JOIV : International Journal on Informatics Visualization
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An Enhanced Routing Protocol For Vehicular Ad Hoc Networks With Swarm Intelligent
Get PDFA Vehicular Ad Hoc Network (VANET) is a transient network of wireless mobile nodes operating without centralized administration or pre-existing infrastructure. VANETs are a subset of Mobile Ad Hoc Networks (MANETs) designed to facilitate vehicular communication. This allows vehicles to communicate directly with roadside devices or with each other. These networks are appropriate for applications like infotainment services, traffic control, and accident avoidance since they are dynamic, decentralized, and highly flexible. However, their lack of established infrastructure presents serious difficulties, especially when preserving dependable routing and energy efficiency. Path selection in VANETs usually attempts to limit the number of intermediary nodes required to reach a destination to reduce latency and possible points of failure. However, as the distance between nodes increases, so does the required transmission power, directly impacting the network's energy consumption. As a result, energy-efficient routing is crucial to maintain network longevity and performance. This paper introduces the Bee Destination Sequenced Distance Vector Routing (B-DSDV) protocol, utilizing swarm intelligence principles via the Artificial Bee Colony (ABC) algorithm to enhance energy efficiency within a DSDV framework. This integration incorporates the Bee Algorithm into the discovery mechanism of DSDV to identify the most accessible node and the shortest route based on node distances. The algorithm assesses both the power levels of nodes and their distances to others. Route selection is optimized by considering the power consumption of intermediate nodes between the source and destination. Performance evaluation of the B-DSDV protocol is compared with established protocols, demonstrating its effectiveness in selecting high-power optimal paths and improving overall performance. The simulation results were conducted based on average throughput, average energy consumption, average end-to-end delay, and packet delivery ratio performance metrics. We conducted a simulation study using Network Simulator (NS) version 2.35 to evaluate the performance metrics of the routing protocols. Regarding energy consumption, the B-DSDV protocol achieved superior results, approximately 0.10% concerning packet size, compared to other protocols
Community Blockchain Record-Keeping Method for Agricultural Land Leases using Design Science Research Approach
Get PDFIn Malaysia, agricultural land lease records are still predominantly maintained on paper, making them vulnerable to loss, damage, and tampering. The study presents a novel, community-based blockchain record-keeping system designed explicitly for agricultural land leasing. Its primary objective is to enhance the transparency, trust, and efficiency of lease transactions between landowners and small-scale farmers. The system leverages Hyperledger Fabric in combination with the Interplanetary File System (IPFS) to ensure lease agreements are stored securely and immutably. By using decentralized storage, the documents remain accessible when needed while reducing the risk of unauthorized modifications. The design of this system is grounded in Work System Theory (WST), which emphasizes the integration of technology with the people, processes, and environmental factors involved in land leasing. To ensure the development approach aligns with the complexities of the real-world context, the study employs Situational Method Engineering (SME). This methodology involves selecting and tailoring components from existing methods to create a solution customized for agricultural land leasing. By combining a robust technical foundation with a design that accounts for community dynamics and legal considerations, the study demonstrates how blockchain can serve not only as a data management tool but also as a means of promoting fairness and transparency in rural land governance. The artefact marks a significant step toward building digital trust in the management and documentation of agricultural land.
Hyperellipsoid Cluster Merging using Hierarchical Analysis of Hyperellipsoid Cluster for Image Segmentation
Get PDFSegmentation is one of the critical stages in digital image processing and computer vision. However, conventional clustering-based segmentation methods, such as K-means and Fuzzy C-means (FCM), are still unable to accurately segment images whose pixels form hyperellipsoid clusters in the feature space. In addition, previous clustering methods based on Mahalanobis distance measurement require a long computational time and still have the potential to fall into local optima. Therefore, in this paper, we propose a new method for segmenting images whose pixels form hyperellipsoid clusters in the feature space, utilizing hyperellipsoid clusters merging through hierarchical analysis of hyperellipsoid clusters. The proposed method comprises eight main steps: histogram extraction, peak and valley identification, elimination of low peaks and valleys, peak combination for centroid initialization, initialization of cluster pixel members, elimination of ineffective clusters, hyperellipsoid cluster merging, and finalization of cluster members. This paper presents a novel approach to segmenting color images by employing an initial centroid discovery process and cluster analysis that considers cluster covariance for cluster merging. Based on the tests conducted using various image characteristics, the proposed method can provide 97.42% accuracy, 98.02% precision, 97.15% recall, 2.58 misclassification error, 97.54 F1-score, 95.29% intersection over union, 97.52% dice coefficient, and 15.37 seconds of computation time. The test results are superior to those of conventional methods, such as K-means and FCM. Based on these results, it can be concluded that the proposed method can effectively segment images with high accuracy. The proposed method can serve as an alternative approach to image segmentation
Toponym Extraction and Disambiguation from Text: A Survey
Get PDFToponym is an essential element of geospatial information. Traditionally, toponyms are collected in a gazetteer through field surveys that require significant resources, including labor, time, and money. Nowadays, we can utilize social media and online news portals to collect event locations or toponyms from the text. This article presents a survey of studies that focus on the extraction and disambiguation of toponyms from textual documents. While toponym extraction aims to identify toponyms from the text, toponym disambiguation determines their specific locations on the earth. The survey covered articles published between January 2015 and April 2023, presented in English, and gathered from five major journal databases. The survey was conducted by adopting the Kitchenham guidelines, consisting of an initial article search, article selection, and annotation process to facilitate the reporting phase. We employed Mendeley as a reference management tool and NVivo to categorize certain parts of the articles that are the focal points of interest in this survey. The primary focus of the survey was on the methods or approaches performed in the research articles to extract and disambiguate toponyms. Additionally, we also discuss some general challenges in toponym research, different applications for toponym extraction and disambiguation, data sources, and the use of languages other than English in the studies. The survey confirms that each approach has its limitations. Extracting and disambiguating toponyms from text is complex and challenging, especially for low-resource languages. We also suggest some research directions related to toponym extraction and disambiguation that could enrich the gazetteer
Visualization of Prediction Potential Hotspots in Multidimensional Datasets
Get PDFCorrelation analysis and visual analysis of multidimensional datasets with the objective of identification of patterns and trends is an essential element of decision-making processes. Conventional visualization models in the considered area, such as correlation heatmaps, are used to visually represent the value of the correlation coefficient measured between pairs of attributes of the multidimensional dataset but are hard to read when working with a large number of attributes. This study concerns the design and implementation of a visualization model, which can be used to identify prediction potential hotspots in analysed datasets - parts of the dataset, which are strongly correlated with a high number of attributes in the dataset. The proposed model focuses on a graphical representation of such hotspots based on planar, multicomponent graphs, with the aim of meta-analysis of large, multidimensional datasets. The implemented approach is evaluated on a case study focused on the analysis of the original cubic graph property dataset where several prediction potential hotspots of different correlation types are constructed. Other than the construction of the hotspots themselves, this study shows a comparison of results gained by the graphical model to the conventional model used in the meta-analysis of multidimensional datasets – Shapley value explanations. The results presented in this study point to the need for a robust visualization framework for the analysis of correlation structures in multidimensional datasets and for models of visualization based on virtual and augmented reality
AquaFlora Smart Terrarium: A Self-Sustaining Internet of Things (IoT)-based Terrarium for Smart Ecosystem Management
Get PDFUsing Internet of Things (IoT) technology in smart terrariums is a growing trend that aligns with the move towards more automated and better- well-managed ecosystem gardening. AquaFlora Smart Terrarium is a state-of-the-art system designed to create and maintain the perfect environment for various plants. It features a network of sensors, actuators, and electronic components, all orchestrated by an ESP32 microcontroller. The system leverages four actuators to control light, humidity, irrigation, and cooling, ensuring optimal conditions for plant growth. Three sensors, which are the Capacitive Soil Moisture Sensor, DHT22, and BH1750FVI to monitor soil moisture, temperature, humidity, and light intensity, providing real-time data to the microcontroller. The terrarium can be conveniently controlled via a mobile app and Node-RED, allowing for remote monitoring, control, and automation through Firebase and MQTT. Node-RED visualization of sensor data over a 10-hour period demonstrated the effectiveness of the automatic mode in maintaining stable plant conditions. Soil moisture remained above 60%, temperature ranged between 30.1°C and 33.1°C, humidity between 69.10% and 74.00%, and light intensity between 23 Lux and 175 Lux. The AquaFlora Smart Terrarium represents a significant innovation in plant care, offering a reliable and automated solution to create and sustain the ideal environment for healthy plant growth
Enhancing Low-Resolution Images of Mustard Leaves Affected by Pests with Thermal Sensor using Super-Resolution Convolutional Neural Network Optimization
Get PDFWith urban areas facing limited agricultural land, hydroponic systems offer a solution to increase food storage and variety. Hydroponics, a farming technique that relies on water as a growing medium rather than soil, provides essential nutrients and oxygen for plants. This paper explores the use of thermal sensors to capture images of mustard leaves in a hydroponic system. In addition, it also explores thermal sensor images. These images are analyzed to detect pest attacks, with red leaves indicating the presence of pests and green/blue leaves unaffected by pests. These pests emit hot air; consequently, they turn red. The method of increasing resolution is to compare the Long Short-Term Memory (LSTM) algorithm with the Super-Resolution Convolutional Neural Network (SR-CNN) to improve the quality of images obtained from low-resolution sensors (AMG8833/Grid-EYE). The results show that the SR-CNN method is better than the LSTM (Long Short-Term Memory) method, although limitations remain due to the sensor resolution. After conducting the research, it could be observed that using LSTM resulted in a Mean Square Error (MSE) value of 0.001551685, while SR-CNN indicated an MSE value of 8.873. Furthermore, LSTM produces a Peak Signal-to-Noise Ratio (PSNR) value of 37.10797726, whereas SR-CNN achieves a PSNR of 39.199. The accuracy rates (SSIM) for LSTM and SR-CNN are 0.991538522961364 and 0.997747, respectively. These findings show that using the SR-CNN algorithm can effectively improve the quality of images produced by thermal sensors, even though the sensor pixel capacity is limited
Multi-Document Summarization Using Tuna Swarm Optimization and Markov Clustering
Get PDFThe Internet contains a large number of documents from various sources with similar content. The contents of documents that are almost identical will lead to news redundancy, making it difficult for readers to distinguish between factual information and opinions. Multi-document summarization has been designed to enable readers to easily understand the meaning of news documents without needing to read multiple documents. Multi-document summarization aims to extract information from several texts written about the same topic. The resulting summary report enables users to obtain a single piece of information from multiple similar pieces of information sourced from various locations. Various approaches have been used in creating multi-document summaries. Issues regarding accuracy and redundancy are still a significant focus of research. In this paper, a new multi-document summarization model was built using Tuna Swarm Optimization (TSO) and Markov Clustering (MCL) methods. The dataset of this research is Indonesian language news from various online media sources. Based on hyperparameter tuning using training data, the best TSO model performance was obtained at variable values a = 0.7, z = 0.9, and the optimal number of tuna fish > 80. From the research results, it was found that TSO outperformed other swarm intelligence methods. The use of MCL has proven to be effective, as evidenced by the performance results, where TSO achieved an average ROUGE value 7.95% higher when MCL was applied. In this performance test, four standard evaluation metrics of the ROUGE toolkit were used
A Secure Cloud Service Game Theory Approach to Demand Response Modelling for Residential Users in Smart Grid
Get PDFIn today's world, electricity has become the keystone for every activity undertaken. As the population increases, the electricity demand has reached unprecedented levels, putting strain on electrical grids. In many developing countries, the residential sector consumes 60% of the peak load. The negative consequences of this trend provide a pathway for frequent brownouts, which lead to enormous losses for industries as well as residential households. To date, the flexibility of energy is usually achieved on the generation side. However, an easier way to counter this would be to manage usage on the demand side. The development of smart grid facilities has enabled communication between utilities and consumers. Therefore, the demand response functionality shows greater potential to stabilize the power supply and demand for the utility and consumers, respectively. In this paper, an intelligent secure cloud service game theory-based demand response modelling algorithm is proposed to handle peak demand in the residential sector. This innovative strategy enables residential consumers to achieve mutually beneficial outcomes. Enhancing communication security between utility providers and consumers, optimizing renewable energy utilization, and improving cost-effectiveness and reliability in electricity production and delivery are vital for meeting the rising demand. The simulation results suggest that the proposed approach efficiently reduces the Peak-to-Average Ratio, leading to mutual advantages for both consumers and utility providers. This approach addresses the growing demand for electricity while promoting sustainable energy through improved energy management practices
STAC Implementation and FAIR Evaluation for Integrating Landsat-8 Analysis Ready Data: Improving Geospatial Data Accessibility and Utilization in Indonesia
Get PDFThe National Research and Innovation Agency (BRIN) provides Landsat-8 Analysis Ready Data (ARD), pre-processed to enable advanced geospatial analysis. Despite its potential, data accessibility and utilization remain limited due to the absence of an integrated data discovery and access system. This study develops an indexing system for ARD data using the SpatioTemporal Asset Catalog (STAC), a standardized approach to managing geospatial metadata. It evaluates its implementation using the FAIR (Findable, Accessible, Interoperable, and Reusable) Data Maturity Model. Landsat-8 ARD sample data were indexed into a STAC database, accessible through an API. STAC implementation achieved significant improvements in data discoverability and accessibility, with both principles reaching maturity level 5, as all essential indicators were met. However, the interoperability and reusability principles remained at level 0 due to incomplete metadata, particularly in data provenance and licensing. The results highlight the effectiveness of STAC in enhancing metadata-driven data search and access but also emphasize the need for comprehensive metadata adhering to community standards. Recommendations include improving metadata completeness, integrating licensing information, and ensuring compliance with international metadata standards to enhance data usability. This study recommends improving geospatial data management in Indonesia, offering scalable solutions for other datasets. Further research should explore metadata automation and user feedback mechanisms to achieve a higher level of FAIR compliance, ultimately fostering better utilization of geospatial data for various applications