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Internet of Things (IoT) technologies with distributed wireless sensors have been increasingly adopted in water utilities to build smart water networks (SWNs) for monitoring purposes. Based on the daily data collected from an accelerometer-based SWN, this paper proposes a new data analytic approach to detect developing cracks in water networks. The daily signals over a continuous period (e.g., 100 days) have been converted to a time-frequency power spectral density (PSD) heatmap. An analytic approach to detect developing cracks on the PSD heatmap has been formulated using a Spearman's rank correlation coefficient. With flexible temporal window lengths and frequency-associated weights adopted, the method involves an optimal search concept in the time-frequency domain for evidence of developing cracks. The implementation of the new method to field data collected from an SWN illustrates that the method can robustly detect developing cracks at their incipient stage, and thus allow adequate time for proactive repair before evolving into pipe breaks. The method is tolerant of noise, which is commonly present in the data collected by the sensors deployed in city areas. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSNs) are a distributed group of Sensor Nodes (SN) that are dispersed geographically in the organized environment to monitor and collect data on environmental conditions. In the network, each node has a limited wireless transmission range hence it is significant for nodes to transfer data through intermediatory nodes to reach the destination, which is a Base Station (BS). However, energy consumption with secure communication is a primary challenge due to resource-constraint nature of device and its communication is unfriendly and open environment. This research proposes Multi-objective Trust-Aware Cosine Tasmanian Devil Optimization Algorithm (M-TACTDOA) to select the Secure Cluster Head (SCH) and routing path in WSN. The TDOA is improved by using Cosine Perturbation Differential Evolution mechanism (CPDE) which addresses local optima issue and rapid convergence speed. By applying fitness function, the M-TACTDOA determine and avoid malicious nodes which provides secure and reliable data transmission. The proposed M-TACTDOA achieves a less energy consumption of 0.08 J compared to existing techniques like Energy Optimization Routing by employing improved Artificial Bee Colony (EOR-iABC). [ABSTRACT FROM AUTHOR]

Since the internal barely visible damage of aircraft composite structures caused by the impact is a critical problem, impact monitoring is essential for the integrity and reliability of aircraft composite structures. This paper presents a multi-frequency probabilistic imaging fusion method for localizing impacts on aircraft composite structures. To capture the impact signals, a network of distributed sensors is mounted on the structure. The impact signals are then processed using the continuous wavelet transform (CWT) to extract the multi-frequency narrowband Lamb wave signals. The time difference of arrival (TDOA), a key feature of the impact source, is measured using averaging techniques employed in the normalized variance sequence. Subsequently, a probabilistic imaging function is established, and the TDOA of narrowband Lamb wave signals at each frequency is used as the feature input to generate the multi-frequency probabilistic imaging results. To determine the performance of the imaging results at each frequency, an efficiency index is introduced, allowing for the retention or abandonment of the imaging results. By utilizing the retained multi-frequency probabilistic imaging results, the proposed method achieves impact localization through imaging fusion. Experimental verification is conducted on a stiffened aircraft composite panel, and a comparison is made with two existing methods: the hyperbolic locus imaging method and the virtual time reversal imaging method. The results show that the proposed method can significantly improve localization accuracy compared to the existing methods, and is effective even in the presence of measurement noise. [ABSTRACT FROM AUTHOR]

This paper presents, for the first time to the best of our knowledge, simultaneous temperature and relative humidity (RH) measurement using a machine learning (ML) model in Rayleigh-based Optical Frequency Domain Reflectometry (OFDR). The sensor unit consists of two segments: bare and polyimide-coated fibers, each with different sensitivities to temperature. The polyimide-coated fiber is RH-sensitive, unlike the bare fiber. We propose the ML approach to avoid manual post-processing data and maintain relatively high accuracy of the sensor. The root mean square error (RMSE) values for the 3 cm length of the sensor unit were 0.36 °C and 1.73% RH for temperature and RH, respectively. Furthermore, we investigated the impact of sensor unit lengths and number of data points on RMSE values. This approach eliminates the need for manual data processing, reduces analysis time, and enables accurate, simultaneous measurement of temperature and RH in Rayleigh-based OFDR. [ABSTRACT FROM AUTHOR]

Global active noise control (ANC) systems reduce noise over the entire car cabin with robust performance even as the human head moves; however, they have not been implemented in real-world applications. A robust error sensing strategy is proposed in this paper that is based on which a feasible global ANC system is realized in an electric car, and real-time ANC experiments demonstrate its effectiveness. Simulations based on measured road noise show that using evenly distributed error sensors is a robust error sensing strategy for different car speeds and the upper limit frequency of 3 dB global control is inversely proportional to the equivalent distance between error sensors. Real-time experiments with a 5-seat A-class sedan demonstrate that an average noise reduction of 3.8 dB was achieved between 50 and 250 Hz with 12 evenly distributed error sensors using the four standard car audio loudspeakers as secondary sources. Virtual sensing techniques can be integrated to constitute a more practical system without obstructing the movement of human heads. The findings in this study establish a benchmark for global noise control in car cabins and can be a starting point for future optimization of the system and implementation of adaptive algorithms. [ABSTRACT FROM AUTHOR]

This paper proposes a multi‐target tracking fusion algorithm based on consensus arithmetic averaging to address the limitations of the sensor detection field of view and information fusion difficulty when tracking multiple targets in a distributed Airborne passive bistatic radar (APBR) network. Labelled Multi‐Bernoulli (LMB) filters are executed locally when each node estimates multi‐target tracks. A consensus strategy is utilised to execute the information interaction among sensors, taking into account the limited detection field of view of a single sensor. The paper proposes the LMB Consensus Arithmetic Average (LMB‐CAA) fusion algorithm to address the issue of label inconsistency in multi‐sensor LMB density fusion. The proposed algorithm is based on label decomposition and reconstruction and aims to avoid the influence of label mismatch on the fusion effect while simultaneously ensuring that the fusion is closed during information interaction and fusion. Simulation results demonstrate that the proposed algorithm can stably estimate multi‐target tracks and improve the multi‐target tracking fusion accuracy of distributed APBR networks. [ABSTRACT FROM AUTHOR]

Detecting distributed targets precisely in homogeneous environments has been a hot topic in radar signal processing. Generally, distributed targets are often modelled with subspace models of unknown coordinates, and clutter is modelled as the complex Gaussian distribution with zero mean and unknown covariance matrix, while covariance matrix is estimated with a set of training data without the target signal. However, in practice, the complexity of the external environment makes the training data that satisfy the condition of independent homogeneous distribution less available. Therefore, it is assumed that the covariance matrix of the clutter is persymmetric structure and the approach of dimensionality reduction using subspace transformations is introduced, two detectors based upon generalised likelihood ratio test criterion and Wald test criterion in homogeneous environments are proposed. Theoretical analyses indicate the constant false alarm rate characteristics of the two proposed detectors for unknown clutter covariance matrices. Simulation analyses indicate that the proposed detector works well even with fewer training data samples, and its detection performance outperforms that of existing contrast detectors. [ABSTRACT FROM AUTHOR]

The present work intends to characterise the combination of two netted software defined radios (SDR) and different radio frequency (RF) front‐ends installed in them, together with a two‐stratum time dissemination and synchronisation system. A modified implementation of the Network Time Protocol is used as coarse event synchronisation for either SDR back‐end. The White rabbit light embedded node implementation of the commonly known white rabbit synchronisation system (IEEE 1588 PTP‐2019) or arbitrary wave form generators are used as fine time dissemination system for either SDR. The resulting netted transceiver system is intended to compose a demonstrator for proof‐of‐concept experiments. The findings presented in this article show that the chosen combination of hardware and software is suitable for radar applications operating within L‐, S‐ and C‐bands. A channel coherency throughout the network with a relative modified Allan deviation of less than 80 fs for averaging intervals of 1 s, a phase noise better than −118 dBc/Hz at 10 Hz frequency offset and a fractional frequency lower than ±2.5⋅10−13 $\pm 2.5\cdot 1{0}^{-13}$ was measured. Within a single transceiver node, a fractional frequency lower than ±2⋅10−13 $\pm 2\cdot 1{0}^{-13}$ and phase noise of −124 dBc/Hz at 10 Hz frequency offset were measured as well. Multistatic radar systems exploit wide spatial diversity to enhance target detection and tracking, albeit with increased complexity when compared to a monostatic configuration. To exploit these benefits, all participating transceiver nodes within the netted radar need to synchronise to a common time base t0 $\left({t}_{0}\right)$. The achieved synchronisation level increases the accuracy with which the chain of timed events at the transmitter and at the receiver side occur, intending to maximise the signal‐to‐noise ratio available at the receiver. The Universal software radio peripheral model X310 with two different daughterboard models as RF front‐end was used as SDR on either radar node. A network combining data and synchronisation purposes allowed the radar nodes under test to operate synchronously. The two‐stratum synchronisation system used glass fibres between 2 m and 5 km of length or coaxial cables with 2 m in length for network traffic, time and frequency dissemination purposes. The multiple RF front‐ends were stimulated by means of arbitrary waveform generators with calibrated traceability. Up‐chirp and sinusoid waveforms were used as stimuli for measuring the offset of either channel with regards of t0 ${t}_{0}$ to ultimately estimate the achievable coherency limits of the system under test. Both analogue and digital evaluation methods were considered. [ABSTRACT FROM AUTHOR]

The fundamental principle of integrated motion measurement (IMM) is integrated navigation with inertial sensors combined with, for example, a satellite navigation receiver. Accordingly, IMM makes use of the specific advantages of complementary sensors and blends them in a filter algorithm. To meet requirements in advanced motion measurements, for instance, for structural health monitoring or for structural control purposes, the approach of a single rigid body like in classical navigation no longer holds for IMM. As a solution, additional degrees of freedom (DOFs) for the moving structure are introduced, which represent deformations and allow the navigated body to be treated as a flexible structure. To cover a variety of flexible deformation shapes, a sufficient number of distributed inertial sensors is required. To restrict accumulating errors of these sensors, additional structural measurements for aiding are necessary. The signals of the inertial sensors and the aiding measurements are fused by an extended Kalman filter (EKF) to obtain an optimal estimation of the usual navigation states, extended by the deformation variables. The contribution presents the preparation of the experimental validation of an IMM system for a flexible structure, which represents an idealization of a wing or rotor blade by a movable beam. The mechanical and electrical setup of a test rig is described. Furthermore, the simulation of the test configuration, that is the model of a test beam with a variety of distributed sensors for generating artificial measurements as a test reference, is discussed. Finally, for an optimal sensor placement, the two methods of effective independence and maximization of modal energy are compared and experimentally tested for different amounts of additional flexible DOFs. [ABSTRACT FROM AUTHOR]

For multi-platform bearings-only cross location and tracking problem in distributed multi-function sensors, a target tracking management method based on asynchronous bearing only is proposed. The distributed multi-function system requires only one integrated sensor to switch to electronic support measure (ESM) mode within each tracking cycle to update the angle measurement information of target radiation source, while the other integrated sensors still work in the original planned mode and task. Compared with traditional cross location tracking method, the proposed asynchronous bearing only tracking method can improve the capability of the distributed integrated system with multiple functions such as reconnaissance, jamming and active detection at the same time in order to cope with the complex combat environment. [ABSTRACT FROM AUTHOR]

In order to further improve the accuracy of the online monitoring spectra of partial discharge in gas insulated switchgear, a partial discharge distributed monitoring system is studied, and distributed sensors are used to simultaneously obtain ultra-high frequency electromagnetic wave signals. Based on the accumulated partial discharge spectra of each sensor, the smooth decoherence noise recognition algorithm is proposed on a long-term cumulative spectral pattern, which is used to analyse the correlation between the spectra of each sensor. Furthermore, a method for determining whether partial discharge is an internal signal is proposed, and its effectiveness is verified by practice, the results confirmed that the algorithm can avoid the misjudgement of partial discharge results caused by distributed sensor interference. [ABSTRACT FROM AUTHOR]

Specialized sensors in wireless networks monitor the environment with minimal power, which is typically provided by batteries but it can also be powered with renewable sources using smart materials. One such solution is piezoelectric wind energy harvesting to power these sensors. However, the low output power of piezoelectric materials is still a concern. This research aims to improve piezoelectric wind energy harvesting by modifying the geometry of the bluff body. A square-shaped bluff body is used as a reference, and geometric changes are made by attaching square-shaped extensions. The study creates a mathematical model of a cantilever beam system with a piezoelectric material at fix end and a bluff body at the free end, using a multi-physics approach that combines electromechanical and aerodynamic models. The study uses MATLAB Simulink to solve the differential equations and verify the results with experiments. A parametric analysis examines the impact of external resistance, wind speed, and bluff body shape on circuit metrics. The study found that at a wind speed of 6 m/s and an optimal load resistance of 200 kΩ, the system generated 4.984 mW of power. This result demonstrates the potential of wind energy harvesting to power various distributed sensors for a wide range of applications. [ABSTRACT FROM AUTHOR]

The single transmitter and multiple receivers (STMR) mobile radar system is a special distributed radar system, which has stronger detection and survival capabilities. However, the high‐speed movement of the radar platforms easily produce the across‐range cell migration (RCM) of radar echo signal, which cause envelope shift effects within the coherent processing interval. Here, a motion compensation method for the STMR mobile radar system in the sea environment, to solve the problem of RCM is proposed. First, the target points and clutter points with the same Doppler velocity are obtained by the radon Fourier transform method. Second, using the range/Doppler difference between clutter and target under multi‐radar views, the target and clutter points are separated by the spatial range mapping. The simulation results show that the method can effectively improve the coherent accumulation effect of the target in the sea environment with low signal‐to‐clutter ratio (SCR). [ABSTRACT FROM AUTHOR]

This paper addresses the problem of optimal sensor selection for ensuring diagnosability of discrete event systems. Given a nondiagnosable discrete event system modeled with labeled Petri nets, a new labeling function can be designed to enforce the system to be diagnosable. An ad‐hoc parallel composition of non‐deterministic finite automata (derived from the original labeled Petri net model) that are observed at multiple observation sites is employed. In order to optimize a given labeling function as well as a sensor selection, an integer linear programming problem is formulated to associate numerical sensor costs with transition relabelings. In a multi‐fault system, this method can determine the type of faults that occur and the fault sequences pertaining to this fault type if multiple faults occur. Examples are presented to demonstrate the proposed method. [ABSTRACT FROM AUTHOR]

Wireless body area networks (WBANs) have gained significant attention due to their versatile applications in healthcare and the rapid development of personal healthcare devices. This paper proposes a novel cell-free (CF) model for WBANs, where multiple access points (APs) instantaneously support distributed sensors placed on the body by allowing all sensors to share the same time and frequency resources. Unlike multiple access schemes, such as non-orthogonal multiple access (NOMA), "cell-free" in our context denotes a decentralized architecture where multiple distributed APs cooperatively serve multiple sensors without the traditional cell boundaries. In NOMA systems, APs communicate with sensors in a cell by superimposing signals at different power levels, whereas CF systems enhance signal quality through joint processing across APs. Different from the traditional star topology WBAN where one AP communicates with only one sensor at a time, the CF model allows all sensors to transceive information data at the same time. Furthermore, a joint CF model and transmit power control algorithm is proposed to control the transmit power of sensors in the uplink and APs in the downlink of the CF model WBAN based on channel conditions in order to reduce the inter-user interference, maintain fairness among all sensors, and thereby improve the per-sensor throughput. The achievable uplink and downlink rates are derived using matched filtering and computational algorithms, resulting in a closed-form solution. The proposed joint method of the CF model and transmit power control algorithm is compared with the CF model without power control and traditional star topology WBANs to assess the effectiveness of the proposed approaches via several key parameters, including the number of sensors, the number of APs, and the length of pilot sequences. Simulation results demonstrate that the proposed approaches enhance system performance in both uplink and downlink across all tested scenarios. [ABSTRACT FROM AUTHOR]

Standard single-mode telecommunication optical fiber is still one of the most popular in distributed acoustic sensing. Understanding the acoustic, mechanical and optical features of various fibers available currently can lead to a better optimization of distributed acoustic sensors, cost reduction and adaptation for specific needs. In this paper, a study of the performances of seven fibers with different coatings and production methods in a distributed acoustic sensor setup is presented. The main results include the amplitude–frequency characteristic for each of the investigated fibers in the range of acoustic frequencies from 100 to 7000 Hz. A single-mode fiber fabricated using the modified chemical vapor deposition technique together with a polyimide coating has shown the best sensitivity to acoustic events in the investigated range of frequencies. All of this allows us to both compare the studied specialty fibers with the standard single-mode fiber and choose the most suitable fiber for a specific application, providing an enhancement for the performance of distributed acoustic sensors and better adaptation for the newly aroused potential applications. [ABSTRACT FROM AUTHOR]

The authors consider the problem of adaptive target detection in the background of interference and heterogeneous disturbance, for a multiple‐input multiple‐output (MIMO) radar system equipped with widely separated distributed antennas. The signal of interest and the interference lie in two corresponding subspaces that are mutually linearly independent. Therein, both the coordinates of two subspaces are unknown. For MIMO radar system, the disturbances of transmit‐receive pairs are heterogeneous, namely, each transmit‐receive pair has different statistics. And the disturbance in one transmit‐receive pair is non‐homogeneous. Therefore a double‐hierarchy heterogeneous disturbance is proposed. By resorting to a two‐step procedure, a double‐hierarchy heterogeneous structural detector (MIMO‐GLRTdh) is proposed in accordance with generalised likelihood ratio test for distributed MIMO radar to suppress interference and disturbance. In real scenarios, the certain structure property, existing in the covariance matrix of disturbance, such as the structural persymmetry or Toeplitz property, may be useful. Furthermore, the authors incorporate the structural persymmetry or Toeplitz property to design two structural detectors (PerMIMO‐GLRTdh and ToeMIMO‐GLRTdh), and study the impact of structure on detectors. Simulation results show that the presented detectors can acquire better detection performance and strong anti‐interference ability. In addition, the results indicate that the detection performance of distributed MIMO radar can be improved by the structure in training‐limited situations. [ABSTRACT FROM AUTHOR]

In Saudi Arabia, drones are increasingly used in different sensitive domains like military, health, and agriculture to name a few. Typically, drone cameras capture aerial images of objects and convert them into crucial data, alongside collecting data from distributed sensors supplemented by location data. The interception of the data sent from the drone to the station can lead to substantial threats. To address this issue, highly confidential protection methods must be employed. This paper introduces a novel steganography approach called the Shuffling Steganography Approach (SSA). SSA encompasses five fundamental stages and three proposed algorithms, designed to enhance security through strategic encryption and data hiding techniques. Notably, this method introduces advanced resistance to brute force attacks by employing predefined patterns across a wide array of images, complicating unauthorized access. The initial stage involves encryption, dividing, and disassembling the encrypted data. A small portion of the encrypted data is concealed within the text (Algorithm 1) in the third stage. Subsequently, the parts are merged and mixed (Algorithm 2), and finally, the composed text is hidden within an image (Algorithm 3). Through meticulous investigation and comparative analysis with existing methodologies, the proposed approach demonstrates superiority across various pertinent criteria, including robustness, secret message size capacity, resistance to multiple attacks, and multilingual support. [ABSTRACT FROM AUTHOR]

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Self-sufficient sensors scattered over different regions of the world comprise distributed wireless sensor networks (DWSNs), which track a range of environmental and physical factors such as pressure, temperature, vibration, sound, motion, and pollution. The use of data fusion becomes essential for combining information from various sensors and system performance. In this study, we suggested the multi-class support vector machine (SDF-MCSVM) with synthetic minority over-sampling techniques (SMOTE) data fusion for wireless sensor network (WSN) performance. The dataset includes 1,334 instances of hourly averaged answers for 12 variables from an AIR quality chemical multisensor device. To create a balanced dataset, the unbalanced data was first pre-processed using the SMOTE. The grey wolf optimization (GWO) approach is then used to reduce features in an effort to improve the efficacy and efficiency of feature selection procedures. This method is applied to classify the fused feature vectors into multiple categories at once to improve classification performance in WSNs and address unbalance datasets. The result shows the proposed method reaches high precision, accuracy, F1-score, recall, and specificity. The computational complexity and processing time were decreased in the study by using the proposed method. This is great potential for accurate and timely data fusion in dispersed WSNs with the successful integration of data fusion technologies. [ABSTRACT FROM AUTHOR]

Abstract Unmanned Aerial Vehicles (UAVs) are widely used in both military and civilian sectors due to their maneuverability and versatility. However, UAVs rely on the Global Navigation Satellite System (GNSS) for real‐time accurate navigation and are therefore vulnerable to attacks, particularly spoofing attacks, in GNSS‐challenging environments. Furthermore, UAV payloads are generally limited to carrying only a single antenna, significantly restricting the spatial Degrees of Freedom (DoFs) available. A new scheme is presented to address the challenges posed by multiple spoofing sources for UAV GNSS navigation. Unlike conventional multi‐antenna techniques, our approach extends centralised multi‐antenna Direction of Arrival (DOA) estimation to distributed scenarios using UAV collaboration techniques. The multi‐source DOA estimation of GNSS spoofing is achieved using the space‐time DOA matrix (ST‐DOAMatrix) technique, which enhances system resilience and spatial DoFs. Simulation results validate the effectiveness of the proposed method and demonstrate the feasibility and potential of the technique in ensuring the proper and safe operation of UAVs using GNSS navigation.

Abstract A distributed multiple‐input multiple‐output (MIMO) dual‐function radar‐communication (D‐MIMO DFRC) system is composed of multiple distributed dual‐function transmitters, multiple radar receivers and multiple communication receivers, which is capable of performing communication and radar tasks simultaneously. In a DFRC system, the goal is on optimising both the sum ‐rate in communication receivers and detection/localisation performance in radar receivers. The secrecy rate is maximised in D‐MIMO DFRC systems by decreasing the eavesdropper data rate as much as possible with a two‐step antenna selection method while maintaining optimal radar performance. In the first step of the proposed method, all transmitter antennas have been classified into groups based on their distance from each other, and each group is called a cluster. Then, a cluster of distributed transmitter antennas is selected based on path fading effects. In the second step of this method, the antenna selection algorithm is performed in the pre‐selected cluster based on channel capacity information utilising QR decomposition. The results show that this antenna selection method, along with low computational complexity and high performance, leads to the maximisation of the secrecy rate. In DFRC systems, it is desirable to minimise the total transmit power while satisfying system requirements to provide low probability of interception (LPI). Finally, after antenna selection, a power allocation strategy is also applied on the selected antennas to optimise the total transmit power and to maximise throughput in communication radar receivers simultaneously, and as a result it leads to provide LPI.

Abstract In a distributed multi‐station system, the observations received by local radar nodes for a single target will have a large signal‐to‐noise ratio (SNR) bias due to inconsistent radar cross‐sections from distinct angles, different distances from the target, various local interference such as harsh weather, and dissimilar background noise. Integrating heterogeneous information in dynamic and uncertain environments can be challenging for the fusion centre. Moreover, the particles in the basic particle filter (PF) may degrade after many iterations, making it difficult to achieve accurate target state estimation in the local tracking process. To address these issues, the authors propose a novel method named DS‐UPF based on the Dempster–Shafer (DS) theory and the unscented particle filter (UPF). By updating the important density function, the UPF efficiently suppresses particle degradation. The weighted Basic Probability Assignments (BPAs) are proposed and integrated under the new synthesis formula. The weight‐modified DS method restrains the impact of significant local estimation errors on weighted BPAs fusion result, improving robustness without local interference prior knowledge. The experimental results demonstrate that the DS‐UPF outperforms the unscented Kalman filter, PF, and UPF in tracking tasks under various local interference. This indicates that the proposed algorithm can improve estimation precision in dynamic and uncertain environments.

Abstract Coherent Rayleigh scattering‐based distributed fibre optic sensing technology enables real‐time acquisition of vibration and acoustic information along the optical fibres. However, the complexity of monitoring environments often leads to false alarms and missed detections during the process of information source identification with distributed acoustic sensing (DAS). Therefore, it becomes crucial to effectively extract meaningful signal features and perform accurate pattern recognition in the presence of external noise disturbance. The authors provide a comprehensive review of signal feature extraction and pattern recognition techniques applied in DAS technology. After introducing the fundamentals of DAS, specific applications are considered, and the following techniques have been analysed and compared: feature extraction algorithms based on wavelet decomposition, feature extraction schemes utilising other decomposition models, traditional recognition classifiers, and neural network‐based recognition classifiers using deep learning. The advantages and limitations of each scheme are discussed, along with their potential applications in various scenarios. The aim is to provide insights into the latest technologies in signal processing and pattern recognition for DAS, fostering further advancements in this field.

A significant challenge arises in powering distributed tiny sensors that gather high‐resolution soil data are crucial for advancing digital and geospatial technologies in the field. Traditional batteries and solar cells are unsuitable due to size mismatches. Microbial fuel cells lack stability in delivering constant power. Soil naturally contains redox‐active ions, such as Zn2+ and Mn2+, which can be harvested and utilized for electrochemical energy storage. To facilitate access to soluble ions, a microfluidic pump based on micro‐Swiss‐roll structures via micro‐origami is developed. Spontaneous capillary flow allows for efficient liquid extraction. The micro‐Swiss‐roll architecture shows significant improvements in redox reversibility and charge transfer within micrometer‐thick interlayer space. Notably, the interference of Zn0/2+ and Mn(II)/Mn(IV) reactions is suppressed. Consequently, the battery using a micro‐Swiss‐roll electrode shows high voltage and energy efficiency at high currents up to 10 µA, with cycling stability of 300 times. A twin‐micro‐Swiss‐roll architecture of 0.385 ± 0.006 mm2 is used for energy storage in wet soil, yielding over 40 times more energy as compared to soil‐microbial fuel cells. A conceptual demonstration of an integrated irrigation water sensor and micro‐Swiss‐roll battery highlights the potential of microbattery development in addressing the critical challenge of powering tiny sensors in the soil. [ABSTRACT FROM AUTHOR]

Structural health monitoring is essential for the lifecycle maintenance of tunnel infrastructure. Distributed fiber‐optic sensor (DFOS) technology, which is capable of distributed strain measurement and long‐range sensing, is an ideal nondestructive testing (NDT) approach for monitoring linear infrastructures. This research aims to develop a distributed sensing network utilizing DFOS for structural integrity assessment of concrete immersed tunnels. The primary innovations of this study lie in the development of a general flowchart for establishing a sensing network and obtaining reliable field data, as well as its subsequent validation through a detailed case study. Concentrated joint deformations in typical immersed tunnels, detectable by the DFOS, are key indicators of structural integrity. This study addresses crucial elements of field monitoring system design, including the selection of appropriate optical fibers or cables and the determination of vital interrogator system parameters. It also covers sensor parameter determination, installation techniques, field data collection, and postanalysis. Furthermore, this research is exemplified by a case study that illustrates the successful implementation of a distributed sensing network in an operational immersed tunnel, and monitoring data reveals cyclic structural deformations under impacts of daily tide and seasonal temperature variations. The data obtained from this network play a significant role in subsequent condition assessments of tunnel structures. The research findings contribute to the assessment of large‐scale infrastructure health conditions through the application of DFOS monitoring. [ABSTRACT FROM AUTHOR]

The application of distributed sensors in smart transmission lines to replace traditional inspection methods is an inevitable trend. Currently, the challenge of energy supply for sensors serves as a bottleneck that hinders the intelligent development of transmission lines. This paper focuses on the application of self‐powered inspection technology based on triboelectric and electromagnetic mechanisms in transmission lines. It proposes a self‐powered temperature and vibration monitoring and warning system (STV‐MWS) for multi‐parameter monitoring of transmission line status. This work utilizes the quasi‐zero stiffness structure and center misalignment design to improve the output performance of STV‐MWS at low vibration amplitude, thereby extending its vibration amplitude response range. The STV‐MWS is capable of harvesting and monitoring vibration of 50 µm and above vibration amplitude and 2–700 Hz vibration frequencies, which fully covers the breeze vibration range of transmission lines. Through the split package design, the flexible deployment of STV‐MWS is achieved, further enhancing its engineering application value. This work can effectively ensure that the transmission line inspection can carry out accurate status monitoring and intelligent analysis in the environment characterized by steep terrain, challenging power extraction, and difficult fault judgment, thereby realizing the visualization and intelligence of the transmission line status. [ABSTRACT FROM AUTHOR]

This paper investigates the estimation problem of a class of nonlinear systems with actuator and sensor faults. The primary objective is to design a distributed fault-tolerant observer which can estimate system states and actuator faults. Firstly, a distributed observer network with intermediate parameters is constructed to compensate the missing information of unobservable nodes of the system. Next, a class of redundant sensors is set up for each distributed observer node to obtain more output measurement samples. More importantly, when some of the redundant sensors occur faults, all sensor signals will be further processed and classified by a new algorithm. An index of sensor health level is constructed to characterize the quality of the fault-free or faulty sensor signals. By using the proposed algorithm, unhealthy sensor signals will be automatically filtered out, while healthy ones will be retained. Based on the healthy sensor signals, the system states and actuator faults are estimated. Finally, an example demonstrates that the proposed method is effective. • In this article, an improved distributed observer network is designed. The actuator fault can be estimated even if the sub-observer nodes are not observable. • We set up a class of redundant sensors to obtain more output measurement samples. Then, a novel algorithm is designed to filter out low quality output information. • We introduce a novel robustness term to against the effect of faulty sensors. The actuator fault can be observed in the case of sensor fault, which does not require employing extra fault detection mechanisms. [ABSTRACT FROM AUTHOR]

A distributed multiple‐input multiple‐output (MIMO) dual‐function radar‐communication (D‐MIMO DFRC) system is composed of multiple distributed dual‐function transmitters, multiple radar receivers and multiple communication receivers, which is capable of performing communication and radar tasks simultaneously. In a DFRC system, the goal is on optimising both the sum ‐rate in communication receivers and detection/localisation performance in radar receivers. The secrecy rate is maximised in D‐MIMO DFRC systems by decreasing the eavesdropper data rate as much as possible with a two‐step antenna selection method while maintaining optimal radar performance. In the first step of the proposed method, all transmitter antennas have been classified into groups based on their distance from each other, and each group is called a cluster. Then, a cluster of distributed transmitter antennas is selected based on path fading effects. In the second step of this method, the antenna selection algorithm is performed in the pre‐selected cluster based on channel capacity information utilising QR decomposition. The results show that this antenna selection method, along with low computational complexity and high performance, leads to the maximisation of the secrecy rate. In DFRC systems, it is desirable to minimise the total transmit power while satisfying system requirements to provide low probability of interception (LPI). Finally, after antenna selection, a power allocation strategy is also applied on the selected antennas to optimise the total transmit power and to maximise throughput in communication radar receivers simultaneously, and as a result it leads to provide LPI. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicles (UAVs) are widely used in both military and civilian sectors due to their maneuverability and versatility. However, UAVs rely on the Global Navigation Satellite System (GNSS) for real‐time accurate navigation and are therefore vulnerable to attacks, particularly spoofing attacks, in GNSS‐challenging environments. Furthermore, UAV payloads are generally limited to carrying only a single antenna, significantly restricting the spatial Degrees of Freedom (DoFs) available. A new scheme is presented to address the challenges posed by multiple spoofing sources for UAV GNSS navigation. Unlike conventional multi‐antenna techniques, our approach extends centralised multi‐antenna Direction of Arrival (DOA) estimation to distributed scenarios using UAV collaboration techniques. The multi‐source DOA estimation of GNSS spoofing is achieved using the space‐time DOA matrix (ST‐DOAMatrix) technique, which enhances system resilience and spatial DoFs. Simulation results validate the effectiveness of the proposed method and demonstrate the feasibility and potential of the technique in ensuring the proper and safe operation of UAVs using GNSS navigation. [ABSTRACT FROM AUTHOR]

A rapid and comprehensive assessment of flood impacts is critical to assist emergency managers in conducting effective relief operations. With advances in information technologies, various types of sensors have been widely used to assess flood impacts promptly as they are capable of providing rapid flood impact information. However, sensor‐driven approaches are limited in the provision of a comprehensive impact assessment as sensors are often sparsely distributed. In this research, the authors integrate the sparse flood impact information obtained from sensors and the spatial autocorrelation of flood‐impacted areas, in order to achieve a rapid and comprehensive flood impact assessment. To achieve such a purpose, a systematic approach is proposed to (1) extract flood impact information from sparsely distributed sensors; (2) model the spatial autocorrelation of flood‐impacted areas based on flood evolution and geography principles; (3) learn the parameters of the spatial autocorrelation model through a gradient descent method; (4) infer the flood impacts of sensor‐uncovered areas based on the sparsely sensed impacts and the modeled spatial autocorrelation. To illustrate the proposed approach, we studied flood impacts on Highways in Houston, Texas during Hurricane Harvey. Results show that the spatial autocorrelation model presents a decent generalization capability in inferring the probability of neighboring highway blocks having the same flood impacts. Compared to purely sensor‐driven approaches, the proposed approach is capable of greatly extending the coverage of flood impact assessment while maintaining the nearly same accuracy. [ABSTRACT FROM AUTHOR]

This paper introduces a novel design for a liquid-deployed Piezoelectric Micromachined Ultrasonic Transducer (PMUT). This design was specifically developed to resonate at a lower ultrasonic frequency than a PMUT with a circular, fully clamped diaphragm with the same diameter. Furthermore, the novel design was also optimised to enhance its ultrasonic radiation reception capabilities. These parametric enhancements were necessary to develop a PMUT device that could form part of an eventual microscale sensory device used for the Structural Health Monitoring (SHM) of reinforced concrete (RC) structures. Through these two enhancements, an eventual microscale sensor can be made smaller, thus taking up a smaller die footprint and also be able to be deployed further apart from each other. Eventually, this would reduce the developed distributed sensor system's cost. The innovative design employed a configuration where the diaphragm was only pinned at particular points along its circumference. This paper presents results from Finite Element Modelling (FEM), as well as experimental work that was conducted to develop and test this novel PMUT. The experimental work presented involved both laser vibrometry and ultrasonic radiation lab work. The results show that when compared to a clamped diaphragm design, the novel device managed to achieve the required reduction in resonant frequency and presented an enhanced sensitivity to incoming ultrasonic radiation. [ABSTRACT FROM AUTHOR]

In this contribution, we use Gaussian posterior probability densities to characterize local estimates from distributed sensors, and assume that they all belong to the Riemannian manifold of Gaussian distributions. Our starting point is to introduce a proper Lie algebraic structure for the Gaussian submanifold with a fixed mean vector, and then the average dissimilarity between the fused density and local posterior densities can be measured by the norm of a Lie algebraic vector. Under Gaussian assumptions, a geodesic projection based algebraic fusion method is proposed to achieve the fused density by taking the norm as the loss. It provides a robust fixed point iterative algorithm for the mean fusion with theoretical convergence, and gives an analytical form for the fused covariance matrix. The effectiveness of the proposed fusion method is illustrated by numerical examples. [ABSTRACT FROM AUTHOR]

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Underground telecommunication cables are highly susceptible to damage from excavation activities. Preventing accidental damage to underground telecommunication cables is critical and necessary. In this study, we present field trial results of monitoring excavation activities near underground fiber cables using an intensity-based phase-sensitive optical time-domain reflectometer (φ-OTDR). The reasons for choosing intensity-based φ-OTDR for excavation monitoring are presented and analyzed. The vibration signals generated by four typical individual excavation events, i.e., cutting, hammering, digging, and tamping at five different field trial sites, as well as five different mixed events in the fifth field trial site were investigated. The findings indicate that various types of events can generate vibration signals with different features. Typically, fundamental peak frequencies of cutting, hammering and tamping events ranged from 30 to 40 Hz, 11 to 15 Hz, and 30 to 40 Hz, respectively. Digging events, on the other hand, presented a broadband frequency spectrum without a distinct peak frequency. Moreover, due to differences in environmental conditions, even identical excavation events conducted with the same machine may also generate vibration signals with different characteristics. The diverse field trial results presented offer valuable insights for both research and the practical implementation of excavation monitoring techniques for underground cables. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) are structured for monitoring an area with distributed sensors and built-in batteries. However, most of their battery energy is consumed during the data transmission process. In recent years, several methodologies, like routing optimization, topology control, and sleep scheduling algorithms, have been introduced to improve the energy efficiency of WSNs. This study introduces a novel method based on a deep learning approach that utilizes variational autoencoders (VAEs) to improve the energy efficiency of WSNs by compressing transmission data. The VAE approach is customized in this work for compressing WSN data by retaining its important features. This is achieved by analyzing the statistical structure of the sensor data rather than providing a fixed-size latent representation. The performance of the proposed model is verified using a MATLAB simulation platform, integrating a pre-trained variational autoencoder model with openly available wireless sensor data. The performance of the proposed model is found to be satisfactory in comparison to traditional methods, like the compressed sensing technique, lightweight temporal compression, and the autoencoder, in terms of having an average compression rate of 1.5572. The WSN simulation also indicates that the VAE-incorporated architecture attains a maximum network lifetime of 1491 s and suggests that VAE could be used for compression-based transmission using WSNs, as its reconstruction rate is 0.9902, which is better than results from all the other techniques. [ABSTRACT FROM AUTHOR]

In a distributed multi‐station system, the observations received by local radar nodes for a single target will have a large signal‐to‐noise ratio (SNR) bias due to inconsistent radar cross‐sections from distinct angles, different distances from the target, various local interference such as harsh weather, and dissimilar background noise. Integrating heterogeneous information in dynamic and uncertain environments can be challenging for the fusion centre. Moreover, the particles in the basic particle filter (PF) may degrade after many iterations, making it difficult to achieve accurate target state estimation in the local tracking process. To address these issues, the authors propose a novel method named DS‐UPF based on the Dempster–Shafer (DS) theory and the unscented particle filter (UPF). By updating the important density function, the UPF efficiently suppresses particle degradation. The weighted Basic Probability Assignments (BPAs) are proposed and integrated under the new synthesis formula. The weight‐modified DS method restrains the impact of significant local estimation errors on weighted BPAs fusion result, improving robustness without local interference prior knowledge. The experimental results demonstrate that the DS‐UPF outperforms the unscented Kalman filter, PF, and UPF in tracking tasks under various local interference. This indicates that the proposed algorithm can improve estimation precision in dynamic and uncertain environments. [ABSTRACT FROM AUTHOR]

For distributed fiber-optic sensors, slowly varying vibration signals down to 5 mHz are difficult to measure due to low signal-to-noise ratios. We propose and demonstrate a forward transmission-based distributed sensing system, combined with a polarization-generated carrier for detection bandwidth reduction, and cross-correlation for vibration positioning. By applying a higher-frequency carrier signal using a fast polarization controller, the initial phase of the known carrier frequency is monitored and analyzed to demodulate the vibration signal. Only the polarization carrier needs to be analyzed, not the arbitrary-frequency signal, which can lead to hardware issues (reduced detection bandwidth and less noise). The difference in arrival time between the two detection ends obtained through cross-correlation can determine the vibration position. Our experimental results demonstrate a sensitivity of 0.63 mrad/με and a limit of detection (LoD) of 355.6 pε/Hz1/2 at 60 Hz. A lock-in amplifier can be used on the fixed carrier to achieve a minimal LoD. The sensing distance can reach 131.5 km and the positioning accuracy is 725 m (root-mean-square error) while the spatial resolution is 105 m. The tested vibration frequency range is between 0.005 Hz and 160 Hz. A low frequency of 5 mHz for forward transmission-based distributed sensing is highly attractive for seismic monitoring applications. [ABSTRACT FROM AUTHOR]

This paper introduces a proof‐of‐concept spatial decision support system (SDSS) that assists decision‐makers to generate equitably distributed sensor networks and evaluate their placement with reference to specific population‐based coverage criteria. Our approach centres equity in infrastructure distribution; we focus on the decision‐making process required to achieve the best possible sensor coverage of the geographical area for selected vulnerable populations and visualise trade‐offs in coverage inherent in infrastructure distribution. The development of the tool brings together expertise from quantitative geography, urban planning, data science and software engineering, and its technical development is underpinned and shaped by interviews with decision‐makers and their iterative feedback. Through this, we ask: how can decision support tools help with the work of building equitable infrastructure? As well as a technical application, our approach develops a conceptual framework for evaluating sensor network purpose and distribution before actual placement. The unique combination of distribution algorithms, user interface and decision‐maker input, developed by an interdisciplinary team, offers a novel approach to sensor network conceptualisation and generation. Our research contributes to the understanding of the distribution of essential infrastructure and can be repurposed for any sensor type and geographical location to promote equity in infrastructure distribution. [ABSTRACT FROM AUTHOR]

An Internet of things (IoT) based system framework integrating a distributed sensor network was implemented to collect real-time data at a construction site. Various sensors were utilized to gather data concerning particulate matter (PM2.5 and PM10 concentrations) as well as meteorological parameters - wind speed, humidity, pressure, and temperature. The real-time measurements results provide an overview of air pollution levels at the construction site, revealing its association with earth excavation work, the primary construction activity. This connection allows for better management aimed at reducing concentrations of suspended particles. Through on-site monitoring of two pollutant concentrations, this study identified that the dust levels resulting from excavation activities were relatively high. It can be concluded that earth excavation significantly impacts air quality in the construction area. While exploring the primary factors influencing construction dust concentrations, the correlations indicate that these concentrations were not significantly associated with meteorological factors. To predict PM2.5 and PM10 concentrations in the air using number of working machines and meteorological parameters as predictors, both multiple linear regression and artificial neural network models were applied. The artificial neural network model demonstrates better alignment with the measured air concentrations compared to the multiple linear regression model. The artificial neural network model demonstrated an R2 value of 0.674 for PM10 and 0.618 for PM2.5, indicating a strong predictive capability. The aim of this research, through modeling PM2.5 and PM10 concentrations in the air at the construction site is to indicate importance of the topic, especially with respect to the health of the construction site workers. [ABSTRACT FROM AUTHOR]

In this paper, a temperature insensitive wide-dynamic-range distributed strain sensor with centimeter-level spatial resolution based on optical frequency domain reflection technology is proposed. It adopts a polarization-maintaining photonic crystal fiber as the sensing fiber, and experimental results demonstrate that its temperature sensitivity is much smaller than that of the standard polarization-maintaining fiber. Benefited from the specially designed cross-correlation algorithm, the sensor achieves the maximum measurable strain reaches 7000 μϵ with spatial resolution of 1.66 cm. This provides a new approach to address current issues with special fiber sensors, such as single-point sensing and low spatial resolution, which shows great significance for structural health monitoring of buildings, bridges and tunnels with urgent requirement of large dynamic range. [ABSTRACT FROM AUTHOR]

Target localization for distributed radars requires precise knowledge of the calibration errors in time, frequency, and space. Previous studies focus on the sensor location error and sensor directional error in space. This paper concentrates on the system errors in time and frequency which directly affect the targets estimation of range and velocity. Based on linear frequency modulation waveform, the impact of the system errors including sample interval, pulse repetition interval, and centre frequency are analysed. An alternating optimization algorithm is proposed to achieve the target localization, velocity estimation and system errors estimation simultaneously. Simulation shows the effectiveness of the algorithm and the influence on localization due to varying number of targets and radars. [ABSTRACT FROM AUTHOR]

Data-driven models used for predictive classification and regression tasks are commonly computed using floating-point arithmetic and powerful computers. We address constraints in distributed sensor networks like the IoT, edge, and material-integrated computing, providing only low-resource embedded computers with sensor data that are acquired and processed locally. Sensor networks are characterized by strong heterogeneous systems. This work introduces and evaluates a virtual machine architecture that provides ML as a service layer (MLaaS) on the node level and addresses very low-resource distributed embedded computers (with less than 20 kB of RAM). The VM provides a unified ML instruction set architecture that can be programmed to implement decision trees, ANN, and CNN model architectures using scaled integer arithmetic only. Models are trained primarily offline using floating-point arithmetic, finally converted by an iterative scaling and transformation process, demonstrated in this work by two tests based on simulated and synthetic data. This paper is an extended version of the FedCSIS 2023 conference paper providing new algorithms and ML applications, including ANN/CNN-based regression and classification tasks studying the effects of discretization on classification and regression accuracy. [ABSTRACT FROM AUTHOR]

Full-scale axial load tests were performed on five screw injection piles founded in medium-dense to dense sand at a site in Delft, the Netherlands. Each pile was instrumented with distributed fiber-optic sensors along its full length, giving detailed insights into the shaft and base response under compression loading. The paper focuses on the pile base response and combines the test results with a newly compiled database of instrumented load tests on screw displacement piles in sand. Given the range of screw displacement piles on the market, the influence of different installation methods and pile geometries on the base resistance can be assessed through the database. In summary, the analysis showed that all screw displacement pile types tended to mobilize base capacities similar to bored or nondisplacement piles. Despite high variability in the database, no significant trend with pile geometry, such as length or diameter, was evident. [ABSTRACT FROM AUTHOR]

Considering the harsh environment of the port, automated straddle carriers, characterized by their large size, tall frame, and high center of gravity, may experience instability during steering and transportation due to inaccurate state estimation. Thus, this paper explores state estimation techniques for automated straddle carriers utilizing smart sensors which are capable of data measurement and processing. First, using the steering principles and lateral characteristics of automated straddle carriers, a dynamic linear model is established based on Newton's second law of motion. Then, in order to enhance the reliability and flexibility of state estimation, a distributed smart sensor network structure is introduced. In addition, considering the challenge of unknown-but-bounded noise and the precision demands of the considered automated straddle carrier, a modified distributed set-membership estimation algorithm is proposed and is derived sufficient conditions for the existence of the estimation set for the considered automated straddle carriers. Finally, the effectiveness and superiority of the proposed method are demonstrated by performance analyses. [ABSTRACT FROM AUTHOR]

Coherent Rayleigh scattering‐based distributed fibre optic sensing technology enables real‐time acquisition of vibration and acoustic information along the optical fibres. However, the complexity of monitoring environments often leads to false alarms and missed detections during the process of information source identification with distributed acoustic sensing (DAS). Therefore, it becomes crucial to effectively extract meaningful signal features and perform accurate pattern recognition in the presence of external noise disturbance. The authors provide a comprehensive review of signal feature extraction and pattern recognition techniques applied in DAS technology. After introducing the fundamentals of DAS, specific applications are considered, and the following techniques have been analysed and compared: feature extraction algorithms based on wavelet decomposition, feature extraction schemes utilising other decomposition models, traditional recognition classifiers, and neural network‐based recognition classifiers using deep learning. The advantages and limitations of each scheme are discussed, along with their potential applications in various scenarios. The aim is to provide insights into the latest technologies in signal processing and pattern recognition for DAS, fostering further advancements in this field. [ABSTRACT FROM AUTHOR]

Temperature difference and its duration are two main factors that affect thermoelectric performance. One can obtain the desired temperature distributions by manipulating heat flow directions; however, it is generally neglected when designing thermoelectric generators (TEGs). In this study, thermal rectifiers work in forward directions to produce in-plane temperature differences (ΔTh), where hot and cold zones are, respectively, provided by the small terminals of rectifiers and gaps between these areas. Thermoelectric legs placed above are arranged in an "X"-shape, keep TEGs' internal resistances, and have a stable range from 0.7 to 2 Ω; even heating temperatures Th have a significant range from 30 to 80 °C. When the rectification coefficient of thermal rectifiers was 1.63 and the thickness of thermoelectric legs decreased from 1 mm to 10 μm, simulated-ΔTh in the steady state rises from 2.62 to 27.10 °C rather than falling. An experimental thermal rectifier with a PI film thickness of 25 μm demonstrates that ΔTh can reach up to 14.7 °C, and the time duration is more than 60 s, where Th and ambient are 50 and 20 °C, respectively. The maximum output power can reach up to 92.48 μW when the temperature bias between Th and ambient increases to 65.33 °C. These novel thin-TEGs with designed in-plane temperature gradient zones by asymmetric thermal rectifiers are expected to be applied in distributed sensors, wearable devices, etc. [ABSTRACT FROM AUTHOR]

Abstract The application and uses of drones in all areas are continuously rising, especially in civilian use cases. This increasing threat requires reliable drone surveillance in urban environments. Radar is the obvious candidate with its ability to detect small objects at range, in all weather conditions. The use of an L‐band networked radar for urban sensing of S‐UAS targets is explored. Small echoes from S‐UAS places a premium on synchronisation, which is the fundamental key for high performance networked radar. The effect of timing errors on the operation of the network radar is investigated theoretically and experimentally, and the processing tools for synchronising data based on the direct signal returns of the transmitter are developed. Also, drone detection using bistatic L‐band staring radar is achieved both in simulation and then in real field trials where the SNR and detection performance are computed and analysed. The updated direct signal synchronisation method for bistatic staring radar is shown to provide comparable SNR and positional accuracy for S‐UAS targets as the monostatic staring radar.

The surge in the number of distributed microelectronics and sensors requires versatile, scalable, and affordable power sources. Heat‐harvesting organic thermoelectric generators (TEGs) are regarded as potential key components of the future energy landscape. Recent advances in the performance of organic thermoelectric materials have made practical applications of organic TEGs more feasible than ever before, yet the challenges of designing and fabricating organic TEGs suitable for real scenarios are scarcely addressed. Specifically, small sensors and wearables demand for micro‐thermoelectric generators (µTEGs) with high power density architectures and small form factors, while typical demonstrations of organic TEGs are characterized by < 10 thermocouples (TCs) per cm2. This work presents a rolled, organic µTEG architecture combining large‐area, solution‐based deposition techniques, such as inkjet and spray‐coating, and an ultrathin parylene substrate to achieve a thermocouple density of 1842 TCs cm−2. Such demonstrative µTEG reaches a thermoelectric conversion performance of 0.15 µW cm−2 at ΔT = 50 K. Such power output is well in line with finite element method simulations, which highlight the benefit of the architecture and show that remarkable power densities, in the mW cm−2 range at ΔT = 10 K, are realistically achievable with geometrical improvements and already ongoing advancements in organic thermoelectric inks. [ABSTRACT FROM AUTHOR]