arXiv subjects

Carmen Delgado

Publications and source records attributed to Carmen Delgado.

18 recordsLinked to original sources

arXiv2026

Unified Evaluation Methodology for AI-Native Integrated Sensing and Communication

Integrated Sensing and Communication (ISAC) couples radio sensing, data transmission, and control actions within a single closed-loop system. When Artificial Intelligence (AI)-driven policies adapt sensing and communication online across a variety of sensing tasks and objectives, end-to-end performance is shaped not only by waveform and channel conditions but also by inference latency, uncertainty, environmental dynamics, and hardware non-idealities, leading to fundamental trade-offs between sensing accuracy, communication reliability, and resource overhead. This manuscript presents a unified system architecture and evaluation methodology for AI-native ISAC, defined as ISAC in which learning-based agents adapt sensing, communication, and actuation policies online under uncertainty. We formalize the design space of closed-loop ISAC, propose a three-stage validation pipeline from bounds and feasibility analysis, through high-fidelity digital-twin simulation, to preliminary over-the-air validation, and provide a minimal reporting checklist that links technical Key Performance Indicators (KPIs) (e.g., data rate, SINR, target detection, parameter estimation, track quality, localization error, outage, latency, overhead, and energy per decision) to application-level Key Value Indicators (KVIs) (e.g., availability and mission effectiveness). Two representative instantiations, specifically Unmanned Aerial Vehicle (UAV)-based outdoor and Reconfigurable Intelligent Surface (RIS)-enabled indoor coverage extensions, are used to illustrate how to structure reproducible baselines and comparable evidence across heterogeneous deployments, helping bridge the gap between theoretical ISAC gains and deployment-ready performance claims.

cs.NI↗

arXiv2026

Enhancing Cellular-enabled Collaborative Robots Planning through GNSS data for SAR Scenarios

Cellular-enabled collaborative robots are becoming paramount in Search-and-Rescue (SAR) and emergency response. Crucially dependent on resilient mobile network connectivity, they serve as invaluable assets for tasks like rapid victim localization and the exploration of hazardous, otherwise unreachable areas. However, their reliance on battery power and the need for persistent, low-latency communication limit operational time and mobility. To address this, and considering the evolving capabilities of 5G/6G networks, we propose a novel SAR framework that includes Mission Planning and Mission Execution phases and that optimizes robot deployment. By considering parameters such as the exploration area size, terrain elevation, robot fleet size, communication-influenced energy profiles, desired exploration rate, and target response time, our framework determines the minimum number of robots required and their optimal paths to ensure effective coverage and timely data backhaul over mobile networks. Our results demonstrate the trade-offs between number of robots, explored area, and response time for wheeled and quadruped robots. Further, we quantify the impact of terrain elevation data on mission time and energy consumption, showing the benefits of incorporating real-world environmental factors that might also affect mobile signal propagation and connectivity into SAR planning. This framework provides critical insights for leveraging next-generation mobile networks to enhance autonomous SAR operations.

cs.RO↗

arXiv2025

Energy-aware Joint Orchestration of 5G and Robots: Experimental Testbed and Field Validation

5G mobile networks introduce a new dimension for connecting and operating mobile robots in outdoor environments, leveraging cloud-native and offloading features of 5G networks to enable fully flexible and collaborative cloud robot operations. However, the limited battery life of robots remains a significant obstacle to their effective adoption in real-world exploration scenarios. This paper explores, via field experiments, the potential energy-saving gains of OROS, a joint orchestration of 5G and Robot Operating System (ROS) that coordinates multiple 5G-connected robots both in terms of navigation and sensing, as well as optimizes their cloud-native service resource utilization while minimizing total resource and energy consumption on the robots based on real-time feedback. We designed, implemented and evaluated our proposed OROS in an experimental testbed composed of commercial off-the-shelf robots and a local 5G infrastructure deployed on a campus. The experimental results demonstrated that OROS significantly outperforms state-of-the-art approaches in terms of energy savings by offloading demanding computational tasks to the 5G edge infrastructure and dynamic energy management of on-board sensors (e.g., switching them off when they are not needed). This strategy achieves approximately 15% energy savings on the robots, thereby extending battery life, which in turn allows for longer operating times and better resource utilization.

cs.RO↗

arXiv2025

REACT: Multi Robot Energy-Aware Orchestrator for Indoor Search and Rescue Critical Tasks

Smart factories enhance production efficiency and sustainability, but emergencies like human errors, machinery failures and natural disasters pose significant risks. In critical situations, such as fires or earthquakes, collaborative robots can assist first-responders by entering damaged buildings and locating missing persons, mitigating potential losses. Unlike previous solutions that overlook the critical aspect of energy management, in this paper we propose REACT, a smart energy-aware orchestrator that optimizes the exploration phase, ensuring prolonged operational time and effective area coverage. Our solution leverages a fleet of collaborative robots equipped with advanced sensors and communication capabilities to explore and navigate unknown indoor environments, such as smart factories affected by fires or earthquakes, with high density of obstacles. By leveraging real-time data exchange and cooperative algorithms, the robots dynamically adjust their paths, minimize redundant movements and reduce energy consumption. Extensive simulations confirm that our approach significantly improves the efficiency and reliability of search and rescue missions in complex indoor environments, improving the exploration rate by 10% over existing methods and reaching a map coverage of 97% under time critical operations, up to nearly 100% under relaxed time constraint.

cs.RO↗

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

Graph Neural Networks as an Enabler of Terahertz-based Flow-guided Nanoscale Localization over Highly Erroneous Raw Data

Contemporary research advances in nanotechnology and material science are rooted in the emergence of nanodevices as a versatile tool that harmonizes sensing, computing, wireless communication, data storage, and energy harvesting. These devices offer novel pathways for disease diagnostics, treatment, and monitoring within the bloodstreams. Ensuring precise localization of events of diagnostic interest, which underpins the concept of flow-guided in-body nanoscale localization, would provide an added diagnostic value to the detected events. Raw data generated by the nanodevices is pivotal for this localization and consist of an event detection indicator and the time elapsed since the last passage of a nanodevice through the heart. The energy constraints of the nanodevices lead to intermittent operation and unreliable communication, intrinsically affecting this data. This posits a need for comprehensively modelling the features of this data. These imperfections also have profound implications for the viability of existing flow-guided localization approaches, which are ill-prepared to address the intricacies of the environment. Our first contribution lies in an analytical model of raw data for flow-guided localization, dissecting how communication and energy capabilities influence the nanodevices' data output. This model acts as a vital bridge, reconciling idealized assumptions with practical challenges of flow-guided localization. Toward addressing these practical challenges, we also present an integration of Graph Neural Networks (GNNs) into the flow-guided localization paradigm. GNNs excel in capturing complex dynamic interactions inherent to the localization of events sensed by the nanodevices. Our results highlight the potential of GNNs not only to enhance localization accuracy but also extend coverage to encompass the entire bloodstream.

cs.LG↗

arXiv2024

Cellular-enabled Collaborative Robots Planning and Operations for Search-and-Rescue Scenarios

Mission-critical operations, particularly in the context of Search-and-Rescue (SAR) and emergency response situations, demand optimal performance and efficiency from every component involved to maximize the success probability of such operations. In these settings, cellular-enabled collaborative robotic systems have emerged as invaluable assets, assisting first responders in several tasks, ranging from victim localization to hazardous area exploration. However, a critical limitation in the deployment of cellular-enabled collaborative robots in SAR missions is their energy budget, primarily supplied by batteries, which directly impacts their task execution and mobility. This paper tackles this problem, and proposes a search-and-rescue framework for cellular-enabled collaborative robots use cases that, taking as input the area size to be explored, the robots fleet size, their energy profile, exploration rate required and target response time, finds the minimum number of robots able to meet the SAR mission goals and the path they should follow to explore the area. Our results, i) show that first responders can rely on a SAR cellular-enabled robotics framework when planning mission-critical operations to take informed decisions with limited resources, and, ii) illustrate the number of robots versus explored area and response time trade-off depending on the type of robot: wheeled vs quadruped.

cs.RO↗

arXiv2024

Joint Application Admission Control and Network Slicing in Virtual Sensor Networks

We focus on the problem of managing a shared physical wireless sensor network where a single network infrastructure provider leases the physical resources of the networks to application providers to run/deploy specific applications/services. In this scenario, we solve jointly the problems of Application Admission Control (AAC), that is, whether to admit the application/service to the physical network, and wireless Sensor Network Slicing (SNS), that is, to allocate the required physical resources to the admitted applications in a transparent and effective way. We propose a mathematical programming framework to model the joint AAC-SNS problem which is then leveraged to design effective solution algorithms. The proposed framework is thoroughly evaluated on realistic wireless sensor networks infrastructures.

cs.NI↗

arXiv2024

On Optimal Resource Allocation in Virtual Sensor Networks

Sensor network virtualization is a promising paradigm to move away from highlycustomized, application-specific wireless sensor networks deployment by opening up to the possibility of dynamically assigning general purpose physical resources to multiple stakeholder applications. In this field, this paper introduces an optimization framework to perform the allocation of physical shared resources of wireless sensor networks to multiple requesting applications. The proposed optimization framework aims at maximizing the total number of applications which can share a common physical network, while accounting for the distinguishing characteristics and limitations of the wireless sensor environment (limited storage, limited processing power, limited bandwidth, tight energy consumption requirements). Due to the complexity of the optimization problem, a heuristic algorithm is also proposed. The proposed framework is finally applied to realistic network topologies to provide a detailed performance evaluation and to assess the gain involved in letting multiple applications share a common physical network with respect to one-application, one-network vertical design approaches.

cs.NI↗

arXiv2024

Energy-aware Dynamic Resource Allocation in Virtual Sensor Networks

Sensor network virtualization enables the possibility of sharing common physical resources to multiple stakeholder applications. This paper focuses on addressing the dynamic adaptation of already assigned virtual sensor network resources to respond to time varying application demands. We propose an optimization framework that dynamically allocate applications into sensor nodes while accounting for the characteristics and limitations of the wireless sensor environment. It takes also into account the additional energy consumption related to activating new nodes and/or moving already active applications. Different objective functions related to the available energy in the nodes are analyzed. The proposed framework is evaluated by simulation considering realistic parameters from actual sensor nodes and deployed applications to assess the efficiency of the proposals.

cs.NI↗

arXiv2024

Battery-Less LoRaWAN Communications using Energy Harvesting: Modeling and Characterization

Billions of IoT devices are deployed worldwide and batteries are their main power source. However, these batteries are bulky, short-lived and full of hazardous chemicals that damage our environment. Relying on batteries is not a sustainable solution for the future IoT. As an alternative, battery-less devices run on long-lived capacitors charged using energy harvesters. The small energy storage capacity of capacitors results in an intermittent on-off behaviour. LoRaWAN is a popular Low Power Wide Area Network technology used in many IoT devices and can be used in these new scenarios. In this work, we present a Markov model to characterize the performance of battery-less LoRaWAN devices for uplink and downlink transmissions and we evaluate their performance in terms of the parameters that define the model (i.e., device configuration, application behaviour and environmental conditions). Results show that LoRaWAN battery-less communications are feasible if choosing the proper configuration (i.e., capacitor size, turn-on voltage threshold) for different application behaviour (i.e., transmission interval, UL/DL packet sizes) and environmental conditions (i.e., energy harvesting rate). Since downlink in the second reception window highly affects the performance, only small DL packet sizes should be considered for these devices. Besides, a 47 mF capacitor can support 1 Byte $SF7$ transmissions every 60 s at an energy harvesting rate of 1 mW. However, if no DL is expected, a 4.7 mF capacitor could support 1 Byte $SF7$ transmissions every 9~s.

cs.NI↗

arXiv2024

On the Feasibility of Battery-Less LoRaWAN Communications using Energy Harvesting

From the outset, batteries have been the main power source for the Internet of Things (IoT). However, replacing and disposing of billions of dead batteries per year is costly in terms of maintenance and ecologically irresponsible. Since batteries are one of the greatest threats to a sustainable IoT, battery-less devices are the solution to this problem. These devices run on long-lived capacitors charged using various forms of energy harvesting, which results in intermittent on-off device behaviour. In this work, we model this intermittent battery-less behaviour for LoRaWAN devices. This model allows us to characterize the performance with the aim to determine under which conditions a LoRaWAN device can work without batteries, and how its parameters should be configured. Results show that the reliability directly depends on device configurations (i.e., capacitor size, turn-on voltage threshold), application behaviour (i.e., transmission interval, packet size) and environmental conditions (i.e., energy harvesting rate).

cs.NI↗

arXiv2024

An Implementation for Dynamic Application Allocation in Shared Sensor Networks

We present a system architecture implementation to perform dynamic application allocation in shared sensor networks, where highly integrated wireless sensor systems are used to support multiple applications. The architecture is based on a central controller that collects the received data from the sensor nodes, dynamically decides which applications must be simultaneously deployed in each node and, accordingly, over-the-air reprograms the sensor nodes. Waspmote devices are used as sensor nodes that communicate with the controller using ZigBee protocol. Experimental results show the viability of the proposal.

cs.NI↗

arXiv2024

Optimal energy-aware task scheduling for batteryless IoT devices

Today's IoT devices rely on batteries, which offer stable energy storage but contain harmful chemicals. Having billions of IoT devices powered by batteries is not sustainable for the future. As an alternative, batteryless devices run on long-lived capacitors charged using energy harvesters. The small energy storage capacity of capacitors results in intermittent on-off behaviour. Traditional computing schedulers can not handle this intermittency, and in this paper we propose a first step towards an energy-aware task scheduler for constrained batteryless devices. We present a new energy-aware task scheduling algorithm that is able to optimally schedule application tasks to avoid power failures, and that will allow us to provide insights on the optimal look-ahead time for energy prediction. Our insights can be used as a basis for practical energy-aware scheduling and energy availability prediction algorithms. We formulate the scheduling problem as a Mixed Integer Linear Program. We evaluate its performance improvement when comparing it with state-of-the-art schedulers for batteryless IoT devices. Our results show that making the task scheduler energy aware avoids power failures and allows more tasks to successfully execute. Moreover, we conclude that a relatively short look-ahead energy prediction time of 8 future task executions is enough to achieve optimality.

cs.NI↗

arXiv2024

OROS: Online Operation and Orchestration of Collaborative Robots using 5G

The 5G mobile networks extend the capability for supporting collaborative robot operations in outdoor scenarios. However, the restricted battery life of robots still poses a major obstacle to their effective implementation and utilization in real scenarios. One of the most challenging situations is the execution of mission-critical tasks that require the use of various onboard sensors to perform simultaneous localization and mapping (SLAM) of unexplored environments. Given the time-sensitive nature of these tasks, completing them in the shortest possible time is of the highest importance. In this paper, we analyze the benefits of 5G-enabled collaborative robots by enhancing the intelligence of the robot operation through joint orchestration of Robot Operating System (ROS) and 5G resources for energysaving goals, addressing the problem from both offline and online manners. We propose OROS, a novel orchestration approach that minimizes mission-critical task completion times as well as overall energy consumption of 5G-connected robots by jointly optimizing robotic navigation and sensing together with infrastructure resources. We validate our 5G-enabled collaborative framework by means of Matlab/Simulink, ROS software and Gazebo simulator. Our results show an improvement between 3.65% and 11.98% in exploration task by exploiting 5G orchestration features for battery savings when using 3 robots.

cs.NI↗

arXiv2024

Analytical Modelling of Raw Data for Flow-Guided In-body Nanoscale Localization

Advancements in nanotechnology and material science are paving the way toward nanoscale devices that combine sensing, computing, data and energy storage, and wireless communication. In precision medicine, these nanodevices show promise for disease diagnostics, treatment, and monitoring from within the patients' bloodstreams. Assigning the location of a sensed biological event with the event itself, which is the main proposition of flow-guided in-body nanoscale localization, would be immensely beneficial from the perspective of precision medicine. The nanoscale nature of the nanodevices and the challenging environment that the bloodstream represents, result in current flow-guided localization approaches being constrained in their communication and energy-related capabilities. The communication and energy constraints of the nanodevices result in different features of raw data for flow-guided localization, in turn affecting its performance. An analytical modeling of the effects of imperfect communication and constrained energy causing intermittent operation of the nanodevices on the raw data produced by the nanodevices would be beneficial. Hence, we propose an analytical model of raw data for flow-guided localization, where the raw data is modeled as a function of communication and energy-related capabilities of the nanodevice. We evaluate the model by comparing its output with the one obtained through the utilization of a simulator for objective evaluation of flow-guided localization, featuring comparably higher level of realism. Our results across a number of scenarios and heterogeneous performance metrics indicate high similarity between the model and simulator-generated raw datasets.

cs.ET↗

arXiv2022

Energy-Aware Packet Schedulers for Battery-Less LoRaWAN Nodes

The Internet of Things (IoT) enables a wide variety of applications where large sensor networks are deployed in remote areas without power grid access. Thus, the sensor nodes often run on batteries, whose replacement and disposal represent important economical and environmental costs. To realize more sustainable IoT solutions, it is therefore desirable to adopt battery-less energy-neutral devices that can harvest energy from renewable sources and store it in super-capacitors, whose environmental impact is much lower than that of batteries. To achieve the energetic self-sustainability of such nodes, however, communication and computational processes must be optimized to make the best use of the scarce and volatile energy available. In this work, we propose different energy-aware packet scheduling algorithms for battery-less LoRaWAN nodes, and compare them in various simulated scenarios, using actual energy-harvesting measurements taken from a testbed. We show that an energy-aware design can significantly increase the number of transmitted packets, also lowering the mean time between packet transmissions, though (as predictable) the gain strongly depends on the harvesting capabilities of the nodes.

cs.NI↗

arXiv2021

An ns-3 implementation of a battery-less node for energy-harvesting Internet of Things

In the Internet of Things (IoT), thousands of devices can be deployed to acquire data from the environment and provide service to several applications in different fields. In many cases, it is desirable that devices are self-sustainable in terms of energy. Therefore,the research community is exploring the possibility of employing battery-less devices, where the energy is derived solely from external and/or environmental sources, such as solar panels. In this work, we propose an ns-3 model of a (super) capacitor, which can be used as the storage of the harvested energy in a battery-less IoT device, and add the support for the intermittent behavior of devices, turning off/on according to their energy level. To exemplify the use of the model, we apply it to a LoRaWAN node, and compare the simulation outcomes with results in the literature obtained with mathematical analysis, confirming the accuracy of the implementation. Then, we show the importance of analyzing the interaction between energy availability and communication performance, paving the way for more accurate and realistic simulations in the field. The implemented code is made available as open source.

cs.NI↗