Czech Republic. This paper also provides data characterizing the long-term behavior of the LoRaWAN channel collected during the two-month measurement campaign. It covers two measurement locations, one at the university premises, and the second situated near the city center. The dataset’s primary goal is to provide the researchers lacking LoRaWAN devices with an opportunity to compare and analyze the information obtained from 303 different outdoor test locations transmitting to up to 20 gateways operating in the 868 MHz band in a varying metropolitan landscape. To collect the data, we developed a prototype equipped with a Microchip RN2483 Low-Power Wide-Area Network (LPWAN) LoRaWAN technology transceiver module for the field measurements. As an example of data utilization, we showed the Signal-to-noise Ratio (SNR) and Received Signal Strength Indicator (RSSI) in relation to the closest gateway distance.

An assessment of radio network coverage, usually in the form of a measurement campaign, is essential for multibase-station (multi-BS) network deployment and maintenance. It can be conducted by a network operator or its served consumers. However, the number of measurement points and their locations may not be known in advance for an efficient and accurate evaluation. The main goal of this study is to propose a new methodology for understanding the selection of measurement points during coverage and signal quality assessment. It is particularly tailored to multi-BS low-power wide-area network (LPWAN) deployments without explicit knowledge of BS locations. To this aim, we first conduct a large-scale measurement campaign for three popular LPWAN technologies, namely, narrowband IoT (NB-IoT), Sigfox, and LoRaWAN. Utilizing this baseline data, we develop a procedure for identifying the minimum set of measurement points for the coverage assessment with a given accuracy as well as study which interpolation algorithms produce the lowest approximation error. Our results demonstrate that a random choice of measurement points is on par with their deterministic selection. Out of the candidate interpolation algorithms, the Kriging method offers attractive performance in terms of the absolute error for NB-IoT deployments. In contrast, for Sigfox and LoRaWAN infrastructures, less complex techniques, such as the natural neighbor, linear interpolation, or inverse-distance weighting, can achieve comparable (and occasionally even better) accuracy levels.

The integration of millimeter wave (mmWave) and low frequency interfaces brings an unique opportunity to unify the communications and positioning technologies in the future wireless heterogeneous networks (HetNets), which offer great potential for efficient handover using location awareness, hence a location-aware handover (LHO). Targeting a self-organized communication system with autonomous vehicles, we conduct and describe an experimental and analytical study on the LHO using a mmWave-enabled robotic platform in a multi-radio environment. Compared to the conventional received signal strength indicator (RSSI)-based handover, the studied LHO not only improves the achievable throughput, but also enhances the wireless link robustness for the industrial Internet-of-things (IIoT)-oriented applications. In terms of acquiring location awareness, a geometry-based positioning (GBP) algorithm is proposed and implemented in both simulation and experiments, where its achievable accuracy is assessed and tested. Based on the performed experiments, the location-related measurements acquired by the robot are not accurate enough for the standalone-GBP algorithm to provide an accurate location awareness to perform a reliable handover. Nevertheless, we demonstrate that by combining the GBP with the dead reckoning, more accurate location awareness becomes achievable, the LHO can therefore be performed in a more optimized manner compared to the conventional RSSI-based handover scheme, and is therefore able to achieve approximately twice as high average throughput in certain scenarios. Our study confirms that the achieved location awareness, if accurate enough, could enable an efficient handover scheme, further enhancing the autonomous features in the HetNets.