Search Results (165)

The H2IOSC project aims to establish a federated cluster of European distributed research infrastructures involved in the humanities and cultural heritage sectors, with operating nodes across Italy. Through four key RIs—DARIAH-IT, CLARIN, OPERAS, and E-RIHS—the project promotes collaboration among researchers with interdisciplinary expertise. Within this framework, DIGILAB functions as the digital access platform for the Italian node of E-RIHS. Conceived as a socio-technical infrastructure for the Heritage Science community, DIGILAB is designed to manage heterogeneous data and metadata through advanced knowledge graph representations. The platform adheres to the FAIR principles and supports the complete data lifecycle, enabling the development and maintenance of Heritage Digital Twins. DIGILAB integrates diverse categories of information related to cultural sites and objects, encompassing historical and artistic datasets, diagnostic analyses, 3D models, and real-time monitoring data. This monitoring capability is achieved through the deployment of cutting-edge Internet of Things (IoT) technologies and large-scale Wireless Sensor Networks (WSNs). As part of DIGILAB, we developed SENNSE (v1.0), a fully open hardware/software platform dedicated to environmental and structural monitoring. SENNSE allows the remote, real-time observation and control of cultural heritage sites (collecting microclimatic parameters such as temperature, humidity, noise levels) and of cultural objects (collecting object-specific data including vibrations, light intensity, and ultraviolet radiation). The visualization and analytical tools integrated within SENNSE transform these datasets into actionable insights, thereby supporting advanced research and conservation strategies within the Cultural Heritage domain. In the following sections, we provide a detailed description of the SENNSE platform, outlining its hardware components and software modules, and discussing its benefits. Furthermore, we illustrate its application through two representative use cases: one conducted in a controlled laboratory environment and another implemented in a real-world heritage context, exemplified by the “Biblioteca Bernardini” in Lecce, Italy. Full article

Unmanned aerial vehicle (UAV) swarms offer an efficient solution for data collection from widely distributed ground users (GUs). However, incomplete environment information and frequent changes make it challenging for standard centralized planning or pure reinforcement learning approaches to simultaneously maintain global solution quality and local flexibility. We propose a hierarchical data collection framework for heterogeneous UAV-assisted wireless sensor networks (WSNs). A small set of high-capability UAVs (H-UAVs), equipped with substantial computational and communication resources, coordinate regional coverage, trajectory planning, and uplink transmission control for numerous resource-constrained low-capability UAVs (L-UAVs) across power-Voronoi-partitioned areas using multi-agent deep reinforcement learning (MADRL). Specifically, we employ Multi-Agent Deep Deterministic Policy Gradient (MADDPG) to enhance H-UAVs’ decision-making capabilities and enable coordinated actions. The partitions are dynamically updated based on GUs’ data generation rates and L-UAV density to balance workload and adapt to environmental dynamics. Concurrently, a large number of L-UAVs with limited onboard resources perform self-organized data collection from GUs and execute opportunistic relaying to a remote access point (RAP) via H-UAVs. Within each Voronoi cell, L-UAV motion follows a weighted Vicsek model that incorporates GUs’ age of information (AoI), link quality, and congestion avoidance. This spatial decomposition combined with decentralized weak-swarm control enables scalability to large-scale L-UAV deployments. Experiments demonstrate that the proposed strong and weak agent MADDPG (SW-MADDPG) scheme reduces AoI by 30% and 21% compared to No-Voronoi and Heuristic-HUAV baselines, respectively. Full article

Monitoring the health of power lines (PL) is essential for ensuring reliable power delivery, facilitating predictive maintenance, and maintaining a resilient grid infrastructure. Given the extensive length of PL networks, large numbers of wireless sensor nodes must be deployed, often in remote and harsh environments where battery replacement is costly and impractical. To address these limitations, this work proposes a hybrid energy-harvesting approach that combines piezoelectric and photovoltaic (PV) technologies to enable long-term, battery-free PL monitoring. The primary energy source is a compact, tunable, magnetically coupled piezoelectric vibrational energy harvester (VEH) that exploits local magnetic field distribution, inducing mechanical excitation of a cantilever and enabling the harvesting of vibrational energy near the PL at a frequency of 50 Hz. A complementary PV harvester is integrated to ensure operation during power outages or conditions where the piezoelectric excitation is reduced, thereby enhancing system robustness. Electromechanical characterization and a lumped-parameter model show good agreement with experimental results of the proposed VEH. The system is validated both on a PL test bench (5 A–10 A) and through inertial excitation using an electrodynamic shaker, demonstrating stable performance across a wide range of operating conditions. The combined hybrid architecture highlights a promising pathway toward self-sustaining, maintenance-free sensor nodes for next-generation power line monitoring. Finally, we demonstrate the feasibility of using such system for powering a WSN node by comparing the power produced by the proposed system with the power consumption of a potential application. Full article

The proliferation of wireless sensor networks in remote and inaccessible areas demands efficient data collection approaches that minimize energy consumption while ensuring comprehensive coverage. Traditional data retrieval methods face significant challenges when sensors are sparsely distributed across extensive areas, particularly in scenarios where direct sensor access is impractical due to terrain constraints or operational limitations. This research addresses these challenges through a novel hybrid optimization framework that combines integer linear programming (ILP) with multiple traveling salesperson problem (mTSP) algorithms for drone-based data collection in wireless sensor networks (WSNs). The methodology employs a two-phase approach, where ILP optimally determines strategic access point locations for sensor clustering based on communication capabilities, followed by mTSP optimization to generate efficient inter-AP flight trajectories rather than individual sensor visits. Comprehensive simulations across diverse network configurations and drone quantities demonstrate consistent performance improvements, with travel distance reductions reaching 32% compared to conventional mTSP implementations. Comparative evaluation against established clustering algorithms including Voronoi, DBSCAN, Constrained K-Means, Graph-Based clustering, and Greedy Circle Packing confirms that ILP consistently achieves optimal access point allocation while maintaining superior routing efficiency. Additionally, a novel quality assessment metric quantifies sensor grouping effectiveness, revealing that ILP-based clustering advantages become increasingly pronounced with higher sensor densities, providing substantial operational benefits for large-scale wireless sensor network deployments. Full article

Wireless sensor networks (WSNs) are an advanced solution for data collection in Internet of Things (IoT) applications and remote and harsh environments. These networks rely on a collection of distributed sensors equipped with wireless communication capabilities to collect low-cost and small-scale data. WSNs face numerous challenges, including network congestion, slow speeds, high energy consumption, and a short network lifetime due to their need for a constant and stable power supply. Therefore, improving the energy efficiency of sensor nodes through solar energy harvesting (SEH) would be the best option for charging batteries to avoid excessive energy consumption and battery replacement. In this context, modern wireless communication technologies, such as Wi-Fi and Li-Fi, emerge as promising solutions. Wi-Fi provides internet connectivity via radio frequencies (RF), making it suitable for use in open environments. Li-Fi, on the other hand, relies on data transmission via light, offering higher speeds and better energy efficiency, making it ideal for indoor applications requiring fast and reliable data transmission. This paper aims to integrate Wi-Fi and Li-Fi technologies into the SEH-WSN architecture to improve performance and efficiency when used in all applications. To achieve reliable, efficient, and high-speed bidirectional communication for multiple devices, the paper utilizes a Markov model, sleep scheduling, and smart switching algorithms to reduce power consumption, increase signal-to-noise ratio (SNR) and throughput, and reduce bit error rate (BER) and latency by controlling the technology and power supply used appropriately for the mode, sleep, and active states of nodes. Full article

Aging infrastructure is a growing concern worldwide, with many bridges exceeding 50 years of service, prompting questions about their structural integrity. Over the past decade, the deterioration of bridges has driven extensive research into Structural Health Monitoring (SHM), a tool for early detection of structural deterioration, with particular emphasis on remote-sensing technologies. This review combines a scientometric analysis and a state-of-the-art review to assess recent advancements in the field. From a dataset of 702 publications (2014–2024), 171 relevant papers were analyzed, covering key SHM aspects including sensing devices, data acquisition, processing, damage detection, and reporting. Results show a 433% increase in publications, with the United States leading in output (28.65%), and Glisic, B., with collaborators forming the largest research cluster (11.7%). Accelerometers are the most commonly used sensors (50.88%), and data processing dominates the research focus (50.29%). Key challenges identified include cost (noted in 17.5% of studies), data corruption, and WSN limitations, particularly energy supply. Trends show a notable growth in AI applications (400%), and increasing interest in low-cost, crowdsource-based SHM using smartphones, MEMS, and cameras. These findings highlight both progress and future opportunities in SHM of footbridges. Full article

Wildfires are complex natural disasters that significantly impact ecosystems and human communities. The early detection and prediction of forest fire risk are necessary for effective forest management and resource protection. This paper proposes an innovative early detection system based on a wireless sensor network (WSN) composed of interconnected Arduino nodes arranged in a hybrid circular/star topology. This configuration reduces the number of required nodes by 53–55% compared to conventional Mesh 2D topologies while enhancing data collection efficiency. Each node integrates temperature/humidity sensors and uses ZigBee communication for the real-time monitoring of wildfire risk conditions. This optimized topology ensures 41–81% lower latency and 50–60% fewer hops than conventional Mesh 2D topologies. The system also integrates artificial intelligence (AI) algorithms (multiclass logistic regression) to process sensor data and predict fire risk levels with 99.97% accuracy, enabling proactive wildfire mitigation. Simulations for a 300 m radius area show the non-dense hybrid topology is the most energy-efficient, outperforming dense and Mesh 2D topologies. Additionally, the dense topology achieves the lowest packet loss rate (PLR), reducing losses by up to 80.4% compared to Mesh 2D. Adaptive routing, dynamic round-robin arbitration, vertical tier jumps, and GSM connectivity ensure reliable communication in remote areas, providing a cost-effective solution for wildfire mitigation and broader environmental monitoring. Full article

Smart agriculture is transforming traditional farming by integrating advanced sensor-based systems, intelligent control technologies, and sustainable energy solutions to meet the growing global demand for food while reducing environmental impact. This review presents a comprehensive analysis of recent innovations in smart agriculture, focusing on the deployment of IoT-based sensors, wireless communication protocols, energy-harvesting methods, and automated irrigation and fertilization systems. Furthermore, the paper explores the role of artificial intelligence (AI), machine learning (ML), computer vision, and big data analytics in monitoring and managing key agricultural parameters such as crop health, pest and disease detection, soil conditions, and water usage. Special attention is given to decision-support systems, precision agriculture techniques, and the application of remote and proximal sensing technologies like hyperspectral imaging, thermal imaging, and NDVI-based indices. By evaluating the benefits, limitations, and emerging trends of these technologies, this review aims to provide insights into how smart agriculture can enhance productivity, resource efficiency, and sustainability in modern farming systems. The findings serve as a valuable reference for researchers, practitioners, and policymakers working towards sustainable agricultural innovation. Full article

Slope failures in open-pit mining pose significant operational and safety issues, underscoring the importance of implementing effective stability monitoring frameworks for early hazard detection to allow for timely intervention and risk mitigation. This systematic review presents a comprehensive synthesis of existing and emerging methods and technologies used for slope stability monitoring in open-pit mining, including both remote sensing and in situ methods, as well as advanced technologies, such as Artificial Intelligence (AI), the Internet of Things (IoT), and Wireless Sensor Networks (WSNs). Using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) 2020 guidelines, a total of 49 studies were selected from a collection of four engineering databases, and a comparative analysis was conducted to determine the underlying differences between the various methods for open-pit slope stability monitoring in terms of their performance across key attributes, such as monitoring accuracy, spatial and temporal coverage, operational complexity, and economic viability. Their juxtaposition highlighted the notion that no universally optimal slope stability monitoring system exists, due to a series of compromises that arise as a result of inherent technological limitations and site-specific constraints. Notably, remote sensing methods offer large-scale, non-intrusive monitoring, but are often limited by environmental factors and data acquisition infrequency, whereas in situ methods provide high precision, but suffer from limited spatial coverage and scalability. This review further highlights the capacity of emerging methods and technologies to address these limitations, providing suggestions for future research directions involving the integration of multiple sensing technologies for the enhancement of monitoring capabilities. This study provides a consolidated knowledge base on open-pit slope stability monitoring methods, technologies, and techniques, to guide the development of integrated, cost-effective, and scalable slope monitoring solutions that enhance mine safety and efficiency. Full article

The working of the Internet of Things (IoT) ecosystem indeed depends extensively on the mechanisms of real-time data collection, sharing, and automatic operation. Among these fundamentals, wireless sensor networks (WSNs) are important for maintaining a countenance with their many distributed Sensor Nodes (SNs), which can sense and transmit environmental data wirelessly. Because WSNs possess advantages for remote data collection, they are severely hampered by constraints imposed by the limited energy capacity of SNs; hence, energy-efficient routing is a pertinent challenge. Therefore, in the case of clustering and routing mechanisms, these two play important roles where clustering is performed to reduce energy consumption and prolong the lifetime of the network, while routing refers to the actual paths for transmission of data. Addressing the limitations witnessed in the conventional IoT-based routing of data, this proposal presents an FL-oriented framework that presents a new energy-efficient routing scheme. Such routing is facilitated by the ADDQL model, which creates smart high-speed routing across changing scenarios in WSNs. The proposed ADDQL-IRHO model has been compared to other existing state-of-the-art algorithms according to multiple performance metrics such as energy consumption, communication delay, temporal complexity, data sum rate, message overhead, and scalability, with extensive experimental evaluation reporting superior performance. This also substantiates the applicability and competitiveness of the framework in variable-serviced IoT-oriented WSNs for next-gen intelligent routing solutions. Full article

The Internet of Military Things (IoMT) is transforming defense operations by enabling the seamless integration of sensors and actuators for the real-time transmission of critical data in diverse military environments. End devices (EDs) collect essential information, including troop locations, health metrics, equipment status, and environmental conditions, which are processed to enhance situational awareness and operational efficiency. In scenarios involving large-scale deployments across remote or austere regions, wired communication systems are often impractical and cost-prohibitive. Wireless sensor networks (WSNs) provide a cost-effective alternative, with Long-Range Wide Area Network (LoRaWAN) emerging as a leading protocol due to its extensive coverage, low energy consumption, and reliability. Existing LoRaWAN network simulation modules, such as those in ns-3, primarily support uniform periodic data transmissions, limiting their applicability in critical military and healthcare contexts that demand adaptive transmission rates, resource optimization, and prioritized data delivery. These limitations are particularly pronounced in healthcare monitoring, where frequent, high-rate data transmission is vital but can strain the network’s capacity. To address these challenges, we developed an enhanced sensor data sender application capable of simulating priority-based traffic within LoRaWAN, specifically targeting use cases like border security and healthcare monitoring. This study presents a priority-based data flow control protocol designed to optimize network performance under high-rate healthcare data conditions while maintaining overall system reliability. Simulation results demonstrate that the proposed protocol effectively mitigates performance bottlenecks, ensuring robust and energy-efficient communication in critical IoMT applications within austere environments. Full article

Consumer interest in medicinal and aromatic herbs is on the rise, with buyers increasingly concerned about the microbiological quality of nutraceutical and aromatic plants. The use of Unmanned Aerial Vehicles (UAVs) and sensor technology allows for high-resolution crop monitoring, particularly in the production of rosemary and sage in Grotte (Italy), Agrigento District. The aim of this study is to evaluate the efficacy of UAV-based time series remote sensing data and multimodal data fusion using RGB and multispectral sensors in rosemary and sage harvesting time individuation and the microbiological quality of these nutraceutical and aromatic plants before and after an innovative and sustainable drying process. The multispectral data were acquired with a DJI multispectral camera mounted on a Phantom 4 UAV. The use of drones in the aromatic plant crops can lead to improved efficiency, productivity, and profitability for farmers and businesses. Italian producers follow strict hygiene regulations to reduce bacterial contamination, particularly during the crucial drying process. A rapid drying method at low temperature using a dryer powered by a photovoltaic renewable energy source (RES) helps preserve the quality of the plants. Real-time monitoring of the drying process is enabled through a system based on wireless sensor networks (WSN), providing valuable data on moisture content, drying rates, and microbial stability. Overall, the innovative use of drones, sensor technology, and renewable energy sources in the production of aromatic herbs like rosemary and sage holds great potential for enhancing crop quality, shelf life, and overall sustainability in the chain food industry. Full article

Wireless Sensor Nodes (WSNs) are becoming increasingly popular in various industrial sectors due to their capability of real-time remote monitoring of assets. Powering these devices with vibrational energy harvesters (EHs) provides multiple benefits, such as minimal maintenance and ideally infinite lifespan. Among the vibrational harvesters, translational electromagnetic ones (TEMEHs) are a promising solution due to their simple and reliable architecture and their ability to harvest energy at low frequencies. However, a major challenge is achieving a high power density. In this paper, recent literature about this typology of harvesters is reviewed. Different techniques to tune the resonance frequencies to the fundamental frequencies of the ambient vibrations are analyzed, such as non-linearities and multi-DOF configurations. The harvesters are classified on the basis of the suspension type, highlighting advantages and disadvantages. A final comparison is carried out in terms of NPD and FoMv, two indexes that evaluate power density in relation to size and excitation amplitudes. Full article

Internet of Things (IoT) technology has facilitated the deployment of autonomous sensors in remote and challenging environments, enabling substantial advancements in environmental monitoring and data collection. IoT sensors continuously gather data, transmitting it to a central Base Station (BS) via designated Cluster Heads (CHs). However, data flow encounters frequent congestion at CH nodes, negatively impacting network performance and Quality of Service (QoS). This paper introduces a novel congestion control strategy tailored for Wireless Sensor Networks (WSNs) to balance energy efficiency and data reliability. The proposed approach follows an eight-step process, integrating Generative Adversarial Networks (GANs) for enhanced clustering and Ant Colony Optimization (ACO) for optimal CH selection and routing. GANs simulate realistic node clustering, achieving better load distribution and energy conservation across the network. ACO then selects CHs based on energy levels, distance, and network centrality, using pheromone-based routing to adaptively manage data flows. A congestion factor (CF) threshold is also incorporated to dynamically reroute traffic when congestion risks arise, preserving QoS. Simulation results show that this approach significantly improves QoS metrics, including latency, throughput, and reliability. Comparative evaluations reveal that our method outperforms existing frameworks, such as Fuzzy Structure and Genetic-Fuzzy (FSFG), Deep Reinforcement Learning Cache-Aware Congestion Control (DRL-CaCC), and Adaptive Cuckoo Search Rate Optimization (ACSRO). Full article

This paper investigates the feasibility of harvesting Radio Frequency (RF) energy from the Wi-Fi frequency band to power low-power Internet-of-Things (IoT) devices. With the increasing prevalence of IoT applications and wireless sensor networks (WSNs), there is a critical need for sustainable energy sources that can extend the operational lifespan of these devices, particularly in remote locations, where access to reliable power supplies is limited. The paper describes the design, simulation, and fabrication of a dual-band antenna capable of operating at 2.4 GHz and 5 GHz, the frequencies used by Wi-Fi. The simulation and experimental results show that the proposed design is efficient based on the reflection coefficient. Using a high-frequency simulator, we developed two C-shaped and an F-shaped microstrip antenna design, optimized for impedance matching and efficient RF–DC conversion.The captured RF energy is converted into usable electrical power that can be directly utilized by low-power IoT devices or stored in batteries for later use. The paper introduces an efficient design for dual-band antennas to maximize the reception of Wi-Fi signals. It also explains the construction of an impedance-matching network to reduce signal reflection and improve power transfer efficiency. The results indicate that the proposed antennas can effectively harvest Wi-Fi energy, providing a sustainable power source for IoT devices. The practical implementation of this system offers a promising solution to the energy supply challenges faced by remote and low-power IoT applications, paving the way for more efficient and longer-lasting wireless sensor networks. Full article