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Aline Carneiro Viana

Publications and source records attributed to Aline Carneiro Viana.

11 recordsLinked to original sources
arXiv2026

Beyond Car Sharing: Uncertainty-Aware Pooling of Vehicular Compute at the Network Edge

Connected vehicles increasingly embed AI accelerators, offering a substantial yet volatile source of supplemental compute near the network edge. Unlike provisioned MEC hosts, vehicular resources are highly dynamic: vehicles may leave the cell, become locally occupied, or offer heterogeneous compute capacities. Therefore, exploiting vehicular resources requires making admission decisions without knowing the compute capacity that will be available during task execution. We present SMART, an uncertainty-aware admission mechanism that enables an \acs{ETSI} \ac{MEC} orchestrator \textit{to opportunistically exploit vehicular compute under predictive uncertainty.} SMART predicts future vehicular capacity using a \ac{BNN} -- whose uncertainty estimates are the best calibrated among the evaluated forecasters at the nominal 95\% level, and incorporates its calibrated predictive uncertainty into a chance-constrained admission program reformulated through \ac{SAA} and \ac{CVaR} approximations. Under a compute-only admission model, SMART admits 95.6\% of tasks while maintaining a median capacity-violation rate of about 0.81\%. It achieves a favorable admission-violation tradeoff compared with seven reactive, mean-only, and uncertainty-aware baselines, by approaching the performance of a compute-capacity oracle under the modeled assumptions. Finally, the sensitivity analyses show that variability in base-station compute availability is a key determinant of admission performance, highlighting the need for a calibrated admission and resource allocation mechanism tailored to opportunistic base-station compute pooling.

cs.NI↗
arXiv2026

When Plausible Is Not Realistic: Evaluating Human Mobility in LLM-Based Urban Simulation

LLM-based generative agents are increasingly used in urban simulators, yet it remains unclear whether they reproduce empirically realistic human mobility patterns or merely generate plausible mobility narratives. We introduce a validation framework for evaluating the mobility of generative agents of LLM-based urban simulators against real-world mobility data. For this, we use mobility laws, temporal rhythms, network motifs, semantic activity transitions, and behavioral mobility profiles. Using datasets from the Greater Paris region and Shanghai, we evaluate AgentSociety and CitySim across multiple dimensions of mobility realism. Our analysis reveals a substantial gap between narrative plausibility and empirical mobility realism. Although the simulators capture some high-level semantic activity distributions, they struggle to reproduce core spatial and temporal constraints, including realistic trip-length distributions, origin-destination flows, dwell times, and transition dynamics. We further observe that realistic mobility diversity is unstable across default prompting configurations and may require explicit profile-aware initialization. To support reproducible evaluation, we also contribute scalable and open LLM-driven infrastructure for regional-scale map generation, observability-enhanced simulation, mobility-metric computation, and traffic simulation. Our findings highlight the need for rigorous empirical validation of LLM-based urban simulators and provide practical tools for building more realistic and reproducible urban simulation systems.

cs.CL↗
arXiv2025

Characterizing User Behavior: The Interplay Between Mobility Patterns and Mobile Traffic

Mobile devices have become essential for capturing human activity, and eXtended Data Records (XDRs) offer rich opportunities for detailed user behavior modeling, which is useful for designing personalized digital services. Previous studies have primarily focused on aggregated mobile traffic and mobility analyses, often neglecting individual-level insights. This paper introduces a novel approach that explores the dependency between traffic and mobility behaviors at the user level. By analyzing 13 individual features that encompass traffic patterns and various mobility aspects, we enhance the understanding of how these behaviors interact. Our advanced user modeling framework integrates traffic and mobility behaviors over time, allowing for fine-grained dependencies while maintaining population heterogeneity through user-specific signatures. Furthermore, we develop a Markov model that infers traffic behavior from mobility and vice versa, prioritizing significant dependencies while addressing privacy concerns. Using a week-long XDR dataset from 1,337,719 users across several provinces in Chile, we validate our approach, demonstrating its robustness and applicability in accurately inferring user behavior and matching mobility and traffic profiles across diverse urban contexts.

cs.NI↗
arXiv2025

SigN: SIMBox Activity Detection Through Latency Anomalies at the Cellular Edge

Despite their widespread adoption, cellular networks face growing vulnerabilities due to their inherent complexity and the integration of advanced technologies. One of the major threats in this landscape is Voice over IP (VoIP) to GSM gateways, known as SIMBox devices. These devices use multiple SIM cards to route VoIP traffic through cellular networks, enabling international bypass fraud with losses of up to $3.11 billion annually. Beyond financial impact, SIMBox activity degrades network performance, threatens national security, and facilitates eavesdropping on communications. Existing detection methods for SIMBox activity are hindered by evolving fraud techniques and implementation complexities, limiting their practical adoption in operator networks.This paper addresses the limitations of current detection methods by introducing SigN , a novel approach to identifying SIMBox activity at the cellular edge. The proposed method focuses on detecting remote SIM card association, a technique used by SIMBox appliances to mimic human mobility patterns. The method detects latency anomalies between SIMBox and standard devices by analyzing cellular signaling during network attachment. Extensive indoor and outdoor experiments demonstrate that SIMBox devices generate significantly higher attachment latencies, particularly during the authentication phase, where latency is up to 23 times greater than that of standard devices. We attribute part of this overhead to immutable factors such as LTE authentication standards and Internet-based communication protocols. Therefore, our approach offers a robust, scalable, and practical solution to mitigate SIMBox activity risks at the network edge.

cs.NI↗
arXiv2023

Zen: LSTM-based generation of individual spatiotemporal cellular traffic with interactions

Domain-wide recognized by their high value in human presence and activity studies, cellular network datasets (i.e., Charging Data Records, named CdRs), however, present accessibility, usability, and privacy issues, restricting their exploitation and research reproducibility.This paper tackles such challenges by modeling Cdrs that fulfill real-world data attributes. Our designed framework, named Zen follows a four-fold methodology related to (i) the LTSM-based modeling of users' traffic behavior, (ii) the realistic and flexible emulation of spatiotemporal mobility behavior, (iii) the structure of lifelike cellular network infrastructure and social interactions, and (iv) the combination of the three previous modules into realistic Cdrs traces with an individual basis, realistically. Results show that Zen's first and third models accurately capture individual and global distributions of a fully anonymized real-world Cdrs dataset, while the second model is consistent with the literature's revealed features in human mobility. Finally, we validate Zen Cdrs ability of reproducing daily cellular behaviors of the urban population and its usefulness in practical networking applications such as dynamic population tracing, Radio Access Network's power savings, and anomaly detection as compared to real-world CdRs.

cs.NI↗
arXiv2021

SimBle: Generating privacy preserving real-world BLE traces with ground truth

Bluetooth has become critical as many IoT devices are arriving in the market. Most of the current literature focusing on Bluetooth simulation concentrates on the network protocols' performances and completely neglects the privacy protection recommendations introduced in the BLE standard. Indeed, privacy protection is one of the main issues handled in the Bluetooth standard. For instance, the current standard forces devices to change the identifier they embed within the public and private packets, known as MAC address randomization. Although randomizing MAC addresses is intended to preserve device privacy, recent literature shows many challenges that are still present. One of them is the correlation between the public packets and the emitters. Unfortunately, existing evaluation tools such as NS-3 are not designed to reproduce this Bluetooth standard's essential functionality. This makes it impossible to test solutions for different device-fingerprinting strategies as there is a lack of ground truth for large-scale scenarios with the majority of current BLE devices implementing MAC address randomization. In this paper, we first introduce a solution of standard-compliant MAC address randomization in the NS-3 framework, capable of emulating any real BLE device in the simulation and generating real-world Bluetooth traces. In addition, since the simulation run-time for trace-collection grows exponentially with the number of devices, we introduce an optimization to linearize public-packet sniffing. This made the large-scale trace-collection practically feasible. Then, we use the generated traces and associated ground truth to do a case study on the evaluation of a generic MAC address association available in the literature. Our case study reveals that close to 90 percent of randomized addresses could be correctly linked even in highly dense and mobile scenarios.

cs.CR↗
arXiv2010

Decoupling data dissemination from the mobile sink's trajectory in wireless sensor networks: Current Research and Open Issues

In this report, firstly, we presents state of the art survey on Data Management and Data Dissemination techniques with Mobile Sink. Moreover we classify these techniques into two ample sub-categories. Under this classification, we identify, review, compare, and highlight these techniques and their pros and cons. We do a SWOT (Strength, Weaknesses, Opportunities, Threats) analysis of each scheme. We also discuss where each scheme is appropriate. Secondly, we presents a new distributed data management scheme which is based upon Random Walk Based Membership Service to facilitate Data Dissemination in Mobile Sink based Wireless Sensor Networks. Our proposed scheme efficiently deals with the aforementioned problems and we also compare the characteristics of our proposed scheme with the state-of-the-art data-dissemination schemes. We propose using Random Walks (RWs) with uniformly distributed views to disseminate data through the WSN with a controlled overhead. This is performed by the use of a Random Walk Based Membership Service - the RaWMS. Our proposal solves then the problems generated when (a) all nodes are storage motes, being no aggregation performed (b) one center node plays the role of storage mote and aggregates data from all the other nodes (c) replication is performed on all nodes in the network. To the best of our knowledge, we are the first to propose an efficient data dissemination approach (in terms of overhead, adaptiveness and representativeness) to allow a mobile sink to gather a representative view of the monitored region covered by n sensor nodes by only visiting any m nodes, where hopefully m << n.

cs.NI↗
arXiv2010

A Cognitive Radio Based Internet Access Framework for Disaster Response Network Deployment

In this paper, we propose a cognitive radio based Internet access framework for disaster response network deployment in challenged environments. The proposed architectural framework is designed to help the existent but partially damaged networks to restore their connectivity and to connect them to the global Internet. This architectural framework provides the basis to develop algorithms and protocols for the future cognitive radio network deployments in challenged environments.

cs.NI↗
arXiv2010

Toward Reliable Contention-aware Data Dissemination in Multi-hop Cognitive Radio Ad Hoc Networks

This paper introduces a new channel selection strategy for reliable contentionaware data dissemination in multi-hop cognitive radio network. The key challenge here is to select channels providing a good tradeoff between connectivity and contention. In other words, channels with good opportunities for communication due to (1) low primary radio nodes (PRs) activities, and (2) limited contention of cognitive ratio nodes (CRs) acceding that channel, have to be selected. Thus, by dynamically exploring residual resources on channels and by monitoring the number of CRs on a particular channel, SURF allows building a connected network with limited contention where reliable communication can take place. Through simulations, we study the performance of SURF when compared with three other related approaches. Simulation results confirm that our approach is effective in selecting the best channels for efficient and reliable multi-hop data dissemination.

cs.NI↗
arXiv2005

Architectural Considerations for a Self-Configuring Routing Scheme for Spontaneous Networks

Decoupling the permanent identifier of a node from the node's topology-dependent address is a promising approach toward completely scalable self-organizing networks. A group of proposals that have adopted such an approach use the same structure to: address nodes, perform routing, and implement location service. In this way, the consistency of the routing protocol relies on the coherent sharing of the addressing space among all nodes in the network. Such proposals use a logical tree-like structure where routes in this space correspond to routes in the physical level. The advantage of tree-like spaces is that it allows for simple address assignment and management. Nevertheless, it has low route selection flexibility, which results in low routing performance and poor resilience to failures. In this paper, we propose to increase the number of paths using incomplete hypercubes. The design of more complex structures, like multi-dimensional Cartesian spaces, improves the resilience and routing performance due to the flexibility in route selection. We present a framework for using hypercubes to implement indirect routing. This framework allows to give a solution adapted to the dynamics of the network, providing a proactive and reactive routing protocols, our major contributions. We show that, contrary to traditional approaches, our proposal supports more dynamic networks and is more robust to node failures.

cs.NI↗