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In this study, we present a novel low-frequency magnetic field energy harvester (EH) employing beryllium bronze/Pb(Zr,Ti)O3 ceramic composited dual-beam structures with tip magnets attached to the inner and outer beams. This design incorporates the internal magnetic-coupled (IMC) effect, resulting in significantly enhanced coupling ability and a wide bandwidth. The validity of the IMC mechanism is confirmed through theoretical formulas and numerical simulations. By leveraging the IMC condition, the EH achieves an expanded bandwidth, which increases from 22 to 43 Hz. Moreover, the total output voltages at the inherent resonance and internal resonance are boosted by 15.4% and 32%, respectively. The performance of the IMC-EH can be further improved by increasing the number of the endmost magnets. Experimental investigations reveal that the IMC-EH generates a maximum RMS output power density of 56.25 μW Oe−2 cm−3, surpassing existing magnetically coupled piezoelectric energy harvesters. Remarkably, even under an ambient magnetic field as low as 1 Oe, the proposed IMC-EH still yields a total output power of 185 μW, sufficient to continuously power 26 LEDs in real time. This demonstrates its potential as a promising solution for low-power consumption small electronics. Furthermore, the implications of this work extend beyond its immediate benefits, as it inspires the design of future self-powered wireless sensor networks in the context of the Internet of Things. [ABSTRACT FROM AUTHOR]

Cities represent a significant and growing portion of global carbon dioxide (CO2) emissions. Quantifying urban emissions and trends over time is needed to evaluate the efficacy of policy targeting emission reductions as well as to understand more fundamental questions about the urban biosphere. A number of approaches have been proposed to measure, report, and verify (MRV) changes in urban CO2 emissions. Here we show that a modest capital cost, spatially dense network of sensors, the Berkeley Environmental Air Quality and CO2 Network (BEACO2N), in combination with Bayesian inversions, result in a synthesis of measured CO2 concentrations and meteorology to yield an improved estimate of CO2 emissions and provide a cost-effective and accurate assessment of CO2 emissions trends over time. We describe nearly 5 years of continuous CO2 observations (2018-2022) in a midsized urban region (the San Francisco Bay Area). These observed concentrations constrain a Bayesian inversion that indicates the interannual trend in urban CO2 emissions in the region has been a modest decrease at a rate of 1.8 ± 0.3%/year. We interpret this decrease as primarily due to passenger vehicle electrification, reducing on-road emissions at a rate of 2.6 ± 0.7%/year.

Summary: Location‐based underwater communication applications such as strategic surveillance, disaster prevention, marine research, and mine detection have given the field of underwater wireless sensor networks (UWSN) a head start. Node localization is a prerequisite for accurate data collection, target monitoring, and network management in UWSNs. However, the unique characteristics of the underwater environment, such as signal attenuation, multipath propagation, and variable acoustic properties, pose a major challenge to effective node localization. Accurate sensor node location data is essential for successful underwater data collection, but difficult to achieve as the GPS system cannot be used in an underwater environment. In this paper, existing node localization techniques such as ALS, SLUM, MASL, SLMP, UDB, USP, etc., and recent advances such as the fusion of range‐based and range‐free techniques, the fusion of RSSI and AoA to improve localization accuracy by using directional information in addition to signal strength, and the use of optimization techniques such as PSO, COA, and WOA algorithms to improve the accuracy of the applied node localization algorithm, e.g., TP‐TSFLA, and challenges related to UWSN are discussed. Also, different localization algorithms that affect the accuracy of UWSN localization techniques have been evaluated and compared with NS2 in terms of localization error, localization coverage, energy consumption, and average communication cost metrics. In addition, this paper also provides an up‐to‐date investigation of localization techniques. Finally, the tools available for simulation are presented, followed by open research questions that need to be addressed in the localization of nodes. [ABSTRACT FROM AUTHOR]

Summary: For the sensor network, greedy forwarding is thought to be the best energy‐efficient routing method. There are two restrictions on greedy forwarding: a minimum local scenario and an intersection problem in which it is unable to locate an existing route. The proposed independent gradient forward (IGF) proves that the Euclidean equation used by greedy forwarding is less capable of dealing with the problems mentioned above. IGF considers each dimension independently to enhance the efficiency of greedy forwarding, sustaining its simplicity. It contributes to strengthening the suggested IGF's ability to manage holes and get around obstructions in the wireless sensor network. We have demonstrated that it travels a shorter potential path and avoids stuck nodes well in advance. It is more energy‐efficient since it can predict the void node and never visits a stalled node. We evaluated the suggested IGF's performance against other traditional boundary traversal‐based methods on both rare and dense networks. In comparison with other boundary traversal‐based algorithms, the simulation results indicate that it generates a 10% smaller route to bypass. The IGF average energy consumption increases slowly, rising by just 7% and 12.5% for dense and rare networks, respectively, with a fivefold increase in void size (from 100 to 500 m). [ABSTRACT FROM AUTHOR]

Von Neumann architecture requires information to be encoded as numerical values. For that reason, artificial neural networks running on computers require the data coming from sensors to be discretized. Other network architectures that more closely mimic biological neural networks (e.g., spiking neural networks) can be simulated on von Neumann architecture, but more important, they can also be executed on dedicated electrical circuits having orders of magnitude less power consumption. Unfortunately, input signal conditioning and encoding are usually not supported by such circuits, so a separate module consisting of an analog-to-digital converter, encoder, and transmitter is required. The aim of this article is to propose a sensor architecture, the output signal of which can be directly connected to the input of a spiking neural network. We demonstrate that the output signal is a valid spike source for the Izhikevich model neurons, ensuring the proper operation of a number of neurocomputational features. The advantages are clear: much lower power consumption, smaller area, and a less complex electronic circuit. The main disadvantage is that sensor characteristics somehow limit the parameters of applicable spiking neurons. The proposed architecture is illustrated by a case study involving a capacitive pressure sensor circuit, which is compatible with most of the neurocomputational properties of the Izhikevich neuron model. The sensor itself is characterized by very low power consumption: it draws only 3.49 μ A at 3.3 V. [ABSTRACT FROM AUTHOR]

Timely pipe break and burst detection and localization are important for proactive management of water distribution systems (WDSs). This paper presents a novel timing-based method for fast localization of hydraulic transient waves that may occur from a pipe break or burst event. The proposed technique deals with the arrival time difference from sensor pairs coupled with a new graph-based transient wave propagation mapping strategy to identify potential link (pipe) candidates for the transient event. The fusion of the wave arrival differences from all combinations of sensor pairs in the network is then used to more comprehensively locate the transient event source among the candidates. This novel integration of topological information avoids missing the correct candidate by identifying all possible solutions, and thus improves the reliability of the localization. Applications to numerical case studies and laboratory experiments show that the method can accurately locate the transient source even with uncertainties from measurement devices and address issues with previous conventional methods concerning detection reliability. [ABSTRACT FROM AUTHOR]

Wastewater surveillance has recently emerged as a valuable tool for environmental and public health monitoring. By analyzing the constituents and biomarkers present in wastewater, stakeholders can gather critical information regarding contamination events and disease outbreaks. However, little attention has been given to the crucial question of where to collect water quality samples or place water quality sensors to maximize the usefulness of wastewater surveillance data. To address this gap, this study introduces a novel framework for sensor placement (SP) optimization in sewer networks. The objective of the optimization is to maximize both the observability and reliability of source identification (SI) under different scenarios. To achieve this objective, a machine learning–based SI model was integrated within the SP optimization framework. The SI model features a multilayer perceptron neural network model that was trained to forecast concentrations at various sensor locations, which were then propagated into a genetic algorithm that finds the optimal sensor network design that maximizes SI performance. The capabilities of the SP framework were demonstrated in a case study featuring a real-life, midsize sewer network. The SP framework was applied to multiple scenarios, including optimal design of a sensor network comprising one or more sensors, as well as optimal extension of existing sensor networks. The results showed that a clear trade-off exists between the sensor network's observability and reliability, highlighting the importance of considering both metrics for SP optimization. Overall, this study offers a practical approach for SP optimization to improve environmental and public health monitoring in a variety of contexts. [ABSTRACT FROM AUTHOR]

Wireless rechargeable sensor network (WRSN) is one of the most important networks in today's world for extending network lifetime. To ensure a collision-free contention in WRSN, this paper proposes the Contention Window Optimization using the Cuttlefish Algorithm (CWO-CA) and addresses existing issues such as the capture effect, the fairness problem, and the queue hike. For safe channel access, the fitness function of CWO-CA is proposed using the retransmission count, queue size, and residual energy of the sensor node. The proposed algorithm divides the Contention Window interval into two halves based on the fitness function, and the nodes are assigned in either half to guarantee service differentiation. The fitness function among groups of the proposed algorithm constitutes the best fitness value. By selecting the best fitness value, the proposed algorithm facilitates adaptive contention management by dynamically balancing resource utilization of the network with the optimal result. The nodal behavior of CWO-CA has been modeled using a discrete Markov model and also simulated to measure throughput, packet delivery ratio, packet loss ratio, average queue size, residual energy, and delay. These results confirm that the proposed CWO-CA algorithm attained better performance than other existing algorithms. [ABSTRACT FROM AUTHOR]

In wireless sensor networks (WSNs), data aggregation techniques are used to reduce energy consumption of the sensors and extend the network lifetime. Recent work in this area considers the utilization of external mobile agents (elements) in data aggregation to further improve the WSN lifespan. The sensitivity of data collected by WSNs varies based on the application environment, necessitating data aggregation that adheres to specific conditions and constraints. One of the challenges in using external mobile elements is establishing trust between these elements and WSNs to ensure that data aggregation conditions set by the WSNs are met. In this paper, we propose a fog-cloud framework for a distributed trust service that enables WSNs to recruit trusted mobile elements for data aggregation. Our proposed trust framework provides WSNs with the flexibility to align their trust policy with the conditions of their data aggregation processes, facilitating efficient access to trust services. Simulation results demonstrate that our framework reduces energy loss while ensuring consistent and reliable data aggregation in WSNs. [ABSTRACT FROM AUTHOR]

The accuracy and reliability of IoT-based sensor networks depend on validating sensed data, including detecting outliers at the node level. This study proposes an online outlier detection approach using Multiple Linear Regression-based adaptive thresholds for real-time IoT/WSN sensor nodes. IoT sensors experience two outlier types: Errors, from sensor malfunctions or low battery, and Events, from sudden environmental changes. The Adaptive Threshold Based Outlier Detection (ATBOD) approach differentiates errors from events using an adaptive threshold that adjusts to real-time data patterns. Unlike existing methods that are used in literature, which lack automated model evolution and suffer from delays and high computational time, ATBOD enhances outlier detection sensitivity without increasing false alarms, which is crucial for efficient IoT sensor board operation. It also improves sensor board lifespan by discarding errors at the node level, preventing energy wastage from transmitting error data to the cloud. ATBOD outperforms existing algorithms, which are referenced for comparison, such as Enhanced Efficient Outlier Detection and Classification Approach (EEODCA), K Nearest Neighbor approximate outlier detection (KNN), and Modified Local Outlier Factor (LOF), in Error Detection Rate, Error False Positive Rate, and Energy Saving Ratio. These advancements represent a significant leap in performance, making ATBOD a superior method for real-time outlier detection in IoT sensor networks. [ABSTRACT FROM AUTHOR]

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The COVID-19 pandemic has dramatically changed human daily life. To mitigate the pandemic’s impacts, different countries and regions implemented various policies to contain COVID-19 and residents showed diverse responses. These human responses in turn shaped the uneven spatial-temporal spread of COVID-19. Such human-pandemic interaction is complex, dynamic, and interconnected. Delineating the reciprocal effects between human society and pandemics is crucial for preparing for and managing future epidemics. Geospatial big data acquired through mobile applications and sensor networks have facilitated near-real-time tracking and assessment of human responses to the pandemic, enabling a surge in researching human-pandemic interactions. However, these investigations involve inconsistent data sources, human activity indicators, relationship detection models, and analysis methods, leading to a fragmented understanding of human-pandemic dynamics. To assess the current state of human-pandemic interactions research using geospatial big data, we conducted a synthesis study based on 67 selected publications between 25 March 2020, and 9 January 2023. We extracted information from each article across six categories, i.e. publication details, research context, research area and time, data, methodological framework, and results and conclusions. Results reveal that the influence of stay-at-home policies on mobility decrease varied regionally, showing limited effectiveness in Europe compared to the US. The positive correlations between human mobility and COVID-19 case rates evolved through time and were highest in the initial outbreak in 2020. Public awareness generally peaked prior to the peaks in COVID-19 cases, with varying intervals of 0 to 19.8 days observed across different countries. This study summarizes the research characteristics of selected articles and highlights the need for future research to spatially and temporally model the long-term, bidirectional causal relationships within human-pandemic systems to inform evidence-based, hyperlocal pandemic mitigation strategies. [ABSTRACT FROM AUTHOR]

Introduction: High annual tomato yields are achieved using high-tech greenhouse production systems. Large greenhouses typically rely only on one central weather station per compartment to steer their internal climate, ignoring possible microclimate conditions within the greenhouse itself. Methods: In this study, we analysed spatial variation in temperature and vapour pressure deficit in a commercial tomato greenhouse setting for three consecutive years. Multiple sensors were placed within the crop canopy, which revealed microclimate gradients. Results and discussion: Different microclimates were present throughout the year, with seasonal (spring - summer - autumn) and diurnal (day - night) variations in temperature (up to 3 °C, daily average) and vapour pressure deficit (up to 0.6 kPa, daily average). The microclimate effects influenced in part the variation in plant and fruit growth rate and fruit yield - maximum recorded difference between two locations with different microclimates was 0.4 cm d-1 for stem growth rate, 0.6 g d-1 for fruit growth rate, 80 g for truss mass at harvest. The local microclimate effect on plant growth was always larger than the bulk climate variation measured by a central sensor, as commonly done in commercial greenhouses. Quality attributes of harvested tomato fruit did not show a significant difference between different microclimate conditions. In conclusion, we showed that even small, naturally occurring, differences in local environment conditions within a greenhouse may influence the rate of plant and fruit growth. These findings could encourage the sector to deploy larger sensor networks for optimal greenhouse climate control. A sensor grid covering the whole area of the greenhouse is a necessity for climate control strategies to mitigate suboptimal conditions. [ABSTRACT FROM AUTHOR]

This paper presents a dual-purpose LED driver system that functions as both a lighting source and a Visible Light Communication (VLC) transmitter integrated with a Powerline Communication (PLC) network under the PRIME G3 standard. The system decodes PLC messages from the powerline grid and transmits the information via LED light to an optical receiver under a binary phase shift keying (BPSK) modulation. The load design targets a light flux of 800 lumens, suitable for LED light bulb applications up to 10 watts, ensuring practicality and energy efficiency. The Universal Asynchronous Receiver-Transmitter (UART) module enables communication between the PLC and VLC systems, allowing for an LED driver with dynamic control and real-time operation. Key signal processing stages are commented and developed, including a hybrid buck converter with modulation capabilities and a nonlinear optical receiver to regenerate the BPSK reference signal for VLC. Results show a successful prototype working under a laboratory environment. Experimental validation shows successful transmission of bit streams from the PLC grid to the VLC setup. A design guideline is presented in order to dictate the design of the electronic devices involved in the experiment. Finally, this research highlights the feasibility of integrating PLC and VLC technologies, offering an efficient and cost-effective solution for data transmission over existing infrastructure. [ABSTRACT FROM AUTHOR]

ABSTRACT This article proposes the design of the resolution‐dependent variance‐constrained state estimation (RDVCSE) algorithm for a class of time‐varying nonlinear coupled complex networks (TVNCCNs) with stochastic communication and correlated noises. Specifically, a continuous‐differentiable nonlinear function with bounded first partial derivative is considered during the exchange among different coupled units and a resolution‐limited model is taken into account to embody the limited data‐processing capabilities of sensors. In order to describe the principle of random allocation in engineering, a stochastic strategy is employed in the sensor/estimator shared channel. An augmented RDVCSE method is developed such that the error covariance upper bound of state estimation (ECUBSE) can be guaranteed and obtained first. Then, the estimator parameter can be concretized via optimizing the trace of ECUBSE. In addition, a sufficient criterion is provided to verify the uniform boundedness of the presented RDVCSE algorithm. Finally, a comparative simulation is carried out to illustrate the validity of the introduced RDVCSE algorithm. [ABSTRACT FROM AUTHOR]

ABSTRACT In this article, the problem of finite‐horizon state estimation is studied for a class of time‐varying complex networks with sensor faults. The phenomenon of measurement quantization is considered such that the measurements are quantized probabilistically before transmitted to the state estimator. To deal with the unknown sensor fault, a neural network is introduced to appropriate the sensor fault whose weights are updated based on estimation error and the gradient descent method. Our aim is to design state estimators so that the state estimation errors are finite‐time bounded. First, sufficient conditions are established to ensure the existence of the desired state estimators. Then, the gains of the state estimators are derived in terms of the solutions to a set of recursive matrix inequalities. Finally, the usefulness of our estimation approach is confirmed by an illustrative example. [ABSTRACT FROM AUTHOR]

ABSTRACT In practical sensor networks, sensor nodes may operate with different sampling periods and initial sampling time instants, and their observations may also be nonuniform. Unfortunately, the research on distributed state estimation problems over such asynchronous sensor networks is very limited. Thus, this article focuses on the distributed consensus filtering problem over sensor networks with asynchronous measurements. First, the asynchronous measurement from each sensor is synchronized by the continuous‐time state evolution equation to a unified filtering fusion time instant within a given filtering period. After measurement synchronization, the statistical characteristics of measurement noise change. The cross‐correlations between the converted measurement noises are analyzed, as well as one‐step correlations between the converted measurement noises and the process noise. Second, an optimal asynchronous distributed consensus filter is designed based on synchronization measurements under the criterion of minimum mean‐square error with the above correlations between various types of noises taken into account. Meanwhile, a suboptimal distributed consensus filtering algorithm is further proposed to reduce computational complexity. Finally, based on the Lyapunov function method, the stability of the estimation error is theoretically demonstrated with an appropriate selection of consensus filtering gain and validated through simulations. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is globally distributed network systems consisting of small low-cost physical sensor nodes that can sense their environment and communicate with other nodes and computers. They are particularly used for key solutions to monitoring systems such as healthcare monitoring, environmental monitoring, and remote patient monitoring and are also preferred for their vast benefits for precision agriculture, smart building, and smart home applications. Medium Access Control (MAC) schemes are vital part of IoT systems, as the network performance metrics such as throughput, end-to-end packet delay, and energy consumption mostly depend upon the MAC scheme utilized. In this paper, a new hybrid MAC protocol called as Low-Delay Hybrid Medium Access Control (LD-HMAC) for data-gathering purposes in high-density IoT networks is presented with its models and detailed performance evaluation. In the networking application area, an unmanned aerial vehicle is considered as an access point for arranging and scheduling channel access, whereas wireless sensor nodes (IoT nodes) are considered as data-gathering elements from a dense IoT networking environment. The proposed LD-HMAC combines the strengths of both TDMA and CSMA, targeting primarily dense IoT network applications. In the development stages, first the proposed LD-HMAC protocol is designed, modeled, and evaluated analytically by using Discrete-Time Markov Chain. Second, in order to validate the analytical results of the proposed model, a simulation model was developed by using the Riverbed network simulation tool. Finally, the performance results obtained are compared to those of the corresponding MDCA and CCS models referenced in Shrestha (IEEE Trans Wirel Commun 13: 4050–4065, 2014). With respect to the end-to-end delay, the proposed LD-HMAC achieves better results than those of the MDCA and CCS models for time-critical dense IoT network applications. [ABSTRACT FROM AUTHOR]

With the rapid development of the oil and gas industry, monitoring the safety and efficiency of pipeline networks has become particularly important. In this context, Wireless Sensor Networks (WSNs) are widely used for monitoring oil and gas pipelines due to their flexible deployment and cost-effectiveness. However, since sensor nodes typically rely on limited battery power, extending the network's lifecycle and improving energy utilization efficiency have become focal points of research. Therefore, this paper proposes an energy-saving scheduling algorithm based on transformer networks, aimed at optimizing energy consumption and data transmission efficiency of wireless monitoring sensors in oil and gas pipelines. Firstly, this study designs a deep learning-based Transformer model that learns from historical data on energy consumption patterns and environmental variables to predict the energy and data transmission needs of each sensor node. Secondly, based on the prediction results, this algorithm employs a dynamic scheduling strategy that automatically adjusts the sensor's operational mode and communication frequency according to the node's energy status and task urgency. Additionally, we have validated the effectiveness of the proposed algorithm through field tests and simulation experiments. According to the experimental results, our model has higher efficiency in energy saving. Compared with Convolutional Neural Networks, Recurrent Neural Networks and Graph Neural Networks, the total energy consumption of sensor networks under the model scheduling in this paper was reduced by 6.7%, 33.4% and 26.3%, respectively. Our algorithms improve the energy efficiency and stability of the monitoring system and provide important technical support for future intelligent pipeline monitoring systems. We hope this paper will inspire future scientific research in this field. [ABSTRACT FROM AUTHOR]

In this study, we aim to conduct real-time sensing of indoor dust concentration distribution to mitigate the airborne transmission of coronaviruses. Airborne infection, facilitated by viruses present in particulate matter, emphasizes the importance of monitoring dust concentration as an indicator of virus spread. Our approach involves the implementation of a time-synchronized wireless sensor network for real-time sensing of dust concentration distribution. The time-synchronized wireless sensor network relies on a proposed time-synchronization algorithm, ensuring a time error of less than ± 1.27 ms. This precision enables the measurement of even fast-moving dust particles. To validate the feasibility of the wireless time-synchronized sensor network, we utilized a dust ejector (air cannon) and positioned time-synchronized sensors in a row. Dust particles released from the ejector passed through each time-synchronized sensor terminal. Simultaneously, a video recording (60 frames) was conducted, and the measured times of the time-synchronized sensor terminals were compared with the lap times of the video. The results of this comparison revealed identical lap times between the time-synchronized sensor data and the video, affirming the successful operation of the time-synchronized wireless distributed sensor node as designed. [ABSTRACT FROM AUTHOR]

To effectively monitor highly heterogeneous urban CO2 emissions using atmospheric observations, there is a need to deploy cost-effective CO2 sensors at multiple locations within the city with sufficient accuracy to capture the concentration gradients in urban environments. These dense measurements could be used as input of an atmospheric inversion system for the quantification of emissions at the sub-city scale or to separate specific sectors. Such quantification would offer valuable insights into the efficacy of local initiatives and could also identify unknown emission hotspots that require attention. Here we present the development and evaluation of a mid-cost CO2 instrument designed for continuous monitoring of atmospheric CO2 concentrations with a target accuracy of 1 ppm for hourly mean measurements. We assess the sensor sensitivity in relation to environmental factors such as humidity, pressure, temperature and CO2 signal, which leads to the development of an effective calibration algorithm. Since July 2020, eight mid-cost instruments have been installed within the city of Paris and its vicinity to provide continuous CO2 measurements, complementing the seven high-precision cavity ring-down spectroscopy (CRDS) stations that have been in operation since 2016. A data processing system, called CO2calqual, has been implemented to automatically handle data quality control, calibration and storage, which enables the management of extensive real-time CO2 measurements from the monitoring network. Colocation assessments with the high-precision instrument show that the accuracies of the eight mid-cost instruments are within the range of 1.0 to 2.4 ppm for hourly afternoon (12:00–17:00 UTC) measurements. The long-term stability issues require manual data checks and instrument maintenance. The analyses show that CO2 measurements can provide evidence for underestimations of CO2 emissions in the Paris region and a lack of several emission point sources in the emission inventory. Our study demonstrates promising prospects for integrating mid-cost measurements along with high-precision data into the subsequent atmospheric inverse modeling to improve the accuracy of quantifying the fine-scale CO2 emissions in the Paris metropolitan area. [ABSTRACT FROM AUTHOR]

Wireless Sensor Network (WSN) is a network with a good organization that provides efficient communication services. However, high energy usage and attack data sometimes become the key challenges that degrade the entire network's Performance. Therefore, in the present research, a novel Chimp-based Curriculum Learning (CbCL) is created for the WSN mobile cloud environment to reduce energy usage and detect the attack node with the CH selection. Initially, the required nodes are deployed in the network. The attack nodes are monitored and removed based on Chimp's optimal functions; optimal CH is selected for the data transfer. Finally, data is shared between the attack node and normal nodes. The created framework was tested in the Matlab environment, and the network efficiency parameters were calculated. To validate the improvement, the results are related to the existing models. The higher Performance of the CbCL satisfied the network's efficient energy management and attack detection process. [ABSTRACT FROM AUTHOR]

In recent decades, inland water remote sensing has seen growing interest and very strong development. This includes improved spatial resolution, increased revisiting times, advanced multispectral sensors and recently even hyperspectral sensors. However, inland waters are more challenging than oceanic waters due to their higher complexity of optically active constituents and stronger adjacency effects due to their small size and nearby vegetation and built structures. Thus, bio-optical modeling of inland waters requires higher ground-truthing efforts. Large-scale ground-based sensor networks that are robust, self-sufficient, non-maintenance-intensive and low-cost could assist this otherwise labor-intensive task. Furthermore, most existing sensor systems are rather expensive, precluding their employability. Recently, low-cost mini-spectrometers have become widely available, which could potentially solve this issue. In this study, we analyze the characteristics of such a mini-spectrometer, the Hamamatsu C12880MA, and test it regarding its application in measuring water-leaving radiance near the surface. Overall, the measurements performed in the laboratory and in the field show that the system is very suitable for the targeted application. [ABSTRACT FROM AUTHOR]

Guided waves (GW) allow fast inspection of a large area and hence have received great interest from the structural health monitoring (SHM) community. Fiber Bragg grating (FBG) sensors offer several advantages but their use has been limited for the GW sensing due to its limited sensitivity. FBG sensors in the edge-filtering configuration have overcome this issue with sensitivity and there is a renewed interest in their use. Unfortunately, the FBG sensors and the equipment needed for interrogation is quite expensive, and hence their number is restricted. In the previous work by the authors, the number and location of the actuators was optimized for developing a SHM system with a single sensor and multiple actuators. But through the use of the phenomenon of acoustic coupling, multiple locations on the structure may be interrogated with a single FBG sensor. As a result, a sensor network with multiple sensing locations and a few actuators is feasible and cost effective. This paper develops a two-step methodology for the optimization of an actuator–sensor network harnessing the acoustic coupling ability of FBG sensors. In the first stage, the actuator–sensor network is optimized based on the application demands (coverage with at least three actuator–sensor pairs) and the cost of the instrumentation. In the second stage, an acoustic coupler network is designed to ensure high-fidelity measurements with minimal interference from other bond locations (overlap of measurements) as well as interference from features in the acoustically coupled circuit (fiber end, coupler, etc.). The non-sorting genetic algorithm (NSGA-II) is implemented for finding the optimal solution for both problems. The analytical implementation of the cost function is validated experimentally. The results show that the optimization does indeed have the potential to improve the quality of SHM while reducing the instrumentation costs significantly. [ABSTRACT FROM AUTHOR]

In order to express the energy flow, motion flow, and control flow in wireless rechargeable sensor networks accurately and intuitively, and to maximize the charging benefit of MVs (mobile vehicles), a type of MTS-HACO (Mobile Transition Sequence Hybrid Ant Colony Optimization) is proposed. Firstly, node places are grouped according to the firing time of node's energy consumption transition to ensure that in each time slot, MV places only enable charging transitions for the node places with lower remaining lifetimes. Then, the FSOMCT (Firing Sequence Optimization of Mobile Charging Transition) problem is formulated under the constraints of MV places capacity, travelling arc weight, charging arc weight, and so on. The elite strategy and the Max–Min Ant Colony system are further introduced to improve the ant colony algorithm, while the improved FWA (fireworks algorithm) optimizes the path constructed by each ant. Finally, the optimal mobile charging transition firing sequence and charging times are obtained, ensuring that MVs have sufficient energy to return to the base station. Simulation results indicate that, compared with the periodic algorithm and the PE-FWA algorithm, the proposed method can improve charging benefit by approximately 48.7% and 26.3%, respectively. [ABSTRACT FROM AUTHOR]

Positioning, coverage, and connectivity play important roles in next-generation wireless network applications. The coverage in a wireless sensor network (WSN) is a measure of how effectively a region of interest (ROI) is monitored and targets are detected by the sensor nodes. The random deployment of sensor nodes results in poor coverage in WSNs. Additionally, battery depletion at the sensor nodes creates coverage holes in the ROI and affects network coverage. To enhance the coverage, determining the optimal position of the sensor nodes in the ROI is essential. The objective of this study is to define the optimal locations of sensor nodes prior to their deployment in the given network terrain and to increase the coverage area using the proposed version of an enhanced particle swarm optimization (EPSO) algorithm for different frequency bands. The EPSO algorithm avoids the deployment of sensor nodes in close proximity to each other and ensures that every target is covered by at least one sensor node. It applies a probabilistic coverage model based on the Euclidean distances to detect the coverage holes in the initial deployment of sensor nodes and guarantees a higher coverage probability. Delaunay triangulation (DT) helps to enhance the coverage of a given network terrain in the presence of targets. The combination of EPSO and DT is applied to cover the holes and optimize the position of the remaining sensor nodes in the WSN. The fitness function of the EPSO algorithm yielded converged results with the average number of iterations of 78, 82, and 80 at 3.6 GHz, 26 GHz, and 38 GHz frequency bands, respectively. The results of the sensor deployment and coverage showed that the required coverage conditions were met with a communication radius of 4 m compared with 6–120 m with the existing works. [ABSTRACT FROM AUTHOR]

Fifth-generation (5G) sensor networks are critical enablers of Industry 4.0, facilitating real-time monitoring and control of industrial processes. However, significant challenges to their deployment in industrial settings remain, such as a lack of support for interoperability and manageability with existing industrial applications and the specialized technical expertise required for the management of private 5G sensor networks. This research proposes a solution to achieve interoperability between private 5G sensor networks and industrial applications by mapping Yet Another Next Generation (YANG) models to Open Platform Communication Unified Architecture (OPC UA) models. An OPC UA pyang plugin, developed to convert YANG models into OPC UA design model files, has been made available on GitHub for open access. The key finding of this research is that the proposed solution enables seamless interoperability without requiring modifications to the private 5G sensor network components, thus enhancing the efficiency and reliability of industrial automation systems. By leveraging existing industrial applications, the management and monitoring of private 5G networks are streamlined. Unlike prior studies that explored OPC UA's integration with other protocols, this work is the first to focus on the YANG–OPC UA integration, filling a critical gap in Industry 4.0 enablement research. [ABSTRACT FROM AUTHOR]

One of the most important problems in directional sensor networks is coverage problem. The coverage can be measured in two ways: positional or temporal. In temporal coverage, the directional sensors rotate periodically round themselves in a repetitive process. Thus, in each time slot, those targets that are positioned within the sensor nodes radius receive their desired coverage. In this model, if a target is left uncovered, it is said that the target has remained in darkness. The main task defined for the temporal coverage model is the minimization of the total dark time for all the targets in the network. This problem has been solved by greedy‐based algorithms in last studies. Greedy‐based algorithms are able to solve the temporal coverage problem in real time. Remember that the performance of greedy algorithms is extremely dependent on the closeness of optimal solution and initial candidates. For this reason, greedy algorithms may obtain local minima due to heuristic search. As far as we know meta‐heuristic algorithms have not been used in past researches to solve such problems. For solving this problem, in this paper two algorithms were developed, GA‐based and hybridized model comprising genetic algorithms and tabu search. A new model was suggested for the chromosome in genetic algorithm. To evaluate the performance of the developed algorithms, they were compared with randomized scenario and greedy‐based algorithm presented in last studies. For better comparison, several parameters, including total dark time, number of sensors, number of targets, sector angle, sensing range were taken into account. The results obtained from the comparison of the algorithms indicated that the developed algorithms are effective in solving the temporal coverage problem in terms of minimizing the total dark time of the targets. [ABSTRACT FROM AUTHOR]

High-pressure die casting (HPDC) is a permanent mold-based production technology that facilitates the casting of near net shape components from nonferrous alloys. The pressure and temperature conditions within the cavity impact the cast product quality during and after the conclusion of the die filling process. Die surface cavity sensors can deliver information describing the conditions at the die-casting interface. They are associated with high costs and limited service lifetimes below the achievable total cycle count of the die inserts and therefore ill-suited for industrial use cases. In this work, the suitability of long short-term memory (LSTM) recurrent neural networks (RNN) for substituting physical cavity temperature and pressure sensors virtually after the production ramp-up or at the end of the sensor service life is investigated. Training LSTMs with data of 233 casting cycles with different process parameters provides networks which are then applied to 99 further cycles. The prediction accuracy is investigated for different time interval lengths in the solidification and cooling phase. For longer time intervals, the cavity pressure prediction deteriorates, potentially due to a highly individual and hardly ascertainable buildup of casting distortion and internal stresses. Overall, however, the accuracy of the developed LSTMs is excellent for the cavity temperatures and good for the cavity pressures. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is a vast network of devices with sensors or actuators connected through wired or wireless networks. It has a transformative effect on integrating technology into people's daily lives. IoT covers essential areas such as smart cities, smart homes, and health-based industries. However, security and privacy challenges arise with the rapid growth of IoT devices and applications. Vulnerabilities such as node spoofing, unauthorized access to data, and cyberattacks such as denial of service (DoS), eavesdropping, and intrusion detection have emerged as significant concerns. Recently, machine learning (ML) and deep learning (DL) methods have significantly progressed and are robust solutions to address these security issues in IoT devices. This paper comprehensively reviews IoT security research focusing on ML/DL approaches. It also categorizes recent studies on security issues based on ML/DL solutions and highlights their opportunities, advantages, and limitations. These insights provide potential directions for future research challenges. [ABSTRACT FROM AUTHOR]

Summary: Wireless sensor networks consist of several autonomous nodes that are outfitted with sensors, radio, processors, memory storage, and power sources. These nodes track, sense, and send data using radio. While establishing a network, the two most essential characteristics are coverage and connectivity. For better connectivity and a longer network life, it's important to make the coverage area as big as possible with the fewest number of sensor nodes. The goal of this research is to make a connected sensor network that uses less energy and can be used in situations where the sensors need to be placed in the best way to make the network last as long as possible. The probabilistic sensing model is used, and improved network lifetime is the goal of the research work by using problem‐specific intelligent optimization techniques like BAT, ACO, and JOA to maximize the coverage area with respect to energy and points of interest. This work introduces a novel approach that optimizes both coverage and connectivity. The modified binary bat algorithm overcomes computational complexities and local optima observed in existing methods. Uniquely, it models the three states of each sensor node and includes innovative features like a greedy initialization and a weighted cost function for improving network efficiency. After investigation, it was analyzed that the proposed solution significantly improves network lifetime by over 10% to 12% compared to existing methods like JOA and ACO. The proposed approach converges faster and performs more efficiently. [ABSTRACT FROM AUTHOR]

This article addresses the state estimator design issue of interval type-2 (IT2) fuzzy systems under deception attacks. The permission of sensors to access the network is determined by a stochastic protocol obeying Markov scheduling rules. An attack-resilient IT2 fuzzy state estimator is proposed to mitigate the adverse effect of deception attacks on estimation performance. By using the known bounds of system membership functions and introducing two new weighting functions, the MFs of fuzzy state estimator are reconstructed. A hidden Markov model is employed to describe the asynchronous behaviour of scheduling rules between the sensor and the estimator. Sufficient conditions are derived to ensure the ultimate boundedness of estimation error. Finally, a numerical example demonstrates the applicability of the proposed attack-resilient asynchronous state estimation method. [ABSTRACT FROM AUTHOR]

Wireless Body Area Networks (WBANs) play a crucial role in healthcare and various other domains by enabling continuous monitoring of vital signs and activities. However, challenges such as low-powered sensors, unstable links, and routing issues hinder their efficiency and reliability. In this paper, we propose the Thermal Aware High Throughput (TAHT) routing protocol to address these challenges. TAHT utilizes a multi-hop topology and prioritizes stable links for efficient routing, considering parameters like energy, link quality, and temperature. Through experimental evaluation, TAHT demonstrates a 10% to 20% improvement in throughput, a 15% reduction in temperature overheads, and enhanced overall performance compared to benchmark protocols. By effectively managing energy consumption, minimizing duplicate data transmissions, and optimizing routing paths, TAHT offers a novel solution to improve the reliability and sustainability of WBANs, thus advancing the field beyond previous efforts in the literature. [ABSTRACT FROM AUTHOR]

The economic cost of repairing corrosion-affected reinforced concrete structures (RCSs) means that reliable and accurate assessment and early detection methods must be sought after. Conventional techniques, such as visual inspections, or measuring either cover layer resistivity or the corrosion potential, are methods that require accessibility and involve personnel having to travel to take in situ measurements. Monitoring by embedded sensors is a much more efficient approach that allows early detection by remote sensing. This work presents the implementation of a new measurement protocol regarding the existing monitoring system called INESSCOM (Integrated Sensor Network for Smart Corrosion Monitoring). Along with the corrosion intensity measurement in embedded sensors, it also proposes monitoring the double layer capacity of the sensors' responses. It aims to determine, along with the rebars' corrosion rate, the triggering agent of the corrosion process. This study was carried out using three reinforced concrete scaled columns that were exposed to different environments. The results demonstrate with this new protocol that the remote INESSCOM monitoring system can establish the corrosion rate and identify the precursor agent of corrosion (carbonation or chlorides), even when the recorded corrosion rates are similar. [ABSTRACT FROM AUTHOR]

Wireless sensor networks consist of numerous sensor nodes that monitor and send environmental data, such as temperature measurements and motion detection information, to a central station. Due to the nature of hop-by-hop data transmission in such networks, the failure of several nodes can adversely affect the functionality of the remaining healthy nodes, making the connection to the central station impossible. This study introduces the Hierarchical Agglomerative Clustering-Based Connectivity Restoration (HACCR) approach to address the problem of network reconnection after node failures. HACCR operates in two phases: backbone formation and recovery process. In the backbone formation phase, nodes exchange data packets to determine their locations and run the Hierarchical Agglomerative Clustering algorithm to form clusters. The clusters' geometric centers are then used to construct an Equivalent Graph, from which a Minimum Spanning Tree (MST) is derived using the Kruskal algorithm. In the recovery process, mobile nodes are deployed to restore connectivity by moving towards the cluster centers and establishing communication links along the MST. HACCR's performance is validated through comprehensive simulation studies, considering metrics such as the number of deployed mobile nodes, total traveled distance, and maximum distance traveled. The results demonstrate that HACCR outperforms baseline approaches in terms of efficiency and effectiveness. [ABSTRACT FROM AUTHOR]

Wireless sensor networks are always a demanding field, no matter how technology advances in recent times. Energy efficiency and security are the major concerns for such networks. While energy efficiency has been taken care of using clustering techniques by many researchers in the past, those energy-efficient clustering techniques still do not focus on security issues. Many researchers in the past considered trust models for the sensor networks that prioritize the detection of packet-dropping malicious nodes by observing the packet forwarding behavior of the nodes in a promiscuous way. This work focuses on the wormhole attack in clustered WSNs for Internet of Things networks and proposes a bio-inspired Ant Colony Optimization trust model to counter the wormhole attack. The proposed model computes the direct trust of the nodes based on the pheromone concentration rather than packet forwarding behavior to avoid packet loss. The proposed model has been compared with existing techniques based on packet delivery ratio, number of packet drops, throughput, remaining energy of the network, average end-to-end delay, average control overhead, average energy consumption per node, and average jitter. Simulation has been done in network simulator 2.35 and results prove that the proposed model can detect the malicious nodes in an energy-efficient way and also causes fewer packet drops. [ABSTRACT FROM AUTHOR]

The main issue in wireless sensor networks (WSNs) is the energy consumption of the nodes. Each sensor node communicates directly with other nodes in its transmission range or uses other nodes to forward the message to nodes outside its range. Software-defined networking (SDN) is a good solution for WSNs by separating the control logic from nodes/drivers. The advantage of SDN-WSNs is that SDN has centralized control over the whole network and deployment of network management protocols and applications becomes easy. In this paper, a new clustering model based on SDN-WSNs is proposed that uses the COOT optimization algorithm and Genetic Algorithm (GA). GA is used in the proposed model to improve COOT. COOT-GA is embedded in the SDN controller and is responsible for forming clusters with optimal structures. The SDN controller sends commands to the sensor nodes and finds the best clustering and energy consumption mode by repeated operations. The COOT-GA model is evaluated by the number of alive nodes, energy consumption, and packet delivery rate. The COOT-GA model is evaluated in two scenarios with 100 and 200 nodes. According to the results, with 100 nodes, the COOT-GA model has reduced energy consumption by 8.55%, 6.23%, and 3.90% compared to the Sine Cosine Algorithm (SCA), Harris Hawks Optimization (HHO) and COOT. According to the results, with 200 nodes, the COOT-GA model has reduced energy consumption by 13.54%, 10.32%, and 4.51% compared to SCA, HHO, and COOT. [ABSTRACT FROM AUTHOR]

Wireless power transfer (WPT) system is gaining prominence for charging various applications, including electric vehicles, biomedical implants, smartphones, and network sensors. Efficient compensation networks are essential to minimize leakage inductance. The performances of singleelement compensation topologies are compared based on different coupling and loading conditions, revealing that series-series (SS) and series-parallel (SP) topologies exhibit lower peak efficiencies of 85% and 90%, respectively, than parallel-series (PS) and parallel-parallel (PP) topologies, having 99% and 98.5% peak efficiency, respectively; with efficiencies deteriorating under different loading and coupling conditions. To address these shortcomings, a hybrid inductor-capacitor capacitor (LC-S) compensation topology is proposed, outperforming the single element topologies and maintaining over 95% efficiency under varying loading and coupling conditions. A slight efficiency drop to 82% due to frequency splitting was observed with low load resistance. In terms of the output characteristics, the LC-S topology achieved 1.2 W. These results showcase the potential of the LC-S topology for enhancing WPT system efficiency. [ABSTRACT FROM AUTHOR]

The 5G‐enabled sensor network systems make it possible to connect cyber and real 'things' in many ways. Even so, the flow of data between 5G‐enabled sensor devices brings big data environment problems, such as huge amounts of data, duplicated data, a lack of scalability etc. Some of these problems are as follows. It is hard to keep an eye on 5G‐enabled sensor systems in a 'big data' environment. Cyberattacks that could put the safety of the sensor network systems at risk are hard to find, which make the situation even more complicated. The security challenges of 5G‐enabled Sensor Network Systems are studied and analyzed due to some constraints associated with the sensor nodes. The proposed advanced algorithm for securing the 5G‐enabled sensor systems is a Multidimensional big data environment using artificial intelligence/machine learning (AI/ML). Using a structure that depends on both geographical and temporal data, an improved clear point selection operation may get important information from multidimensional time series data that is spread across a wide range of sensor nodes. Therefore, the actions of the 5G‐enabled sensor network can be shown accurately and a complete model of its underlying data structure is built to analysis attacking, pattern on 5G‐enabled Sensor Network Systems using the AI/ML Algorithm. [ABSTRACT FROM AUTHOR]

Internet of autonomous unmanned vehicles (IAUV) is a global network of sensors, robots, and autonomous vehicles. Person re‐identification (Re‐ID) is an important intelligent transportation application in IAUV, which needs to be solved using artificial intelligence algorithms. In this study, a domain adaptation metric learning method embedded with structural information (called DAML‐ESI) is designed for person Re‐ID in IAUV. Due to the lack of labeling information in the target domain, DAML‐ESI realizes person Re‐ID with the help of the discriminative and structural information of pedestrian images of related domains. DAML‐ESI projects pedestrian images selected from different domains into a common metric space and establishes a discriminative metric learning model, which requires that the positive sample pair be mapped to a point, and the distance distribution of the negative sample pair be mapped to a fixed value. The projection matrix learned by DAML‐ESI is used to eliminate the distribution differences between different domains, and the distance metric is used to ensure that the learned metric learning model has strong discriminative ability in the metric space. To verify the effectiveness of DAML‐ESI, experimental comparisons are conducted on three person Re‐ID datasets, and DAML‐ESI achieves satisfactory recognition performance. [ABSTRACT FROM AUTHOR]

We propose a framework for classifying acoustic scenes utilizing distributed sound sensor devices capable of sound-to-light conversion, which we term as Blinkies. These Blinkies can convert acoustic signals into varying intensities of light via an inbuilt light-emitting diode. By using Blinkies, we can aggregate the spatial acoustic information across a wide region by recording the fluctuating light intensities of numerous Blinkies distributed throughout the region. Nonetheless, the signal communicated is subject to the bandwidth limitation imposed by the frame rate of the video camera, typically capped at 30 frames per second. Our objective is to refine the process of transforming sound into light for the purpose of acoustic scene classification within these bandwidth confines. While traversing through the air, a light signal is affected by inherent physical limitations such as the attenuation of light and interference from noise. To account for these factors, we have integrated these physical constraints into differentiable physical layers. This approach enables us to jointly train a pair of deep neural networks for the conversion of sound to light and for the classification of acoustic scenes. Our simulation studies, which employed the SINS database for acoustic scene classification, demonstrated that our proposed framework outperforms the previous one that utilized Blinkies. These findings emphasize the effectiveness of Blinkies in the field of acoustic scene classification. [ABSTRACT FROM AUTHOR]

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The shape estimation technique can help solve the end positioning or grasping control of soft robots. However, there is a lack of sensing and modeling techniques for accurate deformation estimation and soft robots with axial elongation, e.g., pneumatic soft actuators (PSAs). This paper presents a shape-self-sensing pneumatic soft actuator (SPSA) with integrated liquid-metal piecewise curvature sensors (LMCSs). Two types of LM composite (Ga-In-Sn/Ga2O3 composites for the sensor and Ga-In-Sn/NdFeB/Ni for the electronic wire) were used to build the strain sensor network. Furthermore, a piecewise variable curvature (PVC) model was developed to predict the bending deformation of the soft actuator. A two-SPSAs-based gripper was built to test the identification performance of LMCSs. The results indicate that SPSA could perform contact and size identification using the PVC model. In addition, the K-nearest neighbors (KNN) algorithm was used to classify the shape of the targets. Finally, the circular, triangular, and square targets were identified with an accuracy rate of 93.3%. The work was expected to be applied to the size and shape perception and deformation planning of soft robots. [ABSTRACT FROM AUTHOR]

The underwater sensor network (UWSN) has become the most demandable area because of its numerous advancements in the real world. Water covers over 70% of the earth's surface, and there is more important information below the water's surface. Sensor networks detect information in the water and transmit it to the base station via the sink. UWSN is made up of autonomous and independent sensor nodes. These are placed spatially in the water to gather and transmit pertinent data. However, the communication may suffer due to several challenges like 3D topology, less bandwidth, routing and power constraints. This paper reviews underwater sensor networks based on routing, localization, and fault tolerance. It also reviews and compares the recently proposed algorithms to fulfil drawbacks in existing methods. Each factor's challenges are briefly discussed and compared in this paper. Future directions for underwater sensor networks are analyzed and discussed, along with their emerging applications and challenges. [ABSTRACT FROM AUTHOR]

This paper focuses on the design of a continuous distributed observer for linear systems under multirate sampled output measurements involving multiple delays. It is mathematically proved that the continuous distributed observer can achieve estimation in a sensor network environment, where output measurements from each sensor are available at different sampling instants, whether these times are periodic or aperiodic, and despite the presence of multiple time-varying delays. Each sampled and delayed measurement represents a node of the network, necessitating a dedicated observer for each node, which has access to only part of the system's output and communicates with its neighbors according to a given network graph. The exponential convergence of the error dynamics is ensured by Lyapunov stability analysis, which accounts for the influence of the sampled and delayed measurements at each node. To demonstrate the effectiveness of the proposed observer, simulation tests were conducted on the tracking control of chasing satellites in low Earth orbit (LEO), encompassing both small and large sampling rates and delays. The continuous distributed observer with sampled output measurements exhibited convergence in scenarios with different sampling intervals, even in the presence of time-varying delays, achieving asymptotic omniscience, as demonstrated in the convergence analysis. [ABSTRACT FROM AUTHOR]

Drones have been attracting significant attention in the field of agriculture. They can be used for various tasks such as spraying pesticides, monitoring pests, and assessing crop growth. Sensors are also widely used in agriculture to monitor environmental parameters such as soil moisture and temperature. Due to the high cost of communication infrastructure and radio-wave modules, the adoption of high-density sensing systems in agriculture is limited. To address this issue, we propose an agricultural sensor network system using drones and Optical Camera Communication (OCC). The idea is to transmit sensor data from LED panels mounted on sensor nodes and receive the data using a drone-mounted camera. This enables high-density sensing at low cost and can be deployed in areas with underdeveloped infrastructure and radio silence. We propose a trajectory control algorithm for the receiving drone to efficiently collect the sensor data. From computer simulations, we confirmed that the proposed algorithm reduces total flight time by 30 % compared to a shortest-path algorithm. We also conducted a preliminary experiment at a leaf mustard farm in Kamitonda-cho, Wakayama, Japan, to demonstrate the effectiveness of the proposed system. We collected 5178 images of LED panels with a drone-mounted camera to train YOLOv5 for object detection. With simple On–Off Keying (OOK) modulation, we achieved sufficiently low bit error rates (BERs) under 10 − 3 in the real-world environment. The experimental results show that the proposed system is applicable for drone-based sensor data collection in agriculture. [ABSTRACT FROM AUTHOR]

This paper describes a revolutionary design paradigm for monitoring aquatic life. This unique methodology addresses issues such as limited memory, insufficient bandwidth, and excessive noise levels by combining two approaches to create a comprehensive predictive filtration system, as well as multiple-transfer route analysis. This work focuses on proposing a novel filtration learning approach for underwater sensor nodes. This model was created by merging two adaptive filters, the finite impulse response (FIR) and the adaptive line enhancer (ALE). The FIR integrated filter eliminates unwanted noise from the signal by obtaining a linear response phase and passes the signal without distortion. The goal of the ALE filter is to properly separate the noise signal from the measured signal, resulting in the signal of interest. The cluster head level filters are the adaptive cuckoo filter (ACF) and the Kalman filter. The ACF assesses whether an emitter node is part of a set or not. The Kalman filter improves the estimation of state values for a dynamic underwater sensor networking system. It uses distributed learning long short-term memory (LSTM-CNN) technology to ensure that the anticipated value of the square of the gap between the prediction and the correct state is the smallest possible. Compared to prior methods, our suggested deep filtering–learning model achieved 98.5% of the sensory filtration method in the majority of the obtained data and close to 99.1% of an adaptive prediction method, while also consuming little energy during lengthy monitoring. [ABSTRACT FROM AUTHOR]

Peatlands across the world are vital carbon stores. However, human activities have caused the degradation of many sites, increasing their greenhouse gas emissions and vulnerability to wildfires. Comprehensive monitoring of peatlands is essential for their protection, tracking degradation and restoration, but current techniques are limited by cost, poor reliability and low spatial or temporal resolution. This paper covers the research, development, deployment and performance of a resilient and modular multi-purpose wireless sensor network as an alternative means of monitoring peatlands. The sensor network consists of four sensor nodes and a gateway and measures temperature, humidity, soil moisture, carbon dioxide and methane. The sensor nodes transmit measured data over LoRaWAN to The Things Network every 30 min. To increase the maximum possible deployment duration, a novel datastring encoder was implemented which reduced the transmitted datastring length by 23%. This system was deployed in a New Forest (Hampshire, UK) peatland site for two months and collected more than 7500 measurements. This deployment demonstrated that low-cost sensor networks have the potential to improve the temporal and spatial resolution of peatland emission monitoring beyond what is achievable with traditional monitoring techniques. [ABSTRACT FROM AUTHOR]