Search Results (190)

The signal-processing architecture of passive surface acoustic wave (SAW) sensors presents significant implementation challenges due to its radar-like operational principle and the inherent complexity of discrete component-based hardware design. While System-in-Package (SiP) has demonstrated remarkable success in miniaturizing electronic systems for smartphones, automotive electronics, and IoT applications, its potential for revolutionizing SAW sensor interrogator design remains underexplored. This paper presents a novel architecture that synergistically combines time-domain ADC design with SiP-based miniaturization to achieve unprecedented simplification of SAW sensor readout systems. The proposed time-domain ADC incorporates an innovative delay chain calibration methodology that integrates physical unclonable function (PUF) principles during time-to-digital converter (TDC) characterization, enabling the simultaneous generation of unique system IDs. The experimental results demonstrate that the integrated security mechanism provides variable-length bit entropy for device authentication, and has a reliability of 97.56 and uniqueness of 49.43, with 53.28 uniformity, effectively addressing vulnerability concerns in distributed sensor networks. The proposed SiP is especially suitable for space-constrained IoT applications requiring robust physical-layer security. This work advances the state-of-the-art wireless sensor interfaces by demonstrating how time-domain signal processing and advanced packaging technologies can be co-optimized to address performance and security challenges in next-generation sensor systems. Full article

Underwater acoustic sensor networks, crucial for marine monitoring, face significant challenges, including limited bandwidth, high delay, and severe energy constraints. Addressing these limitations requires an energy-efficient design to ensure network survivability, reliability, and reduced operational costs. This paper proposes an artificial intelligence-driven framework aimed at enhancing energy efficiency and sustainability in applications of marine wildlife monitoring in underwater sensor networks, according to the vision of implementing an underwater acoustic sensor network. The framework integrates intelligent computing directly into underwater sensor nodes, employing lightweight AI models to locally classify marine species. Transmitting only classification results, instead of raw data, significantly reduces data volume, thus conserving energy. Additionally, a software-defined radio methodology dynamically adapts transmission parameters such as modulation schemes, packet length, and transmission power to further minimize energy consumption and environmental disruption. GNU Radio simulations evaluate the framework effectiveness using metrics like energy consumption, bit error rate, throughput, and delay. Adaptive transmission strategies implicitly ensure reduced energy usage as compared to non-adaptive transmission solutions employing fixed communication parameters. The results illustrate the framework ability to effectively balance energy efficiency, performance, and ecological impact. This research contributes directly to ongoing development in sustainable and energy-efficient underwater wireless sensor network design and deployment. Full article

This work presents an on-field noise analysis during the class breaks in Greek school units (a high school and a senior high school) based on the design and deployment of low-cost IoT sensor nodes and IoT platforms. The course breaks form 20% of a regular school day, during which intense mobility and high noise levels usually evolve. Indoor noise levels, along with environmental conditions, have been measured through a wireless network that comprises IoT nodes that integrate humidity, temperature, and acoustic level sensors. PM₁₀ and PM_2.5 values have also been acquired through data sensors located nearby the school complex. School buildings that have been recently renovated for minimizing their energy footprint and CO₂ emissions have been selected in comparison with similar works in academia. The data are collected, shipped, and stored into a time-series database in cloud facilities where an IoT platform has been developed for processing and analysis purposes. The findings show that low-cost sensors can efficiently monitor noise levels after proper adjustments. Additionally, the statistical evaluation of the received sensor measurements has indicated that ubiquitous high noise levels during the course breaks potentially affect teachers’ leisure time, despite the thermal isolation of the facilities. Within this context, we prove that the proposed IoT Sensor Network could form a tool to essentially monitor school infrastructures and thus to prompt for improvements regarding the building facilities. Several guides to further mitigate noise and achieve high-quality levels in learning institutes are also described. Full article

The underwater domain presents unique challenges and opportunities for scientific exploration, resource extraction, and environmental monitoring. Autonomous underwater vehicles (AUVs) rely on simultaneous localization and mapping (SLAM) for real-time navigation and mapping in these complex environments. However, traditional SLAM techniques face significant obstacles, including poor visibility, dynamic lighting conditions, sensor noise, and water-induced distortions, all of which degrade the accuracy and robustness of underwater navigation systems. Recent advances in deep learning (DL) have introduced powerful solutions to overcome these challenges. DL techniques enhance underwater SLAM by improving feature extraction, image denoising, distortion correction, and sensor fusion. This survey provides a comprehensive analysis of the latest developments in DL-enhanced SLAM for underwater applications, categorizing approaches based on their methodologies, sensor dependencies, and integration with deep learning models. We critically evaluate the benefits and limitations of existing techniques, highlighting key innovations and unresolved challenges. In addition, we introduce a novel classification framework for underwater SLAM based on its integration with underwater wireless sensor networks (UWSNs). UWSNs offer a collaborative framework that enhances localization, mapping, and real-time data sharing among AUVs by leveraging acoustic communication and distributed sensing. Our proposed taxonomy provides new insights into how communication-aware SLAM methodologies can improve navigation accuracy and operational efficiency in underwater environments. Furthermore, we discuss emerging research trends, including the use of transformer-based architectures, multi-modal sensor fusion, lightweight neural networks for real-time deployment, and self-supervised learning techniques. By identifying gaps in current research and outlining potential directions for future work, this survey serves as a valuable reference for researchers and engineers striving to develop robust and adaptive underwater SLAM solutions. Our findings aim to inspire further advancements in autonomous underwater exploration, supporting critical applications in marine science, deep-sea resource management, and environmental conservation. Full article

Models for must fermentation kinetics are investigated and used in simulations for enabling control strategies based on temperature adjustment during the alcoholic fermentation process using the estimated CO₂ rate. An acoustic emission digital processing approach for estimating the CO₂ rate in the must fermentation process is proposed and investigated. The hardware architecture was designed considering easy integration with other sensor networks, remote monitoring of the fermentation-related parameters, and wireless communication between the fermentation vessel and the processing unit. It includes a virtual CO₂ rate sensor, having as input the audio signal collected by a microphone and as output the estimated CO₂ rate. Digital signal processing performed on an embedded board involves acquisition, filtering, analysis, and feature extraction. Time domain-based methods for analyzing the audio signal were implemented. An experimental setup with a small fermentation vessel (100 L) was used for CO₂ rate monitoring during the fermentation of grape must. The estimated CO₂ rate data were fitted with the CO₂ rate profiles generated by simulating the models under similar conditions. Simulations for controlling fermentation kinetics through a CO₂ rate-based temperature control were performed and analyzed. The simulations indicate the proposed approach as valid for a closed-loop system implementation capable of controlling kinetics behavior using estimated CO₂ rate and temperature control. Full article

The inherent energy constraints of sensor nodes render energy efficiency optimization a critical challenge in wireless sensor network deployments. This study presents an innovative acoustic source localization framework incorporating a two-level data aggregation technology, specifically designed to minimize energy expenditure while prolonging network lifetime. A mixed noise model is proposed to describe the characteristics of abnormal noise in real environments. Subsequently, the novel two-level data aggregation technology is proposed. The first level is implemented at individual sensors, where a large number of similar measurements may be collected. The second level data aggregation technology is performed at the cluster head nodes to eliminate the data redundancy between different sensor nodes. After the novel two-level data aggregation, most of the redundant data are eliminated and a significant amount of energy is saved. Then, a nonlinear iterative weighted least squares algorithm is applied to complete the final acoustic source location estimation based on the real remaining sensor measurements. Finally, through extensive simulation experiments, it was verified that the two-level data aggregation technology reduced energy consumption by at least 51% and 43%, respectively, and that the RMSE is less than 0.96. Full article

Traditionally, microphone array networks for 3D sound source localization rely on centralized data processing, which can limit scalability and robustness. In this article, we recast the task of sound source localization (SSL) with networks of acoustic arrays as a distributed optimization problem. We then present two resolution approaches of such a problem; one is computationally centralized, while the other is computationally distributed and based on an Adapt-Then-Combine (ATC) diffusion strategy. In particular, we address 3D SSL with a network of linear microphone arrays, each of which estimates a stream of 2D directions of arrival (DoAs) and they cooperate with each other to localize a single sound source. We develop adaptive cooperation strategies to penalize the arrays with the most detrimental effects on localization accuracy and improve performance through error-based and distance-based penalties. The performance of the method is evaluated using increasingly complex DoA stream models and simulated acoustic environments characterized by various levels of reverberation and signal-to-noise ratio (SNR). Furthermore, we investigate how the performance is related to the connectivity of the network and show that the proposed approach maintains high localization accuracy and stability even in sparsely connected networks. Full article

Urban sound encompasses various acoustic events, from critical safety-related sound to everyday environmental noise. In response to the need for comprehensive and scalable sound monitoring, this study introduces an integrated system combining the Hierarchical Wireless Acoustic Sensor Network (HWASN) with the new proposed end-to-end CNN-CNN-BiLSTM-Attention (CCBA) sound classification model. HWASN facilitates large-scale, scalable sound data collection and transmission through a multi-hop architecture. At the same time, the CCBA model, optimized for Jetson Nano, delivers high-accuracy classification in noisy environments with minimal computational overhead. The CCBA model is trained using distillation techniques, achieving up to a 71-fold speed-up compared to its teacher system. Real-world deployments demonstrate the system’s robust performance under dynamic acoustic conditions. Combining HWASN’s scalability with CCBA’s classification efficiency provides a versatile and long-term solution for comprehensive urban sound monitoring. Additionally, other environmental parameters, such as air quality, light intensity, temperature, humidity, and atmospheric pressure, are sampled using this system to enhance its application in smart city management, urban planning, and public safety, addressing various modern urban needs. Full article

Agricultural sustainability is becoming more and more important for human health. Wireless sensing technology could provide smart monitoring in real time for different parameters in planting, breeding, and the food supply chain with advanced sensors such as flexible sensors; wireless communication networks such as third-, fourth-, or fifth-generation (3G, 4G, or 5G) mobile communication technology networks; and artificial intelligence (AI) models. Many sustainable, natural, renewable, and recycled facility energies such as light, wind, water, heat, acoustic, radio frequency (RF), and microbe energies that exist in actual agricultural systems could be harvested by advanced self-powered technologies and devices using solar cells, electromagnetic generators (EMGs), thermoelectric generators (TEGs), piezoelectric generators (PZGs), triboelectric nanogenerators (TENGs), or microbial full cells (MFCs). Sustainable energy harvesting to the maximum extent possible could lead to the creation of sustainable self-powered wireless sensing devices, reduce carbon emissions, and result in the implementation of precision smart monitoring, management, and decision making for agricultural production. Therefore, this article suggests that proposing and developing a self-powered wireless sensing system for sustainable agriculture (SAS) would be an effective way to improve smart agriculture production efficiency while achieving green and sustainable agriculture and, finally, ensuring food quality and safety and human health. Full article

Underwater Wireless Sensor Networks (UWSNs) are pivotal for advancing maritime capabilities. These networks predominantly utilize acoustic communication, characterized by an open and shared acoustic channel and energy-limited underwater nodes, which underscores the critical importance of node authentication and management. Blockchain technology, recognized for its security, confidentiality, and traceability, is particularly suitable for scenarios requiring secure data exchange. This paper proposes a blockchain-based collaborative node authentication model tailored for clustered networks in UWSNs to tackle the challenges posed by the open nature of acoustic channels and the constrained energy resources of underwater nodes. Autonomous Underwater Vehicles (AUVs) are deployed as blockchain nodes to aid cluster heads in identity verification, while all underwater acoustic nodes are integrated as lightweight blockchain nodes, thus ensuring uniform management and authentication. Furthermore, this study enhances existing clustering algorithms to prolong the operational lifespan of the network and introduces a group signature and authentication mechanism tailored to the unique conditions of underwater blockchain edge computing. This mechanism includes a robust two-round block verification scheme designed to secure the blockchain against potential consensus algorithm attacks. Comprehensive simulations are presented, validating the effectiveness of the proposed group signature solution in enhancing the security and sustainability of underwater clustered networks. Full article

Smart sensors and networks have spread worldwide over the past few decades. In the industry field, these concepts have found an increasing quantity of applications. The omnipresence of smart sensor networks and smart devices, especially in the industrial world, has contributed to the emergence of the concept of Industry 4.0. In a world where everything is interconnected, communication among smart devices is critical to technological development in the field of smart industry. To improve communication, many engineers and researchers implemented methods to standardize communication along the various levels of the ISO-OSI model, from hardware design to the implementation and standardization of different communication protocols. The objective of this paper is to study and implement an unconventional type of communication, exploiting acoustic wave propagation on metallic structures, starting from the state of the art, and highlighting the advantages and disadvantages found in existing literature, trying to overcome them and describing the progress beyond the state of the art. The proposed application for acoustic communication targets the field of smart industries, where implementing signal transmission via wireless or wired methods is challenging due to interference from the widespread presence of metallic structures. This study explores an innovative approach to acoustic communication, with a particular focus on the physical challenges related to acoustic wave propagation. Additionally, communication performance is examined in terms of noise rejection, analyzing the impact of injected acoustic noise on communication efficiency. Full article

The rapid growth of marine industries has emphasized the focus on environmental impacts for all industries, as well as the influence of key environmental parameters on, for instance, offshore wind or aquaculture performance, animal welfare and structural integrity of different constructions. Development of automatized sensors together with efficient communication and information systems will enhance surveillance and monitoring of environmental processes and impact. We have developed a modular Smart Ocean observatory, in this case connected to a large-scale marine aquaculture research facility. The first sensor rigs have been operational since May 2022, transmitting environmental data in near real-time. Key components are Acoustic Doppler Current Profilers (ADCPs) for measuring directional wave and current parameters, and CTDs for redundant measurement of depth, temperature, conductivity and oxygen. Communication is through 4G network or cable. However, a key purpose of the observatory is also to facilitate experiments with acoustic wireless underwater communication, which are ongoing. The aim is to expand the system(s) with demersal independent sensor nodes communicating through an “Internet of Underwater Things (IoUT)”, covering larger areas in the coastal zone, as well as open waters, of benefit to all ocean industries. The observatory also hosts experiments for sensor development, biofouling control and strategies for sensor self-validation and diagnostics. The close interactions between the experiments and the infrastructure development allow a holistic approach towards environmental monitoring across sectors and industries, plus to reduce the carbon footprint of ocean observation. This work is intended to lay a basis for sophisticated use of smart sensors with communication systems in long-term autonomous operation in remote as well as nearshore locations. Full article

Underwater acoustic sensor networks (UASNs) are fundamental assets to enable discovery and utilization of sub-sea environments and have attracted both academia and industry to execute long-term underwater missions. Given the heightened significance of battery dependency in underwater wireless sensor networks, our objective is to maximize the amount of harvested energy underwater by adopting the TDMA time slot scheduling approach to prolong the operational lifetime of the sensors. In this study, we considered the spatial uncertainty of underwater ambient resources to improve the utilization of available energy and examine a stochastic model for piezoelectric energy harvesting. Considering a realistic channel and environment condition, a novel multi-agent reinforcement learning algorithm is proposed. Nodes observe and learn from their choice of transmission slots based on the available energy in the underwater medium and autonomously adapt their communication slots to their energy harvesting conditions instead of relying on the cluster head. In the numerical results, we present the impact of piezoelectric energy harvesting and harvesting awareness on three lifetime metrics. We observe that energy harvesting contributes to 4% improvement in first node dead (FND), 14% improvement in half node dead (HND), and 22% improvement in last node dead (LND). Additionally, the harvesting-aware TDMA-RL method further increases HND by 17% and LND by 38%. Our results show that the proposed method improves in-cluster communication time interval utilization and outperforms traditional time slot allocation methods in terms of throughput and energy harvesting efficiency. Full article

High-strength bolts play a crucial role in ultra-high-pressure equipment such as bridges and railway tracks. Effective monitoring of bolt conditions is of paramount importance for common fault repair and accident prevention. This paper aims to detect and classify bolt corrosion levels accurately. We design and implement a bolt corrosion classification system based on a Wireless Acoustic Emission Sensor Network (WASN). Initially, WASN nodes collect high-speed acoustic emission (AE) signals from bolts. Then, the ReliefF feature selection algorithm is applied to identify the optimal feature combination. Subsequently, the Extreme Learning Machine (ELM) model is utilized for bolt corrosion classification. Additionally, to achieve high prediction accuracy, an improved goose algorithm (GOOSE) is employed to ensure the most suitable parameter combination for the ELM model. Experimental measurements were conducted on five classes of bolt corrosion levels: 0%, 25%, 50%, 75%, and 100%. The classification accuracy obtained using the proposed method was at least 98.04%. Compared to state-of-the-art classification diagnostic models, our approach exhibits superior AE signal recognition performance and stronger generalization ability to adapt to variations in working conditions. Full article

There is an increasing interest in accurately evaluating urban soundscapes to reflect citizens’ subjective perceptions of acoustic comfort. Various indices have been proposed in the literature to achieve this purpose. However, many of these methods necessitate specialized equipment or extensive data collection. This study introduces an enhanced predictor for dwelling acoustic comfort, utilizing cost-effective data consisting of a 30-s audio clip and location information. The proposed predictor incorporates two rating systems: a binary evaluation and an acoustic comfort index called ACI. The training and evaluation data are obtained from the “Sons al Balcó” citizen science project. To characterize the sound events, gammatone cepstral coefficients are used for automatic sound event detection with a convolutional neural network. To enhance the predictor’s performance, this study proposes incorporating objective noise levels from public IoT-based wireless acoustic sensor networks, particularly in densely populated areas like Barcelona. The results indicate that adding noise levels from a public network successfully enhances the accuracy of the acoustic comfort prediction for both rating systems, reaching up to 85% accuracy. Full article