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In this work, we implemented a short-reach real-time optical communication system using MLP for pre-distortion. Lookup table (LUT) algorithms are commonly employed for pre-distortion in intensity modulation and direct detection (IM/DD) systems. However, storage limitations typically restrict the LUT pattern length to 9, limiting its effectiveness in compensating for nonlinear effects. A multilayer perceptron (MLP) can overcome this limitation by predicting errors and generating pre-distorted signals, thus replacing the extensive storage requirements of LUTs with minimal computational resources. The MLP-based digital pre-distortion (MLP-DPD) technique enables the creation of long-pattern LUTs for improved nonlinear compensation. In this work, an MLP-DPD scheme was implemented on a field-programmable gate array (FPGA). The FPGA was used to generate a 14.7456 GBaud pre-distorted pulse amplitude modulation 4-level (PAM4) signal. This signal was then transmitted over 20 km of standard single-mode fiber (SSMF). At the receiver, the parallel constant modulus algorithm (PCMA) was applied for signal processing. The bit error rate (BER) achieved met the 2.4 × 10−2 threshold for soft-decision forward error correction (SD-FEC), enabling a net transmission bit rate of 24.576 Gbit/s. This approach demonstrates the feasibility of using MLP-DPD for effective nonlinear compensation in high-speed optical communication systems with limited resources. [ABSTRACT FROM AUTHOR]

The Chat Engine Project is an advanced software solution designed to enable real-time communication through an intuitive and dynamic chat interface. This platform caters to diverse communication needs by supporting both one-on-one and group chat functionalities. The Group Chat feature is a cornerstone of the project, fostering team collaboration by enabling multi-user conversations in an organized and intuitive environment. With features such as group creation, role assignment, and permission management, it provides a structured space for productive interactions. Additionally, the chat interface supports the exchange of media files, documents, and links, enhancing the overall collaboration experience. On the frontend, the application leverages React.js and a UI Kit to provide a responsive, user-friendly, and visually appealing interface. React.js ensures a smooth and dynamic user experience by utilizing efficient state management and a component-based architecture that promotes reusability and modularity. This allows developers to quickly implement and expand features while maintaining performance and responsiveness. The UI Kit complements React.js by offering a pre-built collection of modern and consistent UI components, significantly speeding up development while adhering to contemporary design principles. It ensures accessibility and seamless adaptability across various devices, including desktops, tablets, and mobile platforms. The backend of the Chat Engine is designed for scalability, performance, and security. It utilizes technologies such as WebSocket for real-time messaging, robust data storage mechanisms for persistent message history, and encryption services to secure communications. A load balancer ensures even distribution of traffic, enhancing reliability and fault tolerance, while modular architecture makes the system flexible for future expansions. The combination of realtime synchronization and a consistent user interface across devices ensures that users can stay engaged and updated regardless of their location. This platform is ideal for a wide range of use cases, including team collaboration, customer support, and community engagement. The Group Chat functionality is particularly suited for organizational use, enabling teams to coordinate effectively in a digital workspace. At the same time, the One-on-One Chat feature facilitates personalized interactions, such as customer service conversations. By combining real-time communication, robust backend infrastructure, and an elegant frontend interface, the Chat Engine delivers a comprehensive, scalable, and secure solution for modern communication needs. Its versatility and extensibility make it a reliable foundation for personal, professional, and organizational use cases. [ABSTRACT FROM AUTHOR]

After natural disasters such as earthquakes, floods, or wars occur, cellular communication networks often sustain significant damage or become impaired. In these critical situations, first responders must coordinate with other rescue teams to communicate essential information to central command and survivors. To address this challenge, we have developed a reliable and rapidly deployable wireless ad hoc system for post-disaster management using Data Distribution Service (DDS) middleware, specifically RTI-DDS, named R-RDSP. The R-RDSP further enhances these metrics, achieving a 14.5% improvement in end-to-end delay and a 20.24% improvement in round-trip delay over the RDSP scheme. The R-RDSP system consists of three main modules: client, relay, and server. Each module connects to others via an ad hoc network, ensuring direct device-to-device communication without relying on existing infrastructure. The client module collects and sends the victim's location and emergency messages. The relay modules forward these messages across the ad hoc networks, ensuring minimal delay and high reliability. Finally, the server module receives the messages, processes them, and coordinates the response. Leveraging RTI-DDS for reliable message distribution, the system demonstrates robust performance even under challenging network conditions. [ABSTRACT FROM AUTHOR]

In response to the shortage of skilled workers and the increasing diversity of products and variants, integrating adaptive and flexible mobile manipulators into line-less mobile assembly systems (LMAS) stands out as a promising solution. To fully benefit from these robotic systems in LMAS, offloading computationally intensive tasks from mobile manipulators to edge computing systems is essential. This offloading of computational tasks not only lightens the processing load on individual robots but also enhances AI based perception, improving the overall system capabilities in dynamic manufacturing environments. Achieving these goals requires a robotic edge computing framework that serves as the key to flexible and adaptive mobile manipulation in LMAS. To construct such an edge computing framework, a comprehensive set of building blocks is required. These building blocks include a powerful edge system with high processing power, sensor-equipped mobile manipulators with the ability to perceive the environment, and a robust edge-to-robot connectivity. A comprehensive software stack, including software modules for localization and navigation in dynamic assembly environments, AI perception for precise pose estimation of assembly components, and motion planning for interaction between manipulators and assembly components, is crucial. Furthermore, virtualization techniques, a comprehensive deployment strategy, and a detailed description of robot hardware, site, and resources are essential. The proposed edge computing framework presents a solution that addresses mobile manipulators in LMAS and paves the way towards advanced industrial automation. Through implementation in a research assembly environment, the realized proof-of-concept showcased the feasibility of the proposed edge computing framework in a real-world pick-and-place scenario, highlighting its potential to enhance adaptivity and flexibility. There is a potential to refine and expand the framework to accommodate a broader range of industrial applications and streamline diverse manufacturing processes through the integration of mobile manipulators and edge computing within industrial settings. [ABSTRACT FROM AUTHOR]

In real-time audio and video transmission, QoS (Quality of Service) metrics reflect the perceived network conditions at the server side, while QoE (Quality of Experience) metrics directly embody the satisfaction level of users with video services. Although QoE metrics are of greater concern to service providers, cloud service providers often cannot obtain QoE data in real-time due to issues such as interface adaptation and user privacy protection, making it difficult to predict and optimize potential QoE anomalies in a timely manner. Given the existing mapping relationship between QoS and QoE, this paper proposed a model that utilizes server-side QoS metrics to detect bottlenecks in QoE metrics, aiming to reduce the workload of operation and maintenance personnel and improve network optimization efficiency. The model employs an imbalanced decision tree for QoS-QoE prediction to achieve QoE anomaly detection. Furthermore, an LSTM regression model is utilized for causal analysis to locate bottlenecks. Experiments show that this model achieves high accuracy in QoE anomaly detection and can identify QoS metrics that significantly impact transmission outcomes. [ABSTRACT FROM AUTHOR]

As information technology progresses, the need for digital transformation within the industrial sector has become increasingly apparent, and digital twin technology has emerged as a significant trend in manufacturing. Digital twins synchronize physical and digital environments, overcoming spatial and temporal limitations to create various added values that are unattainable in reality. This paper presents a model that integrates digital twin technology with production and operational technologies at manufacturing sites, enabling remote, centrally controlled manufacturing services that transcend physical constraints. Specifically, by utilizing Message Queuing Telemetry Transport (MQTT) for real-time synchronization, this approach ensures efficient and timely data transfer between physical and digital environments. While traditional approaches often encounter challenges due to high investment costs and design complexities, this paper proposes a cost-effective and practical solution that reflects actual factory conditions. [ABSTRACT FROM AUTHOR]

In a variety of applications, from industrial control systems to online gaming and video streaming, real-time communication has become increasingly important. However, sending real-time data over networks presented challenges due to bandwidth variability and latency. A study was carried out at the Department of Information Technology of the Pontificia Universidad Católica del Ecuador with the objective of improving the communications of its backend applications. Currently, these applications use different types of communication protocols, but they had not been able to solve the problem of real-time communication. In the study, different communication protocols were analyzed through an online chat to determine which would be the most suitable for a scalable backend architecture. The communication protocols evaluated were: Long polling, WebSockets and Server-Sent Events. The results showed that WebSockets was the most suitable communication protocol for a scalable backend architecture. These results have important implications for web application development. By using WebSockets, it is expected that you can improve the quality of your web applications and provide users with a more seamless and interactive experience. The study showed that WebSockets was the most suitable communication protocol for a scalable backend architecture. This is because WebSockets provides a bidirectional connection between the client and the server, allowing data to be sent in real time without having to reload the page. [ABSTRACT FROM AUTHOR]

In spoken communicative contexts, speakers often convey uncertainty via intonation and through paralinguistic behaviours such as speech rate and gesture—and addressees can use these behaviours to generate inferences about the speaker's epistemic state. In text-based contexts, however, cues of this sort are more restricted. In this study, we examine the expression and reception of epistemic information in the context of real-time written message production. We hypothesised that real-time typing dynamics (like those available in text-based collaborative contexts, such as Google Docs) can function as a rich paralinguistic cue about a partner's epistemic state. In Experiment 1, we collected production data showing that manipulations of typist certainty, instantiated through both the ease of message formulation and repeated experience with the task, are reflected in measures of typing fluency and speed. Then, in Experiment 2, we presented select screen recordings of the typing behaviours from Experiment 1 to a group of independent observers who made epistemic judgements about the typist. Our results show that observable differences in typing speed and fluency contribute to perceptions of typist knowledge and confidence, which has implications for interfaces that enable real-time text-based collaboration. [ABSTRACT FROM AUTHOR]

To enhance communication and collaborative work efficiency in cyber–physical systems (CPSs) within the Industry 4.0 environment, this study investigates a graph-based machine learning approach aimed at optimizing information interaction during multi-party conversations. Devices within CPSs must efficiently exchange information in real time to synchronize operations and responses. This research treats these interactions as intricate graph structures and uses graph learning techniques to accurately identify communication links and dependencies among devices. This improvement leads to more accurate decision-making and smoother operations. Our methodology involves a real-time analysis of structural patterns and node attributes within conversations, improving information flow and comprehension. The empirical findings demonstrate that this approach significantly enhances production efficiency, system adaptability, and minimizes delays attributed to communication misunderstandings. Our method can effectively identify the communication relationships between devices, significantly improving the efficiency and accuracy of information transmission. This improved communication capability leads to an enhanced production efficiency of the entire system. [ABSTRACT FROM AUTHOR]

The 5G core network adopts a Control and User Plane Separation (CUPS) architecture to meet the challenges of low-latency business requirements. In this architecture, a balance between management costs and User Experience (UE) is achieved by moving User Plane Function (UPF) to the edge of the network. However, cross-UPF handover during communication between the UE and the remote server will cause TCP/IP session interruption and affect continuity of delay-sensitive real-time communication continuity. Information-Centric Networks (ICNs) separate identity and location, and their ability to route based on identity can effectively handle mobility. Therefore, based on the 5G-ICN architecture, we propose a seamless mobility support method based on router buffered data (BDMM), making full use of the ICN's identity-based routing capabilities to solve the problem of UE cross-UPF handover affecting business continuity. BDMM also uses the ICN router data buffering capabilities to reduce packet loss during handovers. We design a dynamic buffer resource allocation strategy (DBRAS) that can adjust the buffer resource allocation results in time according to network traffic changes and business types to solve the problem of unreasonable buffer resource allocation. Finally, experimental results show that our method outperforms other methods in terms of average packet delay, weighted average packet loss rate, and network overhead. In addition, our method also has good performance in average handover delay. [ABSTRACT FROM AUTHOR]

Intelligent Transportation Systems are rapidly expanding to meet the growing demand for safer, more efficient, and sustainable transportation solutions. These systems encompass various applications, from traffic management and control to autonomous vehicles, aiming to enhance mobility experiences while addressing urbanization challenges. This paper examines key components of Intelligent Transportation Systems, including Vehicular Ad-hoc Networks, Intelligent Traffic Lights, Virtual Traffic Lights, and Mobility Prediction, emphasizing their role in improving transportation efficiency, safety, and sustainability. It explores recent advancements in communication systems that enable real-time Intelligent Transportation Systems operations, contributing to the realization of environmentally friendly smart cities.Moreover, the paper addresses security challenges associated with Intelligent Transportation Systems deployment, particularly concerning public transit privacy, and presents case studies illustrating the benefits of Intelligent Transportation Systems integration in specific urban areas, emphasizing its role in fostering Sustainable Smart Cities. Additionally, it examines proactive initiatives by automotive manufacturers in adhering to Intelligent Transportation Systems standards, ensuring mutual benefits for drivers and urban centers.

Introduction/purpose: All data exchanged over the Internet as well as other computer networks should be considered exposed to various types of security threats. In light of this, the transmission of voice over applications that do not use any type of crypto-protection allows anyone to discern the content of communication. Since voice transmission requires as little delay as possible, various protocols are used to enable crypto protected real-time communication. This paper presents one solution in a desktop application variant. Methods: The essence in voice exchange systems as well as in other systems where real-time communication is necessary is the establishment of a crypto-protected session which is a virtual secure channel for communication to which only the communicating parties have access. Voice sessions in the application are established with the SIP (Session Initiation Protocol) protocol. The sessions are further protected using the ZRTP (Zimmerman Real-time Transport Protocol) protocol. FusionPBX was used as the SIP server (registrar) for testing purpouses. The application is developed in C++ language using the Qt framework. Results: The final version of the application demostrates that ZRTP and SIP protocols are well suited for establishing crypto protected voice communications with low delay. Conclusion: This solution provides cryptographic functions for data secrecy and the management of cryptographic keys. Improving the solution with digital signatures and certificates will result in additional cryptographic functions: data integrity and personal identification. With this improvement, this solution will be able to withstand modern security threats with low delay.

Introduction/purpose: This paper conducts a comprehensive analysis of a potential implementation of Voice over Internet Protocol (VoIP) systems, focusing on network architecture, VoIP phones, and servers. The study explores potential vulnerabilities and proposes solutions. The paper concludes by advocating for a holistic approach to securing VoIP systems, incorporating supplementary services to ensure the confidentiality, integrity, and availability of voice communications in the digital landscape. Methods: Review of the underlying theory, analysis of the end-user needs and potential solutions, practical viability assesment. Results: The theoretical points discussed were proven in practice, using commercially available resources. Communication was established in an expected manner. Conclusions: Implementing solutions similar to the one presented in the paper would be a relatively inexpensive way to make diverse improvements to the operation of tactical level units, both in peacetime and during war.

Achieving reliable low-latency streaming on real-time immersive services that require seamless interaction has been of increasing importance recently. To cope with such an immersive service requirement, IETF and 3GPP defined Low Latency, Low Loss, and Scalable Throughput (L4S) architecture and terminologies to enable delay-critical applications to achieve low congestion and scalable bitrate control over 5G. With low-latency applications in mind, this paper presents a cloud-based streaming system using WebRTC for real-time communication with an adaptable L4S congestion control (aL4S-CC). aL4S-CC is designed to prevent the target service from surpassing a required end-to-end latency. It is evaluated against existing congestion controls GCC and ScreamV2 across two configurations: 1) standard L4S (sL4S) which has no knowledge of Explicit Congestion Notification (ECN) marking scheme information; 2) conscious L4S (cL4S) which recognizes the ECN marking scheme information. The results show that aL4S-CC achieves high link utilization with low latency while maintaining good performance in terms of fairness, and cL4S improves sL4S's performance by having an efficient trade-off between link utilization and latency. In the entire simulation, the gain of link utilization on cL4S is 1.4%, 4%, and 17.9% on average compared to sL4S, GCC, and ScreamV2, respectively, and the ratio of duration exceeding the target queuing delay achieves the lowest values of 1% and 0.9% for cL4S and sL4S, respectively.

The development of new technological applications is driving the need for deterministic network communication that exhibits reliable behavior. This is particularly relevant for applications like automation and autonomous vehicles, which require real-time network responsiveness. This has led to the development of various technologies, including Time-Sensitive Networking (TSN). Recent studies have explored the optimization of scheduling and routing in TSN, as this is crucial for meeting TSN’s objectives. Current methods primarily focus on scheduling Time-Triggered (TT) transmissions, often overlooking the scheduling of lower-priority traffic types. Many of these methods do not optimize routing, and only a few consider reliability. In this paper, we present the Optimized Hybrid Deterministic Scheduling and Routing Plus (OHDSR+) approach. This method prioritizes communications, optimizing both scheduling and routing of TT communications, while also addressing the requirements of lower-priority best-effort (BE) communications, and ensuring the reliability of TT communications. We evaluated our approach using various problem instances. The results demonstrate our approach’s effectiveness in managing different traffic types and ensuring reliable transmission for TT traffic. Our model achieves significant improvements compared to the state-of-the-art REU-CP-R method, with reductions in total latency of up to 1.03%, total response time of up to 34.70%, and scheduling response time of up to 63.97%. Notably, OHDSR+ exhibits superior scalability, successfully finding solutions for all tested network sizes with a 100% success rate, while REU-CP-R achieved only an 87.5% success rate. This highlights OHDSR+’s ability to provide optimal solutions for networks of various sizes with minimal latency and response times.

Time-Sensitive Networking (TSN) extends Ethernet bridging with features for deterministic transmission. Periodic streams may be scheduled such that their frames hardly interfere in bridges. Additionally, the Time-Aware Shaper (TAS) can keep egress ports free from other traffic when scheduled traffic arrives. TAS scheduling determines transmission starts of scheduled streams at end stations and configures the TAS in bridges. Most TAS scheduling algorithms disregard jitter and synchronization errors at end stations and bridges, race conditions from simultaneously arriving frames with same egress ports, and hardware-based configuration limits of the TAS. We call the resulting schedules tight schedules (TS). However, all these challenges apply to real hardware bridges. Therefore, we present an algorithm using event times with uncertainty to compute efficient robust schedules (ERS) that respect these constraints. We also propose a repair for existing scheduling approaches and call their output naïve robust schedules (NRS). We evaluate and compare their bandwidth usage and stream admission with those of TS. ERS are more efficient than NRS, and the performance gap between ERS and TS quantifies the price for robust schedules. Moreover, the presented algorithm for ERS computes significantly faster than four well-known methods for TS, and it can solve larger problem instances.

As information technology progresses, the need for digital transformation within the industrial sector has become increasingly apparent, and digital twin technology has emerged as a significant trend in manufacturing. Digital twins synchronize physical and digital environments, overcoming spatial and temporal limitations to create various added values that are unattainable in reality. This paper presents a model that integrates digital twin technology with production and operational technologies at manufacturing sites, enabling remote, centrally controlled manufacturing services that transcend physical constraints. Specifically, by utilizing Message Queuing Telemetry Transport (MQTT) for real-time synchronization, this approach ensures efficient and timely data transfer between physical and digital environments. While traditional approaches often encounter challenges due to high investment costs and design complexities, this paper proposes a cost-effective and practical solution that reflects actual factory conditions.

A high-fidelity optical fiber microphone (HF-OFM) with hybrid frequency and fast response is theoretically and experimentally demonstrated by the nanofabrication techniques for real-time communication, which consists of a graphene oxide (GO) film, an Au nanocoating, and an air cavity. The internal stress of the film is increased by the method of mechanical tensile preparation, and the microphone response flatness is improved. Meanwhile, the structural design of the 3 nm Au nanocoating improves the acoustic pressure detection sensitivity by 2.5 times by increasing the reflectivity. The experimental result shows that single, dual, and triple frequency acoustic signal detection in the frequency range of 0.1 kHz–20 kHz are achieved with acoustic pressure sensitivities of 9.64, 9.66, and 8.9 V/Pa, as well as flat frequency response (

Attendance systems are a critical tool to track attendance in various organizations. The integration of Internet of Things (IoT) technology has brought new advancements to attendance systems, with the inclusion of biometric sensors. Biometric sensors, such as fingerprint or facial recognition, ensure the accuracy of attendance records by verifying the identity of the individual. This paper focuses on the implementation of a smart attendance system that leverages the latest IoT technology. The system utilizes a compact and powerful microcontroller board, the ESP32, with in-built Bluetooth and Wi-Fi module for real-time communication through webSocket. The ESP32 also has a low power functionality, operating on a voltage of 3.3V, making it an ideal choice for IoT applications. By combining biometric sensors with webSocket, an instant communication protocol, attendance systems can deliver real-time updates on employee attendance. This allows for increased efficiency, accountability, and security, making IoT-enabled attendance systems an ideal solution for modern businesses. [ABSTRACT FROM AUTHOR]

This research delves into the transformation of agriculture through precision techniques and advanced technology. The focus is on an autonomous AI weed cutter, utilising TinyML, the ESP32 camera module, and a proximity sensor to revolutionise weed management. The study introduces a holistic approach by integrating TinyML's advanced computer vision techniques for real-time weed detection and classification. The ESP32 camera captures field images, processed by TinyML algorithms to identify crops and weeds accurately. Augmenting visual perception, a proximity sensor ensures safe navigation, detecting obstacles and maintaining safe distances from crops. This fusion empowers the system to make informed decisions. Field trials confirm exceptional weed detection and removal accuracy. Integration of TinyML-based vision, ESP32 camera tech, and the proximity sensor optimises efficiency, adaptability, and safety. Economic and environmental analyses highlight benefits: sustainable practices, potential yield increase, and reduced herbicide usage. In conclusion, this research showcases an autonomous AI weed cutter, seamlessly integrating TinyML, ESP32, and a proximity sensor. This technology amalgamation offers precise weed management, boosting efficiency, reducing environmental impact, and promoting sustainability in agriculture—a pivotal stride toward precision farming's future. [ABSTRACT FROM AUTHOR]

Copyright of Military Technical Courier / Vojnotehnicki Glasnik is the property of Military Technical Courier / Vojnotehnicki Glasnik and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Military Technical Courier / Vojnotehnicki Glasnik is the property of Military Technical Courier / Vojnotehnicki Glasnik and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Broadband powerline communication is a technology developed mainly with consumer applications and bulk data transmission in mind. Typical use cases include file download, streaming, or last-mile internet access for residential buildings. Applications gaining momentum are smart metering and grid automation, where response time requirements are relatively moderate compared to industrial (real-time) control. This work investigates to which extent G.hn technology, with existing, commercial off-the-shelf components, can be used for real-time control applications. Maximum packet rate and latency statistics are investigated for different G.hn profiles and MAC algorithms. An elevator control system serves as an example application to define the latency and throughput requirements. The results show that G.hn is a feasible technology candidate for industrial IoT-type applications if certain boundary conditions can be ensured. [ABSTRACT FROM AUTHOR]

In an ever-evolving technological landscape, addressing the performance challenges of real-time communication protocols is crucial. Real-time communication, facilitated by streaming media protocols, utilizes peer-to-peer or client-server models to enhance Quality of Service (QoS). WebRTC (Web Real-Time Communication) stands as a widely adopted, browser-based, opensource, peer-to-peer protocol, offering real-time media transmission through JavaScript APIs without third-party plugins. This paper presents an in-depth performance evaluation of a WebRTC-based video conferencing system using Socket.io services on a Node.js server. Our research expands on recent studies by introducing a comprehensive set of performance parameters, including Processing delay, CPU Utilization, Latency, Jitter, and Packet Loss, and packet delay. Our findings indicate that WebRTC performs exceptionally well within specific latency thresholds. However, scalability concerns emerge when a large number of clients are introduced, especially in bandwidth-constrained environments. [ABSTRACT FROM AUTHOR]

Real-time communications characterized by low-latency, deterministic, and reliable behavior are crucial for the advancement of emerging technologies. Consequently, Time-Sensitive Networking (TSN) has been developed to address the distinct demands of sectors such as automation and autonomous vehicles applications. This is currently achieved through various methods that emphasize the scheduling of critical data traffic. However, many of these methods determine routes independently, potentially impacting the schedulability of transmissions. Additionally, there is a noticeable lack of emphasis on the scheduling and routing of low-priority transmissions within TSN. In this paper, we introduce the Optimized Hybrid Deterministic Scheduling and Routing (OHDSR) approach. This method takes into account the priority of communications to jointly optimize the scheduling and routing of Time-Triggered (TT) communications, while also catering to low-priority Best-Effort (BE) communications. Extensive experimental evaluations show the high efficiency of our proposed method. It ensures not only the prompt delivery of TT communications but also the delivery of BE communications within suitable time frames, with a maximum difference of 14.29% from TT communications, meeting their respective deadlines. Moreover, the evaluation demonstrates the high scalability of the proposed approach, providing improved response times compared to the latest work for both routing and scheduling.

System-of-systems (SoS) have recently been used in several applications and scenarios in the field of safety, defense, and healthcare. In an SoS environment, the entire system is divided into sub-systems, which provides more flexibility and reduces the management cost of the entire system. SoS have been widely used in healthcare monitoring services, where patients are provided with medical sensors that send their medical measurements to a remote unit for further processing and decision-making. These sensors communicate with an access point using the wireless channel, which gives patients flexibility in mobility and makes the monitoring system more convenient and comfortable. However, sending data over the wireless channel presents several challenges, such as contention between the different sensors in accessing the channel and the bit errors associated with the noisy wireless channel. In this paper, an SoS healthcare monitoring framework is proposed, where a wireless communication protocol is proposed that addresses the sensors’ node network access contention and mitigates the bit errors of the communication channel by providing forward error correction bits to the transmitted packets. In addition, the protocol takes into consideration the sensors’ importance and criticality, such that more important sensors are given more network access time and more error correction bits, which in turn results in a robust transmission process with low transmission delay. The simulation results show the proposed wireless communication protocol’s effectiveness in lowering the packet loss, giving higher priority and having higher throughput for the more critical sensors.

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The 5G core network adopts a Control and User Plane Separation (CUPS) architecture to meet the challenges of low-latency business requirements. In this architecture, a balance between management costs and User Experience (UE) is achieved by moving User Plane Function (UPF) to the edge of the network. However, cross-UPF handover during communication between the UE and the remote server will cause TCP/IP session interruption and affect continuity of delay-sensitive real-time communication continuity. Information-Centric Networks (ICNs) separate identity and location, and their ability to route based on identity can effectively handle mobility. Therefore, based on the 5G-ICN architecture, we propose a seamless mobility support method based on router buffered data (BDMM), making full use of the ICN’s identity-based routing capabilities to solve the problem of UE cross-UPF handover affecting business continuity. BDMM also uses the ICN router data buffering capabilities to reduce packet loss during handovers. We design a dynamic buffer resource allocation strategy (DBRAS) that can adjust the buffer resource allocation results in time according to network traffic changes and business types to solve the problem of unreasonable buffer resource allocation. Finally, experimental results show that our method outperforms other methods in terms of average packet delay, weighted average packet loss rate, and network overhead. In addition, our method also has good performance in average handover delay.

Terahertz (THz) communication is widely regarded as the key component of future 6G mobile communication systems. Through comparative analysis of some of the main existing technical routes of THz up-conversion and down-conversion in THz wireless communication systems, a novel ultra-wideband (UWB) fiber-THz-fiber seamlessly converged real-time architecture, which utilizes the commercially mature digital coherent optical module to realize ultrahigh-capacity THz real-time wireless communication, is proposed in this study. (1) The proposed architecture employs the dual-polarization photonic up-conversion technique for THz generation and hybrid optoelectronic down-conversion technique for THz reception to facilitate the seamless integration between optical fiber and THz communications. (2) Because of the limited bandwidth of optoelectronic devices, multidimensional modulation techniques are adopted for UWB THz signals to improve spectral efficiency and transmission capacity. (3) An intelligent nonlinear joint compensation technique based on the deep neural network, which can effectively improve the signal-to-noise ratio of the time-varying hybrid fiber-THz-fiber channel, is proposed. Based on the investigations of the aforementioned key techniques, we, for the first time, realize the photonics-assisted record-high 100/200 GbE real-time THz wireless transmission at 360–430 GHz band, the capacity of which is 10–20 times higher than that of 5G. The proposed fiber-THz-fiber architecture can realize the smooth conversion between high-speed THz and lightwave signals. Moreover, the architecture can significantly reduce the research difficulty and development cost, thereby considerably accelerating the commercialization of 6G THz technology by thoroughly reusing commercial digital coherent optical module (DCO) modules, which are compatible with the physical layer transmission protocols, such as IEEE 802.3 and ITU-T G.798. Finally, this study also introduces some potential directions of research and development for higher-capacity, longer-distance, and more-integrated fiber-THz-fiber seamless communication. [ABSTRACT FROM AUTHOR]

English as a foreign language (EFL) instruction places special emphasis on students' active participation in class discussions. EFL learners encounter obstacles which prevent them from oral participation in the classroom. The purpose of this research is to discover why EFL students are not more engaged in class, as well as recognizing factors that impede students' class engagement. Moreover, the study aims at casting the light on the Techniques and Strategies for Enhancing Students Participation. Data was gathered through an interview in this descriptive-analytical study and analyzed using SPSS. The samples of the study consist of forty EFL professors from two Saudi universities. Findings from the study urged educators to help their students form positive self and social perceptions. Better class participation can be achieved through good rapport and the enhancement of the targeted teaching practices. The study concludes that EFL students are often disinclined to be engaged in oral class participation due to a mélange of a poorly managed classroom, language anxiety, and lack of vocabulary. [ABSTRACT FROM AUTHOR]

Dependable, safety-critical real-time (SCRT) systems are becoming increasingly important and complex. Examples of such systems are autonomous or self-driving cars which are poised to revolutionise the transport industry. A critical part of these SCRT systems is the network communication protocol that is used by components in an SCRT system to exchange data. Communication protocols for SCRT systems are required to exhibit predictable, worst-case execution times and thus have to be designed in a more static and less flexible way. To ensure this predictability, current state-of-art communication protocols for SCRT systems are based on the Time-Triggered Architecture (TTA), where static and equal-length time-slots are used for all nodes to access the communication channel, irrespective of the size of their transmission payload. This determinism forms the basis of predictable timing, behaviour and fault tolerance. However, this determinism comes at the cost of poor channel and bandwidth utilisation, which hinders the development of SCRT systems. In this paper, we propose a more flexible approach, INCUS+, that allocates the slot length of a node based on its transmission requirements in a Time Division Multiple Access (TDMA) round. We achieve this while retaining the level of dependability required for SCRT systems and ensuring fail-silence. We validate this through formal verification of the timing parameters for the transmission windows of all participating nodes as well as independent bus guardians. Our design exhibits a significant improvement in bandwidth and channel utilisation, as we demonstrate in an autonomous vehicle case study.

Broadband powerline communication is a technology developed mainly with consumer applications and bulk data transmission in mind. Typical use cases include file download, streaming, or last-mile internet access for residential buildings. Applications gaining momentum are smart metering and grid automation, where response time requirements are relatively moderate compared to industrial (real-time) control. This work investigates to which extent G.hn technology, with existing, commercial off-the-shelf components, can be used for real-time control applications. Maximum packet rate and latency statistics are investigated for different G.hn profiles and MAC algorithms. An elevator control system serves as an example application to define the latency and throughput requirements. The results show that G.hn is a feasible technology candidate for industrial IoT-type applications if certain boundary conditions can be ensured.

Due to recent cyberattacks on the Cyber-Physical Systems, the traditional security schemes face difficulties in dealing with such attacks as the attackers nowadays have adopted many intelligent mechanisms involving advanced information and communication technologies to launch attacks. This recent type of attack provides a significant impact on the resiliency of the software-defined networks. In this paper, we present a real-time delay attack on fault-resilient software-defined networks. The main objective of the time delay attack is to reduce the resiliency in the SDN-RM by adding a delay in the LLDP packets in the OpenFlow Switch. This addition of delay causes degradation in the network performance resulting in a low success rate in the SDN-RM mechanisms. In this paper, we present a machine learning-based detection system for detecting the attacks. The integration of machine learning techniques with network resilience solutions can effectively address the issue of predicting and classifying the LLDP packets that are delayed at a particular switch. Another issue is the detection of the malicious switch at the controller side which results in the improvement of the resiliency in SDN-RM. We propose a machine learning solution to detect the anomalies that are present in the topology and can be detected at the controller side. We use machine learning models such as k-nearest neighbors, support vector machines (SVM), Random Forest, and decision trees to detect the poisonous switches. The delay-based time attack detection system (DTA-DS) helps the controller to take a reactive decision to improve resilience by detecting poisonous network switches. With the help of the preventive approach, we achieve high-fault resiliency in fault resilient-based software-defined networks. [ABSTRACT FROM AUTHOR]

Marine submerged buoys can effectively obtain various parameters of seawater, which plays an important role in the research of marine physical phenomena, marine environmental changes, and climate change. However, traditional self-contained submerged buoys usually work underwater at a depth of about 100 m, and the observation data cannot be obtained before their recovery, which cannot satisfy the needs of real-time data acquisition for marine scientific research. To solve this problem, this paper proposes a real-time communication subsurface mooring system that consists of a satellite communication buoy (SCB), conductivity–temperature–depth sensors (CTD), and an inductive coupling mooring cable. The underwater inductive coupling link collects the data from the underwater sensors and transmit it to the SCB. Then, the data will be transmitted to the station receiver via satellite communication module integrated into the SCB. In order to ensure a high success rate of data recovery, the stress analysis and hydrodynamic simulation of the SCB were carried out in this paper. The results show that the SCB maintained a relatively stable attitude in the 3–4 sea state. The attitude data obtained from the subsequent sea trial was consistent with the simulation results, and the success rate of satellite communication during this period was more than 95%. In this paper, a modular embedded hardware circuit was designed to meet the functional requirements of the subsurface mooring system. An efficient data recovery strategy was also developed, which ensured that the average power consumption of the system was low and the success rate of data recovery is not less than 90% when operating in the severe sea state for a long time. The system underwent sea trials in the South China Sea for more than 3 months from the end of 2021 to the beginning of 2022. It transmitted more than 2034 sets of seawater profile temperature, salinity, and depth data in real-time, with a success rate of over 91% of the total sample data. The CTD data returned in real-time from our system is consistent with the data of the HYCOM and World Ocean Atlas (WOA), and a cyclonic mesoscale eddy was detected in the operation area. [ABSTRACT FROM AUTHOR]

It is essential to support real-time communication in networked control systems, such as automotive systems or smart factories. Several studies have been conducted to schedule real-time flows by adjusting their priorities using software-defined networking (SDN). However, because its architecture was not initially designed for real-time communication, unexpected challenges may occur if it is not utilized carefully. In particular, because SDN does not provide an atomic method for priority adjustment, some flows may violate their timing constraints by receiving additional interference during priority adjustment. In this letter, we propose novel schemes to update the flow priorities safely in SDN-based real-time systems. To this end, we first analyze such problems through two types of cases. Then, we develop schemes that determine a safe procedure for priority adjustment and synchronize the priority settings of all switches. By network emulations, we demonstrate that the above problem occurs in 149 out of 300 flow sets, but our schemes can effectively prevent all of them. [ABSTRACT FROM AUTHOR]

[Introduction] Aiming at the problems of business concentration, fault decision delay and high data transmission overhead for power grid dispatching control center, a fast and intelligent information processing method for fault recorder master station based on edge computing was proposed. [Method] Based on the edge computing design principle and overall framework of fault recording information processing, we used DataSocket asynchronous parallel real-time communication and Hadoop distributed storage scheme to ensure stable transmission, fast storage and retrieval for data, adopted improved SSDA algorithm to sift out the fault information to analysis quickly, and submitted the fault analysis results electronically. [Result] The experimental results show that this method has good application effects in mass data processing, rapid fault diagnosis, auxiliary decision support, etc. It can alleviate the pressure of information processing of dispatching control center and shorten the time delay of fault decision-making. [Conclusion] The proposed method is effective and feasible to handle massive fault recording information reliably combined with the Guangxi power grid operation case.

Deaf-mute individuals encounter substantial difficulties in their daily lives due to communication impediments. These individuals may encounter difficulties in social contact, communication, and capacity to acquire knowledge and engage in employment. Recent studies have contributed to decreasing the gap in communication between deaf-mute people and normal people by studying sign language interpretation. In this paper, a real-time Arabic avatar system is created to help deaf-mute people communicate with other people. The system translates text or spoken input into Arabic Sign Language (ArSL) movements that the avatar makes using deep-learning-based translation. The dynamic generation of the avatar movements allows smooth and organic real-time communication. In order to improve the precision and effectiveness of ArSL translation, this study depends on a state-of-the-art deep learning model, which makes use of YOLOv8, to recognize and interpret sign language gestures in realtime. The avatar is trained on three diverse datasets of Arabic sign language images, namely Sign-language-detection Image (SLDI), Arabic Sign Language (ArSL), and RGB Arabic Alphabet Sign Language (AASL), enabling it to accurately capture the nuances and variations of hand movements. The best recognition accuracy of the suggested approach was 99.4% on the AASL dataset. The experimental results of the suggested approach demonstrate that deaf-mute people will be able to communicate with others in Arabic-speaking communities more effectively and easily. [ABSTRACT FROM AUTHOR]

The state-of-the-art digital signatures incur undesirable delays, hence are impractical for time-stringent Industrial Control Systems (ICSs). The recent revision to IEC 62351–6 standard stepped back from digital signatures in favor of symmetric key based solutions, thereby sacrificing key properties, e.g., scaling well for multiple destinations, easy key distribution and management, public verifiability, and non-repudiation. Inspired by the Online/Offline signatures, this paper presents a new digital signature model to provide the key properties of digital signatures within the delay requirements, hinting that this step back can be avoided. The Online/Offline signatures concept divides the signature generation into two phases; offline (before the message is given), online (using the outputs of the former for faster signing after the message is given). The conventional solutions following this concept potentially reduce the delay, yet do not meet IEC 61850 delay requirements as they still involve expensive operations in the online phase, and their offline phase hinders throughput. This paper introduces Less-online/More-offline Signatures (LoMoS) to enable minimal end-to-end delay and high message throughput. LoMoS entails avoiding expensive operations entirely during the online phase. We present a construction that converts any digital signature scheme into LoMoS, retains its properties, and unlike existing solutions, benefits from shorter messages. [ABSTRACT FROM AUTHOR]