Real-time Information Dissemination Research Articles

The IoT-Based Early Warning System for Detecting High Tide Floods (ROB-EWS) in Tambak Lorok, Semarang Indonesia

Background and Objectives: The Tambaklorok fishing village area, located on the northern coast of Semarang City, Indonesia, is susceptible to sea level fluctuations, which often lead to tidal flooding and seawater encroaching into residential areas. Consequently, a seawater flood detector (rob) is needed for effective disaster mitigation for affected communities. This study aims to implement a disaster mitigation system by installing an IoT-based Seawater Flood (Rob) Early Warning System to reduce disaster risks and their impacts on the community. Methods: The IoT-based Early Warning System is designed to detect floods by monitoring sea level height parameters. Results: IoT-based devices measure sea level using ultrasonic sensors, with the readings processed by a microprocessor. The data is then periodically analyzed and transmitted to an application in real-time. This technology allows for real-time information dissemination through web applications and alerts in the form of sirens. These warnings enable residents to evacuate and move to safer areas. The siren alerts are categorized into two levels: Alert 1 indicates that seawater is approaching the land boundary, while Alert 2 signals that water has begun to enter residential areas. Additionally, designated evacuation zones will guide affected communities to safer areas. Conclusion: The installation of an IoT-based Early Warning System delivers real-time information to help mitigate the impact of rising sea levels (rob) in the Tambak Lorok fishing village community.

Indonesian GDP movement detection using online news classification

Gross Domestic Product (GDP) stands as a pivotal indicator, offering strategic insights into economic dynamics. Recent technological advancements, particularly in real-time information dissemination through online economic news platforms, provide an accessible and alternative data source for analyzing GDP movements. This study employs online news classification to identify patterns in the movement and growth rate of Indonesia’s GDP. Utilizing a web scraping technique, we collected data for analysis. The classification models employed include transfer learning from pre-trained language model transformers, with classical machine learning methods serving as baseline models. The results indicate superior performance by the pre-trained language model transformers, achieving the highest accuracy of 0.8880 and 0.7899. In comparison, hyperparameter-tuned classical machine learning models also demonstrated commendable results, with the best accuracy reaching 0.845 and 0.7811. This research underscores the efficacy of leveraging online news classification, particularly through advanced language models. The findings contribute to a nuanced understanding of economic dynamics, aligning with the contemporary landscape of information accessibility and technological progress.

Determinants of Blockchain Technology Adoption in the Australian Agricultural Supply Chain: A Systematic Literature Review

Blockchain technology (BCT) is emerging as a key enabler of sustainability in various sectors, including agriculture. This study explores the impact of BCT adoption on sustainability within the Australian agriculture sector. Through a systematic literature review (SLR) of studies published between 2015 and 2021, ten key themes influencing BCT adoption were identified: transparency, traceability, contract exchange, transaction efficiency, trade finance management, quality control, real-time information dissemination, security, trust, and legislative frameworks. The findings suggest that BCT adoption in agriculture can lead to improved sustainability outcomes. By enhancing transparency and traceability, BCT enables stakeholders to track the provenance of products, reducing the risk of fraud and ensuring compliance with environmental standards. The automation and efficiency gains afforded by BCT streamline supply chain processes, reducing waste and resource consumption. BCT enhances trust among stakeholders, fostering collaboration and information sharing to address sustainability challenges. This study contributes to the literature by highlighting how BCT can drive sustainability in agriculture through improved transparency, efficiency, and collaboration.

Development of a Microcontroller-based Anti-violence Billboard

Effective communication is crucial in institutions and public places, where digital display boards play a significant role in disseminating information. Traditional display boards often require constant reprogramming, limiting their flexibility and efficiency. This project addresses these limitations by designing and implementing a Light Emitting Diode (LED) scrolling message display system using an ATmega328 microcontroller. The system is specifically designed to promote anti-violence campaigns, such as "SAY NO TO EXAMINATION MALPRACTICE AND CULTISM," while also accommodating static printed images. The microcontroller-based system allows for easy and efficient message updates via a personal computer (PC) equipped with Visual Basic software (MEKUS), serving as the interface between the user and the display system. Messages are dynamically displayed on the LED board, providing clear and engaging communication. The system operates on a 5V DC power supply, with message scrolling controlled by shift registers using four IN4049 ICs. This design enhances the security and awareness within institutions by offering a reliable and user-friendly platform for real-time information dissemination. The advantages of this LED display system include ease of setup, programming, and handling, along with a clear presentation of scrolling text.

Empowering Communication: A Raspberry PI-Based Smart Noticeboard System

This research paper explores the implementation of a Smart Notice Board that leverages IoT and single-board computers, such as Raspberry Pi, to revolutionize traditional communication systems. It delves into the technical aspects of integrating these technologies to create a dynamic platform for real-time information dissemination. Additionally, it investigates how features like remote updating, authentication mechanisms, and environmental considerations enhance the functionality and sustainability of the Smart Notice Board. Ultimately, the paper aims to showcase the potential of this innovative solution in improving communication efficiency, security, and environmental impact within organizational and institutional settings.

The use of intelligent real-time image acquisition system using data mining technology in news acquisition and editing practice

Aiming to address the timely dissemination of news information, this work explores the clever utilization of data mining (DM) technology and deep learning (DL) algorithms to construct an intelligent real-time news image acquisition system to meet the urgency of news dissemination needs. First, this work introduces an intelligent real-time news image acquisition system and provides a detailed analysis of its principles and advantages. Throughout this process, the crucial role of DM technology in news image classification and automation is emphasized, especially in dealing with rapidly evolving news events. Next, the work establishes an intelligent real-time news image acquisition model based on DL algorithms, which integrates the essence of DM technology. Through this fusion, the research objective is to enhance the performance of the news image acquisition system to achieve higher real-time and accuracy, which is vital for the swift delivery of news information. Finally, this work investigates the application of the intelligent news image acquisition system in network communication to ensure its adaptability to various network communication scenarios while maintaining accuracy and real-time capabilities. The research results demonstrate that the application of DM technology in combination with DL algorithms can effectively meet the practical needs of the news industry, enhancing the automation of news image processing and enabling faster information delivery to the audience. Notably, the AlexNet model employed performs exceptionally well, achieving recognition rates of up to 99.6% after data augmentation or equalization processing, with an accuracy of 90.9% and a high specificity of 93.38%. This indicates outstanding overall classification accuracy and negative class accuracy, even when distinguishing between news and non-news scenarios. These results clearly underscore the connection between DM technology and news acquisition and editing practices, and emphasize its potential to improve the efficiency and accuracy of real-time information dissemination. The research’s contribution and innovation lie in the fusion of DM technology with DL algorithms to build an intelligent real-time news image acquisition system. This fusion enhances the automation and classification performance of news images, thereby improving the real-time and accuracy of news information. Furthermore, the work strongly emphasizes improving the real-time and accuracy of the news image acquisition system to ensure the swift delivery of information, which is of utmost importance in rapidly changing news events.

Narrative literature review: Efficiency enhancement - user trust in chatbots as a tool for improving service quality by humans

Chatbots have become efficient and reliable tools for instant and real-time information dissemination. Despite their effectiveness, user trust in chatbot systems remains relatively low. A holistic approach is necessary, integrating user emotional experiences, trust-building strategies, and continuous technological refinement to maximize chatbot benefits across various sectors. This research explores the potential for selective information dissemination based on user preferences using chatbots combined with artificial intelligence. Through a narrative approach, the study reviews literature and analyzes eight articles related to chatbots' application in information dissemination. The results indicate that chatbots are efficient in providing information and can be customized for various needs, such as population services, reminder notifications, and book processing. Chatbots have the potential to enhance services and can be integrated into information systems to improve service quality. However, challenges such as reliance on high-quality data and machine learning, difficulties in understanding non-formal language or slang, and limitations in handling complex questions need to be addressed for chatbots to reach their full potential.

A Region-wide Deployment of an Automated Real-time Truck Parking Availability Detection and Information Dissemination System

Commercial heavy vehicle-borne freight will continue to be the dominant cross-region shipping mode for materials and goods for the foreseeable future. Federal hours of service (HOS) regulations mandate scheduled minimum times for of-duty and in-transit rest periods to minimize operator fatigue. Without timely, accurate information disseminated to drivers and carriers on where to park, complying to HOS regulations can often become a very challenging task for drivers. This paper describes a region-wide deployment of a unique non-intrusive, multi-camera, truck parking space detection and availability information dissemination architecture. The architecture leverages multi-view Structure and Motion methods to reconstruct a Three-dimensional representation of the environment for estimating unoccupied spatial extents to deduce truck parking availability. Unlike 2D camera sensor-based methodologies, the advantage is its immediate adaptability to a variety of parking facilities and scenarios without the need for any subsequent re-training. The approach also mitigates errors arising from occlusions, and many other environmental conditions that confound many 2D camera-based approaches. The region-wide deployment, operated by a state transportation agency, has been online since early 2019, and has thus far substantiated the technical and day-to-day operational viability of the approach. The performance throughout this period indicates an overall accuracy at or above 90%. This paper provides a general overview of the deployed system architecture and performance characteristics across varied parking facility designs, parking behaviors, and other environmental conditions.

Social adaptive behavior and oscillatory prevalence in an epidemic model on evolving random geometric graphs

Our recent experience with the COVID-19 pandemic amply shows that spatial effects like the mobility of agents and average interpersonal distance, together with the adaptation of agents, are very important in deciding the outcome of epidemic dynamics. Structural and dynamical aspects of random geometric graphs are widely employed in describing processes with a spatial dependence, such as the spread of an airborne disease. In this work, we investigate the interplay between spatial factors, such as agent mobility and average interpersonal distance, and the adaptive responses of individuals to an ongoing epidemic within the framework of random geometric graphs. We show that such spatial factors, together with the adaptive behavior of the agents in response to the prevailing level of global epidemic, can give rise to oscillatory prevalence even with the classical SIR framework. We characterize in detail the effects of social adaptation and mobility of agents on the disease dynamics and obtain the threshold values. We also study the effects of delayed adaptive response of agents on epidemic dynamics. We show that a delay in executing non-pharmaceutical spatial mitigation strategies can amplify oscillatory prevalence tendencies and can have non-linear effects on peak prevalence. This underscores the importance of early implementation of adaptive strategies coupled with the dissemination of real-time prevalence information to manage and control the epidemic effectively.

Social Media Analytics on Russia–Ukraine Cyber War with Natural Language Processing: Perspectives and Challenges

Utilizing social media data is imperative in comprehending critical insights on the Russia–Ukraine cyber conflict due to their unparalleled capacity to provide real-time information dissemination, thereby enabling the timely tracking and analysis of cyber incidents. The vast array of user-generated content on these platforms, ranging from eyewitness accounts to multimedia evidence, serves as invaluable resources for corroborating and contextualizing cyber attacks, facilitating the attribution of malicious actors. Furthermore, social media data afford unique access to public sentiment, the propagation of propaganda, and emerging narratives, offering profound insights into the effectiveness of information operations and shaping counter-messaging strategies. However, there have been hardly any studies reported on the Russia–Ukraine cyber war harnessing social media analytics. This paper presents a comprehensive analysis of the crucial role of social-media-based cyber intelligence in understanding Russia’s cyber threats during the ongoing Russo–Ukrainian conflict. This paper introduces an innovative multidimensional cyber intelligence framework and utilizes Twitter data to generate cyber intelligence reports. By leveraging advanced monitoring tools and NLP algorithms, like language detection, translation, sentiment analysis, term frequency–inverse document frequency (TF-IDF), latent Dirichlet allocation (LDA), Porter stemming, n-grams, and others, this study automatically generated cyber intelligence for Russia and Ukraine. Using 37,386 tweets originating from 30,706 users in 54 languages from 13 October 2022 to 6 April 2023, this paper reported the first detailed multilingual analysis on the Russia–Ukraine cyber crisis in four cyber dimensions (geopolitical and socioeconomic; targeted victim; psychological and societal; and national priority and concerns). It also highlights challenges faced in harnessing reliable social-media-based cyber intelligence.

Albanian Fake News Detection

Recent years have witnessed the vast increase of the phenomenon known as the fake news. Among the main reasons for this increase are the continuous growth of internet and social media usage and the real-time information dissemination opportunity offered by them. Deceiving, misleading content, such as the fake news, especially the type made by and for social media users, is becoming eminently hazardous. Hence, the fake news detection problem has become an important research topic. Despite the recent advances in fake news detection, the lack of fake news corpora for the under-resourced languages is compromising the development and the evaluation of existing approaches in these languages. To fill this huge gap, in this article, we investigate the issue of fake news detection for the Albanian language. In it, we present a new public dataset of labeled true and fake news in Albanian and perform an extensive analysis of machine learning methods for fake news detection. We performed a comprehensive feature engineering and feature selection experiments. In doing so, we explored the Albanian language-related feature categories such as the lexical, syntactic, lying-detection, and psycho-linguistic features. Each article was also modeled in four different ways: with the traditional bag-of-words (BoW) and with three distributed text representations using the state-of-the-art Word2Vec, FastText, and BERT methods. Additionally, we investigated the best combination of features and various types of classification methods. The conducted experiments and obtained results from evaluations are finally used to draw some conclusions. They shed light on the potentiality of the methods and the challenges that the Albanian fake news detection presents.

PENERAPAN HUKUM TERHADAP E - TILANG DALAM UPAYA PENERTIBAN LALU LINTAS PADA UNDANG - UNDANG NO 22 TAHUN 2009 TENTANG LALU LINTAS DAN ANGKUTAN JALAN

To achieve a relevant evidence of infringement (violation ticket) process its is necessary to have an information system that is supported by a network-based software or website that allows the dissemination of realtime information to every member of the police. The implementation of E-Tilang is could be an effective option to sanction the traffic offenders. Even tough the E-Tilang can not be said that it is effective because not every people in Indonesia are technologically literate. There are still many of them who do not know about the existence of E-Tilang so that they need for more vigorous and equitable socialization abaout it. Therefore, this study intends to analyze the effectiveness of the implementation of Sanction of E-Tilang Penalty for Traffic Offenders Based on Law no. 22/2009 on Traffic and Road Transportation in the Territory of the Police, and to analyze how theimplementation, constraints and the effectiveness of E-tilang system in the settlement of criminal cases of traffic. E-Tilang has some advantages, it has faster service than a conventional one. This system is more practical and faster. The implementation of the electronic ticketing system (E-Tilang) is to facilitate speed and convenience, the openness of the execution of the ticketing process or as a substitute for on-site ticketing process. Some of the benefits for traffic violators in the presence of the E-Tilang system are the transparency of public apparatus's actions in government administration activities, community empowerment where people are expected to transmit the orderly attitude of the traffic after knowing the rules to those around them in order not to violate the existing regulations. The responsiveness of the authorities will be higher and more responsive to public complaints in traffic and equity, where violators with the same offense will get the same fine or penalty

Short-Term Passenger Flow Prediction Using a Bus Network Graph Convolutional Long Short-Term Memory Neural Network Model

Short-term passenger flow prediction is critical to managing real-time bus networks, responding to emergencies quickly, making crowdedness-aware route recommendations, and adjusting service schedules over time. Some recent studies have attempted to predict passenger flow using deep learning models. The complexity of transportation networks, coupled with emerging real-time data collection and information dissemination systems, has increased the popularity of these approaches. There has also been a growing interest in using a new deep learning approach, the graph neural network that captures graph dependence by passing messages between its nodes. Researchers in various transportation domains have used such tools for modeling and predicting transportation networks, as many of these networks consist of nodes and links and can be naturally categorized as graphs. This paper develops a bus network graph convolutional long short-term memory (BNG-ConvLSTM) neural network model to forecast short-term passenger flows in bus networks. Validating the proposed model is done using real-world data collected from the Laval bus network in Canada. Based on a set of comparisons between the proposed model and some other popular deep learning approaches, it clearly indicates that the BNG-ConvLSTM model is more scalable and robust than other baselines in making network-wide predictions for short-term passenger flows.

Age of Information Modeling and Optimization for Fast Information Dissemination in Vehicular Social Networks

Autonomous vehicles (AVs) with advanced communication, computing, and control capabilities will provide not only a convenient means of transportation but also an emerging platform for real-time social communications and networking. Thereby, it is crucial to enable timely exchange of information over the dynamic cyber-physical-social system enabled by the AVs. In this paper, we consider age of information (AoI)-centric real-time information dissemination over vehicular social networks (VSNs), where the AoI captures the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">freshness</i> of received data packets. We first propose a mathematical framework based on the mean-field theory (MFT) to analyze the network AoI (NAoI) of VSNs, namely AoI for the AV that lastly receives the information update in the network. The proposed analytical framework considers both the social features of vehicular networks, which are characterized using the scale-free network theory, and the underlying wireless communication process to evaluate the NAoI. Then, we further consider joint optimization of the information update rate at the source node and the transmit probabilities at the AVs for minimization of the average peak NAoI (PNAoI), i.e., the time average of peak values occurred in the evolution of the NAoI, which is solved via a novel parametric optimization scheme. Both analytical and simulation results show that compared with several baseline schemes, the proposed scheme can exploit the scale-free feature of the VSNs to significantly lower the average PNAoI by up to 96 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> .

Senior Citizen Information System

This developmental-descriptive research sought to design and developed a computer-based system, to be known as the Senior Citizens Information System, to provide real-time information dissemination among senior citizen members in the Municipality of Batad, Iloilo pertaining to their membership status and monetary assistance monitoring. The Rapid Application Development model was employed as the SDLC in the development of the system product. More so, the three-tier architecture was used as the architectural model. There were 120 senior citizens as user-respondents and five IT experts for evaluation. The system product was evaluated using a researchers-made survey instrument to determine if level of usability and performance. Results of the investigation shows that the system product was perceived as very good with its level of usability as well as its performance.

Development and Demonstration of an Intelligent Tourism Support System with Real-Time Web Information Dissemination Function and Shelter Route Guidance

Development and Demonstration of an Intelligent Tourism Support System with Real-Time Web Information Dissemination Function and Shelter Route Guidance

Improved Road Segment-Based Geographical Routing Protocol for Vehicular Ad-hoc Networks

The real-time traffic information dissemination among on-road vehicles has been envisioned via realizing vehicular ad hoc networks (VANETs) as smart service-oriented roadside wireless sensor networks. The network enables various types of real-time traffic applications related to safety and infotainment for drivers and passengers. The information dissemination-centric routing protocols for vehicular networks have to dynamically adopt under the constrained network environment while considering the higher mobility of vehicular nodes and unpredictable physical topologies in the network. The issue needs to be addressed through smart and network-aware routing protocols. Geographical routing protocols have witnessed significant attention for information dissemination under these types of dynamic vehicular network environment. To this end, this paper presents an improved road segment-based geographical routing (ISR) protocol focusing on better head node selection for information dissemination. It divides the forwarding area into a number of road segments and selects a head node on each segment by focusing on traffic-aware information including the location, direction, and link quality-centric score for every vehicle on each road segment. Algorithms were developed for the complete process of head node selection and information dissemination among vehicles on the road segments. The simulation results attested the performance benefits of the proposed routing framework as compared to the state-of-the-art protocols considering dynamic vehicular traffic environment-related metrics.

Review of ICT usage in disaster management

Disaster management demands a near real-time information dissemination so that emergency services can be provided to the people immediately. Real time information can be easily collected with the various sources with rapid advancement of information and communication technologies. Use of ICT in different stages of disaster management is studied. Existing solutions are explored and also the study of integrated emerging ICT networks, services and applications are explored. Different techniques during evacuation s are studied. The evacuation model is proposed after comparative analysis of different ICT based technologies.

A Novel Android Based Application for Collaborative Learning in Colleges

Technology has taken a long leap with the introduction of social platforms like instant messaging applications. The easy accessibility of smartphones and internet connectivity has drastically changed the way in which we communicate today. Apart from making instant communication possible, it also has an untapped potential of providing a platform for collaborative learning. Collaborative learning involves the use of social networking tools to enhance the learning process between teachers and students by enabling the sharing of valuable information, study materials, etc. In this paper, we propose a new Android-based application tailored to fulfill the needs of real-time information dissemination in our college as well as for effective sharing of study material between teachers and students by overcoming the drawbacks of the existing systems. Traditionally, all information pertaining to student’s academics are displayed on notice boards or carried out manually which is both time-consuming and laborious. Moreover, there is a lack of a proper channel for sending urgent updates to students. This paper discusses the design and benefits of the proposed Android application and how it successfully caters to the need of academic information dissemination in our college.

Performance Evaluation of Magnetic Wireless Sensor Networks Algorithm for Traffic Flow Monitoring in Chaotic Cities

Traffic flow monitoring involves the capturing and dissemination of real-time traffic flow information for a road network. When a vehicle, a ferromagnetic object, travels along a road, it disturbs the ambient Earth’s magnetic field, causing its distortion. The resulting distortion carries vehicle signature containing traffic flow related information such as speed, count, direction, and classification. To extract such information in chaotic cities, a novel algorithm based on the resulting magnetic field distortion was developed using nonintrusive sensor localization. The algorithm extracts traffic flow information from resulting magnetic field distortions sensed by magnetic wireless sensor nodes located on the sides of the road. The model magnetic wireless sensor networks algorithm for local Earth’s magnetic field performance was evaluated through simulation using Dar es Salaam City traffic flow conditions. Simulation results for vehicular detection and count showed 93% and 87% success rates during normal and congested traffic states, respectively. Travel Time Index (TTI) was used as a congestion indicator, where different levels of congestion were evaluated depending on the traffic state with a performance of 87% and 88% success rates during normal and congested traffic flow, respectively.