Monitoring Network Research Articles

Prognostic‐Based Active Battery Degradation Management in Wireless Sensor Networks for Group Replacement

ABSTRACTBatteries are prevalent energy storage devices, and their failures can cause huge losses such as the shutdown of entire systems. Therefore, the prognostic health management of batteries to increase their availability is highly desirable. This work focuses on improving the serviceability of batteries for wireless sensor networks (WSNs) deployed in remote and hard‐to‐reach places. We propose an active management strategy such that the batteries in a network will attain similar end‐of‐life times, in addition to lifetime extension. The fundamental idea is to adaptively adjust the node quality‐of‐service (QoS) to actively manage their degradation processes, while ensuring a minimum level of network QoS. The framework first executes a prognostic algorithm that can predict the remaining useful life (RUL) of a battery, given its assigned node‐level QoS. A Bayesian optimization framework with an augmented Lagrangian method has been adopted to efficiently solve the developed black‐box constrained optimization problem. A Matlab Simulink model based on a truss bridge structure health monitoring network is built considering the battery aging and temperature effects. Compared with the benchmark models, the proposed strategy demonstrates a more extended network lifespan and uniform working time ratio.

Simultaneous Observation of the Anomerization and Reaction Rates of Enzymatic Dehydrogenation of Glucose-6-Phosphate by Dissolution DNP.

The hyperpolarization of biological samples using dissolution dynamic nuclear polarization (dDNP) has become an attractive method for the monitoring of fast chemical and enzymatic reactions using NMR by taking advantage of a large signal increase. This approach is actively developing but still needs key methodological breakthroughs to be used as an analytical method for the monitoring of complex networks of simultaneous metabolic pathways. In this article, we use the deceptively simple example of glucose-6-phosphate (G6P) oxidation reaction by the enzyme G6P dehydrogenase (G6PDH) to discuss some important methodological aspects of dDNP kinetic experiments, such as its robustness and its ability to provide repeatable results as well as the capacity of this time-resolved methodology to test kinetic models and hypotheses and to provide reliable parameter estimates. To illustrate the potential of our approach, we report the first direct and quantitative evidence of selectivity of G6PDH toward the β anomer of G6P.

A multi-scale and multi-modal convolutional neural network for condition monitoring of transmission line

Abstract Monitoring the fatigue damage of transmission lines is crucial for stable power system operation. However, existing model-driven methods face challenges such as high computational complexity and reliance on expert knowledge, while data-driven methods require large amounts of abnormal state data. To address these issues, a multi-scale and multi-modal convolutional neural network (CNN) is proposed for real-time condition monitoring of transmission lines. Key steps include: firstly, empirical Fourier decomposition is used to decompose the original signals, extracting multi-scale state information at different frequency scales. Then, time-domain, frequency-domain, and time–frequency domain analyses are performed on the decomposed signals to capture multi-modal information. Based on this, a multi-modal fusion network is proposed based on a CNN to extract shallow and deep features, with a fully connected layer used for multi-modal feature fusion. Notably, the algorithm is implemented on a microprocessor for practical application. Experimental results show that the proposed model achieves a diagnostic accuracy of 93.06%, outperforming classical networks. It also surpasses models trained solely on time, frequency, or time–frequency features by 25.18%, 21.8%, and 19.3%, respectively.

Enhancing wave energy farm efficiency: Eigen-stacking ensemble framework

The significant wave height (SWH) plays a pivotal role across diverse domains, ranging from the assessment of wave energy potential to the optimization of marine operations and the advancement of ocean engineering techniques. Understanding the spatial and temporal distribution patterns of SWH data among buoy network stations holds utmost importance for various applications. This research introduces a novel methodology for accurate spatiotemporal SWH prediction across multiple stations within buoy monitoring networks. Leveraging eigen SWH time series enables the identification of dataset patterns, feature extraction, and dimensionality reduction. Examining eigen time series provides insights into SWH dynamics, allowing exclusive use for prediction across all stations. By incorporating this eigen SWH time series as the sole input into a stacking ensemble model, we introduce a highly efficient and accurate framework for SWH prediction. Notably, the methodology achieves success in forecasting hourly SWH values along the western United States coastline, utilizing a stacking ensemble technique for enhanced accuracy. Evaluation metrics confirm outstanding performance, with CE and R2 values surpassing 0.95 and 0.93, respectively. Further, a single Long Short-Term Memory (LSTM) model is incorporated as a benchmark to assess the effectiveness of the stacking ensemble model for significant wave height (SWH) prediction, with results demonstrating that the ensemble model outperforms the single LSTM in terms of accuracy and robustness. Consequently, the introduced stacking ensemble model promises autonomy in forecasting spatial-temporal SWH patterns, extending its applicability within ocean engineering and scientific research.

Accuracy and Precision of Earthquake Location Programs: Insights from a Synthetic Controlled Experiment

Abstract Earthquake location programs employ diverse approaches to address the challenges posed by incomplete knowledge and simplified representation of complex Earth structures. Assessing their reliability in location and uncertainty characterization remains challenging as benchmark datasets with known event locations are rare, and usually confined to particular sources, such as quarry blasts. This study evaluates eight earthquake location methods (GrowClust, HypoDD, Hypoinverse, HypoSVI, NonLinLoc, NonLinLoc_SSST, VELEST, and XCORLOC) through a controlled synthetic computational experiment on 1000 clustered earthquakes based on the setting of the 2019 Ridgecrest, California, earthquake sequence. We construct a travel-time dataset using the fast-marching method and a 3D velocity model extracted from the Community Velocity Model, supplemented with a von Karman perturbation to represent small-scale heterogeneity, and including elevation effects. Picking errors, phase availability, and outliers are introduced to mimic difficulties encountered in seismic network monitoring. We compare the location results from eight programs applied to the same travel-time dataset and 1D velocity structure against the ground-truth locations. For this aftershock sequence, our results reveal the superior accuracy and precision of differential time-based location methods compared to single-event location methods. The results validate the significance of compensating for deviations from assumed 1D velocity structure either by path or site correction modeling or by cancellation of unmodeled structure using differential arrival times. We also evaluate the uncertainty quantification of each program and find that most of the programs underestimate the errors.

Evaluation of privacy protection methods of public service advertising visual design in the perspective of artificial intelligence internet of things.

With the widespread application of monitoring technology and network storage technology, there are some privacy issues in the process of public service advertising visual design and the process of pushing public service advertising visual design works. Researching the privacy protection method of public service advertising visual design is of great significance to protecting the privacy data security of public service advertising visual designers and the personal privacy of other citizens. This paper proposes a digital watermark privacy protection method and a public service advertising visual design work push privacy protection method, and conducts a privacy protection experiment study on public service advertising visual design. The digital watermark privacy protection method and the public service advertising work push privacy protection method are used to provide an innovative solution for public service advertising privacy protection. The study shows that the evaluation score of the digital watermark privacy protection method is 11.05% higher than that of the traditional passive privacy protection method; the average feasibility evaluation score and the average effectiveness evaluation score of the privacy protection method for the public service advertising visual design work push by experts are 86.8 and 90.66 respectively. Combining the perspectives of artificial intelligence and the Internet of Things, this paper proposes a digital watermark privacy protection method, emphasizing practicality, and providing new ideas for privacy protection of public service advertising visual design.

TIRVolcH: Thermal Infrared Recognition of Volcanic Hotspots. A single band TIR-based algorithm to detect low-to-high thermal anomalies in volcanic regions.

Detecting early signs of impending eruptions and monitoring the evolution of volcanic phenomena are fundamental objectives of applied volcanology, both essential for timely assessment of associated hazards. Thermal remote sensing proves to be a cost-effective, yet reliable, information source for these purposes, especially for the hundreds of volcanoes still lacking conventional ground-based monitoring networks. In this work, we present an innovative and effective single band TIR-based (11.45 μm) algorithm (TIRVolcH), capable of detecting thermal anomalies in a broad range of volcanic settings, from low-temperature hydrothermal systems to high-temperature effusive events. Based on the processing of Visible Infrared Imaging Radiometer Suite (VIIRS) scenes, the algorithm offers an unprecedented trade-off between spatial (375 m) and temporal resolution (multiple acquisitions per day), having the potential to detect thermal anomalies for pixel-integrated temperatures as low as 0.5 K above the background, while maintaining a false positive rate of ∼1.8 %. The analysis of decadal time series of VIIRS data (2012−2023), acquired at three different volcanoes, reveals how the algorithm can: (i) detect hydrothermal crises at fumarolic fields (Vulcano, Italy), (ii) unveil thermal unrest preceding dome extrusions and explosive eruptions (Agung, Indonesia), and (iii) spatially trace lava flows extent and quantify their advancement rate, as well as track their long-term cooling behaviour (La Palma, Spain).We envisage that the algorithm will prove instrumental for detecting early signs of volcanic activity and following the evolution of eruptive phenomena, providing a useful tool for hazard management and risk reduction applications. Furthermore, the compilation of statistically robust multidecadal thermal datasets will provide novel insights and new perspectives into volcano monitoring, laying the ground for forthcoming higher-resolution TIR missions.

Spatial and Seasonal Variability of Remote and Urban Speciated Fine Particulate Matter in the United States.

The spatial and seasonal variability in the composition of major PM2.5 (particles with aerodynamic diameters less than 2.5μm) aerosol species in the United States were characterized using data from ground-based aerosol monitoring networks. The IMPROVE (Interagency Monitoring of Protected Visual Environments) network and the Chemical Speciation Network (CSN) operate in mostly rural/remote or urban/suburban sites, respectively. The networks have similar sampling schedules and analysis methods. Regional, monthly, and annual mean concentrations from 2019 to 2022 were calculated for ammonium sulfate (AS), ammonium nitrate (AN), particulate organic matter (POM), elemental carbon (EC), fine dust (FD), and sea salt (SS), as well as their relative contributions to reconstructed PM2.5 mass (RCFM). Organic aerosols were the largest contributor to RCFM across the United States (>40% annually, up to 80% monthly), with significant impacts from biomass smoke on POM and EC concentrations, contributions, and seasonality. AS concentrations and contributions were similar in urban and rural regions and contributed <20% annually to RCFM, considerably less than two decades ago. In general, urban concentrations were greater for AN, POM, and EC, suggesting additional urban sources. Some species, such as POM, FD, and AN, exhibited strong seasonal variability due to episodic source impacts or seasonal formation conditions. Evaluating the urban and rural monthly variability of major aerosol species is necessary for understanding the impacts of emission sources, regional transport, and atmospheric processes governing aerosol concentrations in the atmosphere.

Enhancing flood forecasting accuracy in Data-Scarce regions through advanced modeling approaches

Flood forecasting in data-scarce regions poses significant challenges due to irregular rainfall patterns and limited hydrological monitoring networks, particularly in semi-arid regions in Africa, South America, and Asia. However, despite significant efforts and advancements, there remains a substantial gap in the accurate prediction of flood events necessary for effective risk management and mitigation, evidenced by the recurrence of devastating floods in middle to low-income countries in recent years. Here, we address this problem by testing advanced modeling techniques in a local African case, using a combination of statistical methods for extreme event prediction, hydrodynamic modeling, and remote sensing data, to recommend the most adapted and accurate approach under a variety of settings. Our case study is an emerging urban area in Northern Morocco, situated in a triangular plain interposed between adverse geomorphological and precipitation settings, and unregulated expansion flow, creating an exceptionally overwhelming context for disastrous floods. In the absence of previous studies, we integrate frequency distribution analysis to predict extreme rainfall events and flood flow modeling to simulate floodplain inundation. Data sources included high-resolution remote sensing, local hydrological measurements, fine topographical data, and interviews with stakeholders. We found the Pearson Type 3 distribution to be the most suitable for modeling extreme precipitation in coastal areas, whereas the Generalized Extreme Value (GEV) distribution better fits inland areas. For flood flow assessment, the Gradex method proved to be the most accurate, while other empirical methods outlined critical limitations. Findings reveal that advanced hydrodynamic models significantly enhance flood hazard assessments, even in regions with limited data, showing outstanding correlations with previous flood records and stakeholder feedback. The outcomes carry critical implications for highlighting the importance of selecting appropriate models based on geographical and climatic conditions to inform more resilient urban planning and disaster management practices. We anticipate that these insights will support local decision-makers and urban planners in developing strategies that enhance community resilience and reduce the adverse impacts of flooding. Our work contributes to the broader field of flood risk management, providing a foundation for future developments and practical applications in similar regions worldwide.

On soil districts

In 2023, the European Commission released a legislative proposal for a Directive on Soil Monitoring and Resilience which aims to define a legal framework to achieve healthy soils across the European Union (EU) by 2050. A key component of the initial Directive is the mandate for Member States to establish basic geographic soil governance units, referred to as soil districts, and appoint a district-specific authority to oversee the implementation of soil health assessments. This paper proposes an operational definition of the districts following the conditions outlined in the proposal for the Directive and discusses various attention points for their implementation. Tentative districts were developed for seven EU countries, considering soil type, climate, topography, and land cover factors, starting from the smallest existing administrative unit (i.e. municipalities). Experts were asked to report on the applicability of the proposed districts within well-known pedo-ecological regions and discuss the relevance of the districts for establishing an EU-wide monitoring network and reporting on soil health and degradation. The outcomes highlight the need for detailed soil maps to account for specific soil types when stratifying countries into soil districts. The soilscape approach allows for a consistent method to defining soil districts across Member States. This enables contrasting soils within a district to be managed in a similar manner, with soil degradation/health thresholds applied to each district based on land cover. However, it is unclear whether soil districts as currently formulated in the Directive are in fact the right tool to support local soil management and monitoring of soil health. Districts can help ensure that all soil conditions are covered in a monitoring system, but they may not provide support for soil management or monitoring at a local scale due to short-scale soil variability and threats affecting soil management within the same soilscape. Beyond the use of districts for designing a European/national scale monitoring system, the districts can help create animations and other educational tools to promote soil literacy and connectivity of users to soils locally.

Boron Nitride Nanostructures (BNNs) Within Metal–Organic Frameworks (MOFs): Electrochemical Platform for Hydrogen Sensing and Storage

Boron nitride nanostructures (BNNs), including nanotubes, nanosheets, and nanoribbons, are renowned for their exceptional thermal stability, chemical inertness, mechanical strength, and high surface area, making them suitable for advanced material applications. Metal–organic frameworks (MOFs), characterized by their porous crystalline structures, high surface area, and tunable porosity, have emerged as excellent candidates for gas adsorption and storage applications, particularly in the context of hydrogen. This paper explores the synthesis and properties of BNNs and MOFs, alongside the innovative approach of integrating BNNs within MOFs to create composite materials with synergistic properties. The integration of BNNs into MOFs enhances the overall thermal and chemical stability of the composite while improving hydrogen sensing and storage performance. Various synthesis methods for both BNNs and MOFs are discussed, including chemical vapor deposition, solvothermal synthesis, and in situ growth, with a focus on their scalability and reproducibility. Furthermore, the mechanisms underlying hydrogen sensing and storage are examined, including physisorption, chemisorption, charge transfer, and work function modulation. Electrochemical characterization techniques, such as cyclic voltammetry, electrochemical impedance spectroscopy, and galvanostatic charge–discharge, are used to analyze the performance of BNN-MOF systems in hydrogen storage and sensing applications. These methods offer insights into the material’s electrochemical behavior and its potential to store hydrogen efficiently. Potential industrial applications of BNN-MOF composites are highlighted, particularly in fuel cells, hydrogen-powered vehicles, safety monitoring in hydrogen production and distribution networks, and energy storage devices. The integration of these materials can contribute significantly to the development of more efficient hydrogen energy systems. Finally, this study outlines key recommendations for future research, which include optimizing synthesis techniques, improving the hydrogen interaction mechanisms, enhancing the stability and durability of BNN-MOF composites, and performing comprehensive economic and environmental assessments. BNN-MOF composites represent a promising direction in the advancement of hydrogen sensing and storage technologies, offering significant potential to support the transition toward sustainable energy systems and hydrogen-based economies.

Bulk density and coarse fragment content of the French soil monitoring network for better assessment of changes of soil organic carbon stocks

Bulk density (BD) and coarse fragments (CF) are often missing in soil monitoring networks. In the French Soil Quality Monitoring Network BD and CF have been measured in two campaigns in sites distributed in a grid over mainland France. The objective of this work is to evaluate i) how BD and CF have changed, ii) how volumetric methods and land use affect the observed trends, and iii) to simulate the impact of BD and CF changes on the estimations of changes of soil organic carbon stocks (SOCS). The results showed no significant change of (Δ) CF between campaigns in either the topsoil or subsoil, while there was a significant decrease of BD only in the topsoil (−3.1 ± 0.9 %). When methods were constant in both campaigns, BD decreased in croplands and grasslands topsoil and it did not change in forests. We could explain a rather low part of the ΔBD variance (R2 = 0.21), mostly linked to the within-site variability of CF and to changes in some methodological aspects between campaigns. We simulated changes in topsoil SOCS, assuming SOC content constant over time; we found negative estimates that were the largest in soils with low CF content (−0.2 ± 0.06 % yr−1 an-1) as well as differences between using site-specific average CF values over both campaigns and using site-specific values of each campaign [-0.17 ± 0.04 % yr−1 vs −0.05 ± 0.06 % yr−1, respectively]. These differences were caused by rocky soils, and were not significant when using only sites with low CF content. Our work highlights some limitations in assessing BD changes using broad-scale soil monitoring networks. These findings have important consequences on the methods used to assess changes in SOCS and their uncertainties at broad-scale. They are timely and relevant given the current proposals to implement soil health monitoring at national, continental and global scale.

Low-Cost Sensor Network for Air Quality Assessment in Cabo Verde Islands

This study explores the application of low-cost sensor networks for air quality monitoring in Cabo Verde islands, utilizing Clarity Node-S sensors to measure fine particulate matter with diameters equal to or smaller than 10 µm (PM10) and 2.5 µm (PM2.5) and nitrogen dioxide (NO2) gasses, across various locations. The sensors were strategically placed and calibrated to ensure coverage of the whole archipelago and accurate data collection. The results consistently revealed seasonal patterns of dust variation across the archipelago, with concentrations of particulate matter exceeding World Health Organization (WHO) limits in all regions. However, Praia frequently exhibits the highest levels of air pollution, exceeding a 200 µg/m3 daily average, particularly during the dry season. Seasonal variations indicated that pollutants are significantly higher from November to March due to Saharan dust flux (a phenomenon locally know as Bruma Seca). Other cities showed more stable and lower pollutant concentrations. This study highlights the potential of low-cost sensors to provide extensive and real-time air quality data, enabling better environmental assessment and policy formulation. However, the variability in equipment accuracy and the limited geographical coverage remain the main limitations to be overcome. Future research should focus on these issues, and a sensor network integrated with reference methods could be a great asset to enhance data accuracy and improve outcomes of air quality monitoring in the country.

The Swiss National Air Pollution Monitoring Network (NABEL) – Bridging Science and Environmental Policy

Awareness of atmospheric air quality in Switzerland became a concern in the 1960s, as a result of which the Swiss National Air Pollution Monitoring Network (Nationales Beobachtungsnetz für Luftfremdstoffe - NABEL) was created in the 1970s. This paper describes the establishment and evolution of NABEL, emphasizing its important role in monitoring air quality in Switzerland, and its contribution to international observation networks and research. The network’s history, legal framework, and measurement program are described, and exemplary time-series of air quality parameters are given. NABEL is an excellent example for reliable, long-term air quality monitoring and demonstrates the importance of such monitoring for air pollution control at both national and international levels.

Early Season 2023 Wildfires Generated Record‐Breaking Surface Ozone Anomalies Across the U.S. Upper Midwest

AbstractDuring summer 2023 Canada experienced its most intense wildfire season on record. Smoke plumes from these fires advected across the United States (U.S.) Upper Midwest, producing regional scale surface enhancements of PM2.5 and ozone, as recorded by the U.S. surface monitoring network. These events are notable because they occurred early in the fire season (May 15‐June 30), and they produced the highest regional‐scale surface ozone levels ever recorded across the northern tier of the U.S. during early (May–June) or late (July‐August) summer. Specifically, the Upper Midwest 50th ozone percentile was greater than in any other year since 1995, when the ozone monitoring network had sufficient coverage to assess regional‐scale ozone levels; the 90th percentile was the highest since 2002. Satellite and aircraft measurements demonstrate the availability of ozone precursors and ozone production within the smoke plumes.

NetGSR: Towards Efficient and Reliable Network Monitoring with Generative Super Resolution

Network monitoring systems are a key building block in today's networks. They all follow a common framework where measurement data from network elements is aggregated at a central collector for network-wide visibility. When designing network monitoring systems, two key properties have to be taken into account: (1) efficiency, to minimize the communication overhead from network elements to the collector; (2) high-fidelity, to faithfully represent the network status. However, in presence of network dynamics, tracking the right operating point to ensure both high fidelity and efficiency is hard and we observe that prior monitoring approaches trade off one for the other. In this paper, we show that it is possible to satisfy both these properties with NetGSR, a new deep learning based solution we introduce that reconstructs the fine-grained behavior of network status at the collector while requiring low resolution measurement data from network elements. This is achieved through a combination of a new custom-tailored conditional deep generative model (DistilGAN), and a new feedback mechanism (Xaminer) based on model uncertainty estimation and denoising that allows the collector to adjust the sampling rate for measurement data from network elements, at run-time. We extensively evaluate NetGSR using three different network scenarios with corresponding real-world network monitoring datasets as well as two downstream use cases. We show that NetGSR can faithfully reconstruct fine-grained network status with 25x greater measurement efficiency than prior approaches while requiring only few ms of inference time at the collector.

Spectrum Monitoring in Cognitive Radio Networks for Reduced Data Loss, Resource Wastage, and Interference

ABSTRACTCognitive radio is a technology that allows the unlicensed users to access the licensed spectrum based on sensing information perceived by the users. Cognitive radio user generally attempts to access the unutilized band of licensed user. However, the emergence/reappearance of primary users during ongoing cognitive user's transmission creates the interference at the primary receiver and data loss at cognitive receiver. In this context, the spectrum monitoring is a vital process of the cognitive radio network during data transmission to continuously monitor the primary user's presence or absence on a licensed channel. In this manuscript, we have utilized the spectrum monitoring technique on earlier existing MAC protocol for CR network to detect the emergence/reappearance of primary users throughout the data transmission period of cognitive users. The spectrum monitoring is employed to reduce the interference at primary receiver, which is measured in terms of interference power (IP) and data loss at CU receiver. The closed form expressions of data loss and IP for the monitoring and without monitoring scenarios are provided. In addition, the probability of monitoring error's (which causes misinterpretation of channel's status by the cognitive users) impact on these performance parameters is also provided and simulated. Further, earlier MAC protocol has significant amount of bandwidth wastage in case primary user disappears or reappears during cognitive user's data transmission, which is shown mathematically and simulated as well. However, incorporation of the monitoring phenomenon even with significant amount of monitoring error has reduced bandwidth wastage, which is measured in bps. In addition, the throughput calculation of the proposed system and the effect of monitoring error on the throughput of the proposed system are also seen. As per the results, the maximum data loss and IP occurred at licensed channel's busy probability value of 0.5, which is 23.22 bps and 3.487 W, respectively, at maximum monitoring error value. However, minimum data loss and IP are achieved at licensed busy channel's probability values of zero and one. In addition, the resource wastage at licensed channel's busy probability value of 0.5 is varying from 2.322 to 23.22 bps for varying the monitoring error value from 0.1 to 1, respectively, giving highest resource wastage at maximum monitoring error value.

WaveFlex: A Smart Surface for Private 5G CBRS Networks

We present the design and implementation of WaveFlex, the first smart surface that enhances Private 5G networks operating under the shared-license framework in the Citizens Broadband Radio Service frequency band. WaveFlex works in the presence of frequency diversity: multiple nearby base stations operating on different frequencies, as dictated by a Spectrum Access System coordinator. It also handles time dynamism: due to the dynamic sharing rules of the CBRS band, base stations occasionally switch channels, especially when priority users enter the network. Finally, WaveFlex operates independently of the network itself, not requiring access to nor modification of the gNB or UEs, yet it remains compliant with and effective on prevailing cellular protocols. We have designed and fabricated WaveFlex on a custom multi-layer PCB, software defined radio based network monitor, and supporting control software and hardware. Our experimental evaluation benchmarks operational Private 5G and LTE networks running at full line rate. In a realistic indoor office scenario, 5G experimental results demonstrate an 8.58~dB average SNR gain, and an average throughput gain of 10.77 Mbps under a single gNB, and 12.84 Mbps under three gNBs, corresponding to throughput improvements of 18.4% and 19.5%, respectively.

Air Quality Estimation and Forecasting via Data Fusion With Uncertainty Quantification: Theoretical Framework and Preliminary Results

AbstractIntegrating air quality information from models, satellites, and in situ monitors allows for both better estimation of air quality and better quantification of uncertainties in this estimation. Uncertainty quantification is important to appropriately convey confidence in these estimates and forecasts to users who will base decisions on these. Uncertainty quantification also allows tracing the value of information provided by different data sources. This can identify gaps in the monitoring network where additional data could further reduce uncertainties. This paper presents a framework for data fusion with uncertainty quantification, applicable to multiple air‐quality‐relevant pollutants. Testing of this framework in the context of nitrogen dioxide forecasting at sub‐city scales shows promising results, with confidence intervals typically encompassing the expected number of actual measurements during cross‐validation. The framework is now being implemented into an online tool to support local air quality management decision‐making. Future work will also include the incorporation of low‐cost air sensor data and the quantification of uncertainty at hyper‐local scales.

Offloading Strategy for Forest Monitoring Network Based on Improved Beetle Optimization Algorithm

In forest monitoring networks, the computational capabilities of sensors cannot meet the latency requirements for complex tasks, and the limited battery capacity of these sensors hinders the long-term execution of monitoring tasks. Mobile edge computing (MEC) acts as an effective solution for this issue by offloading tasks to edge servers, significantly reducing both task latency and energy consumption. However, the computational capacity of MEC servers and the bandwidth in the system are limited, and the communication environment in forested areas is complex. To simulate the complexity of the forest communication environment, we incorporate empirical path loss and multipath fading into the calculation of signal transmission rates. The computational offloading problem is then converted into a minimum-cost optimization problem with multiple constraints related to energy consumption and latency, which we formulate as an NP-hard problem. We propose a dung beetle optimization (DBO) strategy for computational offloading, enhancing it with an improved circle chaotic mapping, a dimension decomposition strategy, and Cauchy disturbance. This algorithm has the beauty of symmetry in the search range, and the symmetrical features can comprehensively search for existing solutions. Experimental results demonstrate that the improved dung beetle optimization algorithm (IDBO) achieves better convergence, lower complexity, and superior optimization outcomes compared to local offloading strategies and other metaheuristic algorithms, confirming the effectiveness of the proposed algorithm and ensuring the service quality of the forest monitoring network.