AIU2 – Next Generation Networks and IoT
Thursday, 5 June 2025, 11:00-12:30, room 1C
Session Chair: Ingrid Moerman (Ghent University – IMEC, BE)
Next Generation LoRaWAN: Integrating Multi-Hop Communications at 2.4 GHz
Riccardo Marini (CNIT, National Laboratory of Wireless Communications (WiLab), Italy); Giampaolo Cuozzo (CNIT & WiLab, Italy)
The Internet of Things (IoT) revolution demands scalable, energy-efficient communication protocols supporting widespread device deployments. The LoRa technology, coupled with the LoRaWAN protocol, has emerged as a leading Low Power Wide Area Network (LPWAN) solution, traditionally leveraging sub-GHz frequency bands for reliable long-range communication. However, these bands face constraints such as limited data rates and strict duty cycle regulations. Recent advancements have introduced the 2.4 GHz spectrum, offering superior data rates and unrestricted transmission opportunities at the cost of reduced coverage and severe interference. To solve this trade-off, this paper proposes a novel hybrid approach integrating multi-band (i.e., sub-GHz and 2.4 GHz) and multi-hop communication into LoRaWAN, while preserving compatibility with the existing standard. The proposed network architecture retains Gateways (GWs) and End Devices (EDs) operating within the sub-GHz frequency while introducing multi-band Relays (RLs) that act as forwarding nodes for 2.4 GHz EDs. Utilizing our previously developed open-source and standards-compliant simulation framework, we evaluate the network performance of our solution under realistic deployment scenarios. The results demonstrate substantial improvements compared to standard single-band and single-hop LoRaWAN networks, demonstrating the potential of this approach to redefine LPWAN capabilities and bridge the gap between current solutions and next-generation IoT applications.
Comparing BLE and ESP-NOW for WBAN Applications: a Ring Topology Approach Using Dynamic Extended Advertising
Jan Herbst (German Research Center for Artificial Intelligence, Germany); Robin Müller (DFKI, Germany); Hans D. Schotten (RPTU Kaiserslautern-Landau, Germany)
Wireless Body Area Networks (WBANs) have emerged as critical enablers for healthcare applications, offering low-power, short-range communication for continuous patient monitoring and data transmission. This work compares two prominent communication protocols, ESP-NOW and Bluetooth Low Energy (BLE), regarding a newly implemented Ring topology approach. Therefore latency, energy efficiency, trade-offs between the protocols, and robustness are discussed within the WBAN context. Based on WiFi IEEE Std. 802.11-2012, ESP-NOW provides ultra-low latency and higher data rates, but energy consumption, and susceptibility to interference pose challenges. Conversely, BLE excels in energy efficiency, scalability, and widespread device compatibility but suffers from higher latency and limited data rates. The study evaluates a BLE-based implementation of a new ring topology-based protocol structure, utilizing BLE 5.0 extended advertisements for asynchronous communication, against its implementation on ESP-NOW. Experimental results conducted on a custom-designed PCB platform centered around the ESP32-S3, demonstrate BLE’s suitability for energy-sensitive applications and ESP-NOW’s dominance in real-time scenarios. These results not only show the strengths of the implemented ring topology but also provide information on the choice of protocol for WBAN applications and illustrate the potential for hybrid solutions that utilize the strengths of both approaches.
Interference Aware Ultra Reliable Communications in in-X IoT Sub-Networks
Mamadou Ngom (IMT Nord Europe, France); Laurent Clavier (Institut Mines-Telecom, IMT Nord Europe, France)
In an environment with strict constraints on maximum permissible delay and a high density of interconnected devices, achieving global system coordination becomes impractical. This lack of coordination results in increased noise due to interference. Under such conditions, interference levels can vary significantly from one packet to another, with only their statistical properties being estimable before transmission. Notably, the variance of the resulting noise remains unpredictable. To address this challenge, we propose an approach to optimize the modulation and coding schemes. Our method models interference using a mixture of exponential distributions. To accurately estimate its parameters, we employ a bootstrap method. We also propose a Quickest Change Detection approach to identify changes in the interference distribution. This approach allows us to determine transmission parameters that ensure a predefined success probability for transmitted packets, without requiring additional listening and estimation tasks on the end devices.
Neyman-Pearson Detector for Ambient Backscatter Zero-Energy-Devices Beacons
Shanglin Yang (INSA Lyon, Inria, France & Orange Labs, France); Jean-Marie Gorce and Muhammad Jehangir Khan (INSA-Lyon & CITI, Inria, France); Dinh-Thuy Phan-Huy (Orange, France); Guillaume Villemaud (Université de Lyon, INSA-Lyon, INRIA, CITI, France)
Recently, a novel ultra-low power indoor wireless positioning system has been proposed. In this system, Zero-Energy-Devices (ZED) beacons are deployed in Indoor environments, and located on a map with unique broadcast identifiers. They harvest ambient energy to power themselves and backscatter ambient waves from cellular networks to send their identifiers. This paper presents a novel detection method for ZEDs in ambient backscatter systems, with an emphasis on performance evaluation through experimental setups and simulations. We introduce a Neyman-Pearson detection framework, which leverages a predefined false alarm probability to determine the optimal detection threshold. This method, applied to the analysis of backscatter signals in a controlled testbed environment, incorporates the use of BC sequences to enhance signal detection accuracy. The experimental setup, conducted on the FIT/CorteXlab testbed, employs a two-node configuration for signal transmission and reception. Key performance metrics, which is the peak-to-lobe ratio, is evaluated, confirming the effectiveness of the proposed detection model. The results demonstrate a detection system that effectively handles varying noise levels and identifies ZEDs with high reliability. The simulation results show the robustness of the model, highlighting its capacity to achieve desired detection performance even with stringent false alarm thresholds. This work paves the way for robust ZED detection in real-world scenarios, contributing to the advancement of wireless communication technologies.
A Statistical Evaluation of Indoor LoRaWAN Environment-Aware Propagation for 6G: MLR, ANOVA, and Residual Distribution Analysis
Nahshon Mokua Obiri and Kristof Van Laerhoven (University of Siegen, Germany)
Modeling path loss in indoor Long Range Wide Area Network (LoRaWAN) deployments is inherently challenging due to the interplay of structural obstructions, occupant density, and fluctuating environmental conditions. This study proposes a two-stage approach to capture and analyze these complexities using an extensive dataset of (890,212) field measurements collected over (4,\mathrm{months} ) in a single-floor office at the University of Siegen’s H”olderlinstra{\ss}e Campus, Germany. First, we implement a Multiple Linear Regression (MLR) model that includes the traditional propagation metrics (distance, structural walls) and environmental variables (relative humidity, temperature, carbon dioxide (CO\textsubscript{2}), particulate matter, and barometric pressure). Using analysis of variance (ANOVA), we show that adding these environmental factors can reduce unexplained variance by (38% ). Secondly, we examine the distribution of the model residuals by fitting (5) probability distributions of: (i) a Normal, (ii) a Skew-Normal, (iii) the Cauchy, (iv) the Student’s (t)-distribution, and (v) a two-component Gaussian Mixture Model (GMM). Besides the base factors of distance and structural obstructions (such as walls), our results indicate that environmental parameters substantially influence path loss residuals. Moreover, a two-component GMM consistently outperforms single-distribution models in capturing the heterogeneous nature of indoor signal propagation errors. Given 6G’s push for ultra-reliable, context-aware communications, our analysis shows that environment-aware modeling can significantly improve LoRaWAN network design in dynamic indoor Internet-of-Things (IoT) deployments.
