Search Results (125)

Blockchain technology is an emerging decentralized and distributed technology that can maintain data security. It has the potential to transform many sectors completely. The core component of blockchain networks is the consensus algorithm because its efficiency, security, and scalability depend on it. A consensus problem is a difficult and significant task that must be considered carefully in a blockchain network. It has several practical applications such as distributed computing, load balancing, and blockchain transaction validation. Even though a lot of consensus algorithms have been proposed, the majority of them require many computational and communication resources. Similarly, they also suffer from high latency and low throughput. In this work, we proposed a new consensus algorithm for consortium blockchain for a leader selection using the reputation value of nodes and the voting process to ensure high performance. A security analysis is conducted to demonstrate the security of the proposed algorithm. The outcomes show that the proposed algorithm provides a strong defense against the network nodes’ abnormal behavior. The performance analysis is performed by using Hyperledger Fabric v2.1 and the results show that it performs better in terms of throughput, latency, CPU utilization, and communications costs than its rivals Trust-Varying Algo, FP-BFT, and Scalable and Trust-based algorithms. Full article

The ocean, with its intricate processes, plays a pivotal role in shaping marine life, habitats, and the Earth’s climate. This study addresses issues such as beach erosion, the survival of propagules from species like Posidonia oceanica, and nutrient distribution. To tackle these challenges, we propose an innovative sensor that quantifies hydrodynamic velocity by measuring the output voltage derived from detecting changes in light absorption and scattering using LEDs and LDRs. Our results not only demonstrate the effectiveness of the sensor but also the accuracy of the processing algorithm. Notably, the blue LED exhibited the lowest mean relative error of 7.59% in freshwater, while the yellow LED was most precise in chlorophyll-containing water, with a mean relative error of 6.80%. In a runoff simulation, we observed similar velocities with the blue, green, and white LEDs, 6.89 cm/s, 6.99 cm/s, and 7.05 cm/s, respectively, for nearly identical time intervals. It is important to highlight that our proposed sensor is not only effective but also highly cost-efficient, representing less than 0.43% of the cost of a Nortek Vector 6 MHz and 0.18% of the Teledyne Workhorse II 300 kHz Marine. This makes it a key tool for managing marine ecosystems sustainably. Full article

Drones are being used for agriculture monitoring in many different crops. Nevertheless, the use of drones for green areas’ evaluation is limited, and information is scattered. In this survey, we focus on the collection and evaluation of existing experiences of using drones for turfgrass monitoring. Despite a large number of initial search results, after filtering the information, very few papers have been found that report the use of drones in green areas. Several aspects of drone use, the monitored areas, and the additional ground-based devices for information monitoring are compared and evaluated. The data obtained are first analysed in a general way and then divided into three groups of papers according to their application: irrigation, fertilisation, and others. The main results of this paper indicate that despite the diversity of drones on the market, most of the researchers are using the same drone. Two options for using cameras in order to obtain infrared information were identified. Moreover, differences in the way that drones are used for monitoring turfgrass depending on the aspect of the area being monitored have been identified. Finally, we have indicated the current gaps in order to provide a comprehensive view of the existing situation and elucidate future trends of drone use in turfgrass management. 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

Projections indicate aquaculture will produce 106 million tonnes of fish by 2030, emphasizing the need for efficient and sustainable practices. New technologies can provide a valuable tool for adequate fish farm management. The aim of this paper is to explore the factors affecting fish well-being, the design of control systems for aquaculture, and the proposal of a smart system based on algorithms to improve efficiency and sustainability. First, we identify the domains affecting fish well-being: the production domain, abiotic domain, biotic domain, and control systems domain. Then, we evaluate the interactions between elements present in each domain to evaluate the key aspects to be monitored. This is conducted for two types of fish farming facilities: cages in the sea and recirculating aquaculture systems. A total of 86 factors have been identified, of which 17 and 32 were selected to be included in monitoring systems for sea cages and recirculating aquaculture systems. Then, a series of algorithms are proposed to optimize fish farming management. We have included predefined control algorithms, energy-efficient algorithms, fault tolerance algorithms, data management algorithms, and a smart control algorithm. The smart control algorithms have been proposed considering all the aforementioned factors, and two scenarios are simulated to evaluate the benefits of the smart control algorithm. In the simulated case, the turbidity when the control algorithm is used represents 12.5% of the turbidity when not used. Their use resulted in a 35% reduction in the energy consumption of the aerator system when the smart control was implemented. Full article

Sixth-generation (6G) wireless networks demand a more efficient implementation of non-orthogonal multiple access (NOMA) schemes for severe multipath fading environments to serve multiple users. Using non-orthogonal multiple access (NOMA) schemes in IoT 6G networks is a promising solution to allow multiple users to share the same spectral and temporal resource, increasing spectral efficiency and improving the network’s capacity. In this work, we have evaluated the performance of a novel progressive pattern interleaver (PPI) employed to distinguish the users in interleaved division multiple access (IDMA) schemes, suggested by 3GPP guidelines as a NOMA scheme, with two multi-carrier modulation schemes known as single-carrier frequency-division multiple access (SC-FDMA) and orthogonal frequency-division multiplexing (OFDM), resulting in SC-FDMA-IDMA and OFDM-IDMA schemes. Both schemes are multi-carrier schemes with orthogonal sub-carriers to deal against inter-symbol interference (ISI) and orthogonal interleavers for the simultaneous access of multiple users. It has been suggested through simulation outcomes that PPI performance is adequate with SC-FDMA-IDMA and OFDM-IDMA schemes in terms of bit error rate (BER) under multipath channel conditions. Moreover, regarding bandwidth requirement and the implementation complexity of the transmitted interleaver structure, PPI is superior to the conventional random interleaver (RI). Full article

Turbidity is one of the crucial parameters of water quality. Even though many commercial devices, low-cost sensors, and remote sensing data can efficiently quantify turbidity, they are not valid tools for the classification it. In this paper, we design, calibrate, and test a novel optical low-cost sensor for turbidity quantification and classification. The sensor is based on an RGB light source and a light detector. The analyzed samples are characterized by turbidity values from 0.02 to 60 NTUs, and have four different sources. These samples were generated to represent natural turbidity sources and leaves in the marine areas close to agricultural lands. The data are gathered using 64 different combinations of light, generating complex matrix data. Machine learning models are compared to analyze this data, including training, validation, and test datasets. Moreover, different alternatives for data preprocessing and feature selection are assessed. Concerning the quantification of turbidity, the best results were obtained using averaged data and principal components analyses in conjunction with exponential gaussian process regression, achieving an R² of 0.979. Regarding the classification of the turbidity, an accuracy of 91.23% is obtained with the fine K-Nearest-Neighbor classifier. The cases in which data were misclassified are characterized by turbidity values lower than 5 NTUs. The obtained results represent an improvement over the current solutions in terms of turbidity quantification and a completely novel approach to turbidity classification. Full article

Studying soil composition is vital for agricultural and edaphology disciplines. Presently, colorimetry serves as a prevalent method for the on-site visual examination of soil characteristics. However, this technique necessitates the laboratory-based analysis of extracted soil fragments by skilled personnel, leading to substantial time and resource consumption. Contrastingly, sensor techniques effectively gather environmental data, though they mostly lack in situ studies. Despite this, sensors offer substantial on-site data generation potential in a non-invasive manner and can be included in wireless sensor networks. Therefore, the aim of the paper is to develop a low-cost red, green, and blue (RGB)-based sensor system capable of detecting changes in the composition of the soil. The proposed sensor system was found to be effective when the sample materials, including salt, sand, and nitro phosphate, were determined under eight different RGB lights. Statistical analyses showed that each material could be classified with significant differences based on specific light variations. The results from a discriminant analysis documented the 100% prediction accuracy of the system. In order to use the minimum number of colors, all the possible color combinations were evaluated. Consequently, a combination of six colors for salt and nitro phosphate successfully classified the materials, whereas all the eight colors were found to be effective for classifying sand samples. The proposed low-cost RGB sensor system provides an economically viable and easily accessible solution for soil classification. Full article

One of the key indicators of water quality is dissolved oxygen. Even though oxygen is important in environmental monitoring, the sensors for dissolved oxygen are expensive and require periodic maintenance due to the use of membranes. In this paper, we propose using ultraviolet light absorption to estimate dissolved oxygen saturation in water samples. The absorption spectrum of dissolved oxygen in the ultraviolet range is investigated over a water matrix with different levels of complexity. First, the difference between different water matrixes is studied. The results indicate similar variations between river water and tap water matrices for comparative purposes. Both samples present much higher absorbance signals than distilled water. Thus, the rest of the tests were performed with only three water matrixes (ultrapure, distilled, and river water). By aerating, water samples were completely saturated. Then, nitrogen gas was used to remove dissolved oxygen from samples to obtain saturations of 75, 50, 25, and 3%. The absorption was measured from 190 to 380 nm, using LLG-uniSPEC 2. The obtained data were used to generate regression models for selected wavelengths (190, 210, 240, and 250 nm). The differences beyond 260 nm for the studied dissolved oxygen saturations were null. The generated models had correlation coefficients from 0.99 to 0.97 for ultrapure water, 0.98 to 0.95 for distilled water, and 0.90 to 0.83 for river water. The maximum differences were found between samples with 75 and 100% of saturation. Full article

Vehicle malfunctions have a direct impact on both human and road safety, making vehicle network security an important and critical challenge. Vehicular ad hoc networks (VANETs) have grown to be indispensable in recent years for enabling intelligent transport systems, guaranteeing traffic safety, and averting collisions. However, because of numerous types of assaults, such as Distributed Denial of Service (DDoS) and Denial of Service (DoS), VANETs have significant difficulties. A powerful Network Intrusion Detection System (NIDS) powered by Artificial Intelligence (AI) is required to overcome these security issues. This research presents an innovative method for creating an AI-based NIDS that uses Deep Learning methods. The suggested model specifically incorporates the Self Attention-Based Bidirectional Long Short-Term Memory (SA-BiLSTM) for classification and the Cascaded Convolution Neural Network (CCNN) for learning high-level features. The Multi-variant Gradient-Based Optimization algorithm (MV-GBO) is applied to improve CCNN and SA-BiLSTM further to enhance the model’s performance. Additionally, information gained using MV-GBO-based feature extraction is employed to enhance feature learning. The effectiveness of the proposed model is evaluated on reliable datasets such as KDD-CUP99, ToN-IoT, and VeReMi, which are utilized on the MATLAB platform. The proposed model achieved 99% accuracy on all the datasets. Full article

The eggshell is a biomineral consisting of CaCO₃ in the form of calcite phase and a pervading organic matrix (1–3.5 wt.%). Transforming eggshell calcite particles into calcium phosphate (apatite) micro-nanoparticles opens the door to repurposing the eggshell waste as materials with potential biomedical applications, fulfilling the principles of the circular economy. Previous methods to obtain these particles consisted mainly of two steps, the first one involving the calcination of the eggshell. In this research, direct transformation by a one-pot hydrothermal method ranging from 100–200 °C was studied, using suspensions with a stoichiometric P/CaCO₃ ratio, K₂HPO₄ as P reagent, and eggshells particles (Ø < 50 μm) both untreated and treated with NaClO to remove surface organic matter. In the untreated group, the complete conversion was achieved at 160 °C, and most particles displayed a hexagonal plate morphology, eventually with a central hole. In the treated group, this replacement occurred at 180 °C, yielding granular (spherulitic) apatite nanoparticles. The eggshell particles and apatite micro-nanoparticles were cytocompatible when incubated with MG-63 human osteosarcoma cells and m17.ASC murine mesenchymal stem cells and promoted the osteogenic differentiation of m17.ASC cells. The study results are useful for designing and fabricating biocompatible microstructured materials with osteoinductive properties for applications in bone tissue engineering and dentistry. Full article

The increasing availability of Electric Vehicles (EVs) is driving a shift away from traditional gasoline-powered vehicles. Subsequently, the demand for Electric Vehicle Charging Systems (EVCS) is rising, leading to the significant growth of EVCS as public and private charging infrastructure. The cybersecurity-related risks in EVCS have significantly increased due to the growing network of EVCS. In this context, this paper presents a cybersecurity risk analysis of the network of EVCS. Firstly, the recent advancements in the EVCS network, recent EV adaptation trends, and charging use cases are described as a background of the research area. Secondly, cybersecurity aspects in EVCS have been presented considering infrastructure and protocol-centric vulnerabilities with possible cyber-attack scenarios. Thirdly, threats in EVCS have been validated with real-time data-centric analysis of EV charging sessions. The paper also highlights potential open research issues in EV cyber research as new knowledge for domain researchers and practitioners. Full article

The Internet of Things (IoT) is a network of interconnected physical devices, vehicles, and buildings that are embedded with sensors, software, and network connectivity, enabling them to collect and exchange data. This exchange of data between the physical and digital worlds allows for a wide range of applications, from smart homes and cities to industrial automation and healthcare. However, a key challenge faced by IoT nodes is the limited availability of energy to support their operations. Typically, these nodes can only function for a few days based on their duty cycle. This paper introduces a solution that aims to ensure the sustainability of IoT applications by addressing this energy challenge. Thus, we develop a design of a hybrid sustainable energy system designed specifically for IoT nodes, using solar photovoltaic (PV) and wind turbines (WT) chosen for their multiple benefits and complementarity. The system uses the single-ended primary-inductance converter (SEPIC) and is controlled using a hybrid approach, combining Harris Hawks Optimization and Particle Swarm Optimization (HHHOPSO). Each SEPIC converter boost the electrical energy generated to attain the required voltage level when charging the battery. The proposed methodology is implemented in MATLAB/Simulink and its performance is measured using appropriate metrics. In terms of efficiency and average power, the results show that the suggested method outperforms previous strategies. Our system powers also many sensor nodes, leading to a high level of sustainability and lowering the carbon footprint associated with traditional energy sources. Full article

Essential oils are valuable in various industries, but their easy adulteration can cause adverse health effects. Electronic nasal sensors offer a solution for adulteration detection. This article proposes a new system for characterising essential oils based on low-cost sensor networks and machine learning techniques. The sensors used belong to the MQ family (MQ-2, MQ-3, MQ-4, MQ-5, MQ-6, MQ-7, and MQ-8). Six essential oils were used, including Cistus ladanifer, Pinus pinaster, and Cistus ladanifer oil adulterated with Pinus pinaster, Melaleuca alternifolia, tea tree, and red fruits. A total of up to 7100 measurements were included, with more than 118 h of measurements of 33 different parameters. These data were used to train and compare five machine learning algorithms: discriminant analysis, support vector machine, k-nearest neighbours, neural network, and naive Bayesian when the data were used individually or when hourly mean values were included. To evaluate the performance of the included machine learning algorithms, accuracy, precision, recall, and F1-score were considered. The study found that using k-nearest neighbours, accuracy, recall, F1-score, and precision values were 1, 0.99, 0.99, and 1, respectively. The accuracy reached 100% with k-nearest neighbours using only 2 parameters for averaged data or 15 parameters for individual data. Full article

Acquiring the morphological parameters of fish with the traditional method (depending on human and non-automatic factors) not only causes serious problems, such as disease transmission, mortality due to stress, and carelessness and error, but it is also time-consuming and has low efficiency. In this paper, the speed of fish and their physical characteristics (maximum and minimum diameter, equivalent diameter, center of surface, and velocity of fish) were investigated by using a programmed online video-recording system. At first, using the spatial coordinates obtained from YOLOv2, the speed of the fish was calculated, and the morphological characteristics of the fish were also recorded using this program during two stages of feeding and normal conditions (when the fish are not in feeding condition). Statistical analysis was performed between the measured parameters due to the high correlation between the parameters, and the classification system with high accuracy was able to provide an accurate prediction of the fish in both normal and feeding conditions. In the next step, an artificial neural network (ANN) prediction model (with three neurons; four input, one hidden layer, and one output) was presented to plan the system online. The model has the lowest error (1.4 and 0.14, respectively) and the highest coefficient of explanation (0.95 and 0.94, respectively) in two modes, normal and feeding, which are presented by the ANN system for planning the online system. The high accuracy and low error of the system, in addition to having a high efficiency for continuous and online monitoring of live fish, can have a high economic benefit for fish breeders due to the simplicity of its equipment, and it can also check and diagnose the condition of fish in time and prevent economic damage. Full article