The use of Unmanned Aerial Vehicles (UAVs) or drones has grown rapidly in both civilian and military operations over the last few decades. Multi-UAV systems are preferred to single-UAV systems, as they are more efficient and cost-effective when completing missions collaboratively. However, like other ad-hoc networks, UAV networks face the challenge of effective communication. Existing research in mobile ad-hoc networks (MANETs) and vehicular ad-hoc networks (VANETs) does not fully address the unique characteristics of UAV networks, which can exhibit varying levels of dynamism, intermittent links, and fluid topology. Moreover, drones need to communicate with each other and ground stations in Flying Ad-hoc Networks. Therefore, routing protocols in such networks must select the most effective paths for communication while ensuring reliable and stable data transmission. This article examines several recently developed routing protocols in UAV communication, detailing their construction methods. As routing is quite challenging in UAVs, where communication performance depends on various factors, this research introduces performance metrics to analyze the efficiency of these protocols. Routing protocols in UAV networks must adapt to high mobility, dynamic topology, intermittent links, power constraints, and changing link quality. Since the lifespan of UAV nodes is limited, seamless handovers are crucial, and the energy efficiency of protocols at different layers should also be considered. Although the reviewed protocols address several aspects of designing routing protocols for UAV communication, some challenges remain. Thus, this article provides a comprehensive exploration of routing protocols in UAV communication, along with a discussion of some open research areas.

Energy harvesting sensor systems typically incorporate energy buffers (e.g., rechargeable batteries and supercapacitors) to accommodate fluctuations in supply. However, the presence of these elements limits the miniaturization of devices. In recent years, researchers have proposed a new paradigm, transient computing, where systems operate directly from the energy harvesting source and allow computation to span across power cycles, without adding energy buffers. Various transient computing approaches have addressed the challenge of power intermittency by retaining the processor’s state using non-volatile memory. However, no generic approach has yet been proposed to retain the state of peripherals external to the processing element. This paper proposes RESTOP, flexible middleware which retains the state of multiple external peripherals that are connected to a computing element (i.e., a microcontroller) through protocols such as SPI or I ${}^2$ C. RESTOP acts as an interface between the main application and the peripheral, which keeps a record, at run-time, of the transmitted data in order to restore peripheral configuration after a power interruption. RESTOP is practically implemented and validated using three digitally interfaced peripherals, successfully restoring their configuration after power interruptions, imposing a maximum time overhead of 15% when configuring a peripheral. However, this represents an overhead of only 0.82% during complete execution of our typical sensing application, which is substantially lower than existing approaches.

Transient computing enables application execution to be performed despite power outages. Although it handles the non-deterministic nature of energy harvesting (EH), sensor systems envisioned by the IoT seek more cost-and volume-effective solutions, which are better tailored to application requirements. Additionally, a major drawback of transient computing, keeping track of time, hinders its widespread adoption in the IoT. To overcome these challenges, this paper proposes a control flow for sensor systems by combining two state-of-the-art transient computing schemes in an energy-aware manner, underpinned by a strategy for timekeeping. It enables application execution to be reliably performed even under the most severe EH conditions, with an improved cost and volume efficiency, i.e., smaller energy storage. Benefiting from the combination of the two schemes, dynamic adjustment of system performance is achieved, while the time is accurately tracked. To illustrate the applicability of this flow to actual sensor systems, two case studies: a bicycle trip computer and a step counter, are presented. Empirical results reveal that, even with a tiny amount of energy harvested (≃tens of μJ), our proposed approach can meet application requirements with smaller storage, i.e., 40% and 66% reduction in required capacitance for the presented case studies.

Mixed reality (MR) laboratories combine physical elements with virtual components, providing convenient experiential environments for testing engineering concepts. This article reports the design, validation, and implementation of an MR laboratory for engineering students to practice the implementation of control algorithms in microcontrollers. First, the design of the MR lab is described in detail. In this, a seesaw electromechanical system is emulated, being synchronized with electrical signals that represent sensors’ measurements and actuators’ commands. Thus, a control algorithm implemented by the students in a microcontroller can affect the simulated system in real time. The real seesaw system was used to validate the simulated plant in the MR lab, finding that the same control algorithm effectively controls both the simulated and physical seesaw systems. A practice, designed based on Kolb’s experiential learning cycle, where the students must implement P, PI, and PID controllers in the MR lab, was implemented. A survey was conducted to assess the students’ motivation, and a post-test was administered to evaluate their learning outcomes.

The rapid growth of the Internet of Things (IoT) demands solutions that can secure massive streams of sensitive data without sacrificing performance. Traditional blockchains struggle in IoT environments, facing significant challenges with transaction speed, scalability, and privacy. This paper introduces PRIVOT, a novel blockchain architecture that integrates a Directed Acyclic Graph (DAG) for high-throughput consensus with lightweight zero-knowledge proofs (ZKPs) for confidential transactions, rateless coded computation for private analytics, and an AI-driven manager that dynamically balances security and efficiency. Our simulations show that PRIVOT significantly outperforms traditional blockchain approaches, achieving high transaction throughput (up to 480 TPS on a 500-device network) with confirmation latencies under 2.1 seconds, even under heavy load. The framework provides robust privacy, limiting data leakage to less than 0.1% against significant node collusion, while keeping computational overhead low enough for resource-constrained IoT devices. By unifying these techniques, PRIVOT offers a scalable and resilient solution ideal for large-scale IoT deployments where both high performance and strong privacy are paramount.

Las proteínas, al ser hidrolizadas, liberan péptidos que pue-den tener efectos benéficos en el cuerpo humano. Gracias a la bioinformática, en la actualidad es posible hacer una pre-dicción de su potencial bioactivo usando plataformas compu-tacionales. Estas herramientas permiten simular procesos como la hidrólisis y evaluar la actividad de péptidos contra enfermedades o microorganismos. Así, los investigadores pueden ahorrar tiempo y recursos, guiando sus estudios de forma más precisa. Aunque no reemplazan los experimentos de laboratorio, los métodos in silico se han convertido en aliados clave de la ciencia actual.