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In recent times, the wireless sensor network (WSN) has become an integral part of daily life. WSN forms the necessary foundation for several important applications such as animal monitoring, border surveillance, asset monitoring, etc. These applications help maintain the confidentiality of the location of the occurring event from the attacker. The properties of the sensor nodes such as limited energy source, communication capability, memory, and network deployment at a large scale make it challenging to maintain the location privacy of a source node. To secure the source node location, this paper presents a source location privacy protection scheme that is based on random rings and a limited hop fake packet routing scheme (SLP-RRFPR). In the proposed scheme, an event packet is forwarded away from the base station by the random routing with confounding transmission to change the attacker’s backtracking process. Afterward, it follows the random routing, where the phantom node forwards the fake packet to other randomly selected nodes. In the last phase, a real packet is transmitted at the base station by ring routing. The simulation results of the proposed SLP-RRFPR are compared with phantom, baseline, probabilistic, source location privacy protection scheme based on ring-loop routing (SLPRR), and source location protection protocol based on dynamic routing (SLPDR). The simulation results show that the proposed SLP-RRFPR performs better than the compared protocol for various performance metrics, such as safety time, transmission delay, network lifetime, and randomness in the packet path.

The increased probability of fire occurrence in urban tunnels has led researchers to investigate this issue extensively. Although fire can occur at any point in a tunnel, the effect of fire source position on temperature distribution has not received considerable attention in most of previous investigations. In this research, the influences of varying horizontal fire source locations on temperature diffusion in particular maximum smoke temperature stratification beneath the ceiling has been investigated. A set of scale-down experiments was performed in a model tunnel [3 m (length) × 0.6 m (width) × 0.96 m (height)]. n-Heptane and gasoline were used as fuels in rectangular pools to generate a heat source. The analysis reveals that typical temperature curves have a similar trend when the fire source location changes. Furthermore, the temperature profile tip (maximum smoke temperature) is located between the burner and the origin of the tunnel. The modified model of maximum temperature, which considers the horizontal fire source location, is defined. The results here complement existing literature where the effects of variable fire position in a tunnel have not been considered.

Recently, the acoustic emission (AE) technique has been widely applied in the field of geotechnical engineering. One of the main applications of this technique is to locate damage sources, which is known as the AE source location technique. In this research, the 3D AE source location technique based on the time difference of arrival is verified by conducting pencil lead break (PLB) tests on a cylindrical metal specimen. From the results of this study, the existing method is proven to have the least error in terms of distance from the coordinate origin, but with some errors along x, y, and z axes. When the PLB sources originate in the middle part of the specimen, the calculated result has a higher accuracy, as compared to other positions. It seems that the calculated AE sources tend to be concentrated on the central part with some errors. Moreover, outside noises induced by a hammer hit have virtually no effect on this AE source location technique.

Wireless sensor networks (WSNs) have been applied in networking devices, and a new problem has emerged called source-location privacy (SLP) in critical security systems. In wireless sensor networks, hiding the location of the source node from the hackers is known as SLP. The WSNs have limited battery capacity and low computational ability. Many state-of-the-art protocols have been proposed to address the SLP problems and other problems such as limited battery capacity and low computational power. One of the popular protocols is random path routing (RPR), and in random path routing, the system keeps sending the message randomly along all the possible paths from a source node to a sink node irrespective of the path’s distance. The problem arises when the system keeps sending a message via the longest route, resulting because of high battery usage and computational costs. This research paper presents a novel networking model referred to as calculated random path routing (CRPR). CRPR first calculates the top three shortest paths, and then randomly sends a token to any of the top three shortest calculated paths, ensuring the optimal tradeoff between computational cost and SLP. The proposed methodology includes the formal modeling of the CRPR in Colored Petri Nets. We have validated and verified the CRPR, and the results depict the optimal tradeoff.

In recent years, with the continuing progress of aging social infrastructures such as bridges and tunnels, there has been high demand for the assessment of deterioration of their performance and conditions. Since current inspection methods for those structures have mainly relied on human resources, it is important to reduce their increasing maintenance cost. One of the key methods for achieving effective maintenance without expensive human costs is to use sensors to discriminate between healthy and unhealthy conditions. In this paper, a MEMS (micro electro mechanical systems) wideband frequency sensor, which is referred to as a super acoustic (SA) sensor, is evaluated through the pencil lead break (PLB) test. Due to its wideband frequency characteristics, the SA sensor is expected to be a promising alternative to the existing vibration sensors, including acoustic emission (AE) sensors. Several PLB signals were generated on an aluminum plate (5 mm thick), and propagating Lamb waves were detected by both AE and SA sensors. SA sensors were able to identify the location of PLB sources on the plate by measuring time differences between each sensor. By comparing the wave spectrums of both the AE and SA sensors analyzed by wavelet transform, the applicability of SA sensor for AE measurement is verified.

A single source location method based on a independent doublet array is proposed. It is considered that the source is located at the far-field of each doublet and the near-field of the array. The presented method realises direction of arrival estimation by an ESPRIT-like technique, and the range of the source by array geometry successively. It provides a simple and search-free estimation method for single near-field source location. Simulation results are presented that verify the effectiveness of the method.

The acoustic emission (AE) technique is one of the most widely used in the field of structural monitoring. Its popularity mainly stems from the fact that it belongs to the category of non-destructive techniques (NDT) and allows the passive monitoring of structures. The technique employs piezoelectric sensors to measure the elastic ultrasonic wave that propagates in the material as a result of the crack formation’s abrupt release of energy. The recorded signal can be investigated to obtain information about the source crack, its position, and its typology (Mode I, Mode II). Over the years, many techniques have been developed for the localization, characterization, and quantification of damage from the study of acoustic emission. The onset time of the signal is an essential information item to be derived from waveform analysis. This information combined with the use of the triangulation technique allows for the identification of the crack location. In the literature, it is possible to find many methods to identify, with increasing accuracy, the onset time of the P-wave. Indeed, the precision of the onset time detection affects the accuracy of identifying the location of the crack. In this paper, two techniques for the definition of the onset time of acoustic emission signals are presented. The first method is based on the Akaike Information Criterion (AIC) while the second one relies on the use of artificial intelligence (AI). A recurrent convolutional neural network (R-CNN) designed for sound event detection (SED) is trained on three different datasets composed of seismic signals and acoustic emission signals to be tested on a real-world acoustic emission dataset. The new method allows taking advantage of the similarities between acoustic emissions, seismic signals, and sound signals, enhancing the accuracy in determining the onset time.

The accuracy of sources locations and velocities estimate is very sensitive to the accurate knowledge of sensor locations and velocities. In the presence of sensor position and velocity errors, this study considers the problem of simultaneously locating multiple disjoint sources and refining erroneous sensor positions and velocities using time differences of arrival and frequency differences of arrival. The previous work by Sun and Ho to solve this problem provided an efficient estimator for multiple disjoint sources, but it cannot provide optimum accuracy for the sensor positions and sensor velocities. In many practical applications, it is necessary and helpful to refine sensor locations and velocities while localising multiple sources. The proposed method improves the previous method so that both the source and the sensor position and velocity estimates can achieve the Cramér–Rao lower bound accuracy very well over small noise region. The theoretical derivation is corroborated by simulations.

Distributed acoustic sensing shows great potential for pipeline monitoring. However, internally deployed and unfixed sensing cables are highly susceptible to disturbances from water flow noise, severely challenging impact source localization. This study proposes a novel two-step method to address this. The first step employs Variational Mode Decomposition (VMD) combined with Short-Time Energy Entropy (STEE) for the adaptive extraction of impact signal from noisy data. STEE is introduced as a stable metric to quantify signal impulsiveness and guides the selection of the relevant intrinsic mode function. The second step utilizes the Pruned Exact Linear Time (PELT) algorithm for accurate signal segmentation, followed by an unsupervised learning method combining Dynamic Time Warping (DTW) and clustering to identify the impact segment and precisely pick the arrival time based on shape similarity, overcoming the limitations of traditional pickers under conditions of complex noise. Field tests on an operational water pipeline validated the method, demonstrating the consistent localization of manual impacts with standard deviations typically between 1.4 m and 2.0 m, proving its efficacy under realistic noisy conditions. This approach offers a reliable framework for pipeline safety assessments under operational conditions.

Recent advancements in synchrophasor measurement technologies have introduced an unprecedented level of visibility in power distribution networks (PDNs), providing a high-quality data foundation for the accurate perception of event source locations. However, the high cost and deployment expense pose a significant challenge in balancing system observability and event source location identification (ESLI) accuracy. In this paper, we propose a staged ESLI scheme based on voltage measurement deviation (VMD), which can achieve high-precision ESLI and event current calculations under extremely low-observability conditions, where the measurement devices are deployed only at the head substation and terminal buses. By setting an unknown event injection current and traversing each bus along the target feeder to derive the terminal bus voltage and its outgoing current, an ESLI model based on virtual event current injection (VCI) is constructed, which not only assists in the ESLI task but also confers the solving capability of the event current. Leveraging the event current calculation ability of the ESLI model, a VMD-based staged ESLI algorithm is developed, achieving an ordered and accurate search for the exact location of the event source in a goal-oriented manner. The effectiveness of the developed ESLI algorithm is evaluated on the IEEE 33-bus test system. Experimental results demonstrate that our VMD achieves high-precision ESLI and event current solving in PDNs under extremely low observability, significantly outperforming the state-of-the-art ESLI methods.