Search Results (198)

Non-orthogonal multiple access (NOMA) enables the parallel offloading of multiuser tasks, effectively enhancing throughput and reducing latency. Backscatter communication, which passively reflects radio frequency (RF) signals, improves energy efficiency and extends the operational lifespan of terminal devices. Both technologies are pivotal for the next generation of wireless networks. However, there is little research focusing on optimizing the transmit power in backscatter-assisted NOMA-MEC systems from a green IoT perspective. In this paper, we aim to minimize the transmit energy consumption of a Hybrid Access Point (HAP) while ensuring task deadlines are met. We consider the integration of Backscatter Communication (BackCom) and Active Transmission (AT), and leverage NOMA technology and user cooperation to mitigate the double near–far effect. Specifically, we formulate a transmit energy consumption minimization problem, accounting for task deadline constraints, task offloading decisions, transmit power allocation, and energy constraints. To tackle the non-convex optimization problem, we employ variable substitution and convex optimization theory to transform the original non-convex problem into a convex one, which is then efficiently solved. We deduce the semi-closed form expression of the optimal solution and propose an energy-efficient algorithm to minimize the transmit power of the entire wireless powered MEC. The extensive simulation results demonstrate that our proposed scheme significantly reduces the HAP transmit power by around 8% compared to existing schemes, validating the effectiveness of our approach. This study provides valuable insights for the design of green IoT systems by optimizing the transmit power in NOMA-MEC networks. Full article

In this paper, we propose an underlay cognitive radio (CR) system that includes subscribers, termed secondary users (SUs), which are designed to coexist with the spectrum owners, termed primary users (PUs). The suggested network includes the PUs system and the SUs system. The coexistence between them is achieved by using a novel dynamic spectrum co-access multicarrier-based cognitive radio (DSCA-MC-CR) technique. The proposal uses a quadrature phase shift keying (QPSK) modulation technique within the orthogonal frequency-division multiplexing (OFDM) scheme that maximizes the system data rate and prevents data inter-symbol interference (ISI). The proposed CR transmitter station (TX) and the CR receiver node (RX) can use an advanced smart antenna system, i.e., a multiple-input and multiple-output (MIMO) system that provides high immunity against channel impairments and provides a high data rate through its different combining techniques. The proposed CR system is applicable to coexist within different existing communication applications like fifth-generation (5G) applications, emergence applications like the Internet of Things (IoT), narrow-band (NB) applications, and wide-band (WB) applications. The coexistence between the PUs system and the SUs system is based on using power donation from the SUs system to improve the quality of the PU signal-to-interference-and-noise ratios (SINRs). The green communication concept achieved in this proposal is compared with similar DSCA proposals from the literature. The simulations of the proposed technique show enhancement in the PUs system throughput and data rate along with the better performance of the SUs system. Full article

In response to growing environmental concerns, the scientific community is increasingly incorporating green chemistry principles into modern analytical techniques. Electrochemical paper-based analytical devices (ePADs) have emerged as a sustainable and efficient alternative to conventional analytical devices, offering robust applications in point-of-care testing, personalized healthcare, environmental monitoring, and food safety. ePADs align with green chemistry by minimizing reagent use, reducing energy consumption, and being disposable, making them ideal for eco-friendly and cost-effective analyses. Their user-friendly interface, alongside sensitive and selective detection capabilities, has driven their popularity in recent years. This review traces the evolution of ePADs from simple designs to complex multilayered structures that optimize analyte flow and improve detection. It also delves into innovative electrode fabrication methods, assessing key advantages, limitations, and modification strategies for enhanced sensitivity. Application-focused sections explore recent advancements in using ePADs for detecting diseases, monitoring environmental hazards like heavy metals and bacterial contamination, and screening contaminants in food. The integration of cutting-edge technologies, such as wearable wireless devices and the Internet of Things (IoT), further positions ePADs at the forefront of point-of-care testing (POCT). Finally, the review identifies key research gaps and proposes future directions for the field. Full article

Nowadays, it is very important to secure our home perfectly. To make our life easier and more secure, we are presenting our smart door lock system project. We implement an IoT-based smart door lock system using an ESP32-CAM and an Android application in this project. Most of the time in our daily life, we forget to lock our doors and later we suffer from confusion about whether we locked all doors perfectly or not. In this project, we implement a smart door lock system, by which the owner can see the visitor’s picture and then lock or unlock their doors from anywhere and at any time using the Android application. Whenever visitors come to visit the home and press the doorbell, the owner will receive a notification on his/her smartphone and then the owner can see the visitor’s picture by using the Android app. After checking the visitor, the owner can let them enter the house by unlocking the door remotely. If the door is locked perfectly, then the door lock signal in the application will show a green signal. If the door is not locked perfectly, the signal will show red and then the owner can remotely lock their door easily from anywhere. In this project, we have also utilized a theft alert. If anyone comes in front of the door and tries to enter the house forcefully then a theft alert notification will be sent to the owner’s smartphone and a Buzzer Alert will ring in the house loudly so that the neighbors can be aware of the theft and can take action. The automatic door lock feature is also available in this system. Full article

The quantitative inversion of the leaf area index (LAI) of green plum trees is crucial for orchard field management and yield prediction. The data on the relative content of chlorophyll (SPAD) in leaves and environmental data from orchards show a significant correlation with LAI. Effectively integrating these two data types for LAI inversion is important to explore. This study proposes a multi−source decision fusion LAI inversion model for green plums based on their adjusted determination coefficient (MDF−ADRS). First, three statistical methods—Pearson, Spearman rank, and Kendall rank correlation analyses—were used to measure the linear relationships between variables, and the six environmental factors most highly correlated with LAI were selected from the orchard’s environmental data. Then, using multivariate statistical analysis methods, LAI inversion models based on environmental feature factors (EFs−PM) and SPAD (SPAD−PM) were established. Finally, a weight optimization allocation strategy was employed to achieve a multi−source decision fusion LAI inversion model for green plums. This strategy adaptively allocates weights based on the predictive performance of each data source. Unlike traditional models that rely on fixed weights or a single data source, this approach allows the model to increase the influence of a key data source when its predictive strength is high and reduce noise interference when it is weaker. This dynamic adjustment not only enhances the model’s robustness under varying environmental conditions but also effectively mitigates potential biases when a particular data source becomes temporarily unreliable. Our experimental results show that the MDF−ADRS model achieves an $R^2$ of 0.88 and an $RMSE$ of 0.39 in the validation set, outperforming other fusion methods. Compared to the EFs−PM and SPAD−PM models, the $R^2$ increased by 0.19 and 0.26, respectively, and the $RMSE$ decreased by 0.16 and 0.22. This model effectively integrates multiple sources of data from green plum orchards, enabling rapid inversion and improving the accuracy of green plum LAI estimation, providing a technical reference for monitoring the growth and managing the production of green plums. Full article

The grain-drying process plays a critical role in grain storage and quality assurance. In recent years, with the advancement of low-carbon and intelligent technologies, the control technology of grain dryers has significantly improved. This paper systematically reviews the development status of grain dryer control technology from the perspective of low-carbon and intelligentization, analyzing the technological differences in control systems between domestic and international approaches. Current research challenges include the insufficient integration of control technologies with the drying process, limited control variables, the inadequate application of intelligent control strategies, and unstable sensor accuracy. To enhance the performance of grain-drying systems, this paper suggests optimizing control mechanisms, adopting efficient and environmentally friendly energy sources, improving sensor performance, introducing advanced intelligent control algorithms, and strengthening system monitoring capabilities. Looking ahead, with the further integration of AI, IoT, and green energy, grain-drying control systems are expected to evolve towards greater intelligence, remote operation, and low carbonization, providing technical support for enhancing drying efficiency and environmental performance. Full article

The Internet of Things (IoT) is set to play a significant role in the future development of smart cities, which are designed to be environmentally friendly. However, the proliferation of these devices, along with their frequent replacements and the energy required to power them, contributes to a significant environmental footprint. In this paper we provide scientific evidences on the advantages of using an energy design pattern named ‘Batch Operations’ (BO) to optimize energy consumption on mobile devices. Big ICT companies like Google already batch multiple API calls instead of putting the device into an active state many times. This is supposed to save tail energy consumption in communication peripherals. To confirm this, we set up an experiment where we compare energy consumption and carbon emission when BO is applied to two communication peripherals on Android mobile device: 4G and GPS. Results show that (1) BO can save up to 40% energy when sending HTTP requests, resulting in an equivalent reduction in $C{O}_2$ emissions. (2) no advantages for the GPS interface. Full article

Cobalt oxide-based in-plane microsupercapacitors (IPMSCs) stand out as a favorable choice for various applications in energy sources for the Internet of Things (IoT) and other microelectronic devices due to their abundant natural resources and high theoretical specific capacitance. However, the low electronic conductivity of cobalt oxide greatly hinders its further application in energy storage devices. Herein, a new manufacturing method of electric discharging machining (EDM), which is simple, safe, efficient, and environment-friendly, has been developed for synthesizing Mo-doped and oxygen-vacancy-enriched Co-CoO (Mo@Co-CoO) integrated microelectrodes for efficiently constructing Mo@Co-CoO IPMSCs with customized structures in a single step for the first time. The Mo@Co-CoO IPMSCs with three loops (IPMSCs3) exhibited a maximum areal capacitance of 30.4 mF cm⁻² at 2 mV s⁻¹. Moreover, the Mo@Co-CoO IPMSCs3 showed good capacitive behavior at a super-high scanning rate of 100 V s⁻¹, which is around 500–1000 times higher than most reported CoO-based electrodes. It is important to note that the IPMSCs were fabricated using a one-step EDM process without any assistance of other material processing techniques, toxic chemicals, low conductivity binders, exceptional current collectors, and conductive fillers. This novel fabrication method developed in this research opens a new avenue to simplify material synthesis, providing a novel way for realizing intelligent, digital, and green manufacturing of various metal oxide materials, microelectrodes, and microdevices. Full article

Green finance, defined as channeling money into sustainable development activities, is still far lower than needed to achieve net-zero emissions objectives. In this paper, we discuss the role of technologies in developing green finance. We identify that green finance faces three major challenges, including the risk management of green projects, the scarcity of innovative green financing products, and compliance with the regulations. Then, in the context of the existing literature, we explore recent technologies, including blockchain, artificial intelligence (AI), machine learning (ML), data analytics, Internet of Things (IoT), and robotics that are helping to deal with the challenges in green finance. We show that data-driven approaches utilizing AI and ML help in the risk assessment of green projects; FinTech-based crowdfunding platforms provide innovative green financial products and regulatory technologies (RegTech) support in compliance with regulations. We also identify that the environmental footprint of cryptocurrencies is an emerging area in the technologies and green finance domain. Our framework could be helpful to further extend the debate on the role of technology in green finance. Full article

In this work, a novel quad-band rectifier circuit is introduced for RF energy harvesting and Internet of Things (IoT) applications. The proposed rectifier operates in the Wi-Fi frequency band and can supply low-power sensors and systems used in IoT services. The circuit operates at $2.4$, $3.5$, 5, and $5.8$ GHz. The proposed RF-to-DC rectifier is designed based on Delon theory and Greinacher topology on an RT/Duroid 5880 substrate. The results show that our proposed circuit can harvest RF energy from the environment, providing maximum power conversion efficiency (PCE) greater than 81% when the output load is $0.511$ kΩ and the input power is 12 dBm. In this work, we provide a comprehensive design framework for an affordable RF-to-DC rectifier. Our circuit performs better than similar designs in the literature. This rectifier could be integrated into an IoT node to harvest RF energy, thereby proving a green energy source. The IoT node can operate at various frequencies. Full article

Recent developments and the widespread use of IoT-enabled technologies has led to the Research and Development (R&D) efforts in green communication. Traditional dynamic-source routing is one of the well-known protocols that was suggested to solve the information dissemination problem in an IoT environment. However, this protocol suffers from a high level of energy consumption in sensor-enabled device-to-device and device-to-base station communications. As a result, new information dissemination protocols should be developed to overcome the challenge of dynamic-source routing, and other similar protocols regarding green communication. In this context, a new energy-efficient routing protocol (EFRP) is proposed using the hybrid adopted heuristic techniques. In the densely deployed sensor-enabled IoT environment, an optimal information dissemination path for device-to-device and device-to-base station communication was identified using a hybrid genetic algorithm (GA) and the antlion optimization (ALO) algorithms. An objective function is formulated focusing on energy consumption-centric cost minimization. The evaluation results demonstrate that the proposed protocol outperforms the Greedy approach and the DSR protocol in terms of a range of green communication metrics. It was noticed that the number of alive sensor nodes in the experimental network increased by more than 26% compared to the other approaches and lessened energy consumption by about 33%. This leads to a prolonged IoT network lifetime, increased by about 25%. It is evident that the proposed scheme greatly improves the information dissemination efficiency of the IoT network, significantly increasing the network’s throughput. Full article

Convolutional neural networks (CNNs) are commonly employed for demanding applications, such as speech recognition, natural language processing, and computer vision. As CNN architectures become more complex, their computational demands grow, leading to substantial energy consumption and complicating their use on devices with limited resources (e.g., edge devices). Furthermore, a new line of research seeking more sustainable approaches to Artificial Intelligence development and research is increasingly drawing attention: Green AI. Motivated by an interest in optimizing Machine Learning models, in this paper, we propose Optimizing Convolutional Neural Network Architectures (OCNNA). It is a novel CNN optimization and construction method based on pruning designed to establish the importance of convolutional layers. The proposal was evaluated through a thorough empirical study including the best known datasets (CIFAR-10, CIFAR-100, and Imagenet) and CNN architectures (VGG-16, ResNet-50, DenseNet-40, and MobileNet), setting accuracy drop and the remaining parameters ratio as objective metrics to compare the performance of OCNNA with the other state-of-the-art approaches. Our method was compared with more than 20 convolutional neural network simplification algorithms, obtaining outstanding results. As a result, OCNNA is a competitive CNN construction method which could ease the deployment of neural networks on the IoT or resource-limited devices. Full article

Agricultural productivity is one of the critical factors towards ensuring food security across the globe. However, some of the main crops, such as potato, tomato, and mango, are usually infested by leaf diseases, which considerably lower yield and quality. The traditional practice of diagnosing disease through visual inspection is labor-intensive, time-consuming, and can lead to numerous errors. To address these challenges, this study evokes the AgirLeafNet model, a deep learning-based solution with a hybrid of NASNetMobile for feature extraction and Few-Shot Learning (FSL) for classification. The Excess Green Index (ExG) is a novel approach that is a specified vegetation index that can further the ability of the model to distinguish and detect vegetative properties even in scenarios with minimal labeled data, demonstrating the tremendous potential for this application. AgirLeafNet demonstrates outstanding accuracy, with 100% accuracy for potato detection, 92% for tomato, and 99.8% for mango leaves, producing incredibly accurate results compared to the models already in use, as described in the literature. By demonstrating the viability of a deep learning/IoT system architecture, this study goes beyond the current state of multi-crop disease detection. It provides practical, effective, and efficient deep-learning solutions for sustainable agricultural production systems. The innovation of the model emphasizes its multi-crop capability, precision in results, and the suggested use of ExG to generate additional robust disease detection methods for new findings. The AgirLeafNet model is setting an entirely new standard for future research endeavors. Full article

The research problem of this systematic review was whether green 6G networks can integrate energy-efficient Industrial Internet of Things (IIoT) in terms of distributed artificial intelligence, green 6G pervasive edge computing communication networks and big-data-based intelligent decision algorithms. We show that sensor data fusion can be carried out in energy-efficient IoT smart industrial urban environments by cooperative perception and inference tasks. Our analyses debate on 6G wireless communication, vehicular IoT intelligent and autonomous networks, and energy-efficient algorithm and green computing technologies in smart industrial equipment and manufacturing environments. Mobile edge and cloud computing task processing capabilities of decentralized network control and power grid system monitoring were thereby analyzed. Our results and contributions clarify that sustainable energy efficiency and green power generation together with IoT decision support and smart environmental systems operate efficiently in distributed artificial intelligence 6G pervasive edge computing communication networks. PRISMA was used, and with its web-based Shiny app flow design, the search outcomes and screening procedures were integrated. A quantitative literature review was performed in July 2024 on original and review research published between 2019 and 2024. Study screening, evidence map visualization, and data extraction and reporting tools, machine learning classifiers, and reference management software were harnessed for qualitative and quantitative data, collection, management, and analysis in research synthesis. Dimensions and VOSviewer were deployed for data visualization and analysis. Full article

In recent years, the integration of Internet of Things technologies in smart agriculture has become critical for sustainability and efficiency, to the extent that recent improvements have transformed greenhouse farming. This study investigated the complexity of IoT architecture in smart greenhouses by introducing a greenhouse language family (GreenH) that comprises three domain-specific languages designed to address various tasks in this domain. The purpose of this research was to streamline the creation, simulation, and monitoring of digital twins, an essential tool for optimizing greenhouse operations. A three-stage methodology was employed to develop the GreenH DSLs, a detailed metamodel for enhanced smart monitoring systems. Our approach used high-level metamodels and extended Backus–Naur form notation to define the DSL syntax and semantics. Through a comprehensive evaluation strategy and a selected language usability metrics, the expressiveness, consistency, readability, correctness, and scalability of the DSL were affirmed, and areas for usability improvement were highlighted. The findings suggest that GreenH languages hold significant potential for advancing digital twin modeling in smart agriculture. Future work should be aimed at refining usability and extending its application range. The anticipated integration with additional model-drive engineering and code generation tools will improve interoperability and contribute to digital transformation in the smart greenhouse domain and promote more sustainable food production systems. Full article