Onel L. A. Lopez

David E. Ruiz-Guirola, Samer Nasser, Bikramjit Singh et al.

Ambient Internet of Things (A-IoT) targets energy harvesting (EH), battery-less devices as a simple connectivity solution for extensive ultra-low-power deployments. These devices typically face intermittent energy availability, making uplink reports increasingly susceptible to access collisions and energy outages. In this paper, we build upon the cellular standardization of A-IoT and examine the paging-triggered contention-based random access (CBRA) framework for uplink reporting. We analyze the effects of energy availability and collisions on these systems and introduce an EH-aware access control mechanism. In this mechanism, the reader broadcasts an access probability in the paging message, which helps regulate the number of devices attempting random access. Results show that, unlike the baselines, the proposed method scales well under dense deployments by keeping collisions nearly constant, improving access efficiency, and substantially reducing the number of paging rounds required for successful reporting. These results highlight the importance of lightweight reader-side access control for reliable and resource-efficient reporting in A-IoT environments.

David E. Ruiz-Guirola, Onel L. A. Løpez, Samuel Montejo-Sanchez

The Internet-of-Things (IoT) is rapidly expanding, connecting numerous devices and becoming integral to our daily lives. As this occurs, ensuring efficient traffic management becomes crucial. Effective IoT traffic management requires modeling and predicting intrincate machine-type communication (MTC) dynamics, for which machine-learning (ML) techniques are certainly appealing. However, obtaining comprehensive and high-quality datasets, along with accessible platforms for reproducing ML-based predictions, continues to impede the research progress. In this paper, we aim to fill this gap by characterizing the Smart Campus MTC dataset provided by the University of Oulu. Specifically, we perform a comprehensive statistical analysis of the MTC traffic utilizing goodness-of-fit tests, including well-established tests such as Kolmogorov-Smirnov, Anderson-Darling, chi-squared, and root mean square error. The analysis centers on examining and evaluating three models that accurately represent the two most significant MTC traffic types: periodic updating and event-driven, which are also identified from the dataset. The results demonstrate that the models accurately characterize the traffic patterns. The Poisson point process model exhibits the best fit for event-driven patterns with errors below 11%, while the quasi-periodic model fits accurately the periodic updating traffic with errors below 7%.

Onel L. A. López, Amirhossein Azarbahram

Resilience is becoming crucial for future wireless networks, which must withstand, adapt to, and recover from rare but potentially cascading disruptions. This paper develops a sequential Monte Carlo (SMC) simulation framework for such systems, in which resilience failures are formulated as path-dependent rare events arising from staged degradation and delayed recovery, and are decomposed into semantically interpretable levels defined by a reaction coordinate. Building on this structure, we present a fixed-level splitting approach with budget-aware population control, enabling efficient estimation of rare non-recovery probabilities. We discuss the potential reuse of SMC checkpoints as representative near-critical states for policy evaluation and simulation-based selection. We further extend the methodology to learned stochastic simulation by using generative sequence models as restartable surrogates within data-driven digital twins. We showcase the framework in a delay-critical wireless network use case, where SMC substantially improves over standard Monte Carlo in rare-event regimes with both physical and learned simulators.