Efficient Communication Research Articles

CLOUD DATA PROTECTION USING WEIBULL DISTRIBUTED RECURRENT NEURAL ERGODIC SIGNCRYPTION

Cloud computing has become an integral part of modern computing, offering scalable storage and processing resources. However, the security of data stored in the cloud remains a major concern, especially when dealing with sensitive information. Traditional encryption schemes, while effective, often face limitations in terms of computational overhead and vulnerability to advanced attacks. To address these challenges, we propose a novel Weibull Distributed Recurrent Neural Ergodic Skewed Certificateless Signcryption scheme aimed at enhancing data protection in cloud environments. The key problem addressed by this work is the inherent inefficiency of existing cryptographic solutions that either rely on certificate-based systems or suffer from high computational and communication costs. This is especially crucial in cloud systems where real-time data processing is essential. Our approach integrates Weibull distribution for key management and optimization, recurrent neural networks (RNNs) for secure data transmission prediction, and ergodic skewed signcryption to eliminate the need for certificate authorities. This results in improved security, reduced computational overhead, and efficient communication, ensuring that the data remains secure even in dynamic cloud environments. The proposed scheme was tested using various metrics, including encryption/decryption time, data throughput, and attack resistance. Results demonstrate a significant reduction in computational cost by approximately 28% compared to traditional certificateless encryption. Furthermore, encryption times decreased from an average of 1.8 ms to 1.2 ms, and the scheme showed robustness against man-in-the-middle and chosen-ciphertext attacks with a detection accuracy of 98.6%. These results confirm the efficacy of the proposed system for enhancing security in cloud computing environments while maintaining high performance.

Artificial Intelligence Plays Crucial Role in Education Sector of Assam

“AI is a mirror, reflection not only our intellect, but our values and fears” Artificial intelligence (AI) is shaping the future of technology and revolutionizing our education system. Due to evolution of artificial intelligence, we have seen the drastic change in our educational sector throughout the globe. It indicates a positive signal for present and future generations of our country and also throughout the globe. Artificial Intelligence (AI) can play a crucial role in the school functioning process, streamlining and optimizing the procedure for both institutions and applicants. Through AI-driven algorithms, schools can efficiently sift through a large pool of applicants, identifying the most suitable candidates based on predefined criteria such as student’s enrollment according to their academic performance, extracurricular activities, and personal attributes. One of the most remarkable advantages of employing AI in the admission process is its ability to eliminate human bias, ensuring fairness and equal opportunities for all applicants. AI systems are programmed to assess candidates solely based on their qualifications and merit, reducing the influence of subjective factor, viz., race, gender, or socioeconomic background. We have seen the drastic changes in case of new admission in our School and College for last 3 to 4 years in the state of Assam and country as well. Moreover, AI facilitates personalized and efficient communication with applicants, providing them with updates on their application status and guiding them through the admission process. Virtual assistants powered by AI can promptly address queries from applicants, offering them the support they need. Furthermore, AI enables predictive analysis, allowing schools to forecast future trends in admission and tailor their strategies accordingly. By analyzing past admission data and trends, AI systems can provide valuable insights into the preferences and behaviors of prospective students, helping schools make informed decisions and develop targeted recruitment strategies. AI is revolutionizing the school admission process by making it more efficient, transparent, and fair. By leveraging AI technologies, schools can streamline their admission procedures, improve the candidate experience, and ultimately ensure that the most qualified and deserving students are admitted. In Assam, the education department of Government of Assam has introduced Shiksha Setu App to enhance communication between students, parents, teachers, and educational institutions. It facilitates access to various educational resources, attendance tracking of both teachers and students, exam schedules, leave for teachers, and performance reports. Its significance lies in improving transparency, efficiency, and accessibility in the whole education system of Assam, and as result contributing to better student outcomes and parental involvement.

DT-Block: Adaptive vertical federated reinforcement learning scheme for secure and efficient communication in 6G

The necessities of security and data sharing have focused on federated learning because of using decentralized data sources. The existing works used federated learning for security, however, it still faces many challenges such as poor security and privacy, computational complexity, etc. In this research, we propose adaptive vertical federated learning using a reinforcement learning approach and blockchain. The proposed work includes three phases: user registration and authentication, machine learning-based client selection, and adaptive secure federated learning. Initially, all the users register their credentials to the cognitive agent, which generates a private key, public key, and random number using a Chaotic Isogenic Post Quantum Cryptography (CIPQC) algorithm. Second, optimal clients are selected for participating in federated learning which improves learning rate and reduces complexity. Here, optimal clients are selected by the Enhanced Multilayer Feed Forward Neural Network (EMFFN) algorithm by considering CSI, RSSI, bandwidth, energy, communication efficiency, and statistical efficiency. Finally, adaptive secure federated learning is performed by the Distributed Distributional Deep Deterministic Policy Gradient (D4PG) algorithm, where the local models are adaptively used by the private strategy based on its sensitivity. The aggregated global models are stored in DT-block (dendrimer tree-based blockchain) which stores the data in a dendrimer tree structure for increasing scalability and reducing search time during data retrieval. The simulation of this research is conducted by NS-3.26 network simulator and the performance of the proposed DT-Block model is estimated based on various performance metrics such as accuracy, delay, loss, f1-score, and security strength this demonstrated that the suggested effort produced better results both in terms of quantitative and qualitative aspects.

Adaptive compressed learning boosts both efficiency and utility of differentially private federated learning

In the federated learning (FL) research field, current research is confronted with several pivotal challenges, e.g., data privacy, model utility and communication efficiency. Furthermore, these challenges are further amplified by statistical data heterogeneous in the FL system. Thus, a novel Communication-efficient and Utility-assured Gaussian differential privacy-based Personalized Federated Adaptive Compressed Learning method, called CUG-PFACL, is proposed. Specifically, an end-to-end local adaptive compressed learning strategy is designed, including three crucial modules, namely the measurement matrix, the personalized compressed data transformation and the local model. Especially, jointly training the measurement matrix module and the personalized compressed data transformation module can mitigate the inherent statistical heterogeneity while preserving all important characteristics of the compressed private data of each local client, and alleviate the additional heterogeneity induced by Gaussian differential privacy in each global communication round. Numerous experimental simulation and comparisons demonstrate that CUG-PFACL has three notable advantages: data privacy guarantee, enhanced personalized model utility and high-efficient communication.

Design and implementation of a multifunctional desktop robot for computer peripherals

Abstract As an important component of the intelligent machinery field, desktop robots not only enrich people’s entertainment lives but are also gradually expanding into applications in education, healthcare, and other fields. However, existing products often struggle to meet the increasingly diverse needs due to short battery life and limited computing resources. In response to this challenge, this study designs a multifunctional desktop robot that can be used as a computer peripheral, which significantly improves power supply and computing capabilities. The design uses the STM32F4 series microcontroller as the main control chip, combined with high-performance servos and their self-developed driver boards, to achieve precise motion control. By integrating USB-HS PHY and USB-CDC protocol, the communication efficiency with the computer is greatly improved, and new screen-driving technology is developed using open-source routines, thus optimizing the user interaction experience. In addition, an innovative adaptive algorithm is incorporated into the firmware program, enhancing the robot’s working stability in different environments. Through systematic hardware debugging and software testing, the robot can achieve efficient performance and stable response when executing complex tasks. This research will promote the expansion of desktop robot technology into broader application fields and support future intelligent work and lifestyle.

Performance analysis of a blockchain-based messaging system implementation for air cargo supply chains

ABSTRACT The air cargo supply chain’s goal is to achieve fast shipment movement through multistage coordination among multiple air cargo stakeholders. The lack of transparency in the end-to-end supply chain and complex digital connectivity is what limits the traditional peer-to-peer communication flow in the fragmented air cargo industry. Blockchain technology can enhance communication transparency by streamlining multistage message flows via smart contracts and by acting as an intermediary to simplify digital connections among air cargo stakeholders. Despite the benefits of applying blockchain for air cargo messaging, as blockchain is an emerging technology, technical concerns in the areas of performance, privacy, and interoperability can hinder the practical adoption of the blockchain system. To address practical implementation concerns, a blockchain-based messaging system using the Algorand public blockchain is developed in our work to demonstrate the practicality of blockchain. Transaction data privacy can be protected by the encryption key exchange algorithm, which supports forward secrecy. Our experimental results provide practical performance insights into the Algorand blockchain for streamlined messaging from various aspects related to transaction submission, confirmation, and retrieval. Our results show that by leveraging the Algorand blockchain, our proposed system can offer scalable, efficient, reliable, and cost-effective communication channels for air cargo messaging.

Application of indoor thermal energy cycle and visual communication simulation scene based on Machine vision and optical image enhancement

Traditional thermal analysis methods have limitations in complex environments, and innovative technologies are urgently needed to improve the accuracy of indoor thermal monitoring and optimization. In this paper, machine vision and optical image enhancement technology are combined to simulate the indoor heat energy cycle process to achieve more efficient heat management and visual communication, so as to improve the energy utilization efficiency and residential comfort of buildings. In this paper, a high-resolution camera is used to capture real-time images of indoor environment, image quality is enhanced by image processing algorithm, and heat distribution characteristics are extracted. The machine learning model is used to analyze the dynamic changes of the thermal energy cycle, and the visual model of the indoor thermal energy cycle is constructed by combining the fluid dynamics theory. The experimental results show that the proposed method can effectively identify and display the key areas of indoor heat circulation and their changing trends. The simulation results show that the optical enhanced images can show different temperature regions more clearly, directly reflect the heat flow situation, and have a high agreement with the actual measurement data. Therefore, the indoor thermal energy cycle simulation method based on machine vision and optical image enhancement not only improves the accuracy and real-time performance of thermal energy analysis, but also provides visual support for indoor environmental management.

HF skywave communication for air-to-ground networks: A cooperative transmission framework

High-Frequency (HF) communication is widely used for long-distance transmission in remote and disaster areas. However, the dynamic nature of the ionosphere and multipath propagation in the HF channel pose significant challenges to designing efficient and robust communication systems. In this paper, we propose a Fixed Station (FS) and frequency matching method, as well as a power allocation method, to improve the sum-rate of air-to-ground HF communication networks. We derive optimal power allocation among users that share the same frequency, based on which a modified water-filling algorithm is used to solve the power allocation problem in multi-user scenarios, while a low-complexity algorithm is proposed to solve the integer optimization problem of frequency-FS matching. Simulation results demonstrate that the proposed algorithm outperforms the naive algorithm, indicating its effectiveness.

Non-coherent short-packet communications: Novel z-domain user multiplexing

In the evolution of fifth generation (5G) and beyond wireless communication systems, non-coherent (NC) short packet communication (SPC) is crucial for achieving ultra-reliable low-latency communication (URLLC). Frame design, latency, and reliability are some of the challenges associated with short-packet communication. Recently, to address these challenges, a novel modulation scheme known as modulation on conjugate-reciprocal zeros (MOCZ) has been proposed. MOCZ modulates information on conjugate reciprocal zeros in the z-domain, thereby eliminating the need for channel estimation and providing a robust solution for NC communication. However, in multi-user MOCZ (MU-MOCZ) scheme, adding guard intervals to each short packet to mitigate channel impact remains an issue, as it increases the transmission time and consequently reduces efficiency. To address the aforementioned problem, this paper introduces a novel frame design approach called z-domain user multiplexing MOCZ (ZDUM-MOCZ or ZDM-MOCZ). Unlike traditional time division multiplexing (TDM), which serves users consecutively in the time domain, this method multiplexes users in the z-domain. In this approach, each user is allocated a specific set of zeros in the z-domain, which collectively form a unique sequence in the time domain. The findings illustrate the potential for reduced latency in downlink transmission, highlighting the benefits of this novel methodology over conventional MU-MOCZ method. The proposed ZDUM-MOCZ scheme not only addresses the existing issues in the frame design of the MU-MOCZ scheme but also facilitates more efficient and reliable short packet communication in 5G and beyond wireless systems.

Communication Technology for Renewable Energy Access Grid System Based on Multi-source Heterogeneous Data Fusion

Abstract Incorporating renewable energy solutions into the power grid is essential for facilitating a shift in energy sources. Ensuring the effectiveness and dependability of communication technologies within smart distribution grids is vital, as these grids serve as the link between end users and the central station, thereby contributing to the grid’s stable functioning and the effective dissemination of energy. This study introduces a framework for communication that tackles the challenges associated with the integration of multi-source, heterogeneous data in smart distribution grids. The framework leverages multi-source heterogeneous data fusion and capitalizes on edge computing to efficiently process and consolidate data from diverse sensors and devices. An optimal weight allocation algorithm is proposed to mitigate data redundancy and to improve the energy efficiency of communication. The paper demonstrates, through theoretical examination and empirical tests, the proposed system’s enhanced communication capabilities, especially in terms of data transmission effectiveness and energy usage optimization. The discussion highlights the advantages of smart distribution networks over conventional communication methods and offers insights for further advancements in communication technologies within this sector.

OASR-WFBP: An overlapping aware start-up sharing gradient merging strategy for efficient communication in distributed deep learning

Wait-Free-Back-Propagation (WFBP) is a practical method for distributed deep-learning, but it suffers from a high communication overhead. To address this issue, the communication overhead can be reduced by overlapping gradient communication and computation, and sharing the startup time among multiple gradient communication phases. However, existing optimizations choose to share the startup time greedily and fail to coordinately exploit the overlapping opportunity between computation and communication. We propose an overlapping aware startup sharing Wait-Free-Back-Propagation (OASR-WFBP). An analytic model is designed to guide the sharing procedure. Evaluations show that OSAR-WFBP achieves a 5%-16% optimization in iteration time over the state-of-the-art WFBP algorithm.

Decoding Silent Speech Cues From Muscular Biopotential Signals for Efficient Human‐Robot Collaborations

AbstractSilent speech interfaces offer an alternative and efficient communication modality for individuals with voice disorders and when the vocalized speech communication is compromised by noisy environments. Despite the recent progress in developing silent speech interfaces, these systems face several challenges that prevent their wide acceptance, such as bulkiness, obtrusiveness, and immobility. Herein, the material optimization, structural design, deep learning algorithm, and system integration of mechanically and visually unobtrusive silent speech interfaces are presented that can realize both speaker identification and speech content identification. Conformal, transparent, and self‐adhesive electromyography electrode arrays are designed for capturing speech‐relevant muscle activities. Temporal convolutional networks are employed for recognizing speakers and converting sensing signals into spoken content. The resulting silent speech interfaces achieve a 97.5% speaker classification accuracy and 91.5% keyword classification accuracy using four electrodes. The speech interface is further integrated with an optical hand‐tracking system and a robotic manipulator for human‐robot collaboration in both assembly and disassembly processes. The integrated system achieves the control of the robot manipulator by silent speech and facilitates the hand‐over process by hand motion trajectory detection. The developed framework enables natural robot control in noisy environments and lays the ground for collaborative human‐robot tasks involving multiple human operators.

Performance Study of Collection Tree Protocol in Mobile Wireless Sensor Network

The Mobile Wireless Sensor Networks (MWSN) are defined as the successor of Wireless Sensor Networks (WSN); geographically distributed autonomous mobile sensors for physical and environmental conditions monitoring. Mobile sensor networks are more flexible than static sensor networks as they can cope with rapid topology changes. Sensor networks are deployed by having their sensors collect information reliably and then relay the sensor readings to a central base station using wireless multi-hop transmission. Multi-hop routes within a rapid-changed topology requires a routing protocol that can adapt to network changes with an efficient communication mechanism among the nodes; to comply with the energy-constraint nature of the wireless sensor networks. Through significant simulation on this study using the AVRora simulator, the Collection Tree Protocol’s (CTP) capacity to respond to MWSN’s changing network topology was assessed by analysing the performance indicators; specifically, the average of packet loss and energy use of mobile nodes with different circumstances considering varied quantities of mobile nodes in the simulation region, with different velocities and network density. In several circumstances, the implementation of CTP in MWSN indicates a rise in energy usage because of broken linkages along with regular tree regeneration brought on by node mobility.

Research on visible light communication channel model in underground mines

Abstract In order to effectively model the Visible Light Communication (VLC) channel in underground mines, this paper delves into the challenges of the underground environment and develops a mathematical model by analyzing the propagation characteristics of visible light. The objective is to deepen our understanding of light signal behavior in this unique setting. Most existing research focuses on visible light channel modeling in indoor environments, with limited studies on underground mines. Our model takes into account the irregular surfaces of mine walls, as well as the effects of factors such as high dust concentration, extreme temperatures and pressures, light absorption by mine walls, and obstruction caused by large machinery on the propagation of visible light signals. This paper introduces refined optical propagation models that incorporate both scattering and light absorption considerations, resulting in a maximum received power of -46.12 dBm, which is significantly lower than that in indoor environments. These models enhance our ability to analyze and improve channel performance, thereby optimizing communication efficiency.

The Design of a Long-Distance Signal Transmission System in a Bistatic RCS Testing System

In wireless communication and radar systems, long-distance signal transmission poses significant challenges that affect overall system performance. In this paper, we propose an innovative bistatic radar cross section (RCS) testing system designed to address these challenges, with a particular focus on its long-distance signal transmission capabilities. This system is capable of accurately measuring the RCS of a target and improving multipath channel modeling accuracy by using precise RCS values. The integrated upper computer software extracts amplitude and phase information from received echo signals, processes these data, and provides detailed outputs including the target’s RCS, one-dimensional image, and two-dimensional image. The experimental results obtained prove that this system can not only achieve effective long-distance signal transmission but also substantially enhance the accuracy of RCS measurements, offering reliable support for multipath channel modeling. However, the conclusions drawn are preliminary and require further experimental validation to fully substantiate this system’s performance. Future work will focus on improving system accuracy, minimizing the impact of environmental noise, and optimizing data-processing methods to enhance the efficiency of wireless communication and radar applications.

Application of BIM and Virtual Reality of System Integration Design and Development in Medical Building Projects: A Case Study in China

Design outcomes in the Medical Building Projects (MBPs) must satisfy the demand of the medical staff and stakeholders. However, they often do not have the relevant engineering expertise, so the design team is prone to misunderstandings when communicating with them using traditional two-dimensional (2D) Computer-Aided Design (CAD) drawings. The medical staff may not readily visualize and understand the design and then provide feedback to the designer through professional medical requirements or advice, which results in the need to redesign and carry out additional work during the design phase, thus delaying the progress of the MBPs. To address these challenges, this study aims to develop an integrated system based on BIM, advanced rendering engines, and VR technology for use in non-immersive and immersive VR environments. Moreover, in this study, the integrated system was applied to the infection-building project of the People's Hospital of Qingbaijiang District, Chengdu City, and semi-structured interviews were distributed to 16 engineers, designers, contractors, and medical staff who had been involved in the whole project to validate the practicality and effectiveness of the integrated system. The results indicate that this system integration is an efficient visual communication and simulation tool in medical design. Key advantages include enhanced communication efficiency between the design team and medical stakeholders, improved visual interaction, and streamlined decision-making processes within a 3D VR/BIM environment. The successful implementation of this integrated system is expected to significantly aid design teams and stakeholders in effectively managing medical design tasks in future projects.

Nurses’ Experiences with the Use of Electronic Nursing Record: A Qualitative Study

Background: The electronic nursing record has contributed to improving nursing practice and ensuring nursing quality, but it is still incomplete in supporting nursing workflow. The purpose of this study is to explore the experience of using the electronic nursing record by nurses working in frontline care for trauma, emergency, and infectious disease-exposed patients.Methods: The study employed a qualitative descriptive design and qualitative content analysis. In-depth interviews were conducted with 10 nurses working in emergency, trauma, and critical care units in the tertiary hospital.Results: The nurses’ experiences with the use of the electronic nursing record emerged into three subjects (improvements to electronic nursing record implementation, benefits of electronic nursing record implementation, electronic nursing record user factors), five categories (information finding and sharing function, information input function, facilitating efficient communication, ensuring record quality, need for education), and eight subcategories.Conclusions: Electronic nursing record supports the visualization of special information such as emergency and infection, and has the advantage of facilitating communication between medical staff. Both support better clinical decision-making. However, system instability and low nursing friendliness delay nursing work in the process of inputting, confirming, and sharing information. Copy-andpaste practices, poor quality of records, and insufficient electronic nursing record education were identified as factors to be changed on the user side. Improvement of nursing-friendly interfaces, optimization of nursing workflows, and participation of nurses in the improvement process are necessary. We should also focus on developing electronic nursing record education programs.

ВИКОРИСТАННЯ ІНФОРМАЦІЙНО-ЦИФРОВИХ ТЕХНОЛОГІЙ ДЛЯ ПОКРАЩЕННЯ МЕНЕДЖМЕНТУ ЯКОСТІ ОСВІТИ В ЗАКЛАДАХ ЗАГАЛЬНОЇ СЕРЕДНЬОЇ ОСВІТИ

The article explores the role of information and digital technologies (IGT) in the formation of quality education management in secondary educational institutions. The author notes that in the modern world, the development of IGT has a significant impact on all areas of life, including education. The implementation of IGT in secondary educational institutions can contribute to improving the quality of education by enhancing the organization of the learning process, providing access to information resources, increasing student motivation for learning, and ensuring effective communication among all participants in the educational process. The article investigates the application of Deming’s recommendations, known as one of the pioneers in quality management, to educational institutions, particularly schools. An analysis of Deming’s fundamental principles and recommendations, such as a focus on continuous improvement, a systematic approach, leadership commitment, problem-solving based on facts and data, and promoting collaboration and interaction, is conducted. The article emphasizes the importance of quality management systems that help educational institutions effectively manage the quality of education, such as the PDCA methodology and Lean approaches. The author examines specific steps for implementing the IGT utilization strategy in education, including the creation of an integrated information-analytical system, the implementation of an electronic reporting system, the use of electronic textbooks and educational materials, providing access to electronic resources, organizing remote learning, involving students in active participation, and supporting the professional development of teachers. Overall, the article elucidates the importance and role of IGT in shaping the quality management of education in secondary educational institutions. The integration of IGT into the educational process contributes to improved learning organization, the creation of favorable conditions for student development, enhanced communication efficiency, and expanded access to information resources.

Beacon-Based Uplink Transmission for LoRaWAN Direct to LEO Satellite Internet of Things

Direct-to-satellite IoT (DtS-IoT) communication structure is a promising solution to provide connectivity and extend the coverage of traditional low-power and long-range technologies, especially for isolated and remote areas where deploying traditional infrastructure is impracticable. Despite their bounded visibility, the Low Earth Orbit (LEO) satellites complement the terrestrial networks, offering broader gateway coverage and terrestrial network traffic offloading. However, the dynamics of LEO and the nature of such integration come with several challenges affecting the efficacy of the network. Therefore, this paper proposes Beacon-based Uplink LoRaWAN (BU-LoRaWAN) to enhance satellite-terrestrial communication efficiency. The proposed scheme exploits the LoRaWAN class B synchronization mechanism to provide efficient uplink transmission from LoRaWAN devices placed on the ground to satellite gateways. BULoRaWAN proposes an uplink transmission slot approach to synchronize ground devices’ uplink traffic with LEO-based orbiting gateways. It also uses a queue data structure to buffer end devices’ ready-to-send packets until the appropriate moment. BU-LoRaWAN avoids possible transmission collision by optimizing a random transmission slot for an end device within the beacon window. The proposed system is implemented and evaluated using OMNeT++ network simulator and FLoRaSat framework. The result demonstrates the feasibility of the proposed system. BU-LoRaWAN achieves better performance compared to the standard LoRaWAN, which manages to deliver almost double the traffic delivered by the standard one.

Medical Federated Model with Mixture of Personalized and Shared Components.

Although data-driven methods usually have noticeable performance on disease diagnosis and treatment, they are suspected of leakage of privacy due to collecting data for model training. Recently, federated learning provides a secure and trustable alternative to collaboratively train model without any exchange of medical data among multiple institutes. Therefore, it has draw much attention due to its natural merit on privacy protection. However, when heterogenous medical data exists between different hospitals, federated learning usually has to face with degradation of performance. In the paper, we propose a new personalized framework of federated learning to handle the problem. It successfully yields personalized models based on awareness of similarity between local data, and achieves better tradeoff between generalization and personalization than existing methods. After that, we further design a differentially sparse regularizer to improve communication efficiency during procedure of model training. Additionally, we propose an effective method to reduce the computational cost, which improves computation efficiency significantly. Furthermore, we collect five real medical datasets, including two public medical image datasets and three private multi-center clinical diagnosis datasets, and evaluate its performance by conducting nodule classification, tumor segmentation, and clinical risk prediction tasks. Comparing with 14 existing related methods, the proposed method successfully achieves the best model performance, and meanwhile up to 60% improvement of communication efficiency. Source code is public, and can be accessed at: https://github.com/ ApplicationTechnologyOfMedicalBigData/pFedNet-code.