arXiv subjects

Jorge Torres Gómez

Publications and source records attributed to Jorge Torres Gómez.

9 recordsLinked to original sources

arXiv2026

Communicating Smartly in Molecular Communication Environments: Neural Networks in the Internet of Bio-Nano Things

Recent developments in the Internet of Bio-Nano-Things (IoBNT) are laying the foundation for innovative healthcare applications that envision a network of remotely coordinated nanodevices within the human body to monitor and actuate over potential diseases. However, interconnecting such nanodevices requires communication strategies that can cope with molecular communication (MC) channels, whose complex, stochastic, and dynamic behavior often makes accurate physical modeling infeasible. To explore the limits of nanodevice interconnectivity under these conditions, this survey focuses on data-driven communication strategies for MC systems, with particular emphasis on machine learning (ML) methods and neural network (NN) architectures for a robust and adaptive communication scheme at the nanoscale. Research on NN-enabled MC spans several aspects covered in this survey, including NNs for communication in IoBNT networks, the feasibility of biocompatible NN realization, explainable approaches, and the generation of training datasets. We also include open-source code examples to support reproducible research across key MC scenarios. Finally, we identify emerging challenges, including the need for robust NN architectures, biologically integrated NN modules, and scalable training strategies.

eess.SP↗

arXiv2026

How Time-Sensitive are IoBNT Networks? An Age of Information Perspective for In-Body Monitoring

This thesis develops a theoretical framework to evaluate the monitoring capability of IoBNT networks. We consider a scenario in which nanosensors passively flow in the bloodstream and detect biomarkers associated with potential diseases, reporting their detections to external gateways on the skin that host a monitoring device. The nanosensors thus realize an artificial point-to-point communication channel between the disease region and the monitor: some packets reach the destination directly, while others are lost through vessel paths that bypass the gateway. We evaluate the network's monitoring capability over this artificial channel using the \ac{AoI} concept, which jointly integrates sample generation (at the disease region), carrying (nanosensor travel through vessels), and delivery (nanosensor-to-gateway) as random events. These are modeled through (i) a Markov model that follows cardiovascular physiology and (ii) channel models of reported nanocommunication technologies. We compute the Markov transition probabilities using a cardiovascular simulator built as a low-complexity electric circuit model of the human vessels. For the nanosensor-to-gateway link, we model two well-known schemes: ultrasonic and terahertz channels. Integrating these components within the \ac{AoI} framework, we report information freshness via the average \ac{PAoI} metric. Under realistic physiological and communication assumptions, fresh information appears on the monitor within tens of seconds. The network is therefore suitable for monitoring tissue-level processes such as bacterial infections, while more adequate architectures are needed to monitor cellular-scale processes, which occur on timescales below tens of seconds.

eess.SP↗

arXiv2026

Building a Bridge between the Two Schools: Realizing a Practical Path to Include Literacy-based Skills within the STEM Curricula

Developing students as well-rounded professionals is increasingly important for our modern society. Although there is a great consensus that technical and professional ("soft") skills should be developed and intertwined in the core of computer science subjects, there are still few examples of alike teaching methodologies at technical schools. This contribution investigates the integration of technical and professional skills while teaching specialized curricula in computer science. We propose a broadly applicable, step-by-step methodology that connects core technical concepts (e.g., information entropy, network security) with fine arts practices such as music, video production, gaming, and performing arts (e.g., Oxford-style debates). The methodology was applied in several computer science courses at technical universities, where quantitative and qualitative assessments, including student questionnaires and exam scores, showed improved learning outcomes and increased student engagement compared to traditional methods. The results indicate that this art-based integration can effectively bridge the historical divide between the two schools of thought, offering a practical direction for educators. Within this context, we also identify open issues that will guide future research on topics such as instructor engagement, female motivation in technical subjects, and scalability of these approaches.

cs.CY↗

arXiv2025

CNN-based solution for mango classification in agricultural environments

This article exemplifies the design of a fruit detection and classification system using Convolutional Neural Networks (CNN). The goal is to develop a system that automatically assesses fruit quality for farm inventory management. Specifically, a method for mango fruit classification was developed using image processing, ensuring both accuracy and efficiency. Resnet-18 was selected as the preliminary architecture for classification, while a cascade detector was used for detection, balancing execution speed and computational resource consumption. Detection and classification results were displayed through a graphical interface developed in MatLab App Designer, streamlining system interaction. The integration of convolutional neural networks and cascade detectors proffers a reliable solution for fruit classification and detection, with potential applications in agricultural quality control.

cs.CV↗

arXiv2025

Rejuvenating IRS: AoI-based Low Overhead Reconfiguration Design

Intelligent reflective surface (IRS) technologies help mitigate undesirable effects in wireless links by steering the communication signal between transmitters and receivers. IRS elements are configured to adjust the phase of the reflected signal for a user's location and enhance the perceived signal-to-noise ratio (SNR). In this way, an IRS improves the communication link but inevitably introduces more communication overhead. This occurs especially in mobile scenarios, where the user's position must be frequently estimated to re-adjust the IRS elements periodically. Such an operation requires balancing the amount of training versus the data time slots to optimize the communication performance in the link. Aiming to study this balance with the age of information (AoI) framework, we address the question of how often an IRS needs to be updated with the lowest possible overhead and the maximum of freshness of information. We derive the corresponding analytical solution for a mobile scenario, where the transmitter is static and the mobile user (MU) follows a random waypoint mobility model. We provide a closed-form expression for the average peak age of information (PAoI), as a metric to evaluate the impact of the IRS update frequency. As for the performance evaluation, we consider a realistic scenario following the IEEE 802.11ad standard, targeting the mmWave band. Our results reveal that the minimum achievable average PAoI is in the microsecond range and the optimal IRS update period is in the seconds range, causing 9% overhead in the link when the MU moves at a velocity of 1 m/s.

eess.SP↗

arXiv2024

Toward Standardized Performance Evaluation of Flow-guided Nanoscale Localization

Nanoscale devices with Terahertz (THz) communication capabilities are envisioned to be deployed within human bloodstreams. Such devices will enable fine-grained sensing-based applications for detecting early indications (i.e., biomarkers) of various health conditions, as well as actuation-based ones such as targeted drug delivery. Associating the locations of such events with the events themselves would provide an additional utility for precision diagnostics and treatment. This vision yielded a new class of in-body localization coined under the term "flow-guided nanoscale localization". Such localization can be piggybacked on THz communication for detecting body regions in which biological events were observed based on the duration of one circulation of a nanodevice in the bloodstream. From a decades-long research on objective benchmarking of "traditional" indoor localization, as well as its eventual standardization (e.g., ISO/IEC 18305:2016), we know that in early stages the reported performance results were often incomplete (e.g., targeting a subset of relevant performance metrics), carrying out benchmarking experiments in different evaluation environments and scenarios, and utilizing inconsistent performance indicators. To avoid such a "lock-in" in flow-guided localization, in this paper we propose a workflow for standardized performance evaluation of such localization. The workflow is implemented in the form of an open-source simulation framework that is able to jointly account for the mobility of the nanodevices, in-body THz communication between with on-body anchors, and energy-related and other technological constraints (e.g., pulse-based modulation) at the nanodevice level. Accounting for these constraints, the framework is able to generate the raw data that can be streamlined into different flow-guided localization solutions for generating standardized performance benchmarks.

cs.NI↗

arXiv2024

Insights from the Design Space Exploration of Flow-Guided Nanoscale Localization

Nanodevices with Terahertz (THz)-based wireless communication capabilities are providing a primer for flow-guided localization within the human bloodstreams. Such localization is allowing for assigning the locations of sensed events with the events themselves, providing benefits along the lines of early and precise diagnostics, and reduced costs and invasiveness. Flow-guided localization is still in a rudimentary phase, with only a handful of works targeting the problem. Nonetheless, the performance assessments of the proposed solutions are already carried out in a non-standardized way, usually along a single performance metric, and ignoring various aspects that are relevant at such a scale (e.g., nanodevices' limited energy) and for such a challenging environment (e.g., extreme attenuation of in-body THz propagation). As such, these assessments feature low levels of realism and cannot be compared in an objective way. Toward addressing this issue, we account for the environmental and scale-related peculiarities of the scenario and assess the performance of two state-of-the-art flow-guided localization approaches along a set of heterogeneous performance metrics such as the accuracy and reliability of localization.

cs.NI↗

arXiv2023

Focusing on Information Context for ITS using a Spatial Age of Information Model

New technologies for sensing and communication act as enablers for cooperative driving applications. Sensors are able to detect objects in the surrounding environment and information such as their current location is exchanged among vehicles. In order to cope with the vehicles' mobility, such information is required to be as fresh as possible for proper operation of cooperative driving applications. The age of information (AoI) has been proposed as a metric for evaluating freshness of information; recently also within the context of intelligent transportation systems (ITS). We investigate mechanisms to reduce the AoI of data transported in form of beacon messages while controlling their emission rate. We aim to balance packet collision probability and beacon frequency using the average peak age of information (PAoI) as a metric. This metric, however, only accounts for the generation time of the data but not for application-specific aspects, such as the location of the transmitting vehicle. We thus propose a new way of interpreting the AoI by considering information context, thereby incorporating vehicles' locations. As an example, we characterize such importance using the orientation and the distance of the involved vehicles. In particular, we introduce a weighting coefficient used in combination with the PAoI to evaluate the information freshness, thus emphasizing on information from more important neighbors. We further design the beaconing approach in a way to meet a given AoI requirement, thus, saving resources on the wireless channel while keeping the AoI minimal. We illustrate the effectiveness of our approach in Manhattan-like urban scenarios, reaching pre-specified targets for the AoI of beacon messages.

cs.MA↗

arXiv2022

Wi-Fi Meets ML: A Survey on Improving IEEE 802.11 Performance with Machine Learning

Wireless local area networks (WLANs) empowered by IEEE 802.11 (Wi-Fi) hold a dominant position in providing Internet access thanks to their freedom of deployment and configuration as well as the existence of affordable and highly interoperable devices. The Wi-Fi community is currently deploying Wi-Fi 6 and developing Wi-Fi 7, which will bring higher data rates, better multi-user and multi-AP support, and, most importantly, improved configuration flexibility. These technical innovations, including the plethora of configuration parameters, are making next-generation WLANs exceedingly complex as the dependencies between parameters and their joint optimization usually have a non-linear impact on network performance. The complexity is further increased in the case of dense deployments and coexistence in shared bands. While classical optimization approaches fail in such conditions, machine learning (ML) is able to handle complexity. Much research has been published on using ML to improve Wi-Fi performance and solutions are slowly being adopted in existing deployments. In this survey, we adopt a structured approach to describe the various Wi-Fi areas where ML is applied. To this end, we analyze over 250 papers in the field, providing readers with an overview of the main trends. Based on this review, we identify specific open challenges and provide general future research directions.

cs.NI↗