Wireless Acoustic Sensor Networks Research Articles

A long-term global and clustering analysis of the COVID-19 pandemic effects on the sound environment of Milan, Italy: Road traffic and other noise sources

The different measures taken by the competent authorities to tackle the effects of the COVID-19 pandemic affected the acoustic environment worldwide, resulting in significant reductions of noise levels. However, most of the investigations mainly focused on the lockdown during spring 2020, in which all non-essential industrial and commercial activities were prohibited, being people requested to stay at home. The analysis of the data collected and processed by the wireless acoustic sensor network deployed in Milan, Italy, by the DYNAMAP project showed a significant drop of sound levels, mainly due to road traffic as well as other noise sources, denoted as anomalous noise events (ANEs). This work extends that research through a long-term global analysis of the COVID-19 pandemic effects from January 2020 to August 2021, including data clustering to identify local patterns of the sound environment of Milan. As a result of the first hard and subsequent second soft lockdown imposed by the Italian authorities, a double V-shape reduction pattern of the mean sound levels Lden (with and without ANEs) is observed, with maximum drops of around 6 dB and 3 dB, respectively, surrounded with similar progressive reductions in both pre- and post-lockdown periods, showing statistically significant differences. A complementary pattern is obtained regarding the detection of ANEs. The clustering of Lden indicators yields three different sound patterns in the second lockdown, both in terms of noise levels and ANEs, depending on sensor locations and traffic flow variations, unlike the first lockdown with a homogeneous behaviour throughout the city.

Statement of Removal

A randomly fluctuated vortex-induced vibration (VIV) affects the localization of moving targets in an underwater acoustic wireless sensor network (UAWSN) to cause an offset time and time synchronization error which disturbs the localization of the non-cooperative moving target. Definition 2.1, theorem 2.2 has been introduced in the present work to describe the genesis and consequences of the VIV effect in UAWSN. An elliptic localization introduces the direct and indirect path measurements of the moving target. The standard CRLB parameter quantifies the VIV effect. The compensation of vortex-induced vibrations (CVV) algorithm is proposed to quantify the degradation of localization performance and to estimate the position of the moving target. The theoretical measurements of position error, velocity error with respect to the variation of noise, number of receiver sensors, and probability of line of sight (LOS) errors are compared to the standard methods. The proposed CVV algorithm improves the localization performance by 32.24% and reduces 34.36% of position errors, and 37.46% of velocity errors up to 600 meters. It can be useful for the sub-aquatic internet of underwater things.

A Systematic Analysis, Outstanding Challenges, and Future Prospects for Routing Protocols and Machine Learning Algorithms in Underwater Wireless Acoustic Sensor Networks

Water has covered a wide part of the earth’s surface. Oceans and other water bodies contain significant natural and environmental resources as well as aquatic life. Due to humans’ hazardous and unsuitable underwater (UW) settings, these are generally undiscovered and unknown. As a result of its widespread utility in fields as diverse as oceanography, ecology, seismology, and oceanography, underwater wireless sensor networks (UWSNs) have emerged as a cutting-edge area of study. Despite their usefulness, the performance of the network is hampered by factors including excessive propagation delay, a changing network architecture, a lack of bandwidth, and a battery life that is too short on sensor nodes. Developing effective routing protocols is the best way to overcome these challenges. An effective routing protocol can relay data from the network’s root node to its final destination. Therefore, the state of the art in underwater wireless acoustic sensor network (UWASN) routing protocols is assessed with an eye toward their potential for development. In real-world applications, sensor node positions are frequently used to locate relevant information. As a result, it is crucial to conduct research on routing protocols. Reinforcement learning (RL) algorithms have the ability to enhance routing under a variety of conditions because they are experience-based learning algorithms. Underwater routing methods for UWSN are reviewed in detail, including those that rely on machine learning (ML), energy, clustering and evolutionary approaches. Tables are incorporated for the suggested protocols by including the benefits, drawbacks, and performance assessments, which make the information easier to digest. Also, several applications of UWSN are discussed with security considerations. In addition to this, the analysis of node deployment and residual energy is discussed in this review. Furthermore, the domain review emphasizes UW routing protocol research difficulties and future directions, which can help researchers create more efficient routing protocols based on ML in the future.

Online distributed waveform-synchronization for acoustic sensor networks with dynamic topology

Acoustic sensing by multiple devices connected in a wireless acoustic sensor network (WASN) creates new opportunities for multichannel signal processing. However, the autonomy of agents in such a network still necessitates the alignment of sensor signals to a common sampling rate. It has been demonstrated that waveform-based estimation of sampling rate offset (SRO) between any node pair can be retrieved from asynchronous signals already exchanged in the network, but connected online operation for network-wide distributed sampling-time synchronization still presents an open research task. This is especially true if the WASN experiences topology changes due to failure or appearance of nodes or connections. In this work, we rely on an online waveform-based closed-loop SRO estimation and compensation unit for nodes pairs. For WASNs hierarchically organized as a directed minimum spanning tree (MST), it is then shown how local synchronization propagates network-wide from the root node to the leaves. Moreover, we propose a network protocol for sustaining an existing network-wide synchronization in case of local topology changes. In doing so, the dynamic WASN maintains the MST topology after reorganization to support continued operation with minimum node distances. Experimental evaluation in a simulated apartment with several rooms proves the ability of our methods to reach and sustain accurate SRO estimation and compensation in dynamic WASNs.

Low Power Wake-Up Receivers for Underwater Acoustic Wireless Sensor Networks

This article reports two low power wake-up receiver (WuRx) circuits (Type A and B) for underwater acoustic wireless sensor networks. The proposed circuits realize the same novel method to extract the wake-up call from the acoustic signals received by a sensor node using different circuit topologies. This is accomplished by comparing the amplitude and the duration of the received pulses to a predefined code. A combination of these two parameters provides a safe and robust decoding system. The values and duration of the predefined code can be adjusted by tuning the biasing voltages in the circuits. The main advantages of the presented designs are modularity and tunability. To verify the operation of the proposed system, an ASIC has been realized. The WuRxs have been characterized in terms of tuning, duty cycle, and power consumption. Both WuRxs can consume less than 0.5. Type A occupies an area of mode = text0.058mm2, has a data rate of mode = text250bit/s, and presents the lowest power consumption reported, consuming 265nW and 61nW in active and sleep modes, respectively.

Pilot Agent-Driven Wireless Acoustic Sensor Network for Uninterrupted Data Transmission

Wireless acoustic sensor networks (WASN) usually demand uninterrupted and reliable data transmissions and an efficient path from the source nodes to the destination nodes, thus ensuring reliable delivery of sensitive or critical data with the use of multipath routing protocols. This paper presents a novel agent-driven WASN relying on a set of static and mobile agents. In the proposed technique, the location address is functioned into the operating node and the destination node. Calculations of the midpoint between the operating and the destination nodes are followed by identifying the shortest path based on a reference axis, using a mobile agent and a location address. Such an approach aims to ensure maximum utilization of the communicating paths. Based on the information concerning partial topology of the network, the destination node computes the weight factor and multiple paths using node distances, energy ratios and efficiencies of specific links. Then, the destination node selects the appropriate path from the multiple paths available, to transmit the data. This article also analyzes the performance of the proposed system with various parameters taken into consideration and shows its efficiency in comparison to other existing techniques.

Impact of moving target on underwater positioning by using state measurement

The localization of moving targets in an underwater acoustic wireless sensor network (UAWSN) is inaccurate due to the various underwater forces (viscous, hydrodynamic forces, perturbation of underwater). The false measurements in the sensor network cause position errors and velocity errors which disrupt the localization of the moving target. A randomly fluctuated spillover effect is introduced in the present paper. The absorption losses generated due to the spillover effect cause false measurements of the moving target. Theorem 1 describes the genesis of these absorption losses and their consequences in UAWSN. The measurements from each moving target in the presence of absorption losses are formulated in the elliptical region. A joint probabilistic data association (JPDA) method is proposed to quantify the false measurements in the elliptical region. A moving target state estimation (MTSE) algorithm is proposed to eliminate the false measurements from the moving targets and to measure the localization of moving targets with the help of the propagation speed of targets. The theoretical measurements of position RMSE and velocity RMSE are verified with standard methods. The proposed MTSE method improves the localization performance of the moving targets by 29.42 % and reduces 32.16 % of position errors and 36.23 % of velocity errors up to 550 ​m. The proposed algorithm will be useful for the sub-aquatic Internet of underwater things (IoUT).

Vortex‐induced vibration effects in underwater acoustic wireless sensor networks

AbstractA randomly fluctuated vortex‐induced vibration (VIV) affects the localization of moving targets in an underwater acoustic wireless sensor network (UAWSN) to cause an offset time and time synchronization error which disturbs the localization of the non‐cooperative moving target. Definition 1, Theorem 1 has been introduced in the present work to describe the genesis and consequences of the VIV effect in UAWSN. An elliptic localization introduces the direct and indirect path measurements of the moving target. The standard Crammer Rao lower bound parameter quantifies the VIV effect. The compensation of vortex‐induced vibrations (CVV) algorithm is proposed to quantify the degradation of localization performance and to estimate the position of the moving target. The theoretical measurements of position error, velocity error with respect to the variation of noise, number of receiver sensors, and probability of line of sight errors are compared to the standard methods. The proposed CVV algorithm improves the localization performance by 32.24% and reduces 34.36% of position errors, and 37.46% of velocity errors up to 600 m. It can be useful for the sub‐aquatic internet of underwater things.

Asynchronous Pattern-Designed Channel Access Protocol in Underwater Acoustic Wireless Sensor Networks

Due to the significant propagation delay in underwater sensor networks, conflict retransmission in channel access protocols comes at a high cost. This poses a challenge in scenarios where multiple sensor nodes generate data frames with strong temporal correlations, such as in disaster warning applications. Traditional channel allocation and timeout-based retransmission mechanisms lead to considerable access delays, making it difficult to meet the requirements. To tackle this issue, we propose the asynchronous pattern-designed random access (APDRA) protocol. This protocol enhances the access probability by designing retransmission time intervals for data frames based on pattern design. Additionally, we introduce a successive interference cancellation (SIC) mechanism at the receiver for decoding. This mechanism facilitates the transformation of the conventional method of discarding conflicted data frames into iterative decoding, thereby enhancing transmission efficiency. Via the utilization of simulations, we compare the APDRA protocol conventional underwater medium access control (MAC) protocols and existing retransmission mechanisms. The results demonstrate that the APDRA protocol has the ability to improve both the transmission success ratio (TSR) and reduces the access delay to some extent.

Closed‐form solution for scaling a wireless acoustic sensor network

AbstractA closed‐form solution for localising and synchronising an acoustic sensor node with respect to a Wireless Acoustic Sensor Network (WASN) is presented. The aim is to allow efficient scaling of a WASN by individually calibrating newly joined sensor nodes instead of recalibrating the entire array. A key contribution is that the sensor to be calibrated does not need to include a built‐in emitter. The proposed method uses signals emitted from spatially distributed sources to compute time difference of arrival (TDOA) measurements between the existing WASN and a new sensor. The problem is then modelled as a set of multivariate non‐linear TDOA equations. Through a simple transformation, the non‐linear TDOA equations are converted into a system of linear equations. Then, weighted least squares is applied to find an accurate estimate of the calibration parameters. Signal sources can either be known emitters within the existing WASN or arbitrary sources in the environment, thus allowing for flexible applicability in both active and passive calibration scenarios. Simulation results under various conditions show high joint localisation and synchronisation performance, often compared to the Cramér‐Rao lower bound.

Improved wireless acoustic sensor network for analysing audio properties

Improved wireless acoustic sensor network for analysing audio properties

Parallel processing of distributed beamforming and multichannel linear prediction for speech denoising and deverberation in wireless acoustic sensor networks

More and more smart home devices with microphones come into our life in these years; it is highly desirable to connect these microphones as wireless acoustic sensor networks (WASNs) so that these devices can be better controlled in an enclosure. For indoor applications, both environmental noise and room reverberation may severely degrade speech quality, and thus both of them need to be removed to improve users’ experience. For this goal, this paper proposes a parallel processing framework of distributed beamforming and multichannel linear prediction (DB-BFMCLP), which consists of generalized sidelobe canceler and multichannel linear prediction for simultaneous speech dereverberation and noise reduction in WASNs. By sharing a common desired response vector, the proposed DB-BFMCLP can provide a significant reduction in communication bandwidth without sacrificing performance. The convergence guarantee of the DB-BFMCLP to its centralized implementation is derived mathematically. Simulation results verify the superiority of the proposed method to the existing related methods in noisy and reverberant scenarios.

Study of rail damage diagnosis and localization method based on intelligent wireless acoustic sensor network

Many major derailments are closely related to rail damage, so it is especially important to conduct regular damage detection on in-service rails. In this paper, with the main objective of nondestructive detection and localization of rail crack damage, a wireless acoustic sensor network (WASN) based rail flaw detection and localization diagnosis system is designed and implemented: firstly, the WASN nodes rapidly sample the acoustic emission signal on the rail surface, then calculate and wirelessly transmit its characteristic parameters to the gateway, and then use the proposed information wasserstein generative adversarial network-gradient penalty (InfoWGAN-GP) algorithm for data augmentation of the collected AE features, followed by feature optimization of the augmented hybrid data set based on the proposed categorical boosting-borutashap (Catboost-BorutaShap) algorithm, furthermore, the proposed improved aquila optimizer-deep extreme learning machine (IAO-DELM) and binary dwarf mongoose simulated annealing optimization-newton iteration-triangulation algorithm (BDMSAO-NI-TA) strategies are then proposed to establish accurate identification and localization of rail crack damage derivation models. The experimental comparison results show that the IAO-DELM model constructed based on the preferred feature subset has the best effect on the performance enhancement of rail crack damage diagnosis, with the accuracy, precision, F1-score, weighted average and AUC of 99.86%, 99.58%, 98.75%, 0.9962 and 0.9923, respectively, which are higher than those of the optimization algorithm such as honey badger combined with DELM model. The evaluation indexes indicate that this method has excellent monitoring effectiveness.

On the Challenges of Acoustic Energy Mapping Using a WASN: Synchronization and Audio Capture

Acoustic energy mapping provides the functionality to obtain characteristics of acoustic sources, as: presence, localization, type and trajectory of sound sources. Several beamforming-based techniques can be used for this purpose. However, they rely on the difference of arrival times of the signal at each capture node (or microphone), so it is of major importance to have synchronized multi-channel recordings. A Wireless Acoustic Sensor Network (WASN) can be very practical to install when used for mapping the acoustic energy of a given acoustic environment. However, they are known for having low synchronization between the recordings from each node. The objective of this paper is to characterize the impact of current popular synchronization methodologies as part of the WASN to capture reliable data to be used for acoustic energy mapping. The two evaluated synchronization protocols are: Network Time Protocol (NTP) y Precision Time Protocol (PTP). Additionally, three different audio capture methodologies were proposed for the WASN to capture the acoustic signal: two of them, recording the data locally and one sending the data through a local wireless network. As a real-life evaluation scenario, a WASN was built using nodes conformed by a Raspberry Pi 4B+ with a single MEMS microphone. Experimental results demonstrate that the most reliable methodology is using the PTP synchronization protocol and audio recording locally.

Reinforcement learning for autonomous vehicle movements in wireless multimedia applications

We develop a Deep Reinforcement Learning (DeepRL)-based, multi-agent algorithm to efficiently control autonomous vehicles that are typically used within the context of Wireless Sensor Networks (WSNs), in order to boost application performance. As an application example, we consider wireless acoustic sensor networks where a group of speakers move inside a room. In a traditional setup, microphones cannot move autonomously and are, e.g., located at fixed positions. We claim that autonomously moving microphones improve the application performance. To control these movements, we compare simple greedy heuristics against a DeepRL solution and show that the latter achieves best application performance.As the range of audio applications is broad and each has its own (subjective) performance metric, we replace those application metrics by two immediately observable ones: First, quality of information (QoI), which is used to measure the quality of sensed data (e.g., audio signal strength). Second, quality of service (QoS), which is used to measure the network’s performance when forwarding data (e.g., delay). In this context, we propose two multi-agent solutions (where one agent controls one microphone) and show that they perform similarly to a single-agent solution (where one agent controls all microphones and has a global knowledge). Moreover, we show via simulations and theoretical analysis how other parameters such as the number of microphones and their speed impacts performance.

Intelligent Microsystem for Sound Event Recognition in Edge Computing Using End-to-End Mesh Networking

Wireless acoustic sensor networks (WASNs) and intelligent microsystems are crucial components of the Internet of Things (IoT) ecosystem. In various IoT applications, small, lightweight, and low-power microsystems are essential to enable autonomous edge computing and networked cooperative work. This study presents an innovative intelligent microsystem with wireless networking capabilities, sound sensing, and sound event recognition. The microsystem is designed with optimized sensing, energy supply, processing, and transceiver modules to achieve small size and low power consumption. Additionally, a low-computational sound event recognition algorithm based on a Convolutional Neural Network has been designed and integrated into the microsystem. Multiple microsystems are connected using low-power Bluetooth Mesh wireless networking technology to form a meshed WASN, which is easily accessible, flexible to expand, and straightforward to manage with smartphones. The microsystem is 7.36 cm3 in size and weighs 8 g without housing. The microsystem can accurately recognize sound events in both trained and untrained data tests, achieving an average accuracy of over 92.50% for alarm sounds above 70 dB and water flow sounds above 55 dB. The microsystems can communicate wirelessly with a direct range of 5 m. It can be applied in the field of home IoT and border security.

Cross-Layer Protocol Based on Directional Reception in Underwater Acoustic Wireless Sensor Networks

The long propagation delay of acoustic links leads to the complex randomness of packet collision, which reduces the network packet delivery rate (PDR) and aggravates network congestion. A single vector hydrophone with directional reception characteristics can concentrate the reception gain on a certain direction, which can increase spatial reuse, reduce packet collision, and help to improve the performance of the underwater acoustic wireless sensor networks (UASNs). Herein, this paper proposes a cross-layer protocol with low interference and low congestion (CLIC) based on directional reception. An integrated routing-medium access control (MAC) design is also devised in the CLIC scheme to use the directional beams to create the least-interfering, highest-capacity data transmission links, weighing key factors affecting network performance to obtain routes with low collisions and low congestion. Simulation results show that the CLIC has a higher packet delivery rate (PDR) and higher energy efficiency compared to the QELAR, CITP, and VBF protocols.

Reliable Data Transmission in Underwater Wireless Sensor Networks Using a Cluster-Based Routing Protocol Endorsed by Member Nodes

Considering Underwater Wireless Sensor Networks (UWSNs) have limited power resources (low bandwidth, long propagation delays, and non-rechargeable batteries), it is critical that they develop solutions to reduce power usage. Clustering is one solution because it not only saves energy consumption but also improves scalability and data integrity. The design of UWSNs is vital to the development of clustering algorithms. The limited energy of sensor nodes, narrow transmission bandwidth, and unpredictable topology of mobile Underwater Acoustic Wireless Sensor Networks (UAWSNs) make it challenging to build an effective and dependable underwater communication network. Despite its success in data dependability, the acoustic underwater communication channel consumes the greatest energy at a node. Recharging and replacing a submerged node’s battery could be prohibitively expensive. We propose a network architecture called Member Nodes Supported Cluster-Based Routing Protocol (MNS-CBRP) to achieve consistent information transfer speeds by using the network’s member nodes. As a result, we use clusters, which are produced by dividing the network’s space into many minute circular sections. Following that, a Cluster Head (CH) node is chosen for each circle. Despite the fact that the source nodes are randomly spread, all of the cluster heads are linked to the circle’s focal point. It is the responsibility of the MNS-CBRP source nodes to communicate the discovered information to the CH. The discovered data will then be sent to the CH that follows it, and so on, until all data packets have been transferred to the surface sinks. We tested our techniques thoroughly using QualNet Simulator to determine their viability.

An integrated system for the acoustic monitoring of goat farms

Effective precision livestock farming requires the availability of reliable and up-to-date data characterizing the animals and environment of interest. This work presents the design, architecture, and implementation of a wireless acoustic sensor network for monitoring goat farms on a 24/7 basis. In addition, we define a hierarchical organization of the involved sound classes exhaustively covering every aspect of the encountered goat vocalizations. Moreover, we developed an annotation tool tailored to the specifics of the problem at hand, i.e. a big, real-world data environment, able to meaningfully assist the annotation of goat vocalizations via a suitable sound classification module. On top of that, a mobile phone application was developed facilitating authorized users to remotely access information describing the situation on the farm site. Importantly, such a non-invasive monitoring framework was installed in 4 different sites located in Northern Italy while taking into account their diverse characteristics.

A Wireless Acoustic Emission Sensor System with ACMD-IGWO-XGBoost Algorithm for Living Tree Moisture Content Diagnosis.

Trunk water has an important influence on the metabolism and ecological balance of living trees, which affects the vegetation growth and moisture cycle of the whole forest ecosystem. The accurate and real-time measurement of moisture content (MC) is of vital guiding meaning to living tree cultivation and forest management. In this paper, a water content diagnosis system based on a wireless acoustic emission sensor network (WASN) was designed and implemented with the aim of the nondestructive detection of water content in living wood trunks. Firstly, the acoustic emission (AE) signal of the trunk epidermis was sampled at high speed; then, its characteristic parameters were calculated and transmitted wirelessly to the gateway. Furthermore, the optimal characteristic wavelet sequence was decomposed by the adaptive chirp mode decomposition (ACMD), and the improved grey wolf optimizer (IGWO) optimization XGBoost established the MC prediction model, which was improved by the multi-strategy joint optimization. Finally, field monitoring was carried out on Robinia Pseudoacacia, Photinia serrulata, Pinus massoniana and Toona sinensis. The average diagnostic accuracy reached 96.75%, which shows that the diagnosis system has excellent applicability in different working conditions.