Dinesh Tamang

Traffic conditions in modern cellular networks vary considerably over time and across locations, making efficient coordination increasingly difficult for distributed learning systems. Existing distributed traffic forecasting methods have largely focused on improving prediction models or optimizing individual coordination mechanisms, whereas the coordination layer itself is commonly kept static under non-stationary traffic conditions. To address this issue, we develop a closed-loop adaptive orchestration framework in which a lightweight composite volatility index (CVI) continuously monitors traffic dynamics and jointly regulates client participation, aggregation, and participation fairness. The framework is evaluated using both controlled simulations and three independent real-world cellular traffic traces collected from two different measurement methodologies. Across all real-world traces, the proposed fairness-aware selection strategy consistently achieves the maximum participation fairness while maintaining prediction accuracy comparable to existing methods and reducing communication overhead by 12–23%. Controlled synthetic experiments further demonstrate that CVI-driven adaptive participation significantly outperforms fixed-budget coordination under the same communication budget ([Formula: see text]). Overall, the results show that lightweight closed-loop orchestration can substantially improve coordination robustness and fairness in non-stationary cellular environments without modifying the underlying prediction models.

\beginabstract In the context of the Factories at Major Accident Risk (FMAR), we consider a scenario consisting of multiple sensor nodes that detect dangerous conditions and raise alarms over a LoRaWAN network. Upon event detection, a large number of nodes try to transmit concurrent alarm messages and the reception of at least one message is sufficient for the central server to react. We propose a scheme in which nodes operate in a slotted-time mode after the detection of the triggering event and they can only transmit one packet in a randomly chosen slot. The choice of the transmission slot follows a specific probability distribution that maximizes the probability of successful reception of at least one packet subject to real-time latency constraints. We provide an optimization framework to find the optimal distribution for choosing a transmission slot. We validate the proposed scheme by simulations and provide numerical results to compare the performance for three different slot choice distributions: i) uniform, ii) the distribution used in the Sift protocol \cite Tay2004, and iii) the proposed optimal distribution. The results show that the proposed optimal distribution leads to much better probability of successful packet delivery than other distributions. \endabstract

In this article, we propose a reliable and low-latency Long Range Wide Area Network (LoRaWAN) solution for environmental monitoring in factories at major accident risk (FMAR). In particular, a low power wearable device for sensing the toxic inflammable gases inside an industrial plant is designed with the purpose of avoiding peculiar risks and unwanted accidents to occur. Moreover, the detected data have to be urgently and reliably delivered to remote server to trigger preventive immediate actions so as to improve the machine operation. In these settings, LoRaWAN has been identified as the most proper communications technology to the needs owing to the availability of off the shelf devices and software. Hence, we assess the technological limits of LoRaWAN in terms of latency and reliability and we propose a fully LoRaWAN compliant solution to overcome these limits. The proposed solution envisages coordinated end device (ED) transmissions through the use of Downlink Control Packets (DCPs). Experimental results validate the proposed method in terms of service requirements for the considered FMAR scenario.