Data Processing Communication Research Articles

Environmental Protection Control System Based on IoT and Deep Learning Intelligent Monitoring Sensors

In order to improve the multi-sensor intelligent control performance of environmental pollution detection, this paper proposes a design model of environmental pollution detection intelligent system based on Internet of Things technology. Firstly, this paper uses ZigBee’s Internet of Things networking technology to design the network of the pollution detection system. Secondly, in order to verify the effectiveness of the environmental pollution detection method, this paper carries out system debugging analysis and simulation test, and uses visual dsp++simulation system to debug the system. The network of ZigBee Internet of Things networking technology is adopted to realize the network design of pollution detection system, and VIX bus technology is adopted to carry out environmental pollution detection data communication and information processing, so as to realize the optimal design of intelligent system of environmental pollution detection system. Finally, the experimental test and analysis were performed. The collection period of environmental pollution data is 10 s and the monitoring time is 22 h. Finally, according to the environmental model and parameters, we determine that the system can accurately detect environmental pollution information, and the results are accurate and reliable.

SIMULATION INTELLECTUAL SIGN #Aniespresiden2024 IN SOCIAL MEDIA BIGDATA ANALYSIS

Talking about 2024 is of course talking about the political year in Indonesia. 2024 is the year when legislative candidates and of course presidential and vice presidential candidates will begin to fight. Currently, one of the presidential candidates from the party that is the current government party is Anies Baswedan. Based on what has been regulated in political ethics and election ethics, it is certainly not allowed to campaign by presidential candidates before the campaign period is carried out, but what happened was the emergence of social media content from Anies Baswedan containing campaigns with disguised signs and also camouflaged content so that what the public eye sees presented in cyberspace is only the content of his daily activities. This study aims to determine how Anies Baswedan’s political branding simulation in preparation for the 2024 Presidential Election. This research uses simulation analysis on Jean Baudrillarde’s theory based on big data analysis on Tiktok social media through content related to Anies’ candidacy as a 2024 Presidential candidate. The focus of the research is the arrangement and meaning of #aniesPresiden2024 signs and messages on mainstream social media received by the digital community and then forming a simulation of Anies Baswedan’s political branding which is the result of the digital communication process towards Anies’ political representation as a 2024 presidential candidate. The results of this study found that tiktok social media has a huge influence on the political branding of anies baswedan, digital society widely receives and consumes information related to the intellectual sign of anies baswedan.

Simulation Political Branding #Ganjarpresiden2024 Base on Media Sosial Analysis

Talking about 2024 is certainly talking about the political year in Indonesia. Year of 2024 is the year when legislative candidates and of course presidential and vice-presidential candidates starts contesting. Currently, one of the presidential candidates from the party that won the 2014 and 2019 elections is Ganjar Pranowo. As one of the presidential candidates in 2024, Ganjar Pranowo really needs to present himself as a person who deserves to be the president of Indonesia in 2024, so it is important for Ganjar Pranowo to utilize his social media and then through the concept of hyperreality give an effect to a wide audience through social media. This research was conducted to find out how Ganjar Pranowo's political branding simulation in preparation for the 2024 Presidential Election is based on big data analysis on Tiktok social media through content related to Ganjar's candidacy as a Presidential candidate in 2024. The focus of the research is the arrangement and meaning of #GanjarPresiden2024 signs and messages on mainstream social media received by digital society and then forming a simulation of Ganjar Pranowo's political branding which is the result of the digital communication process towards Ganjar's political representation as a 2024 presidential candidate. The research method uses Jean Baudrillard's theory with a simulation approach.

An efficient reconfigurable code rate cooperative low-density parity check codes for gigabits wide code encoder/decoder operations

In recent days, extensive digital communication process has been performed. Due to this phenomenon, a proper maintenance of authentication, communication without any overhead such as signal attenuation code rate fluctuations during digital communication process can be minimized and optimized by adopting parallel encoder and decoder operations. To overcome the above-mentioned drawbacks by using proposed reconfigurable code rate cooperative (RCRC) and low-density parity check (LDPC) method. The proposed RCRC-LDPC is capable to operate over gigabits/sec data and it effectively performs linear encoding, dual diagonal form, widens the range of code rate and optimal degree distribution of LDPC mother code. The proposed method optimize the transmission rate and it is capable to operate on 0.98 code rate. It is the highest upper bounded code rate as compared to the existing methods. The proposed method optimizes the transmission rate and is capable to operate on a 0.98 code rate. It is the highest upper bounded code rate as compared to the existing methods. the proposed method's implementation has been carried out using MATLAB and as per the simulation result, the proposed method is capable of reaching a throughput efficiency greater than 8.2 (1.9) gigabits per second with a clock frequency of 160 MHz.

Sb2S3-based optical switch exploiting the Brewster angle phenomenon [Invited

Optical switches based on phase change materials (PCMs) hold great promise for various photonic applications such as telecommunications, data communication, optical interconnects, and signal processing. Their non-volatile nature as well as rapid switching speeds make them highly desirable for developing advanced and energy-efficient optical communication technologies. Ongoing research efforts in exploring new PCMs, optimizing device designs, and overcoming existing challenges are driving the development of innovative and high-performance optical switches for the next generation of photonics applications. In this study, we design and experimentally demonstrate a novel optical amplitude switch design incorporating PCM antimony trisulfide (Sb2S3) based on the Brewster angle phenomenon.

Vehicular network energy storage system with renewable analysis using deep learning architectures

The prevalence of renewable energy sources (RES) like solar photovoltaic (PV) systems that use power electronics as interfaces, grid frequency instability has been an issue in recent years. This study proposes a novel method to analysis of communication data in a Vehicular Ad Hoc Network (VANET)-based energy storage system based on renewable energy sources. Here, photovoltaic cells and other renewable energy sources are used for VANET energy storage. Spatial regressive adversarial neural networks are used in the VANET data communication process. In a non-Markovian environment, this method is known to exhibit high performance as well as high rate of convergence. In order to reduce battery replacement costs and optimize total operation costs, the output is simulated to record observed readings. the proposed technique attained energy efficiency of 91%, power consumption of 45%, QoS(quality of service) of 88%, accuracy of 98%, sensitivity of 66%, computational cost of 48%.

Adaptive client and communication optimizations in Federated Learning

Federated Learning (FL) is a very effective distributed machine learning framework that enables a large number of devices to jointly train models without sharing raw data. However, the process of iterative learning and data communication in FL can be time-consuming, depending heavily on the number of clients participating in training and the number of local iterations between two consecutive global aggregations (communication period). In this paper, we analyze the runtime of the FL framework and propose a joint optimization problem, which considers both the number of local clients involved in global training and the communication period to minimize the error-runtime convergence of FedAvg. By theoretical analysis, we obtain the optimal solutions of this optimization problem in closed forms under the assumption of different probability distributions of the client’s local computation time. In addition, we design an adaptive control algorithm that can dynamically select the number of clients and communication period during FL training to speed up the convergence of the model. Experimental results validate our theoretical analysis and show that the proposed algorithm has outstanding performance compared with other related FL control algorithms.

Securing web documents by using piggybacked framework based on Newton's forward interpolation method

The modern telecommunication system that transfers data over the web requires a secure channel so that integrity, authentication, and confidentiality of data are ensured. To achieve this, the SSL/TLS protocol of the transport layer provides end-to-end security to transfer data without any intrusion. But this security fails due to vulnerabilities reported in the last few years on SSL/TLS not ignorable which makes the data communication process vulnerable. The handshake protocol used by SSL/TLS decides a common session key to share the message among the communicating nodes. The common key remains the same in a session to encrypt entire data payload. If a hacker breaks into the session and accesses the key, then the entire session would be compromised. Therefore, there is a need for a mechanism that ensures the proper key exchanges so that the messages transferred during a session uses different keys for encryption and decryption process. This is achieved by implementing the key exchange mechanism using Newton's interpolation method which is used to generate a polynomial function that derives the key for the ongoing communications. The paper represents the result of the research conducted and also recommends a few changes to the existing key exchange mechanism.

A multi-agent framework for collaborative geometric modeling in virtual environments

The use of collaborative applications in which several individuals interact to solve a problem is an important strategy both in educational settings as well as professional environments. Virtual Reality (VR) technology, in particular, has acquired a predominant role in many industries. However, as the level of immersion and realism of the VR experience increases, so does the demand for computational resources, as rendering processes become increasingly intensive and can negatively affect other communication processes that are critical in collaborative environments. In this paper, we present a software framework based on multi-agent systems that enables the separation of rendering processes from internal data management tasks and communication processes between users, which are prevalent in collaborative virtual reality applications. The results of our validation studies show that, compared to other techniques based on protocol or network enhancements, the proposed architecture can significantly improve collaborative processes in general, and virtual reality-based applications in particular.

Research on smart city data encryption and communication efficiency improvement under federated learning framework

To improve the data communication processing capability of smart city informatization construction and defend against malicious joint attacks of internal communication participants, we studied the use of function encryption, blockchain, differential privacy and other technologies to defend against weight disclosure, participation in collusion attacks, single point failures and other issues in the federal learning process, and introduced edge computing and asynchronous communication in the classic federal learning framework, Improve the communication efficiency of the model while ensuring the accuracy. The research results show that the accuracy of FE-BDP algorithm can maintain above 95% for a long time, and the change is not significant with the increase of the number of users, indicating that the algorithm has strong stability. The loss value of FE-LDP model is significantly smaller than that of other models and can be stabilized below 0.05. The data aggregation time of blockchain encryption technology is less than 1.0s. The edge-asynchronous communication framework can be applied to multiple urban scenarios and achieve effective data communication, with the highest accuracy rate of 93.87%and the lowest communication cost of 1315.29s. The research results show that the security encryption fusion technology can effectively protect user data privacy, and the edge-asynchronous communication framework has obvious effect on improving communication efficiency, which has important application value for promoting the construction of smart city informatization.

Emergency Vehicle Driving Assistance System Using Recurrent Neural Network with Navigational Data Processing Method

Emergency vehicle transportation is important for responding to and transporting individuals during emergencies. This type of transportation faces several issues, such as road safety, navigation and communication, time-critical operations, resource utilisation, traffic congestion, data processing and analysis, and individual safety. Vehicle navigation and coordination is a critical aspect of emergency response that involves guiding emergency vehicles, such as ambulances, to the location of an emergency or medical centre as quickly and safely as possible. Therefore, it requires additional effort to reduce driving risks. The roadside units support emergency vehicles and infrastructure to decrease collisions and enhance optimal navigation routes. However, during the emergency vehicle’s data communication and navigation process, communication is interrupted due to vehicle outages. Therefore, this study proposes the Navigation Data Processing for Assisted Driving (NDP-AD) method to address the problem. The proposed approach assimilates infrastructure and neighbouring location information during driving. The integrated information is processed for distance and traffic during the previous displacement interval. The NDP-AD method employs a recurrent neural network learning approach to analyse opposing vehicle distance and traffic to provide accurate, independent guidance. This effective learning-based guidance process minimises false navigations and deviation in displacement. System efficiency is evaluated based on processing latency, displacement error, data utilisation, false rate, and accuracy metrics.

A Substation Live Detection Method on Communication Protocol Data Parsing and Data-level Fusion of Neural Networks

In this paper, a data parsing method based on database communication protocol technology and a data fusion method based on the neural network are proposed, which solves the shortcomings in the data communication process of the substation, and provides an intelligent method for data analysis and fusion. The data parsing, transforming method, process, and implementation scheme based on the communication protocol is described in this paper. Firstly, the digital expression method of the data message is proposed. Secondly, the message recognition method based on the feature field is introduced. Through the analysis and heuristic of the feature fields, the protocols of the messages are determined. then the data interface relationships in the system are stored in a separate database. When the parsing process is completed, we use data pre-processing, data cleaning, data integration, and other methods to fuse the data of each source. Finally, the unstructured data fusion method based on the VGG-19 model is used to fuse the data. At the same time, this paper also describes the scheme of some principles step by step and introduces the relevant technical details.

Value-based Smart Retrofitting of Maintenance in the Hydropower Plants of Celsia

Smart Retrofitting in maintenance indicates the development of maintenance services through the retrofitting of legacy devices with the functionalities of data collection, data communication and data processing. Many companies nowadays desire to retrofit their assets through the implementation of Prognostics and Health Management (PHM) services to assist in better predicting their future state and making timely and sound decisions. In this context, this work presents the PHM center developed by the company Celsia in the Alto Anchicayá hydropower plant and discusses the available approaches for estimating the value brought by PHM investments. Even if different works can be found in the literature with this purpose, only the economic feasibility of PHM implementations is generally considered. Whereas, in value-based decision-making different dimensions are embraced and not only the performance and cost ones. Therefore, the need of a value model to support decision-making concerning PHM implementations is presented and the potential of System Dynamics for this purpose is discussed.

Short Text Clustering Algorithms, Application and Challenges: A Survey

The number of online documents has rapidly grown, and with the expansion of the Web, document analysis, or text analysis, has become an essential task for preparing, storing, visualizing and mining documents. The texts generated daily on social media platforms such as Twitter, Instagram and Facebook are vast and unstructured. Most of these generated texts come in the form of short text and need special analysis because short text suffers from lack of information and sparsity. Thus, this topic has attracted growing attention from researchers in the data storing and processing community for knowledge discovery. Short text clustering (STC) has become a critical task for automatically grouping various unlabelled texts into meaningful clusters. STC is a necessary step in many applications, including Twitter personalization, sentiment analysis, spam filtering, customer reviews and many other social network-related applications. In the last few years, the natural-language-processing research community has concentrated on STC and attempted to overcome the problems of sparseness, dimensionality, and lack of information. We comprehensively review various STC approaches proposed in the literature. Providing insights into the technological component should assist researchers in identifying the possibilities and challenges facing STC. To gain such insights, we review various literature, journals, and academic papers focusing on STC techniques. The contents of this study are prepared by reviewing, analysing and summarizing diverse types of journals and scholarly articles with a focus on the STC techniques from five authoritative databases: IEEE Xplore, Web of Science, Science Direct, Scopus and Google Scholar. This study focuses on STC techniques: text clustering, challenges to short texts, pre-processing, document representation, dimensionality reduction, similarity measurement of short text and evaluation.

Formation of the enterprise's digital communications system: aspects of economic business security

The study revealed the relevance of the issue of forming the digital communications system of the enterprise in modern conditions of information globalization and digital transformation of socio-economic relations; the relationship between the formation and improvement of the digital communications system and the strengthening of the economic security of business is shown. The purpose of the research is to determine the ways and justify the means of building the digital communications system of the enterprise, taking into account the requirements for ensuring the economic security of business. It has been established that the enterprise's digital communications system consists of such elements as search systems (in the form of a software and hardware complex for conducting data searches on the Internet, their accumulation and processing, placement in their own databases and sources of access to them, search queries), communication tools (with the use of which "communication" is carried out), digital technologies (methods of hosting and further dissemination of information) and digital communications in the global digital space, including semantic webs. The composition and structure of the elements of the enterprise's digital communications system have been determined. The parameters of the digital communication process management system of enterprises were built, and the goals and objectives of such work were defined. The conceptual sequence of the formation and use of the enterprise's digital communications system has been developed and substantiated. The directions of formation and immediate advantages, as well as risks and threats of the impact of the formation (improvement) of the digital communications system on the economic security of the enterprise are identified. The applied value of the research results is in the development of methodological and applied support for the management of the processes of formation (improvement) of the digital communications system at the business entity, as well as in the identification of risks and threats of digitalization to the economic security of the enterprise. The scientific novelty of the research results lies in the identification of both advantages (strengths), as well as risks and threats to the economic security of the enterprise by resource-functional components of economic security. Keywords: digital communications; digital system; economic security of business; economy and enterprise management; digital transformation; digitalization of business relations.

Challenges in Interpreting Epidemiological Surveillance Data – Experiences from Germany

As early as March 2020, the authors of this letter started to work on surveillance data to obtain a clearer picture of the pandemic’s dynamic. This letter outlines the lessons learned during this peculiar time, emphasizing the benefits that better data collection, management, and communication processes would bring to the table. We further want to promote nuanced data analyses as a vital element of general political discussion as opposed to drawing conclusions from raw data, which are often flawed in epidemiological surveillance data, and therefore underline the overall need for statistics to play a more central role in public discourse.

Autonomous Vehicles: The Cybersecurity Vulnerabilities and Countermeasures for Big Data Communication

The possible applications of communication based on big data have steadily increased in several industries, such as the autonomous vehicle industry, with a corresponding increase in security challenges, including cybersecurity vulnerabilities (CVs). The cybersecurity-related symmetry of big data communication systems used in autonomous vehicles may raise more vulnerabilities in the data communication process between these vehicles and IoT devices. The data involved in the CVs may be encrypted using an asymmetric and symmetric algorithm. Autonomous vehicles with proactive cybersecurity solutions, power-based cyberattacks, and dynamic countermeasures are the modern issues/developments with emerging technology and evolving attacks. Research on big data has been primarily focused on mitigating CVs and minimizing big data breaches using appropriate countermeasures known as security solutions. In the future, CVs in data communication between autonomous vehicles (DCAV), the weaknesses of autonomous vehicular networks (AVN), and cyber threats to network functions form the primary security issues in big data communication, AVN, and DCAV. Therefore, efficient countermeasure models and security algorithms are required to minimize CVs and data breaches. As a technique, policies and rules of CVs with proxy and demilitarized zone (DMZ) servers were combined to enhance the efficiency of the countermeasure. In this study, we propose an information security approach that depends on the increasing energy levels of attacks and CVs by identifying the energy levels of each attack. To show the results of the performance of our proposed countermeasure, CV and energy consumption are compared with different attacks. Thus, the countermeasures can secure big data communication and DCAV using security algorithms related to cybersecurity and effectively prevent CVs and big data breaches during data communication.

An optimization-based machine learning technique for smart home security using 5G

Generally, cellular networks are divided into discrete geographic zones where a secure routing protocol is important. In this study, Sailfish-based Distributed IP Mobility Management (SbDMM) architecture for security protocol in a smart home using 5G is suggested. Smart homes first gathered data via IoT devices which are then communicated with the use of a Home Gateway (HGW). Mobile Nodes (MN) and Corresponding Nodes (CN) process data communication (CN). In addition, the acquired data are encrypted and secured using the session key. Additionally, use an authenticated key and a cipher key to secure the routing optimization. As a result, the fitness of sailfish is updated in a protocol path that is optimized for securing data from attackers. The designed framework is then implemented in Python and the obtained results are compared to those of other methodologies in terms of execution time, confidentiality rate, efficiency, delay, and task completion.

ON SOCIAL NETWORKS, DIGITAL MEDIA, AND MENTAL HEALTH.

The aim: The paper attempts to investigate the impact of social networks and digital media on mental health and psychological well-being. Materials and methods: The data collection was carried out using PubMed, Scopus, Web of Science, Google Scholar databases. Research papers were identified according to the search terms "cyberspace", "cyberpsychology", "digital media", "virtual reality", "social networks", "mental health", "psychological well-being", "beauty", "beauty standards". The authors used integrative anthropological approach, interpretive research paradigm, and multidisciplinary analysis. Conclusion: The digital world is a relatively recent phenomenon, therefore, the potential relationship between its use and mental health and psychological well-being has not yet been adequately explored. Most of the research has been conducted over the last decades; and until now the scientific community has not been able to fully interpret the obtained results and come to final conclusions. As Timpano and Beard rightly pointed out, the future research should be focused on types and motivations for the use of social networks; identify potential mechanisms linking the social media use and mental health symptoms; reveal an impact of social networks on declines in face-to-face social interactions; understand the link of social networks with maladaptive coping strategies and health behaviors. The relationship between social networks, digital media use and psychological functioning is not straightforward and depends on many factors. However, we should not forget that there is nothing new under the sun. The same people act in both digital and physical spaces. And the psychological problems that are revealed in the digital universe are the same that "accompany" the actors of the digital communication process in the real, physical world. Anyway, we cannot avoid cyberspace, so we have to learn how to interact within this challenging world.

Digital Signature Method to Overcome Sniffing Attacks on LoRaWAN Network

LoRa or Long Range with LoRaWAN technology is a protocol for low-power wireless networks. The absence of an encryption process on the data payload becomes a challenge for the LoRaWAN network. When the process of sending messages is running inter devices, sniffing might occur, thereby reducing the confidentiality aspect of the data communication process. This paper optimized the digital signature method to secure messages sent by LoRaWAN network devices, along with Advanced Encryption Standard (AES) algorithm and Ed25519 algorithm. AES was used for message encryption, while Ed25519 was used for signature purposes. The aim of applying digital signatures in this paper was to verify that the payload data sent was original and not changed during the transmission process and to ensure data confidentiality. The addition of security mechanisms to the LoRaWAN network, such as the process of encryption, decryption, and verification results, has caused some overheads. The overhead caused by the usage of a digital signature is also analyzed to ensure that the digital signature is feasible to be implemented in LoRa devices. Based on the experimental results, it was found that there was an increase in the size of memory usage and some additional processing delay during the deployment of digital signatures for LoRa devices. The overall overhead caused by implementing digital signatures on the LoRa devices was relatively low, making it possible to implement it on the LoRa network widely.