Service-oriented Architecture Research Articles

Building a Secure IoT Platform for Smart Home Automation: A Comprehensive Integration

Abstract: The creation and use of a novel Internet ofThings (IoT) platform designed for smart home automation arepresented in this research study. The platform combines powerful hardware elements with cutting-edge software technologies. It makes use of Docker ptimization on, an Ubuntu-based Linux server, and essential utilities like Traefik, MySQL, Node-RED, Mosquitto, Adminer, and InfluxDB. The infrastructure is painstakingly built to provide scalable deployment, effective data management, and smooth communication. The core of the technique is the creation of a Linux server running Ubuntu. A flexible reverse proxy called Traefik ptimizat web traffic and makes sure that components communicate with one other effectively. Docker ptimization on is used for the modular deployment of services,such as the central orchestrator Node-RED, the lightweightMQTT broker services provided by Mosquitto, and MySQL for structured data storage. Time-series data processing and database administration are improved by the combination of Adminer and InfluxDB. The visual programming tool Node- RED acts as the central data flow orchestrator, allowing databases and devices to be connected with ease. Real-time datainterchange is ensured via MQTT implementation usingMosquitto, which is essential for Internet of Things applications.Network protocols, Docker best practices, and optional SSL/TLS implementation are just a few of the security features that provide a stable and secure environment. Security audits are planned on a regular basis to find and fix such weaknesses. Hardware for home automation control includes DHT11 sensors, 4-channel relay modules, and ESP32 development boards. Beyond simple automation, the platform includes functionalities for sensor data collecting, remote device administration, and intuitive appliance control via a Node-RED Dashboard. Applications include temperature, humidity, and air quality monitoring, as well as security and surveillance withbuilt-in cameras and motion sensors.

Validasi Data Teknis Tidak sama dan Optical Distribution Poin (ODP) tidak sama dengan UIM Tools di PT TELKOM

Abstract — At present the public's need for internet network access for voice, data and video services continues to increase, to run these services requires very high bandwidth and internet speed. Fiber To The Home (FTTH) is a telecommunications service architecture that utilizes optical network transmission media that supports transmission at bandwidth and internet speeds of up to 100 Mbps. One of FTTH component is Optical Ditributon Point,so we must equate the existing data in the field and the system .Therefore it is necessary to validate the ODP and the technical data are not the same in order to make it easier for field workers to increase internet speed. Because if validation is not carried out, what will happen is connection loss so that the internet cannot be used even though the bandwidth speed has been increased.Keywords— ODP, FTTH, technical data.

Makelar dalam Transaksi Ekonomi (Analisis Fatwa DSN MUI No. 151/DSN-MUI/VI/2022 Tentang Akad Samsarah)

Brokers in economic transactions are common jobs undertaken by society. However, in practice, brokers often pose problems, especially for consumers or buyers. It is not uncommon for buyers to find that the price set during the transaction is not the price set by the owner of the goods, but rather a price added by the broker. The significant price difference from the original price becomes a separate problem for the community.
 This article aims to explain the practice of brokers in Islam by analyzing the Fatwa DSN MUI NO. 151/DSN-MUI/VI/2022 on Samsarah Contracts. The focus of this article is: What is the nature of brokers in Islam? How does the Fatwa DSN MUI No. 151/DSN-MUI/VI/2022 on Samsarah Contracts play a role in ensuring the legality of broker practices in Indonesia?
 In this article, researchers use a research library approach. Primary data sources include literature on brokers in Islam. In Islam, brokerage is considered an allowed practice; a broker aims to provide assistance to both the buyer and the owner of the goods to be sold. Meanwhile, the role of Fatwa DSN MUI NO. 151/DSN-MUI/VI/2022 has outlined the technical implementation of broker services and provided arguments in accordance with Islamic Sharia teachings.

Multi-Agent System in Web Services

The term "service-oriented architecture" (SOA) refers to a software paradigm for creating systems made up of a variety of services that interact with one another to accomplish a certain task. The communications involve more than just transmitting data back and forth; they also connect two or more services to coordinate the required operations. Cross-platform communication is necessary for the coordinating process when services are spread across several platforms. Several web service standards and specifications are used in the present SOA implementation. Based on the prototype's implementation and simulation, this proposed research study has been validated and evaluated. The suggested cross-platform communications architecture is implemented using NetBeans, JADE, WSIG, and OWL-S. An integrated development environment for Java is offered by Oracle in the form of NetBeans. Telecom Italia's Java Agent DEvelopment Framework (JADE) is an agent software framework that is entirely built in the Java language. Web Service Integration Gateway (WSIG), which helps to facilitate JADE agent services being called by Web service clients. The core fundamental engine of the suggested framework is OWL-S. It is a web ontology language used to describe Semantic Web Services in the Semantic Web's OWL-based framework. A quantitative approach is used in this study's performance analysis and comparative investigation for evaluation and validation. The prototype's major element is the Java agent development framework (JADE), which was used to create a multi-agent system for the agent-based MOM framework that has been presented. The creation of the multi-agent systems was facilitated by the JADE platform. JADE 3.7 was the version that was employed. Two key features of JADE are a FIPA-compliant agent platform and a package for creating Java agents. The implementation results show that the proposed agent-based MOM framework was successful communicate between multiple types of SOA application with a better performance of the average of round-trip time where the proposed framework was successful in responding to all the requests.

Challenges Of Population Services Through Population Administration Information Systems In Jakarta Buffering Area

The role of the population in driving economic growth and development is fundamental, functioning as an active consumer and contributor. An important foundation for informed policymaking and fostering smart citizens lies in the quality of human resources and the availability of accurate population data. The study addresses specific challenges within Jakarta's densely populated Buffer Area, particularly the urgent need for an updated population database. To address this problem, we propose a comprehensive Population Information System that integrates data from three provinces and nine districts/cities in the Greater Jakarta area, which includes geographic and demographic information. This integrated approach ensures the availability of real-time data for quick responses to population-related matters. The significance of our study is rooted in the constitutional mandate to regulate matters concerning Citizens and Residents, emphasizing the state's role in protecting human rights and providing legal certainty in Population Administration. We also emphasize important laws, including Law Number 24 of 2013 concerning Population Administration, protecting personal and legal status while facilitating data collection and utilization. Examining the various challenges facing population services in Jakarta's buffer city districts, focusing on local government initiatives and the Population and Civil Registration Office, is essential. Understanding these challenges and proposed solutions is critical to improving population services and promoting informed policymaking in this critical region. In addition, our findings underscore the urgent need for comprehensive reforms in Disdukcapil services, including addressing issues such as bribery and using the services of brokers who persist in administrative practices.

Research on the Construction of an Efficient and Lightweight Online Detection Method for Tiny Surface Defects through Model Compression and Knowledge Distillation

In response to the current issues of poor real-time performance, high computational costs, and excessive memory usage of object detection algorithms based on deep convolutional neural networks in embedded devices, a method for improving deep convolutional neural networks based on model compression and knowledge distillation is proposed. Firstly, data augmentation is employed in the preprocessing stage to increase the diversity of training samples, thereby improving the model’s robustness and generalization capability. The K-means++ clustering algorithm generates candidate bounding boxes, adapting to defects of different sizes and selecting finer features earlier. Secondly, the cross stage partial (CSP) Darknet53 network and spatial pyramid pooling (SPP) module extract features from the input raw images, enhancing the accuracy of defect location detection and recognition in YOLO. Finally, the concept of model compression is integrated, utilizing scaling factors in the batch normalization (BN) layer, and introducing sparse factors to perform sparse training on the network. Channel pruning and layer pruning are applied to the sparse model, and post-processing methods using knowledge distillation are used to effectively reduce the model size and forward inference time while maintaining model accuracy. The improved model size decreases from 244 M to 4.19 M, the detection speed increases from 32.8 f/s to 68 f/s, and mAP reaches 97.41. Experimental results demonstrate that this method is conducive to deploying network models on embedded devices with limited GPU computing and storage resources. It can be applied in distributed service architectures for edge computing, providing new technological references for deploying deep learning models in the industrial sector.

Enabling applications for variable vehicle configurations with Software-defined Vehicle and Service-oriented Architecture

Modern vehicle applications must be able to handle variable vehicle configurations, including the vehicle sales configuration, feature-as-a-service and subscription configurations, as well as user configuration preferences. How can the foundations for modern vehicle applications – service-oriented architecture and software-defined vehicle – be utilized to enable such variable configurations? This paper is introducing the “passenger welcome” case to discuss different implementation options.

Energy Consumption Monitoring and Prediction System for IT Equipment

This paper focuses on the monitoring and prediction of the energy consumption of IT equipment to make informed decisions in terms of energy efficiency. The challenge with current monitoring systems lies in their specialization, scalability and integration complexities. To overcome these challenges, we propose a monitoring and prediction system for energy consumption of IT equipment. The proposed solution combines an adaptable, cost-effiective and energy-Efficient embedded device with open source software and a service-oriented architecture (SOA), which offers flexibility and integration capabilities, facilitating the easy inclusion of several workstation working from different environments. Several traditional Linear Regression (LR) models were evaluated prediction models using a temporal window of hour taking into account several features. As a result of the LR models evaluation, it is established that the Bayesian Ridge model was the best model since it presented the lowest error and the highest coefficient of determination. Finally, two approaches were evaluated to predict energy consumption: a Kernel Density Estimation (KDE)-based mechanism proposed to generate predictor variables in order to predict future energy using the best LR model, and a KDE-based energy model. Numerical results show that the energy prediction using KDE for the energy measurements provides lower time response than the LR based mechanism for the available dataset.

Towards a container-based architecture for CMS data acquisition

The CMS data acquisition (DAQ) is implemented as a service-oriented architecture where DAQ applications, as well as general applications such as monitoring and error reporting, are run as self-contained services. The task of deployment and operation of services is achieved by using several heterogeneous facilities, custom configuration data and scripts in several languages. In this work, we restructure the existing system into a homogeneous, scalable cloud architecture adopting a uniform paradigm, where all applications are orchestrated in a uniform environment with standardized facilities. In this new paradigm DAQ applications are organized as groups of containers and the required software is packaged into container images. Automation of all aspects of coordinating and managing containers is provided by the Kubernetes environment, where a set of physical and virtual machines is unified in a single pool of compute resources. We demonstrate that a container-based cloud architecture provides an acrossthe-board solution that can be applied for DAQ in CMS. We show strengths and advantages of running DAQ applications in a container infrastructure as compared to a traditional application model.

Balkhash-Alakol Basin animal world database creation process for the purpose of cadastre maintenance

This paper describes the database (DB) development process of the cadastre of the fauna of the Balkhash-Alakol Basin that will be used for analyzing the spatial and temporal dynamics of population status and the graphical display of spatial data. The data was organized using the methods of relational database organization (SQL) with sets of individual criteria adapted for different groups of animals (parasitofauna, malacofauna, arachnofauna, entomofauna, ichthyofauna, herpetofauna, ornithofauna, and theriofauna). This database considers several additional technical aspects of its further integration with other databases, for example, geographical, climatic, etc., which allows for a deep analysis of the relationship between various environmental factors and population dynamics. For the functioning of the user interface, server architecture, and program interface, an application on the Django framework was developed, which simplified the processes of software development and allowed specialists to flexibly adapt the database to the own needs and project goals while simultaneously increasing the autonomy, security, and stability of the database.

A C-ITS Architecture for MEC and Cloud Native Back-End Services

A C-ITS Architecture for MEC and Cloud Native Back-End Services

Towards a service-oriented architecture for information systems in the circular economy

Information gaps are one of the significant barriers to a flourishing circular economy (CE). Information systems (IS) are implemented to overcome these gaps as part of digitalization efforts. In the CE context, IS can provide insights into the sustainability and circularity of a product and its value chain. In fact, many products come into existence through supply chains encompassing plentiful suppliers and complex in-use and after-use streams. Implementing CE IS, like obligatory product passports, will significantly scale up information technologies (IT) use. Environmental impacts through IT use, and diverse product lifecycles call for an architecture that allows such a degree of complexity while minimizing burdens. This conference paper summarizes our preliminary results on designing an architecture for CE IS. First, we reviewed the literature on product passports. We created an architecture based on the design science methodology, including a meta-model providing an overview of all concepts. We also show how the architecture can facilitate the CE IS implementation by an example case of remanufacturing. Our results advocate rethinking the concept of product-centered systems towards IS that are more based on CE and digital services.

Decentralized Federated Learning With Intermediate Results in Mobile Edge Computing

The emerging Federated Learning (FL) permits all workers ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , mobile devices) to cooperatively train a model using their local data at the network edge. In order to avoid the possible bottleneck of conventional parameter server architecture, the decentralized federated learning (DFL) is developed on the peer-to-peer (P2P) communication. In DFL, model exchanging among workers is usually regarded as an atomic operation, which largely affects the total bandwidth consumption during model training. Given the limited communication resource on workers, model exchanging will pose a great challenge when meeting with the large-scale models. Herein, we propose to let workers exchange the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">intermediate results</i> , instead of the entire model, with each other. We provide theoretical analysis of DFL based on intermediate result exchanging, which reveals the relationship between the training performance and the exchanging interval ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , the number of local updating iterations) of intermediate results. According to the convergence bound, we propose an adaptive exchanging interval (or frequency) algorithm called Fed-IR, which optimizes the trade-off between communication cost and training performance. Extensive simulation results show that compared with the model exchanging methods, our proposed algorithms can save communication traffic of around 42% <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 81% while still achieving the similar accuracy.

Multicenter Hierarchical Federated Learning With Fault-Tolerance Mechanisms for Resilient Edge Computing Networks.

In the realm of federated learning (FL), the conventional dual-layered architecture, comprising a central parameter server and peripheral devices, often encounters challenges due to its significant reliance on the central server for communication and security. This dependence becomes particularly problematic in scenarios involving potential malfunctions of devices and servers. While existing device-edge-cloud hierarchical FL (HFL) models alleviate some dependence on central servers and reduce communication overheads, they primarily focus on load balancing within edge computing networks and fall short of achieving complete decentralization and edge-centric model aggregation. Addressing these limitations, we introduce the multicenter HFL (MCHFL) framework. This innovative framework replaces the traditional single central server architecture with a distributed network of robust global aggregation centers located at the edge, inherently enhancing fault tolerance crucial for maintaining operational integrity amidst edge network disruptions. Our comprehensive experiments with the MNIST, FashionMNIST, and CIFAR-10 datasets demonstrate the MCHFL's superior performance. Notably, even under high paralysis ratios of up to 50%, the MCHFL maintains high accuracy levels, with maximum accuracy reductions of only 2.60%, 5.12%, and 16.73% on these datasets, respectively. This performance significantly surpasses the notable accuracy declines observed in traditional single-center models under similar conditions. To the best of our knowledge, the MCHFL is the first edge multicenter FL framework with theoretical underpinnings. Our extensive experimental results across various datasets validate the MCHFL's effectiveness, showcasing its higher accuracy, faster convergence speed, and stronger robustness compared to single-center models, thereby establishing it as a pioneering paradigm in edge multicenter FL.

Customs Broker Services Operational Performance: Basis for the Development of Corporate Strategy

This study investigated the operational performance of customs brokers and its implications for developing a corporate strategy. In total, 277 respondents participated, comprising 121 customs brokers and 156 importers from the Philippines, providing demographic information and responding to selfadministered questionnaires. We evaluated operational performance based on quality, speed, dependability, flexibility, and cost using a 4-point Likert scale. The findings revealed that both groups of respondents highly rated customs brokers in terms of quality, dependability, flexibility, and cost. Specific examples illustrating these high ratings were noted. We analyzed differences based on demographic profiles between customs brokers and importers, including gender, speed, and educational attainment. Significant differences were found based on gender and speed. Additionally, variations in educational attainment were identified regarding dependability and flexibility. A significant difference was observed between customs brokers and importers regarding operational performance objectives in terms of speed, with customs brokers rated higher than importers. Addressing this identified difference by implementing targeted measures to enhance speed can contribute to overall operational improvement, meeting the time-sensitive needs of importers and further strengthening the reputation of customs brokers. These findings have practical implications for developing a strategy to enhance the operational performance of customs broker services. By recognizing their strong performance in quality, dependability, flexibility, and cost, customs brokers can continue to prioritize and maintain high standards in these areas, thereby improving overall service delivery.

Multi-layer parallel-perceptual-fusion spatiotemporal graph convolutional network for cross-domain, poor thermal information prediction in cloud-edge control services

Thermal error, which has spatiotemporal behavior, severely reduces machining accuracy of high-accuracy machine tools and should be controlled in real time. The deep learning is used to establish spatiotemporal thermal error model with large-sample thermal information as input. But the acquisition of large-sample thermal information is extremely difficult and costly. So, in the actual application, thermal error is predicted with poor thermal information as input, and then the robustness is weak because the cross-domain and poor spatiotemporal thermal information prediction is still a severe challenge. In this study, the transfer learning model of multi-layer parallel-perceptual-fusion spatiotemporal graph convolutional network is proposed without deepening network depth, instead, its width is expanded. Specifically, the adjacency matrix is constructed to consider the spatial information by defining the distance between each two sensors, and then the graph convolutional network is integrated into long short-term memory and gated recurrent unit to propose graph convolutional long short-term memory and graph convolutional gated recurrent unit with constructed adjacency matrix as input, respectively. The graph convolutional long short-term memory and graph convolutional gated recurrent unit are superimposed to propose the multi-layer parallel-perceptual-fusion spatiotemporal graph convolutional network. To improve the robustness, the adjacency matrix is retrained, and the transfer learning model of multi-layer parallel-perceptual-fusion spatiotemporal graph convolutional network is proposed and applied to forecast thermal error, and its prediction accuracy is 94.926%. Spatiotemporal characteristics of temperature and thermal error are fully captured by transfer learning model of graph convolutional long short-term memory and graph convolutional gated recurrent unit. Finally, the transfer learning model of multi-layer parallel-perceptual-fusion spatiotemporal graph convolutional network is embedded into thermal error control service architecture based on cloud-edge collaboration. With implementation of thermal error control service architecture based on cloud-edge collaboration, machining error of machine tools is reduced by more than 80%.

Adaptive retrofitting for industrial machines: utilizing webassembly and peer-to-peer connectivity on the edge

Leveraging previously untapped data sources offers significant potential for value creation in the manufacturing sector. However, asset-heavy shop floors, extended machine replacement cycles, and equipment diversity necessitate considerable investments for achieving smart manufacturing, which can be particularly challenging for small businesses. Retrofitting presents a viable solution, enabling the integration of low-cost sensors and microcontrollers with older machines to collect and transmit data. In this paper, we introduce a concept and a prototype for retrofitting industrial environments using lightweight web technologies at the edge. Our approach employs WebAssembly as a novel bytecode standard, facilitating a consistent development environment from the cloud to the edge by operating on both browsers and bare-metal hardware. By attaining near-native performance and modularity reminiscent of container-based service architectures, we demonstrate the feasibility of our approach. Our prototype was evaluated with an actual industrial robot within a showcase factory, including measurements of data exchange with a cutting-edge data lake system. We further extended the prototype to incorporate a peer-to-peer network that facilitates message routing and WebAssembly software updates. Our technology establishes a foundational framework for the transition towards Industry 4.0. By integrating considerations of sustainability and human factors, it further extends this groundwork to facilitate progression into Industry 5.0.

Adaptive exchange protocol for multi-agent communication in augmented reality system

Augmented Reality (AR) is one of the modern ways of providing users with different types of information. In applications based on this technology, the main influence on the subjective assessment of product quality is the speed of interaction between the system and the user and between users of the system. The main purpose of the work series are creation and modification for algorithms to assessing and improving the quality of AR services. This paper describes and justifies an adaptive communication protocol for multi-agent interaction. We consider a general nonlinear dynamic system with introducing feedback control which is based on measurements under almost arbitrary noise. In practice, this control is realized as a superposition of neural networks with the estimation of the result of mutual additional learning based on the system identification method (M.K.Campi and E.Weyer’s LSCR is used). The prototype is built for a system with a distributed server architecture and multiagent behavior of both clients and servers in the system.

Исследование трансформации рынка частных инвестиций в России на современном этапе

The need to ensure the stable development of the Russian economy and financial market in the current geopolitical conditions presupposes the use of domestic financial resources as the main source of investment. Of particular scientific and practical importance is the identification and research of key trends and growth drivers in the development of the Russian private investment market, the main participants of which are resident individuals. The analysis of the features and changes in the functioning of the domestic stock market at the present stage allowed us to substantiate the conclusion about the strengthening of the role of the retail investor, including due to the universal digitalization of society and increasing the availability of basic exchange instruments for the mass consumer, whose investment behavior is now becoming decisive in the transformation of the Russian stock system as the largest element of the financial market in the country. A study of the quantitative and qualitative characteristics of the activities of resident individuals on the stock exchange in the segment of trust management and brokerage services, which allowed to assess the volume of their investment, investment activity, share of participation in auctions, including in certain market segments, industry rotation of investments, investment horizon, as well as to identify the significant changes in the retail investor's investment preferences, which affected the structure and quality of his portfolio. The factors and areas of activity that contribute to the solution of the task identified at the state level as a priority to stimulate the process of transforming the savings of the population into long-term investments are identified.

ADAPTABILITY IN INDUSTRY 4.0: SERVICE-ORIENTED ARCHITECTURE TO DEPLOY ARTIFICIAL INTELLIGENCE ON INDUSTRIAL AUTOMATION

Industry 4.0 represents a revolution in the business environment, driving the integration of information technologies and industrial automation, with the main objective of reducing latency in decision-making. Artificial Intelligence (AI) plays an essential role in advanced data analysis and resource optimization, allowing accurate predictions and agile decisions, however its implementation presents challenges, such as algorithm complexity and integration with industrial automation systems. An innovative solution to overcome these challenges is the implementation of a service-oriented architecture, which creates modular and interoperable systems, a concept especially relevant in the practical application of industrial automation, in which the integration between Information Technology (I.T) and Automation Technology (A.T) is crucial. This work presents, through experimental research, an innovative solution based on the development of a computer vision application, isolated in a Docker container. This application is designed to inspect the assembly of parts by a robotic system and establish communication with a Programmable Logic Controller (PLC) to approve or disapprove the assembly. The results of the adopted architecture demonstrate a flexible approach that simplifies the operation of AI systems, allowing operation with both AI enabled and disabled, reducing potential disruptions to the workflow. This research promises to open paths for future innovations and advances in the field of industrial automation and Artificial Intelligence, offering a model that effectively combines the agility of AI with the robustness of automation.