Communication Capabilities Research Articles

Enhancing Wireless Network Efficiency with the Techniques of Dynamic Distributed Load Balancing: A Distance-Based Approach.

The unique combination of the high data rates, ultra-low latency, and massive machine communication capability of 5G networks has facilitated the development of a diverse range of applications distinguished by varying connectivity needs. This has led to a surge in data traffic, driven by the ever-increasing number of connected devices, which poses challenges to the load distribution among the network cells and minimizes the wireless network performance. In this context, maintaining network balance during congestion periods necessitates effective interaction between various network components. This study emphasizes the crucial role that mobility management plays in mitigating the uneven load distribution across cells. This distribution is a significant factor impacting network performance, and effectively managing it is essential for ensuring optimal network performance in 5G and future networks. The study investigated the complexities associated with congested cells in wireless networks to address this challenge. It proposes a Dynamic Distance-based Load-Balancing (DDLB) algorithm designed to facilitate efficient traffic distribution among contiguous cells and utilize available resources more efficiently. The algorithm reacts with congested cells and redistributes traffic to its neighboring cells based on specific network conditions. As a result, it alleviates congestion and enhances overall network performance. The results demonstrate that the DDLB algorithm significantly improves key metrics, including load distribution and rates of handover and radio link failure, handover ping-pong, and failed attached requests.

Wideband 32-Element 3D-MIMO antenna for vehicular applications

A wideband 32-element vehicular 3D-MIMO system is proposed by utilizing eight-element MIMO antenna for Internet-of-Vehicle/Vehicle-to-Everything. The eight-element MIMO antenna consists of four sets of two identical antenna elements are arranged 900 symmetrically on an octagon-shaped substrate cross-section area of 0.4374 λ02. A combination of ground stubs between two same oriented antennas is loaded with shared ground at the bottom of the same substrate for better matching and low correlation, which are fed through the tapered microstrip line. Similarly, dual I-shaped stubs interconnected with shared ground for high isolation between a pair of corner elements. The proposed antenna achieved measured impedance bandwidth range (Sij ∈ i = j < − 10 dB) of 3.03–15.33 GHz with a maximum mutual coupling (Sij ∈ i ≠ j) value of − 15.5 dB. Furthermore, a set of eight-element MIMO antenna with extended ground is vertically orthogonally symmetrically rotated around the central axis, forming a 32-element 3D-MIMO antenna and its found results are almost close to eight-element antenna with an enhanced peak gain value of 12.1 dBi. The performances of the 3D-MIMO antenna with radome and large metallic sheet are also examined. The satisfactory results observed in the 3D system-in-package highlight its capability for advanced vehicular communication.

Adaptive ubiquitous learning for server deployment and distributed offloading in UAV-enhanced IoV

Through creating an environment rich in computational and communication capabilities, ubiquitous computing gradually integrates it with human activities. Inspired by adaptive ubiquitous learning, various intelligent devices (e.g., roadside units and infrared sensors) deployed in the Internet of Vehicles (IoV) are expected to be critical to mitigating urban traffic congestion and enhancing travel safety. In addition, benefiting from the advantages of high mobility and real-time response, Unmanned Aerial Vehicles (UAVs) embody substantial prospects to assist IoV in efficiently and flexibly handling latency-sensitive, computation-intensive tasks. Nevertheless, due to time-varying demands and heterogeneous computing resources, it is challenging to provide effective service for mobile devices while guaranteeing high-quality data transmission. Therefore, a distributed service offloading system framework in UAV-enhanced IoV is designed. To minimize the service latency, a game theory-based distributed service offloading algorithm, named G-DSO, is proposed to realize adaptive ubiquitous learning for service request distribution. Finally, numerous experiments are implemented based on real-world service requirement datasets. Experimental results demonstrate that the proposed G-DSO approach improves the hit rate by 2.68% to 74.42% compared with four existing service offloading methods, verifying the effectiveness and good scalability of G-DSO.

Performance Analysis of an Aerial Remote Sensing Platform Based on Real-Time Satellite Communication and Its Application in Natural Disaster Emergency Response

The frequency of natural disasters has increased recently, posing a huge threat to human society. Rapid, accurate, authentic, and comprehensive acquisition and transmission of disaster information are crucial in emergency response. In this paper, we propose a design scheme for an aerial remote sensing platform based on real-time satellite communication. This platform mainly includes a civilian heavy-duty unmanned aerial vehicle, ground observation system with the self-developed orthographic image stabilization device, wireless communication system with an airborne mobile communication device using Ku band, ground satellite information receiving station, and data processing and application analysis system. The image stabilization capability of the ground observation system and the communication capability of the wireless communication system were verified through ground and flight tests respectively. The results showed that the stability accuracy of the platform was better than the theoretical threshold, the system transmission rate was not less than 2 M bandwidth, the data packet loss rate was low, and the time delay was not more than 2 s. The images captured in the experiment were clear, with a resolution of less than 1cm and an overlap rate of more than 70%. These all results meet the emergency observation requirement, which indicates that the aerial remote sensing platform based on real-time satellite communication has great potential for application in natural disaster emergency response.

Operational Tests for Delay-Tolerant Network between the Moon and Earth Using the Korea Pathfinder Lunar Orbiter in Lunar Orbit

The Korea Pathfinder Lunar Orbiter (KPLO) was launched on 5 August 2022, equipped on the SpaceX Falcon 9 launch vehicle. At present, the KPLO is effectively carrying out its scientific mission in lunar orbit. The KPLO serves as a cornerstone for the development and validation of Korean space science and deep space technology. Among its payloads is the DTNPL, enabling the first-ever test of delay-tolerant network (DTN) technology for satellites in lunar orbit. DTN technology represents a significant advancement in space communication, offering stable communication capabilities characterized by high delay tolerance, reliability, and asymmetric communication speeds—a necessity for existing satellite and space communication systems to evolve. In this paper, we briefly give an overview of the Korea Lunar Exploration Program (KLEP) and present scientific data gathered through the KPLO mission. Specifically, we focus on the operational tests for DTN-ION conducted for message and file transfer, as well as real-time video streaming, during the initial operations of the KPLO. Lastly, this study offers insights and lessons learned from KPLO DTNPL operations, with the goal of providing valuable guidance for future advancements in space communication.

Wearable Sensor Node for Safety Improvement in Workplaces: Technology Assessment in a Simulated Environment.

Personal protective equipment (PPE) has been universally recognized for its role in protecting workers from injuries and illnesses. Smart PPE integrates Internet of Things (IoT) technologies to enable continuous monitoring of workers and their surrounding environment, preventing undesirable events, facilitating rapid emergency response, and informing rescuers of potential hazards. This work presents a smart PPE system with a sensor node architecture designed to monitor workers and their surroundings. The sensor node is equipped with various sensors and communication capabilities, enabling the monitoring of specific gases (VOC, CO2, CO, O2), particulate matter (PM), temperature, humidity, positional information, audio signals, and body gestures. The system utilizes artificial intelligence algorithms to recognize patterns in worker activity that could lead to risky situations. Gas tests were conducted in a special chamber, positioning capabilities were tested indoors and outdoors, and the remaining sensors were tested in a simulated laboratory environment. This paper presents the sensor node architecture and the results of tests on target risky scenarios. The sensor node performed well in all situations, correctly signaling all cases that could lead to risky situations.

Adjustable focusing property of circular Airyprime beam through Fourier space modulation.

Airyprime beams are known for their powerful autofocusing property, which are further enhanced by the introduction of a circular structure-circular Airyprime beam (CAPB). We derive an asymptotic expression of the CAPB in Fourier space (FS) and verify its accuracy by the numerical Fourier transform (FT) method. Through FS modulation on it, adjustable control of autofocusing property of the FS-modulated CAPB can be achieved, whose lower and upper limits can reach 8.7% reduction and 2.6 times enhancement compared to the unmodulated one. The experimental results agree well with the numerical analyses. Our findings offer promising possibilities for efficient particle trapping and enhancing free-space optical communication capabilities.

Classroom Leadership Communication Capabilities and Communal Interface of Teachers in Public Elementary Schools

Classroom Leadership Communication Capabilities and Communal Interface of Teachers in Public Elementary Schools

Robotic exploration of Martian caves: Evaluating operational concepts through analog experiments in lava tubes

Caves on Mars are a tantalizing target for exploration, as they could harbor evidence of extinct or extant life, clues to the planet’s geological history, and even potential future human habitation. However, the inherent challenges of navigating unknown and challenging terrain, coupled with limited communication capabilities, pose significant obstacles for robotic exploration in these caves. This paper presents the results of an effort to evaluate different operational concepts for a mission dedicated to exploration of caves on Mars. We conducted a series of analog exploration experiments in lava tubes on Earth, testing two hypotheses: (1) that two robots are more effective than one, and (2) that high levels of autonomy are more effective than low levels of autonomy. Our findings suggest that two robots are indeed more effective, except in low autonomy cases, where more operational resources are required. We also found that full autonomy is more efficient than low autonomy, as it enables quicker exploration and detection of targets of interest. However, the low autonomy cases benefited from the operator input to acquire higher quality data, an area of autonomy requiring further development. This paper provides insight into the design of our experiments, as well as detailing the results and implications for the design of future missions to explore caves on Mars. By shedding light on the operational concepts tested and their corresponding outcomes, we contribute to the knowledge base required to formulate optimal strategies for the realization of successful cave exploration missions on the Red Planet.

Node Localization Method in Wireless Sensor Networks Using Combined Crow Search and the Weighted Centroid Method.

Node localization is critical for accessing diverse nodes that provide services in remote places. Single-anchor localization techniques suffer co-linearity, performing poorly. The reliable multiple anchor node selection method is computationally intensive and requires a lot of processing power and time to identify suitable anchor nodes. Node localization in wireless sensor networks (WSNs) is challenging due to the number and placement of anchors, as well as their communication capabilities. These senor nodes possess limited energy resources, which is a big concern in localization. In addition to convention optimization in WSNs, researchers have employed nature-inspired algorithms to localize unknown nodes in WSN. However, these methods take longer, require lots of processing power, and have higher localization error, with a greater number of beacon nodes and sensitivity to parameter selection affecting localization. This research employed a nature-inspired crow search algorithm (an improvement over other nature-inspired algorithms) for selecting the suitable number of anchor nodes from the population, reducing errors in localizing unknown nodes. Additionally, the weighted centroid method was proposed for identifying the exact location of an unknown node. This made the crow search weighted centroid localization (CS-WCL) algorithm a more trustworthy and efficient method for node localization in WSNs, with reduced average localization error (ALE) and energy consumption. CS-WCL outperformed WCL and distance vector (DV)-Hop, with a reduced ALE of 15% (from 32%) and varying communication radii from 20 m to 45 m. Also, the ALE against scalability was validated for CS-WCL against WCL and DV-Hop for a varying number of beacon nodes (from 3 to 2), reducing ALE to 2.59% (from 28.75%). Lastly, CS-WCL resulted in reduced energy consumption (from 120 mJ to 45 mJ) for varying network nodes from 30 to 300 against WCL and DV-Hop. Thus, CS-WCL outperformed other nature-inspired algorithms in node localization. These have been validated using MATLAB 2022b.

Sentimientos estratégicos: la respuesta de la Comisión Europea en las redes sociales a la crisis entre Rusia y Ucrania

The use of online platforms by the European Commission, particularly during the Russia-Ukraine crisis in the context of the digital age, offers informative insights about current crisis communication. Between February 1 and October 26, 2022, this study analysed the Commission’s activities on the primary platforms, Facebook and Twitter. To carry out this analysis, the study used RStudio and the SentimentAnalysis package within R. The Blake &amp; Mouton’s Grid was additionally employed as a reference point to extract the strategic dimensions. The data revealed distinct usage patterns by the Commission: Twitter acted as a channel for quick and immediate updates, making use of its real-time communication capabilities, while Facebook was used for more detailed conversations with a wider audience. Regarding sentiment, most of the Commission’s communications had a neutral or positive tone. Nevertheless, there were cases where certain critical issues prompted the Commission to take definite positions, providing insight into their strategic intent and priorities. To summarise, the European Commission’s online communication during the relevant period showed an informed and platform-specific methodology. Through comprehending and improving the platforms' functionalities and audience preferences, the Commission established a communication structure that can be utilised as a model for entities that intend to simplify their digital interaction procedures, particularly in the event of a crisis.

Analysis of Real-time Processing Approaches for Large Data Volumes in Metering Infrastructure

Smart grid systems and communication technologies, such as Advanced Metering Infrastructure (AMI), have revolutionized utility service management and monitoring. AMI leverages smart meters equipped with advanced communication capabilities, facilitating bidirectional communication between utilities and consumers. The increasing deployment of smart meters and the adoption of sub-hourly data collection requirements by utility companies highlight significant data volume growth. Thus, there is a need for efficient real-time data processing solutions as existing approaches may not meet previously established Service-Level Agreements (SLAs) concerning performance, accuracy, and scalability metrics. This research aims to comprehensively review the latest publications relevant to distributed real-time data processing methods for smart grid applications and outline problems for further research. Specifically, the study delves into the effectiveness and application of reviewed approaches in managing the constant stream of data from smart meters and IoT devices within the smart grid context. By analysing existing methodologies and advancements, this study seeks to identify challenges and opportunities in real-time data processing for smart grid infrastructures, focusing on addressing the complexities of processing, managing, and storing large volumes of real-time data. The literature review revealed two primary applications of real-time data processing: optimization of data streaming performance and data analysis. The review encompasses various studies, each presenting distinct methodologies and technologies applied to address the challenges of processing large volumes of real- time data from smart meters and IoT devices. Future research should address the challenges and limitations discovered in this study.

Crop Monitoring System with Water Moisture Levels Using Controller

This paper presents a Crop Monitoring System (CMS) designed to effectively manage crop water moisture levels through automated irrigation control. The system incorporates soil moisture sensors and environmental sensors for real-time data collection, which is then processed by a controller to determine optimal irrigation schedules and water application rates. By integrating predefined thresholds and algorithms, the controller ensures precise irrigation tailored to specific crop and soil conditions, thus preventing both under- watering and over-watering. Additionally, the CMS features wireless communication capabilities, enabling remote monitoring and adjustment of irrigation settings by farmers. This remote accessibility facilitates timely interventions and optimizations, even in the absence of farmers in the fields. Overall, the CMS enhances crop health, improves resource efficiency, and conserves water in agriculture. By leveraging advanced sensor technologies and intelligent control algorithms, this system offers a sustainable solution for precision agriculture, contributing to increased yields while minimizing water usage and environmental impact. This project intends to make farming as mechanized and labor-free as possible.

A framework to examine the potential social influence of scientists and nongovernmental organizations in marine conservation

AbstractEffective marine conservation requires science‐based decisions and strong support from the public and local communities. Understanding the potential social influence of scientists and nongovernmental organizations (NGOs) in marine conservation is key, yet this aspect has been underexplored in conservation social science. In this study, we created a simple index of potential social influence (PSII) for scientists and NGOs, initiating an analytical framework to identify key variables correlated with the PSII and their underlying pathways. We illustrated this framework using China as a case study, where marine conservation advancement is urgently needed. Our results indicated that many of the variables we identified were correlated with the PSII, even though some are rarely mentioned in the related literature. Notably, both communication capability and annual funding positively impacted the potential social influence of scientists and NGOs, albeit through different pathways. This study provides valuable insights to enhance marine conservation efforts in China and addresses a critical research gap in assessing social influence on nature conservation.

Recent Developments in AI and ML for IoT: A Systematic Literature Review on LoRaWAN Energy Efficiency and Performance Optimization.

The field of the Internet of Things (IoT) is dominating various areas of technology. As the number of devices has increased, there is a need for efficient communication with low resource consumption and energy efficiency. Low Power Wide Area Networks (LPWANs) have emerged as a transformative technology for the IoT as they provide long-range communication capabilities with low power consumption. Among the various LPWAN technologies, Long Range Wide Area Networks (LoRaWAN) are widely adopted due to their open standard architecture, which supports secure, bi-directional communication and is particularly effective in outdoor and complex urban environments. This technology is helpful in enabling a variety of IoT applications that require wide coverage and long battery life, such as smart cities, industrial IoT, and environmental monitoring. The integration of Machine Leaning (ML) and Artificial Intelligence (AI) into LoRaWAN operations has further enhanced its capability and particularly optimized resource allocation and energy efficiency. This systematic literature review provides a comprehensive examination of the integration of ML and AI technologies in the optimization of LPWANs, with a specific focus on LoRaWAN. This review follows the PRISMA model and systematically synthesizes current research to highlight how ML and AI enhance operational efficiency, particularly in terms of energy consumption, resource management, and network stability. The SLR aims to review the key methods and techniques that are used in state-of-the-art LoRaWAN to enhance the overall network performance. We identified 25 relevant primary studies. The study provides an analysis of key findings based on research questions on how various LoRaWAN parameters are optimized through advanced ML, DL, and RL techniques to achieve optimized performance.

Fair resource optimization for cooperative non-terrestrial vehicular networks

Non-terrestrial networks aim to extend wireless communications capabilities beyond traditional terrestrial infrastructure by utilizing various platforms such as satellites, high-altitude platform stations (HAPS), and unmanned aerial vehicles to provide global or localized coverage. However, improving data rates in non-terrestrial cooperative communications (NTCC) networks presents unique challenges, necessitating innovative solutions. This paper presents a novel approach to enhance the request–response ratio (RRR) of NTCC in time asynchrony. By leveraging the advantages of HAPS, such as wide coverage, high altitude, and flexible deployment, we aim to optimize the performance of NTCC networks. We formulate an optimization problem considering the simultaneous connection between the HAPS and ground users, power distribution, and decoding order. The joint optimization problem is formulated as non-convex-non-linear and is also NP-hard, making it very challenging to obtain a globally optimal solution. To reduce the complexity of the problem and make it more tractable, we decouple it into subproblems and achieve an efficient solution in two steps. In the first step, we use the Gale–Shapley algorithm to solve the many-to-many two-sided matching problem of HAPS user terminal association, given the fixed decoding and power allocation. Then, we solve the decoding order and power allocation problem using the Dinkeback-like algorithm, given the optimal association in the second step. Our proposed method iteratively updates the preference lists, mitigating interference among HAPS and enhancing overall system performance. Through extensive simulations, we demonstrate that implementing our proposed schemes achieves high RRR compared to benchmark schemes. Additionally, when temporal asynchrony is employed alongside our approach, there is an increase in the overall performance obtained from the process.

A Systematic Review of Computational Intelligence Techniques for Channel Selection in P300-Based Brain Computer Interface Speller

Electroencephalography (EEG)-based P300 speller aids in restoring the communication and control capabilities in patients suffering from motor disabilities. However, the quality and quantity of the data collected from EEG recordings have a substantial influence on the P300 speller’s performance. Hence, selecting the optimum number of recording electrodes, i.e., channels for each user, is a significant difficulty for the P300 speller. There are two fundamental objectives of the channel selection process: (1) to extract the most crucial information from the relevant channels, hence reducing the computing complexity of P300/non-P300 signal processing operation, and (2) to lessen the potential overfitting that could result from using unwanted channels to boost performance. For obtaining the best channel subsets, different channel selection techniques, including manual, filtering, wrapper, and embedded approaches, have been applied by past researchers. This research provides an in-depth examination of recent advancements, status, challenges, and potential solutions related to channel selection strategies in P300 speller systems. Each channel selection technique is thoroughly explored, including detailed comparisons between them. The notable advantages and drawbacks of each method are emphasized along with the discussion on the future direction and scope of work in the field of channel selection in P300 speller. The review underscores that channel selection methods enable the use of a reduced number of channels without compromising classification performance. By eliminating noisy or irrelevant channels, these approaches contribute to enhanced system performance.

Software-Bus-Toolchain (SBT): Introducing a Versatile Method for Quickly Implementing (I)IoT-Scenarios

The Internet of Things (IoT) has become ubiquitous. IoT devices are applied in a multitude of applications, e.g., in smart home scenarios, building automation, smart energy and smart cities, healthcare, and industrial environments. Fast and efficient implementation and roll-out of IoT devices is a critical factor for successs and acceptance of IoT devices. At the same time, the variety of hardware platforms that can be used for IoT applications, as well as the number of IoT orchestration platforms is increasing. Finding the right combination of tooling and hardware is not trivial, but essential for building applications that provide value. In this work, a Software-Bus-Toolchain (SBT) is introduced that encapsulates firmware design, data point definition, and communication protocol usage. Furthermore, an IoT control platform is provided to control and evaluate the IoT modules. Thus, using the SBT, solely the business logic has to be designed, while the hardware-design is automated to a high degree. Usage of the Zephyr framework allows the interchange of hardware modules, while interfaces provide easy adaption of data points and communication capabilities. The implementation of interfaces to the IoT-platform as well as to the communication layer provides a universal usage of logic and data elements. The SBT is evaluated in two application scenarios, where its flexible nature is shown.

A Throughput Performance Analysis Method for Multimode Underwater Acoustic Communication Network Based on Markov Decision Process

The multimode underwater acoustic communication network is a novel form of underwater acoustic communication that adjusts its communication mode to enhance overall performance. Current performance analysis methods are primarily applied to single-mode networks and assume uniform communication capability across all nodes, making them unsuitable for multimode networks. This paper investigates the multimode communication of the physical layer, considering factors such as the marine environment, the node transmitting sound source level, and the transmitting distance. A decoding conflict model is proposed to support multimode concurrent transmission scenarios. The communication mode is designed to be compatible with the channel and node characteristics. Additionally, using a Markov decision process, this paper establishes a performance evaluation and analysis model for multimode underwater acoustic networks to determine throughput performance limits in real underwater environments. Simulations across various scenarios validate that the throughput performance limits obtained by this method are more accurate under multimode networks, with an improvement in accuracy of over 67.5% compared to existing methods.

The Role of Tele-Orthodontics in Enhancing Patient Compliance and Treatment Monitoring

ABSTRACT Background: Tele-orthodontics, the application of telecommunications technology in orthodontic care, has emerged as a promising tool to enhance patient compliance and facilitate treatment monitoring. In traditional orthodontic practice, patient compliance and monitoring are pivotal but often challenging aspects. Tele-orthodontics offers a solution by providing remote communication and monitoring capabilities, potentially improving patient engagement and treatment outcomes. Materials and Methods: This study conducted a retrospective analysis of orthodontic patients who utilized tele-orthodontics as part of their treatment plan. A total of 100 patients were included in the study, with ages ranging from 12 to 40 years old. Patients were provided with mobile applications or web-based platforms enabling them to communicate with their orthodontists, submit progress images, and receive feedback remotely. Treatment compliance was assessed based on adherence to scheduled appointments, wearing of orthodontic appliances, and completion of prescribed exercises. Results: The analysis revealed a significant improvement in patient compliance with the implementation of tele-orthodontics. Adherence to scheduled appointments increased by 30%, with patients attending 95% of their appointments compared to 65% before the introduction of tele-orthodontics. Moreover, compliance with wearing orthodontic appliances showed a remarkable rise, with patients consistently wearing their appliances for an average of 22 h per day, up from 14 h per day previously. Additionally, the completion rate of prescribed exercises saw a notable enhancement, with 80% of patients completing their exercises as instructed, compared to 50% previously. Conclusion: Tele-orthodontics plays a significant role in enhancing patient compliance and treatment monitoring in orthodontic care. The remote communication and monitoring capabilities provided by tele-orthodontic platforms empower patients to actively participate in their treatment process, leading to improved adherence to appointments, better compliance with wearing orthodontic appliances, and increased completion of prescribed exercises. These findings underscore the importance of integrating tele-orthodontics into orthodontic practice to optimize patient outcomes and satisfaction.