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.

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.

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.

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.

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 hold promise in precision medicine, offering 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 intuitively 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 communication and 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, reinforced by the adaptability and resilience of Heterogeneous Graph Transformers (HGTs), 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.

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 on-board 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 energy-saving 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.

Battery life for collaborative robotics scenarios is a key challenge limiting operational uses and deployment in real life. Mission-Critical tasks are among the most relevant and challenging scenarios. As multiple and heterogeneous on-board sensors are required to explore unknown environments in simultaneous localization and mapping (SLAM) tasks, battery life problems are further exacerbated. Given the time-sensitivity of mission-critical operations, the successful completion of specific tasks in the minimum amount of time is of paramount importance. In this paper, we analyze the benefits of 5G-enabled collaborative robots by enhancing the Robot Operating System (ROS) capabilities with network orchestration features for energy-saving purposes. We propose OROS, a novel orchestration approach that minimizes mission-critical task completion times of 5G-connected robots by jointly optimizing robotic navigation and sensing together with infrastructure resources. Our results show that OROS significantly outperforms state-of-the-art solutions in exploration tasks completion times by exploiting 5G orchestration features for battery life extension.

Tiny batteryless Internet of Things (IoT) devices that depend on the harvested energy from their environment provide a promising alternative for a sustainable IoT vision. These devices use small capacitors as energy storage, which together with the unpredictable and dynamic harvesting environment results in intermittent on–off behavior of the device. The crucial issue to effectively use batteryless IoT devices is to find a way of enabling the successful execution of application tasks in face of this intermittency. As the conventional computing models cannot handle this behavior, in this article, we present an energy-aware task scheduler for batteryless IoT devices based on dependencies and priorities, which can intelligently schedule the application tasks avoiding power failures and maintaining forward progress. With the properly defined voltage thresholds for each application task, using our energy-aware task scheduler a safer execution can be ensured. We evaluate our approach based on emulated and real experiments and validate it using two types of power management units (PMUs) (environment emulator and intelligent PMU based on the AEM10941 chip). Our results show that the energy-aware task scheduler is able to react and adapt the execution to environmental changes, avoiding power failures. Comparing to the state-of-the-art scheduling approaches, which are mostly not aware of the energy, we show that our energy-aware task scheduler can keep the device on during the full time of the experiment, executing more tasks when a relatively small capacitor of 10 mF or less is used at harvesting currents as low as$40 ~\mu \text{A}$.

Many IoT scenarios, such as smart cities, wild life monitoring, or smart agriculture, involve thousands of battery-powered devices. The disposal and replacement of such batteries represent an important economical and environmental cost. To realize Green IoT solutions, it is therefore desirable to adopt battery-less energy-neutral devices that can harvest power from renewable sources, such as solar or wind energy and store it in much more sustainable capacitors. The limited and inconstant energy supply and the limited energy storage capacity of such devices, however, require special care in the design of communication and computational processes, which have a major impact on the energy consumption of the devices. In this work, we explore multiple elements that could affect the device energy and communication capabilities of LoRaWAN devices. We propose and compare different energy-aware packet transmission algorithms, and test them in a scenario where values for the harvested power are collected from real testbeds. We show that the number of successfully transmitted packets can be doubled by using an energy-aware design approach.

The numerous applications of industrial automation have always posed many challenges for wireless connectivity. In the last decade, IEEE 802.15.4e time-slotted channel hopping (TSCH) networks have provided high reliability and low-power operation in such challenging industrial environments. Typically, TSCH networks employ one modulation at the physical layer and are thus limited by the characteristics of the chosen modulation in terms of, among others, data rate, reliability and energy efficiency. To tackle these limitations and to improve network performance and flexibility in those challenging industrial environments, this work explores the simultaneous use of multiple modulations in a TSCH network. Traditionally, TSCH relies on fixed-duration slots, large enough to send a packet of any size given the fixed data rate. In order to avoid wasting airtime when simultaneously using modulations with different data rates, we propose the concept of slot bonding. This allows the creation of different-sized bonded slots with a duration adapted to the data rate of each chosen modulation. To analyze the proposed slot bonding technique, we formally describe the TSCH slot bonding problem in terms of optimizing the packet delivery ratio while minimizing radio on time, with the inclusion of parent selection and interference avoidance. Afterward, we propose a genetic algorithm that allows us to implement the problem and find solutions heuristically. Finally, we provide insights into preferred parent selection and modulation configurations by using this heuristic approach during extensive simulation experimentation in which the scalability advantage of slot bonding over longer fixed-duration slots is also shown.

Billions of Internet-of-Things (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 future IoT. As an alternative, batteryless devices run on long-lived capacitors charged using energy harvesters. The small energy storage capacity of capacitors results in an intermittent on-off behavior. 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 batteryless LoRaWAN devices for uplink and downlink (UL/DL) transmissions and we evaluate their performance in terms of parameters that define the model (i.e., device configuration, application behavior, and environmental conditions). Results show that LoRaWAN batteryless communications are feasible if choosing the proper configuration (i.e., capacitor size and turn-on voltage threshold) for different application behavior [i.e., transmission interval, UL/DL packet sizes (PSs)], and environmental conditions (i.e., energy harvesting rate). Since DL in the second reception window highly affects the performance, only small DL PSs should be considered for these devices. Besides, a 47-mF capacitor can support 1 B 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 B SF7 transmissions every 9 s.

Low-power wireless mesh networks provide connectivity for a wide range of applications in industrial scenarios. For many years, IEEE 802. 15.4e Time-Slotted Channel Hopping (TSCH) networks have proven their efficiency in such environments, providing high reliability and low-power operation. TSCH networks run on top of one physical (PHY) layer and are thus limited by the characteristics of the chosen PHY layer in terms of, among others, data rate, reliability and energy efficiency. To tackle these limitations and to improve network performance and flexibility in those challenging industrial environments, this work explores the simultaneous use of multiple PHYs, and more specifically multiple modulation and coding schemes (MCSs), in a TSCH network. Traditionally, TSCH relies on fixed-duration slots, large enough to send a packet of any size given the fixed data rate. In order to avoid wasting airtime when simultaneously using multiple PHYs or MCSs with different data rates, we first introduce the concept of slot bonding. This allows the creation of different-sized bonded slots with a duration adapted to the data rate of each chosen PHY. Afterwards, we formally describe TSCH slot bonding using a Mixed Integer Linear Program (MILP) model. Finally, we use this model to determine the optimal MCS configuration with a short slot frame length that causes network saturation and show the scalability advantage of slot bonding in terms of packet delivery ratio.

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).

We focus on the problem of managing a shared physical wireless sensor network (WSN) 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 WSNs infrastructures.

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.

We propose an optimization framework to perform resource allocation in virtual sensor networks. Sensor network virtualization is a promising paradigm to improve flexibility of wireless sensor networks which allows to dynamically assign physical resources to multiple stakeholder 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). The proposed framework is finally applied to realistic network topologies to assess the gain involved in letting multiple applications share a common physical network with respect to one-application, one-Network vertical design approaches.