Sensor Network For Monitoring Research Articles

Metrology for sensor networks: metrological traceability and measurement uncertainties for air quality monitoring

Abstract In situ calibration of sensors delivering SI traceable measurement results still provides an open question to the design and operation of sensor networks. Particularly when addressing low-cost sensors, currently, the use of sensor networks for air quality monitoring is limited by the low or unknown accuracy of measurements that they can achieve, while the data quality of individual sensor networks is mainly derived by algorithms. Standardization bodies like DIN and CEN therefore announced the need for investigations of validation methods on gas phase species and particulate matter on the one hand side, and for the development of fully digitized quality assurance/quality control and calibration techniques for sensor networks on the other (CEN/CENELEC, Opportunity for Standardisation to Contribute to the European Partnership on Metrology EPM under Horizon Europe). This contribution concentrates on the metrological traceability of sensor networks for air quality monitoring to the international system of units (SI) based on FAIRified intra-network communications (M. Wilkinson, et al., “The FAIR guiding principles for scientific data management and stewardship,” Sci. Data, vol. 3, 2016, Art. no. 160018) and including delocalized Optical Gas Standards operated according to the digital TILSAM method (O. Werhahn, et al., The TILSAM Method Adapted into Optical Gas Standards – Complementing Gaseous Reference Materials, PTB Open Access Repository, 2021). Informed by related activities in EURAMET (Partnership project FunSNM, EMNs COO & POLMO, TC-IM 1551) (European Metrology Network Climate and Ocean Observation (COO), European Metrology Network Pollution Monitoring (POLMO), EURAMET Project TC-IM 1551, Project Database) this contribution discusses the importance of measurement uncertainties in the context of sensor networks, comprising different sensor principles and promoting an efficient uptake of state-of-the-art methods. We discuss how the sensor network case can be addressed with sensors individually using the GUM principles (Joint Committee for Guides in Metrology, Guide to the Expression of Uncertainty in Measurement (GUM), JCGM 100: 2008 (E)). For sensor network measurements becoming metrologically traceable to the SI, documented and unbroken chains of calibrations need to be implemented each contributing to the measurement uncertainty. This applies to each individual sensor of the network including the potential gold standard among them, but also to the network’s output viewed as a single entity. The contribution provides first approaches to be tested and validated that are underpinned by fundamental design strategies for sensor networks. It follows on practical applications in real world scenarios aside from model uncertainties discussed in artificial intelligence prospects.

Design of a low power consumption wireless sensor network for structural monitoring of bridges

Design of a low power consumption wireless sensor network for structural monitoring of bridges

Integrating Mobile and Fixed-Site Black Carbon Measurements to Bridge Spatiotemporal Gaps in Urban Air Quality.

Urban air pollution can vary sharply in space and time. However, few monitoring strategies can concurrently resolve spatial and temporal variation at fine scales. Here, we present a new measurement-driven spatiotemporal modeling approach that transcends the individual limitations of two complementary sampling paradigms: mobile monitoring and fixed-site sensor networks. We develop, validate, and apply this model to predict black carbon (BC) using data from an intensive, 100-day field study in West Oakland, CA. Our spatiotemporal model exploits coherent spatial patterns derived from a multipollutant mobile monitoring campaign to fill spatial gaps in time-complete BC data from a low-cost sensor network. Our model performs well in reconstructing patterns at fine spatial and temporal resolution (30 m, 15 min), demonstrating strong out-of-sample correlations for both mobile (Pearson's R ∼ 0.77) and fixed-site measurements (R ∼ 0.95) while revealing features that are not effectively captured by a single monitoring approach in isolation. The model reveals sharp concentration gradients near major emission sources while capturing their temporal variability, offering valuable insights into pollution sources and dynamics.

Data Evaluation of a Low-Cost Sensor Network for Atmospheric Particulate Matter Monitoring in 15 Municipalities in Serbia.

Conventional air quality monitoring networks typically tend to be sparse over areas of interest. Because of the high cost of establishing such monitoring systems, some areas are often completely left out of regulatory monitoring networks. Recently, a new paradigm in monitoring has emerged that utilizes low-cost air pollution sensors, thus making it possible to reduce the knowledge gap in air pollution levels for areas not covered by regulatory monitoring networks and increase the spatial resolution of monitoring in others. The benefits of such networks for the community are almost self-evident since information about the level of air pollution can be transmitted in real time and the data can be analysed immediately over the wider area. However, the accuracy and reliability of newly produced data must also be taken into account in order to be able to correctly interpret the results. In this study, we analyse particulate matter pollution data from a large network of low-cost particulate matter monitors that was deployed and placed in outdoor spaces in schools in central and western Serbia under the Schools for Better Air Quality UNICEF pilot initiative in the period from April 2022 to June 2023. The network consisted of 129 devices in 15 municipalities, with 11 of the municipalities having such extensive real-time measurements of particulate matter concentration for the first time. The analysis showed that the maximum concentrations of PM2.5 and PM10 were in the winter months (heating season), while during the summer months (non-heating season), the concentrations were several times lower. Also, in some municipalities, the maximum values and number of daily exceedances of PM10 (50 μg/m3) were much higher than in the others because of diversity and differences in the low-cost sensor sampling sites. The particulate matter mass daily concentrations obtained by low-cost sensors were analysed and also classified according to the European AQI (air quality index) applied to low-cost sensor data. This study confirmed that the large network of low-cost air pollution sensors can be useful in providing real-time information and warnings about higher pollution days and episodes, particularly in situations where there is a lack of local or national regulatory monitoring stations in the area.

A Blade-Type Triboelectric-Electromagnetic Hybrid Generator with Double Frequency Up-Conversion Mechanism for Harvesting Breeze Wind Energy.

Triboelectric nanogenerators (TENGs) have garnered substantial attention in breeze wind energy harvesting. However, how to improve the output performance and reduce friction and wear remain challenging. To this end, a blade-type triboelectric-electromagnetic hybrid generator (BT-TEHG) with a double frequency up-conversion (DFUC) mechanism is proposed. The DFUC mechanism enables the TENG to output a high-frequency response that is 15.9 to 300 times higher than the excitation frequency of 10 to 200 rpm. Coupled with the collisions between tribomaterials, a higher surface charge density and better generating performance are achieved. The magnetization direction and dimensional parameters of the BT-TEHG were optimized, and its generating characteristics under varying rotational speeds and electrical boundary conditions were studied. At wind speeds of 2.2 and 10 m/s, the BT-TEHG can generate, respectively, power of 1.30 and 19.01 mW. Further experimentation demonstrates its capacity to charge capacitors, light up light emitting diodes (LEDs), and power wireless temperature and humidity sensors. The demonstrations show that the BT-TEHG has great potential applications in self-powered wireless sensor networks (WSNs) for environmental monitoring of intelligent agriculture.

Region Based Cluster Aided Routing Protocol for Environment Monitoring in Heterogeneous Wireless Sensor Networks

Heterogeneous wireless sensing networks (HWSNs) that are limited by their batteries' power consumption might benefit greatly from more efficient routing algorithms. It is essential that routing accounts for the diversity of network nodes to achieve optimal performance. This letter considers sensor nodes with random initial energies and random disparities in data output rate (traffic) to build a realistic clustering-based WSN suitable for heterogeneous sensing applications. The protocol divides the space into numerous zones with different distance thresholds to deal with the hotspot problem that the multi-hop method creates. The advantages of region-divided routing ensure that only qualified nodes compete for the role of cluster's head during the choice phase and that the cluster heads with the greatest remaining energy in the extremely energetic area are selected as the relay node throughout the inter-cluster routing that includes several hops phase. The simulation findings demonstrate that the protocol may efficiently equalize energy consumption throughout the network, eliminate the issue of "hot spots," be used in an energy-diverse system, and lengthen the service life of the network.

An improved three factor authentication protocol for wireless body area networks

Wireless Body Area Networks (WBAN) is a concrete solution for the healthcare system, and it is paramount to ensure the sensed data by the WBAN sensors is secure against vulnerable threats and inaccessible by unauthorized entities. In light of this, highly robust authentication schemes and solutions are essential for the successful adoption of WBANs. To this end, a plethora of secure authentication solutions have been recommended by investigators over the past few decades. However, the absence of cohesive research work in view of secure mutual authentication schemes does not light on the extensive purpose of offering a bird’s-eye solution to the domain. To address the security issues in the previous schemes, we approach them in the following manner: We reviewed the Ali et al. scheme, a recently proposed authentication scheme for wireless medical sensor networks for healthcare monitoring, asserting that their scheme can withstand various attacks. Despite this, we proved that their scheme still has significant security weaknesses and is vulnerable to various attacks through rigorous cryptanalysis. Afterward, we proposed our three-factor authentication scheme to overcome the vulnerable attacks found in the Ali et al. scheme. Thereafter, we performed an extensive informal and formal analysis using Burrows-Abadi-Needham (BAN) logic to verify the robustness of the proposed scheme. From the analysis, we proved that the proposed authentication scheme is able to withstand various attacks on WBAN. Finally, a critical performance evaluation is done on our scheme. From the results, we identified that our scheme is a robust and identical solution for feature-rich WBAN health-care applications.

Monitoring and management of green information in water ecological environment based on sensors and big data

With the continuous development of industrial production, the pollution of water resources is becoming more and more serious, and the damage to aquatic organisms is also increasing. Therefore, strengthening water environment monitoring has become a key measure to prevent and solve this problem. Aiming at the limitation of traditional water environment monitoring methods, a water ecological environment monitoring scheme is proposed. This study uses advanced sensor technology, including water quality sensor, water level sensor, weather sensor, etc., to monitor the indicators of water in real time. Through data acquisition and storage technology, the data obtained by the sensor is integrated and analyzed. At the same time, big data analysis method is used to predict and simulate the change trend of water ecological environment. This scheme designs and develops a sensor network monitoring system, which can collect water temperature data in real time at the monitoring point, transmit the sampled information to the aggregation node through the sensor network, and finally transmit to the information intelligent monitoring equipment through GPRS, so as to realize timely display and early warning. At the same time, combined with the water quality monitoring instrument, it can realize the remote query and processing of monitoring data. The experimental results show that the water ecological environment monitoring and management system based on sensor and big data technology can efficiently and accurately monitor the indicators of water. Through the comprehensive monitoring of the water ecological environment, abnormal situations can be found in time, and corresponding measures can be taken to protect and repair. The results of big data analysis provided by the system can provide scientific basis and guidance for decision makers.

Designing a low-cost wireless sensor network for particulate matter monitoring: Implementation, calibration, and field-test

Poor air quality can provoke severe impacts on health, necessitating environmental monitoring of atmospheric particulate matter (PM) to assess potential threats to human well-being. However, traditional continuous air quality monitoring systems are often costly and time-consuming in data treatment. Lately, there is a growing trend towards the use of low-cost wireless PM sensors, providing more detailed information than standard systems. This paper presents a system designed to measure air quality, specifically, a wireless sensor network composed of a distributed sensor network linked to a cloud system. The proposed system can efficiently measure air quality as it is cost-effective, small-sized, and consumes little power. Sensor nodes based on low-power long range (LoRa) motes transmit field measurement data to the cloud via a gateway, and a cloud computing system is implemented to store, monitor, process, and visualise the data. Advanced techniques were included in our cloud for data processing and analysis to optimise the detection of PM. Laboratory and field tests in the historic Riotinto mine validate the system's viability, offering real-time air quality information for nearby populations. Once calibrated, sensors demonstrate high accuracy, presenting mean error of −0.3% and low deviation (R2 = 0.96) when compared to regulatory systems for both low (<10 μgPM10/m3) and hazardous concentrations (300 μgPM10/m3), which makes them perfect as early warning systems for atmospheric pollution in mining.

Development of sustainable conductive cementitious composite using graphite-coated spent catalyst waste

This study presents an innovative approach to developing sustainable conductive composites by coating graphite onto the surface of spent catalyst waste through nano-surface engineering techniques. The process ensures uniform adsorption of graphite onto the surface of the spent catalyst waste particles, followed by oven treatment and milling. This results in better integrity and effective bonding, leading to the production of graphite-coated spent catalyst waste (G-SCW). Scanning electron microscopy indicates the successful coating of spent catalyst waste with graphite. The research investigates the effect of G-SCW on the cementitious properties of paste and mortar. Incorporating G-SCW results in acceptable workability and setting time, while the compressive strength increases at early and later stages, with up to 20 % G-SCW content. The addition of G-SCW in the mortar significantly reduces the electrical resistivity, resulting in a 63 % reduction in resistivity compared to the reference mix, thereby enhancing the conductivity. Hydration studies confirm the presence of pozzolanic reaction in blended paste, as evidenced by a decrease in calcium hydroxide content. The sustainability assessment indicates a substantial reduction in embodied carbon and possibly producing mortar with lower cement content. These findings suggest great potential for developing sustainable conductive mortar with G-SCW, enabling smart building construction, and supporting sensor networks for structural health monitoring.

3D printed sub-terahertz photonic crystal for wireless passive biosensing

Monitoring pathogens has become a major challenge for society and research in recent years. Of great interest are refractive index sensors, which are based on the interaction between analytes and electromagnetic waves and allow label-free and fast detection. In addition, the electromagnetic waves can be exploited for wireless communication. However, current refractive index biosensors can only be read from a few centimeters. Here, we demonstrate an innovative concept of a passive wireless sensor based on a sub-terahertz photonic crystal resonator. The fabricated sensors have a reading range of up to 0.9 m and elevation and azimuth acceptance angles of around 90°. We demonstrate the stand-off detection of sub-µm thin-film proteins as test analytes. The proposed wireless sensor opens the door to a non-electronic, compact, and low-cost solution and can be extended to a wireless sensor network monitoring airborne pathogen, which may provide a pre-infection detection to prevent their spread efficiently.

An efficient dual layer data aggregation scheme in clustered wireless sensor networks

AbstractIn wireless sensor network (WSN) monitoring systems, redundant data from sluggish environmental changes and overlapping sensing ranges can increase the volume of data sent by nodes, degrade the efficiency of information collection, and lead to the death of sensor nodes. To reduce the energy consumption of sensor nodes and prolong the life of WSNs, this study proposes a dual layer intracluster data fusion scheme based on ring buffer. To reduce redundant data and temporary anomalous data while guaranteeing the temporal coherence of data, the source nodes employ a binarized similarity function and sliding quartile detection based on the ring buffer. Based on the improved support degree function of weighted Pearson distance, the cluster head node performs a weighted fusion on the data received from the source nodes. Experimental results reveal that the scheme proposed in this study has clear advantages in three aspects: the number of remaining nodes, residual energy, and the number of packets transmitted. The data fusion of the proposed scheme is confined to the data fusion of the same attribute environment parameters.

Transforming construction: The growing role of mechatronics in building our infrastructure

Mechatronics, the synergistic integration of mechanical engineering, electrical engineering, control engineering, and computer science, is progressively transforming the construction industry. This paper explores how mechatronic technologies are being applied across the construction lifecycle to enhance productivity, quality, safety, sustainability, and cost-effectiveness. Core mechatronic technologies changing construction include automated material handling systems, advanced construction robotics, sensor networks for monitoring, laser scanning for accurate modeling and control, and building information modeling (BIM) software. Mechatronics offers multifaceted benefits spanning from enhanced labor productivity and construction velocity to superior quality control, safety, and process optimization. However, realizing the immense potential of mechatronics in construction requires overcoming key challenges including high upfront costs, lack of technical skills, organizational resistance, integration difficulties, and reliability concerns. With thoughtful leadership and implementation, mechatronics promises to revolutionize construction, shifting it towards more automated, streamlined, and optimized techniques. This paper delineates the tremendous potential of mechatronics in construction regarding benefits, technologies, and challenges. The outlook is promising for intelligent mechatronic systems to profoundly enhance productivity, quality, and safety as construction progresses towards greater automation and optimization.

Landslide monitoring and prediction system using geosensors and wireless sensor network

Landslides in hilly regions are a frequently occurring natural phenomenon which takes a heavy toll of human lives and causes damage to different properties. Hence, prediction of landslide is essential for averting its deleterious effects by providing early warning to neighboring residents about the impending hazard of landslide. A landslide monitoring system has been designed using geosensors and high-range wireless network for on-line monitoring and prediction of landslides with the application of multivariate statistical analysis of prevailing site parameters. The system consists of various geosensors, wireless sensor network, server, and landslide monitoring and forecasting software. Crackmeter, in-place inclinometer, raingauge, tiltmeter, piezometer and other sensors are set up in the selected hill slope prone to landslide for continuous monitoring of influencing parameters. The system measures real-time landslide parameters using the said geosensors connected with wireless nodes and establishing a dynamic wireless network to overcome redundancy issue using wireless nodes of around 1200 m communication range using a high performance low power microcontroller, integrated solar panel and additional external omni-directional antenna for monitoring landslide in a large and hazardous hilly region from a distant safer location. The application software consists of different modules, namely data monitoring, analysis, storing, viewing, prediction, and generation of audio-visual, SMS and email alerts for 3 levels of landslides situations. The paper enumerates the system architecture and the application software details.Graphical

Artificial Intelligence-Assisted RFID Tag-Integrated Multi-Sensor for Quality Assessment and Sensing.

Radio frequency identification (RFID) is well known as an identification, track, and trace approach and is considered to be the key physical layer technology for the industrial internet of things (IIoT). However, IIoT systems have to introduce additional complex sensor networks for pervasive monitoring, and there are still challenges related to item-level sensing and data recording. To overcome the shortage, this work proposes an artificial intelligence (AI)-assisted RFID-based multi-sensing technology. Both passive and semi-passive RFID tag-integrated multi-sensors are developed. The main contributions and the novelty of this investigation are as follows. A UHF RFID tag-integrated multi-sensor with a boosted charge pump is proposed; it enables high RF signal sensitivity and a long operational range. The whole hardware design, including the antenna and energy harvester, are studied. Moreover, a demonstration with real-world ham product sensing data is conducted. This work also proposes and successfully demonstrates the integration of machine learning algorithms, specifically the NARX neural network, with RFID sensing data for food product quality assessment and sensing (QAS). This application of machine learning to RFID-generated data for quality assessment is also a novel aspect of the research. The deployment of an autoregressive model with an exogenous input (NARX) neural network model, tailored for nonlinear processes, emerges as the most effective, achieving a root mean square error (RMSE) of 0.007 and an R-squared value of 0.99 for ham product QAS. By deploying the technology, low-cost, timely, and flexible product QAS can be achieved in manufacturing industries, which helps product quality improvement and the optimization of the manufacturing line and supply chain.

Multi-objective SHM sensor path optimisation for damage detection in large composite stiffened panels

This work proposes a novel methodology for the automatic multi-objective optimisation of sensor paths in structural health monitoring (SHM) sensor networks using archived multi-objective simulated annealing. Using all of the sensor paths within a sensor network may not always be beneficial during damage detection. Many sensor paths may experience significant signal noise, attenuation, and wave mode conversion due to the presence of features, such as stiffeners, and hence impair the detection accuracy of the overall system. Many paths will also contribute little to the overall coverage level or damage detection accuracy of the network and can be ignored, reducing complexity. Knowing which paths to include, and which to exclude, can require significant prior expert knowledge, which may not always be available. Furthermore, even when expert knowledge is considered, the optimum path selection might not be achieved. Therefore, this work proposes a novel automatic procedure for optimising the sensor paths of an SHM sensor network to maximise coverage level, maximise damage detection accuracy and minimise the overall signal noise in the network due to geometric features. This procedure was tested on a real-world large composite stiffened panel with many geometric features in the form of frames and stiffeners. Compared to using all of the available sensor pairs, the optimised network exhibits superior performance in terms of detection accuracy and overall noise. It was also found to provide very similar performance, in terms of coverage level and overall signal noise, to a sensor path network designed based on prior expert knowledge but provided up to 35% higher damage detection accuracy. As a result, the novel procedure proposed in this work has the capability to design high-performing SHM sensor path networks for structures with complex geometries but without the need for prior expert knowledge, making SHM more accessible to the engineering community.

5G NR launching in Greece: Preliminary in situ and monitoring network measurements of electromagnetic fields exposure levels at rooftops.

In Greece, 5G New Radio (NR) has started launching in the end of 2020, at the 3400-3800 MHz (FR1) frequency band. Focusing on 117 Base Stations (BSs) which were already equipped with 5G NR antennas, in situ broadband and frequency selective measurements have been conducted at minimum three points of interest, at adjacent rooftops (when accessible). The points have been selected according to the sweeping method and the electric field strength (E) value has been stored on the selected worst-case scenario point. Spectrum analysis was conducted in the FR1, for the allocated spectrum that corresponds to each mobile communication provider, in order to get preliminary results concerning the contribution of the 5G NR emissions in the general public exposure levels. The vast majority of the in situ measurements has been conducted in urban environments from the beginning of 2021 until the mid of 2022, since in Greece 5G NR services launching started from the big cities. Additionally, a 5G NR BS, installed in a suburban environment (in the city of Kalamata) is thoroughly investigated during its pilot and regular operation, based on broadband and frequency selective measurements data derived by the National Observatory of Electromagnetic Fields (NOEF) monitoring sensor network. Insitu measurement data within the 5G NR frequency range are verified via the NOEF's output. The 5G NR contribution to the total E-field levels is assessed in time, from pilot to regular operation of the BS. In all cases, compliance with the reference levels for general public exposure is affirmed.

Synergistic Integration of Nanogenerators and Solar Cells: Advanced Hybrid Structures and Applications

AbstractThe rapid growth of global energy consumption and the increasing demand for sustainable and renewable energy sources have urged vast research into harnessing energy from various sources. Among them, the most promising approaches are nanogenerators (NGs) and solar cells (SCs), which independently offer innovative solutions for energy harvesting. This review paper presents a comprehensive analysis of the integration of NGs and SCs, exploring advanced hybrid structures and their diverse applications. First, an overview of the principles and working mechanisms of NGs and SCs is provided for seamless hybrid integrations. Then, various design strategies are discussed, such as piezoelectric and triboelectric NGs with different types of SCs. Finally, a wide range of applications are explored that benefit from the synergistic integration of NGs and SCs, including self‐powered electronics, wearable devices, environmental monitoring, and wireless sensor networks. The potential for these hybrid systems is highlighted to address real‐world energy needs and contribute to developing sustainable and self‐sufficient technologies. In conclusion, this review provides valuable insights into the state‐of‐the‐art developments in NGs and SCs integration, shedding light on advanced hybrid structures and their diverse applications.

A digital twin-based energy-efficient wireless multimedia sensor network for waterbirds monitoring

Wetlands play a critical role in maintaining the global climate, regulating the hydrological cycle, and protecting human health. However, they are rapidly disappearing due to human activities. Waterbirds are valuable bio-indicators of wetland health, but it is challenging to monitor them effectively. Wireless Multimedia Sensor Networks (WMSNs) offer a promising technology for monitoring wetlands. Nonetheless, these networks are constrained in terms of energy, and also encounter challenges associated with large-scale deployments under natural environmental conditions. These conditions introduce harsh circumstances that may not have been anticipated during the pre-deployment testing phase. This paper proposes a Digital Twin (DT) based energy-efficient WMSN monitoring system specifically tailored for waterbirds in wetlands. The system utilizes a unique approach that combines local audio identification and image compression with DT technology to optimize network performance and minimize energy consumption. To reduce unnecessary image transmissions, the system employs a real-time, low-complexity local audio identification phase before triggering image capture. A denoising step is employed to achieve highly accurate bird recognition despite surrounding noises. Each image undergoes a low-complexity compression scheme prior to transmission, further enhancing energy efficiency. To enhance the system’s overall efficiency and effectiveness, DT technology is integrated to create real-time replicas of the WMSN and the monitoring application. A synergistic interaction between the two DTs enables cooperative data-making decision that ensures both QoS (Quality of Service) and QoE (Quality of Experience) requirements are met. Transmission rate control is done using a fuzzy logic decision-making technique. Real-time feedback provides rapid and accurate analysis of the current state of the WMSN, allowing for dynamic adjustments. The ”what-if scenarios” feature of the implemented DTs has been effectively leveraged to find the most suitable settings for the controller. The effectiveness and performance enhancements achieved by integrating DT into our WMSN-based surveillance system are validated through comprehensive experiments in scenarios that correspond to a real-world wetland. Comparative analyses demonstrate the undeniable benefits of the DT-integrated system compared to a conventional WMSN-based surveillance setup. In particular, the results demonstrate the system’s superior performance in terms of energy efficiency, real-time monitoring capabilities, and ability to handle multiple video sources.

Net Zero Dairy Farming—Advancing Climate Goals with Big Data and Artificial Intelligence

This paper explores the transformative potential of Big Data and Artificial Intelligence (AI) in propelling the dairy industry toward net zero emissions, a critical objective in the global fight against climate change. Employing the Canadian dairy sector as a case study, the study extrapolates its findings to demonstrate the global applicability of these technologies in enhancing environmental sustainability across the agricultural spectrum. We begin by delineating the environmental challenges confronting the dairy industry worldwide, with an emphasis on greenhouse gas (GHG) emissions, including methane from enteric fermentation and nitrous oxide from manure management. The pressing need for innovative approaches in light of the accelerating climate crisis forms the crux of our argument. Our analysis delves into the role of Big Data and AI in revolutionizing emission management in dairy farming. This includes applications in optimizing feed efficiency, refining manure management, and improving energy utilization. Technological solutions such as predictive analytics for feed optimization, AI in herd health management, and sensor networks for real-time monitoring are thoroughly examined. Crucially, the paper addresses the wider implications of integrating these technologies in dairy farming. We discuss the development of benchmarking standards for emissions, the importance of data privacy, and the essential role of policy in promoting sustainable practices. These aspects are vital in supporting the adoption of technology, ensuring ethical use, and aligning with international climate commitments. Concluding, our comprehensive study not only suggests a pathway for the dairy industry towards environmental sustainability but also provides insights into the role of digital technologies in broader agricultural practices, aligning with global environmental sustainability efforts.