Aug 2026· Engineering Research Express· Vol 8· 0 citations· 31 references
Physics
TL;DR
A collision-aware ADR (CA-ADR) technique to handle collisions in dense LoRaWAN deployments is presented, which incorporates a capture-aware model based on signal-to-interference ratio thresholds, enabling a more realistic assessment of packet reception under overlapping transmissions.
Abstract
LPWANs, especially LoRaWAN, are commonly employed in large IoT deployments. The adaptive data rate (ADR) scheme enhances LoRaWAN by modifying spreading factor and transmit power. But ADR usually depends only on the device-specific links and does not consider the effects of collisions or the capture effect at the network layer, making it less effective in dense scenarios. The present paper presents a collision-aware ADR (CA-ADR) technique to handle collisions in dense LoRaWAN deployments. In contrast to other ADR schemes, CA-ADR is an algorithm running at the network layer. Parameter selection is formulated as a multi-objective optimisation problem, which minimises cost based on multiple criteria, including collision probabilities and energy consumption. The collision evaluation also incorporates a capture-aware model based on signal-to-interference ratio thresholds, enabling a more realistic assessment of packet reception under overlapping transmissions. The proposed approach is evaluated using an NS-3 LoRaWAN simulation framework. First, baseline, standard ADR, and default CA-ADR are compared for network sizes from 10 to 500 devices with a 300 s transmission period. Then, 144 CA-ADR weight configurations are explored in the dense 500-device scenario. Results show that CA-ADR provides controllable energy–reliability trade-offs. High-reliability configurations achieve more than 99% Last 4 packet delivery ratio with an average transmit power between 13.51 and 13.60 dBm over 10 independent random seeds.
LoRa-LQE, a link quality estimation scheme specifically designed for mobile environments, outperforms state-of-the-art solutions and achieves packet delivery ratio improvements and energy efficiency improvements, thereby enabling more reliable communication for mobile IoT assets.
Geraldo A. Sarmento, Thiago A. Ribeiro Da Silva, Fernando J. V. Santos et al.· Journal of Internet Services...· 0 citations
Low Power Wide Area Networks (LPWANs), particularly LoRaWAN, are increasingly being deployed in mobile Internet of Things (IoT) applications. In these applications, adaptive data rate (ADR) control is essential for maintaining reliable and energy-efficient communication under dynamic wireless conditions. However, ADR performance remains constrained when channel state information (CSI) is imperfect. Although reinforcement learning (RL)-based ADR methods have demonstrated notable improvement over conventional approaches, their effectiveness under imperfect CSI deployment scenarios remains insufficiently explored. In this paper, we investigate the robustness of RL-based ADR approaches with imperfect CSI for mobile LoRaWAN networks using a mobility-aware simulation framework. Standard ADR, heuristic ADR, Deep Q-Network (DQN)-based ADR, and Proximal Policy Optimisation (PPO)-based ADR are evaluated across varying mobility patterns, network layouts, and channel dynamics. Key performance metrics include packet delivery ratio, latency, runtime efficiency, and retention of baseline capability are evaluated. The Results show that PPO achieves the best performance under imperfect CSI conditions, attaining a mean packet delivery ratio of 0.9954, outperforming standard ADR, heuristic ADR, and DQN by 29.49%, 19.12%, and 1.55%, respectively. In addition, PPO reduced latency by 53.12% and runtime by 81.94% compare to DQN while preserving over 99% of baseline communication reliability under scenario variation. These findings demonstrate that policy-gradient learning provides better transferability, robustness, and deployment readiness compared with conventional and value-based ADR approaches. The study highlights the importance of evaluating intelligent wireless controllers not only for optimisation performance, but also for cross-scenario generalisation before real-world deployment in mobile LPWAN systems.
Unknown authors· FUDMA Journal of Sciences· 0 citations
This study provides an efficient joint optimization scheme for building high-capacity, wide-coverage, and long-lifespan complex Internet of Things (IoT) networks.
Shengli Pang, Yuanyuan Ma, Xianjin Cheng et al.· Italian National Conference...· 1 citation
RSrSF-LoRa is proposed, an integrated implementation of the reserved spreading factor (rSF) mechanism within the RS-LoRa framework (lightweight scheduling), designed to improve reliability for critical traffic in smelter IIoT deployments.
Sonile K. Musonda, Musa Ndiaye, Zilole Simate et al.· Italian National Conference...· 0 citations
The deployment of Ultra-Dense Networks (UDNs) in 5G systems is to meet the growing demand for high data rates and massive connectivity. However, the dense deployment of small cells increases handover frequency, leading to challenges such as handover failures (HOF), unnecessary handovers, and the ping-pong effect, leading to degradeuser Quality of Service (QoS). This paper proposes a velocity-aware adaptive handover control approach for efficient mobility management in 5G ultra-dense networks. The proposed approach dynamically adjusts Handover Control Parameters (HCPs) called Time-to-Trigger (TTT) and Handover Margin (HOM) on the real-time velocity of User Equipment (UE) and signal conditions. The system is modeled as a two-tier heterogeneous network consisting of a macrocell overlaid with multiple small cells, and performance is evaluated using the Cost 231-Hata propagation model. The findings demonstrate that the proposed algorithm significantly reduces the total number of handovers, mitigates the ping-pong effect, and lowers handover failure rates compared to conventional static schemes. The results confirm that velocity-aware adaptive control enhances network reliability, reduces signaling overhead, and improves overall mobility performance in 5G ultra-dense environments.
Halah Hassen Aldumaini, Hanadi Esmeail Yahya, Oloof Ameen Mohmmed et al.· 2026 6th International Confe...· 0 citations
The proposed SPN model enables system architects to compare routing configurations, identify performance bottlenecks, and size infrastructure components without requiring physical deployment, and enables the identification of communication bottlenecks without requiring physical deployment.
José Miquéias Araújo, L. Lopes, Luiz Nelson Lima et al.· Journal of Internet Services...· 0 citations
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