Acoustic Sensor Nodes Research Articles

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.

A novel routing protocol for underwater wireless sensor networks based on shifted energy efficiency and priority

Underwater Wireless Sensor Networks (UWSNs) are among the most promising research areas these days due to their unique characteristics and diverse underwater applications. Though a number of routing protocols have been designed and implemented for UWSNs over the past few years, the researchers face several challenges, e.g., low speed of propagation, small bandwidth, limited battery power, etc., while designing routing protocols for communication in UWSNs. Acoustic sensor nodes are equipped with batteries with limited power and it is quite costly to replace or recharge them. The network will not survive for the desired period of time if the power of node batteries is not efficiently used. To effectively resolve this issue, this paper proposes a Shifted Energy Efficiency and Priority (SHEEP) routing protocol for UWSNs. The proposed protocol aims to enhance the efficiency of the state-of-the-art Energy Balanced Efficient and Reliable Routing (EBER2) protocol for UWSNs. SHEEP is built upon the depth and energy of the current forwarding node, the depth of the expected next forwarding node, and the average energy difference among the expected forwarders. Simulation results demonstrate that SHEEP improves the energy efficiency and packet delivery ratio in comparison to EBER2 by 7.4% and 13% respectively.

A Non-Destructive Measurement of Trunk Moisture Content in Living Trees Based on Multi-Sensory Data Fusion

Water plays an important role in various physiological activities of living trees. Measuring trunk moisture content (MC) in real-time without damage has important guiding significance for transpiration research in forest ecosystems. However, existing standing tree MC detection methods are either too cumbersome to install or cause different degrees of damage. Here, we propose a novel Internet of Things (IoT) monitoring system that includes wireless acoustic emission sensor nodes (WASNs) and underground soil MC sensor nodes to efficiently detect and diagnose the MC level of living tree trunks. After the characteristic parameters were collected by the two sensors, a feature selection and multi-sensory global fusion method for MC diagnosis was designed and developed and several statistical parameters were selected as the input variables to predict the heartwood MC level with a support vector machine (SVM) model. Moreover, to achieve the highest prediction accuracy, an improved sparrow search algorithm (ISSA) is applied to ensure the most suitable parameter combinations in a two-objective optimization model. Extensive experiments result in a fusion of the environment, and AE signals show that the proposed mechanism has better diagnostic performance than state-of-the-art methods and is more adaptable to the fluctuation of working conditions.

A Centralized Control-Based Clustering Scheme for Energy Efficiency in Underwater Acoustic Sensor Networks

Underwater acoustic sensor networks (UASNs) are among the most critical tools for underwater environment sensing and information acquisition. Since the complexity of the underwater environment renders battery replenishment and replacement more difficult and UASNs are mainly powered by batteries built into the underwater acoustic sensor nodes, the optimization of node energy consumption is particularly important for extending the network lifetime. Recently, energy consumption optimization solutions for UASNs have mainly focused on node clustering and multihop transmission. However, most schemes are implemented using distributed algorithms; in these types of approaches, decision information usually comes from only a subset of nodes, and it is difficult to make the most globally beneficial decision. Therefore, in this paper, the idea of centralized control is applied for the optimization of network energy consumption from a global perspective. Specifically, a centralized control-based clustering scheme (CCCS) for UASNs is proposed. In this scheme, a node density-based adaptive clustering approach is adopted, intracluster controllers are established within clusters, and the choices of both relay nodes and relay clusters are optimized to achieve energy balancing and route optimization. The performance achieved in simulations shows that compared to similar methods, CCCS is able to balance the node energy consumption and effectively increase the lifetime of a UASN.

Performance analysis of compressive sensing and reconstruction by LASSO and OMP for audio signal processing applications

Audio signal processing is used in acoustic IoT sensor nodes which have limitations in data storage, computation speed, hardware size and power. In most audio signal processing systems, the recovered data constitutes far less fraction of the sampled data providing scope for compressive sensing (CS) as an efficient way for sampling and signal recovery. Compressive sensing is a signal processing technique in which a sparse approximated signal is reconstructed at the receiving node by a signal recovery algorithm, using fewer samples compared to traditional sampling methods. It has two main stages: sparse approximation to convert the signal into a sparse domain and reconstruction through sparse signal recovery algorithms. Recovery algorithms involve complex matrix multiplication and linear equations in sampling and reconstruction, increasing the computational complexity and leading to highly resourceful hardware implementations. This work reconstructs the sparse audio signal using LASSO and orthogonal matching pursuit (OMP) algorithm. OMP is an iterative greedy algorithm involving least square method that takes a compressed signal as input and recovers it from the sparse approximation, while LASSO is L1 norm based with a controlled L2 penalty. The paper reviews the reconstruction and study of sparsity and error obtained for reconstructing an audio signal by OMP and LASSO.

Distributed Sampling Rate Offset Estimation Over Acoustic Sensor Networks Based on Asynchronous Network Newton Optimization

Sampling rate synchronization is an inevitable issue in distributed acoustic sensor networks. In this paper, an analytical sampling rate offset (SRO) estimation approach is first proposed, and then, it is extended to a distributed method that suitable for acoustic sensor networks with arbitrary communication graphs. Specifically, a linear-phase drift model in the short-time Fourier transform domain is used to approximate the SRO between each pair of microphone nodes. Next, after unwrapping the temporally averaged phase information, SROs are recovered analytically via a new weighted-sum criterion. Based on this, a distributed cost function is established at each node to obtain the SROs of all nodes simultaneously in a distributed manner. Finally, a state-of-the-art distributed algorithm named asynchronous network Newton optimization is adopted to carry out the distributed SRO estimation. The proposed method can effectively estimate the SROs among acoustic sensor nodes in noisy and reverberant environments. Compared with the existing approaches, it does not require an external central processor, and only local communications among nodes are needed. Experimental results confirm the validity of the proposed method.

A Fuzzy Rule-Based System to Infer Subjective Noise Annoyance Using an Experimental Wireless Acoustic Sensor Network

Over the last few years, several works have been conducted on the design and development of wireless acoustic sensor networks (WASNs) to monitor acoustic noise levels and create noise maps. The information provided by these WASNs is based on the equivalent noise pressure level over time T (Leq,T), which is used to assess the objective noise level. According to some authors, noise annoyance is an inherently vague and uncertain concept, and Leq,T does not provide any information about subjective annoyance to humans. Some fuzzy models have been proposed to model subjective annoyance. However, the use of fuzzy rule-based systems (FRBS) that have been adapted to acoustic sensor node resource limitations in real WASN to provide the degree of subjective noise annoyance in real-time remains a largely unexplored region. In this paper, we present the design and implementation of an FRBS that enables the sensor nodes of a real WASN deployed in the city of Linares (Jaen), Spain to infer the degree of subjective noise annoyance in real-time. The hardware used for the sensor nodes is a commercial model, Arduino Due. The results demonstrate that the sensor nodes have sufficient processing capacity and memory to infer the subjective annoyance in real-time, and the system can correctly detect situations that can be considered more annoying by humans.

Parametermeasurement of aircraft-radiated noise from a single acoustic sensor node in three-dimensional space

A line spectrum presents the form of a narrow-band time-varying signal due to Doppler effect when the single hydrophone node observes flight-radiated noise. The modulation law of the time-varying signal contains a large number of feature information of moving targets, which can be used for detection and classification. This paper studies the possibility of using instantaneous frequency measurements from the hydrophone node to improve the precision of the flight parameter estimates when the source spectrum contains a harmonic line of constant frequency. First of all, we build up and analyze the underwater sound field excited by the aircraft using the ray theory model; then the Doppler shift in the two isospeed media, which is caused by the aircraft, is established; finally, a robust time–frequency transform describes the time–frequency distribution of the received signal, and a geometric approach solves the flying parameters.

BDREA Betta and Dolphin Pods Routing via Energy Scarcity Aware Protocol for Underwater Acoustic Wireless Sensor Networks (UAWSNs)

There exist numerous applications for deploying Underwater Wireless Sensor Networks (UWSNs), including submarine detection, disaster prevention, oil and gas monitoring, off-shore exploration, and military target tracking. The acoustic sensor nodes are deployed to monitor the underwater environment, considering the area under observation. This research work proposes an energy scarcity-aware routing protocol for energy efficient UWSNs. Moreover, it aims to find the feasible region on the basis of the objective function, in order to minimize the energy tax and extend the network life. There are three different sensors nodes in the network environment, i.e., anchor nodes, relay nodes, and the centralized station. Anchor nodes originate data packets, while relay nodes process them and broadcast between each other until the packets reach the centralized station. The underline base scheme Weighting Depth and Forwarding Area Division Depth-Based Routing (WDFAD-DBR) for routing is based on the depth differences of the first- and second-hop nodes of the source node. The propose work, Betta and Dolphin Pods Routing via Energy Scarcity Aware protocol (BDREA) for packet forwarding from the forwarding nodes considers the first and second hops of the source node, i.e., the packet advancement, the network traffic, the distance to the centralized station, and the inverse normalized energy of the forwarding zone. It is observed that the proposed work improves the performance parameters by approximately 50% in terms of energy efficiency, and prolongs the network life compared to Dolphin and Whale Pod (DOW-PR) protocols. Furthermore, the energy efficiency directly relates to the other parameters, and its enhancement can be seen in terms of an 18.02% reduction in end-to-end delay when compared with the Weighting Depth and Forwarding Area Division Depth-Based Routing (WDFAD-DBR) protocol. Furthermore, BDREA improves the Packet Delivery Ratio (PDR) by approximately 8.71%, compared to DOW-PR, and by 10% compared with the benchmark, WDFAD-DBR, the energy tax by 50% in comparison to DOW-PR, the end-to-end delay by 18%, and the APD by 5% in comparison to WDFAD-DBR.

An evolved algorithm for underwater acoustic sensor node localization enhancement using reference node

Accurate localization of underwater acoustic sensor networks is one of the essential operations. The range-free DV-Hop technique is more acceptable due to its low hardware cost and simplicity than the range-based approach. But the main drawback of this basic DV-Hop method is low accuracy and link failure due to maintenance or short battery life during the continuous localization calculation in the underwater scenario. Most algorithms in this field do not pay enough attention to the mobility of the nodes. By examining the movement patterns of water, this manuscript uses a technique for UWSN localization based on a mobility prediction algorithm. This manuscript proposed a reference node-based multi-hop improved DV-Hop​ technique using School Topper Optimization (STO) and mobility prediction algorithms. This proposed scheme’s highlighted advantage is to reduce the localization error to identify the unknown target nodes using a reference node-based localization scheme using a mobility prediction algorithm without increasing the beacon nodes, and the multi-hop improved DV-Hop​ algorithm using STO significantly improved the link failure problem by iteratively selecting the different sets of the beacon nodes in the same scenario The simulation report has proved that our framework takes better computational time, has a better convergence rate by introducing the STO technique, provides 23.76% and 37.18% better avg. localization error and localization error variance, and 16.12% better coverage from PSODV-Hop and GSODV-Hop techniques.

Ultra-Low-Cost and Ultra-Low-Power, Miniature Acoustic Modems Using Multipath Tolerant Spread-Spectrum Techniques

To enable long-term, large-scale, dense underwater sensor networks or Internet of Underwater Things (IoUT) this research investigates new novel waveforms and experimental prototypes for robust communications on ultra-low-cost and ultra-low-power, miniature acoustic modems. Spread-spectrum M-ary orthogonal signalling (MOS) is used with symbols constructed from subsequences of long pseudorandom codes. This decorrelates multipath signals, even when the time-spread spans many symbols, so they present as random noise. A highly cost-engineered and miniaturised prototype acoustic modem implementation was created, for the 24 kHz–32 kHz band, with low receive power consumption (12.5 mW) and transmit power of <1 W. Simulations show that the modulation scheme achieves 640 bit/s at −4.5 dB with AWGN or the equivalent level of multipath energy. Experimental validation of the hardware shows successful point-to-point communication at ranges of >3 km in lakes and >2 km in the sea including severe multipath. In lake testing of a 7-node, multi-hop, sensor network with TDA-MAC protocol, packet delivery was near 100% for all nodes. Trials of acoustic sensor nodes in the North Sea achieved 99.5% data delivery over a 3-month period and a wide range of sea conditions. Modulation and hardware have proven reliable in a variety of underwater environments. Competitive range and throughput with low cost and power are attractive for large-scale and long-term battery-operated networks. This research has delivered a viable and affordable communication technology for future IoUT applications.

Sequence Design Technique for Accurate Timing and Cell ID Estimation in Underwater Acoustic Cellular Systems with a High Doppler

In underwater acoustic cellular (UWAC) systems, underwater equipment or sensor nodes (UE/SN) should perform downlink synchronisation and a cell search during the initial access stage using the preambles received from adjacent underwater base stations (UWBSs). The UE/SN needs to estimate accurate timing and cell ID (CID) using the received preambles, and synchronise with a serving UWBS, even in high-Doppler environments. In this paper, a sequence design technique for joint estimation of accurate timing and CID in UWAC systems with a high Doppler is proposed to decrease the receiver complexity and processing time. A generalised Zadoff–Chu sequence is proposed for the preamble design. This sequence is decomposed into multiple short sub-sequences to reduce the effect of Doppler shift on the timing and CID estimation. The performance loss caused by the short sequence length is compensated by combining the sub-sequences using the repetition property of the ZC sequence. The properties (autocorrelation and cross-correlation) of the proposed sequence are derived analytically in the presence of Doppler shift and compared with the simulation results. The simulation results reveal that the proposed technique performs better than existing techniques in both additive white Gaussian noise and multipath channels with a high-Doppler. It is concluded that the proposed technique is suitable for accurate timing estimation and CID detection in UWAC systems with a high Doppler.

Game-Theory-Based Clustering Scheme for Energy Balancing in Underwater Acoustic Sensor Networks

The underwater acoustic sensor network (UASN) is a specific deployment of Internet-of-Things (IoT) technology in the underwater environment, since energy constraints limit the lifetime of UASNs, effectively balancing the energy consumption of acoustic sensor nodes in UASNs is important to maximize the amount of information collected and to prolong the network lifetime. Node clustering is widely regarded as one of the most important energy-efficient schemes for UASNs. However, most existing clustering schemes focus on the cooperation-based election of cluster headers (CHs) in a centralized manner. Due to the limited energy capacity, acoustic sensor nodes are designed to save their own energy, hindering the realization of such cooperation. To address this issue in this article, game theory is applied to UASNs to balance network energy consumption and model acoustic sensor nodes as rational and selfish players. Specifically, a game-theory-based clustering (GTC) scheme for UASNs is developed. In the CH election phase, each node makes a decision in pursuit of a greater payoff based on the Nash equilibrium. An incentive mechanism is invented to induce nodes to make more beneficial collective decisions and plays a role in the CH rotation to effectively balance the energy consumption. Meanwhile, the network area is divided into nonuniform sectors to ensure the energy consumption of the CH is more evenly distributed. Simulation results show that the proposed GTC scheme can effectively balance network energy consumption and extend the network lifetime.

Development of a low‐power wireless acoustic emission sensor node for aerospace applications

Acoustic emission (AE) is the spontaneous release of energy caused by the growth of damage, the monitoring of which gives an indication of the presence of damage within a structure. The current standard for AE localisation is difficult to apply in a low‐power system as sensors must either be wired together or Node's time synchronised, which is power intensive. This paper proposes the use of a method of bonding three piezoelectric sensors in a small triangular array, which has previously been shown by Aljets et al. to be capable of locating sources in simple structures. In this prior work the wave's A0 mode was used to predict the angle of arrival and the distance the wave has travelled through single sensor modal analysis. This paper presents the development of hardware to apply this technique and testing that showed artificial sources could be located in simple plates to a good level of accuracy. The addition of complexity to structures significantly reduced accuracy. This prompted hardware modifications to use the S0 mode for angle prediction. Testing showed that this significantly improved performance in a complex composite structure. The power consumption of the device is very low, consuming 0.33 mW in sleep mode, 17.44 mW whilst waiting for an event and 38 mW to record, process and transmit an event. This level of consumption has the potential to be self‐powered via energy harvesting.

Hybrid optimization routing management for autonomous underwater vehicle in the internet of underwater things

Internet of Underwater Things (IoUTs) is completely embraced by acoustic sensor nodes that are commonly battery consumption. The sensor’s battery life is restricted and it is problematic while it needs to recharge. Moreover, these kinds of underwater sensor nodes may form the cluster to store the huge amount of energy. The cluster head formation is the main problem that the cluster head needs to use added energy for aggregation and data collection to broadcast the data packets to the destination. The autonomous underwater vehicles (AUVs) have to be used to connect the sensor nodes with the internet or particular devices, the path discovery within the AUV is the primary issue for producing the efficient routing in IoUTs. In this paper, the proposed routing strategy is used to provide energy proficiency while performing the cluster-based routing. AUV is responsible for selecting the cluster head and maintaining cluster-based scheduling. The AUV path is constructed to increase the residual energy for the sensor nodes in the network. It coordinates the AUV’s effective way of arranging calculations into the steering convention. This incorporated specialized strategy depends on two stages: A-ANTD (AUV-Assisted Network Transmit Devising) and TARD (Transmit Assisted Route Devising). A-ANTD uses the collaboration of performance of multiple ventures to decrease energy utilization for the system and stays away from the problem area and sector issue with a vital GN (Gateway Nodules) plans. The experimental results show that the proposed methodology has improved performance compared with the other methods.

Multi-Agent-Based Acoustic Sensor Node Deployment in Underwater Acoustic Wireless Sensor Networks

The deployment of acoustic sensor nodes in 3-D underwater acoustic wireless sensor networks (UAWSN) is a difficult task due to various aquatic conditions and physical obstacles. This work proposes multi-agent-based acoustic sensor node deployment (MASD) to deploy the acoustic nodes at ideal positions to enhance coverage and seamless connectivity. The proposed scheme works is threefold: 1) AUV initiates random walk in the network to gather the information and prospective common reference points; 2) the base station gets this information through surface buoys and computes the routing path, feasible locations for deploying new nodes; and 3) AUV collects this information and follows the path to deploy nodes with the help of agents. The multi-agent-enabled deployment framework (MADF) is proposed to support the deployment process at each level of the proposed MASD scheme. The performance of propagation loss, coverage, and overhead tradeoffs are analyzed to validate the proposed scheme. Mobility issues can be further re-investigated in shallow water as a future direction to the MASD scheme.

Cell ID and Timing Estimation Techniques for Underwater Acoustic Cellular Systems in High-Doppler Environments.

In an underwater acoustic cellular (UAC) system, underwater equipment or sensor nodes need to detect the identity of an underwater base station (UBS) and synchronise it with a serving UBS. It is known that, in an underwater acoustic channel, the temporal variability of the ocean coupled with the low speed of sound in water may induce a significant Doppler shift. In this paper, two different types of cell search techniques (CSTs) are proposed to detect the cell ID and correct timing of the UBS in UAC systems with a Doppler shift: CST based on linear frequency modulation with full bandwidth in the time domain (LFM-FT) and CST based on linear frequency modulation in the frequency domain (LFM-FF). The performances (auto-correlation, cross-correlation, ambiguity function, and cross ambiguity function) of the proposed techniques are analysed and compared with simulation results. It is demonstrated by simulation that the proposed techniques perform better than previous techniques in both AWGN and multipath channels when a Doppler shift exists. It is also shown that the LFM-FF-CST achieves the best performance in the presence of a Doppler shift and is suitable for mobile UAC systems.

Bio‐inspired evolutionary computing approach for distributed active noise control problem

In this study, a distributed active noise control (DANC) system for spatial noise control in a network of acoustic sensor nodes based on the behavioural traits of felines is presented. An unified strategy based on incremental co-operative learning and cat swarm intelligence is proposed for noise mitigation in spatial region. The hybrid nature of the proposed incremental cat swarm optimisation (ICSO) algorithm provides efficient noise control without prior estimation of multiple secondary paths. In the developed ICSO-based DANC scheme, the individual sensor nodes communicate the intermediate solutions using incremental mode of cooperation to attain overall global noise mitigation over the distributed network. The performance of the proposed ICSO based DANC scheme is validated for tonal, broadband and practical air conditioner noise control test scenarios. Evaluation results show that the proposed system achieves faster convergence with computational efficiency of over 36% and ∼2–9 dB improvement in noise cancellation for different noise cases and acoustic environments over genetic algorithm and particle swarm optimisation based DANC counterparts.

Incremental Learning Based Adaptive Filter for Nonlinear Distributed Active Noise Control System

Active control of noise for multi-channel applications is affected by the existence of nonlinear primary and secondary paths. There is a degradation in the performance of linear multi-channel active noise control (LMANC) systems based on minimization of sum of squared errors obtained from multiple sensors in presence of nonlinear primary path (NPP) and nonlinear secondary path (NSP) conditions. The NPP and NSP problems are more prominent and challenging for multi-point noise control applications owing to different locations of silent zones and acoustic coupling between secondary sources and error sensors. In order to surmount this problem, an incremental strategy based nonlinear distributed ANC (NDANC) system is developed in this article. The adaptive exponential functional link network (AE-FLN) is employed as an adaptive control unit at the acoustic sensor nodes (ASNs) for the design of NDANC system. The incremental co-operation scheme is utilized to provide uniform noise cancellation in presence of NPP and NSP conditions. Simulation study is conducted extensively to demonstrate the efficiency of the proposed system for different practical NPP and NSP scenarios. The detailed computational load analysis and subjective evaluation of reduction in perceptual noise levels are performed for different real noise conditions.

Data Gathering from a Multimodal Dense Underwater Acoustic Sensor Network Deployed in Shallow Fresh Water Scenarios

The Robotic Vessels as-a-Service (RoboVaaS) project intends to exploit the most advanced communication and marine vehicle technologies to revolutionize shipping and near-shore operations, offering on-demand and cost-effective robotic-aided services. In particular, the RoboVaaS vision includes a ship hull inspection service, a quay walls inspection service, an antigrounding service, and an environmental and bathymetry data collection service. In this paper, we present a study of the underwater environmental data collection service, performed by a low-cost autonomous vehicle equipped with both a commercial modem and a very low-cost acoustic modem prototype, the smartPORT Acoustic Underwater Modem (AHOI). The vehicle mules the data from a network of low cost submerged acoustic sensor nodes to a surface sink. To this end, an underwater acoustic network composed by both static and moving nodes has been implemented and simulated with the DESERT Underwater Framework, where the performance of the AHOI modem has been mapped in the form of lookup tables. The performance of the AHOI modem has been measured near the Port of Hamburg, where the RoboVaaS concept will be demonstrated with a real field evaluation. The transmission with the commercial modem, instead, has been simulated with the Bellhop ray tracer thanks to the World Ocean Simulation System (WOSS), by considering both the bathymetry and the sound speed profile of the Port of Hamburg. The set up of the polling-based MAC protocol parameters, such as the maximum backoff time of the sensor nodes, appears to be crucial for the network performance, in particular for the low-cost low-rate modems. In this work, to tune the maximum backoff time during the data collection mission, an adaptive mechanism has been implemented. Specifically, the maximum backoff time is updated based on the network density. This adaptive mechanism results in an 8% improvement of the network throughput.