Data Transmission Services Research Articles

A lightweight RDMA connection protocol based on post-hoc confirmation

With the increasing scale and complexity of high-performance computing systems, the rising failure rate poses significant challenges for RDMA networks that aim for high bandwidth and low latency. RDMA networks require hardware-level end-to-end reliable data transmission services to avoid the high cost of software failure recovery. Tianhe HPC interconnection network adopts a NIC-based RDMA reliable connection protocol, RCP. RCP establishes a connection for each message that enters the NIC and releases it after the transmission is complete. However, this introduces an additional round-trip time RTT connection overhead for each message, which severely impacts the performance of networks dominated by short messages in high-performance computing systems. We have found that utilization of receiver-side connection resources has been consistently low because maintaining message-grained connections on the NIC results in rapid release of connections. Therefore, we propose a lightweight RDMA connection protocol based on post-hoc confirmation, PCP. PCP assumes the receiver has connection resources by default and eliminates the need for confirmation from the receiver before sending a message, thus reducing the connection overhead of almost all messages by one RTT. At the same time, PCP also includes mechanisms to address the special case where the receiver lacks connection resources. Evaluation results demonstrate that PCP significantly optimizes short messages and applications dominated by short messages. Moreover, PCP further reduces the usage of receiver-side connection resources. Additionally, PCP does not experience performance degradation even under large-scale heavy loads and severe endpoint congestion.

An Authentic and Privacy-Preserving Scheme Towards E-Health Data Transmission Service

An Authentic and Privacy-Preserving Scheme Towards E-Health Data Transmission Service

Towards Millions of Database Transmission Services in the Cloud

Alibaba relies on its robust database infrastructure to facilitate realtime data access and ensure business continuity despite regional disruptions. To address these operational imperatives, Alibaba developed the Data Transmission Service (DTS), which has become critical for internal applications and public cloud services alike. This paper presents a comprehensive study of the architectural innovations, resource scheduling mechanisms, and performance optimization strategies that have been implemented within DTS to tackle the significant challenges of cross-network, heterogeneous data transmission in a cost-effective manner. We explore the novel Any-to-Any (A2A) architecture, which simplifies the complexity of data paths between diverse databases and mitigates network connectivity issues, thereby significantly reducing development overhead. Additionally, we examine a dynamic network bandwidth scheduling algorithm that effectively maintains Service-Level Objectives (SLOs), complemented by a serverless mechanism that ensures efficient resource utilization. Furthermore, DTS utilizes advanced strategies such as transaction dependency tracking, hot data consolidation, and batching to enhance synchronization performance and efficiency. DTS has distilled the lessons learned from years of serving our customer base and currently supports nearly 1 million public cloud instances annually. Our evaluation results show that DTS can effectively and efficiently handle real-time data transmission in both experimental and production environments.

A novel and optimized computational framework for energy efficient data dissemination in wireless sensor network

Wireless sensor network (WSN) is an integral part of internet-of-things (IoT), where a large scale of data transmission and various complex services are identified to be delivered. In order to facilitate these services, energy efficiency is one critical demand for resource-constraint sensor nodes. Carrier sense multiple access (CSMA) has been considered for effective traffic management for high-end data delivery services. A review of existing literature on CSMA-based schemes shows that it has not yet achieved an optimal case of energy efficiency. Hence, the proposed study presents a novel computational framework in order to address this research gap. The prime contribution of the proposed study is towards presenting an optimal computational model for maximized fairness in data dissemination services in WSN especially focusing on energy efficiency. The presented study model also contributes towards optimizing route buffer and buffer power, which facilitates towards availability of energy-efficient path information. The study also introduces a mobile auxiliary node that aggregates the data from sensor nodes and delivers it to the sink node considering dynamic location updates for seamless transmission. Scripted in MATLAB, the proposed scheme exhibited 70% energy saving compared to conventional schemes of CSMA in WSN.

Correction: Improving the quality of real-time data transmission service in VANETS by balancing the load on road side units

Correction: Improving the quality of real-time data transmission service in VANETS by balancing the load on road side units

Improving the quality of real-time data transmission service in VANETS by balancing the load on road side units

Improving the quality of real-time data transmission service in VANETS by balancing the load on road side units

INVESTIGATION AND THE ANALYSIS OF MEDICAL BIG DATA AND THE EMPLOYMENT OF ARTIFICIAL INTELLIGENCE FOR DISEASE PREDICTION

The development of technologies like big data, cloud services, Internet+, and artificial intelligence has greatly improved data communication services in the traditional pharmaceutical industry, driving the progress of the healthcare sector in our country. These advancements have facilitated healthcare system reforms, enhanced the quality and efficiency of medical services, and fostered technological innovation. In this context, our research focuses on the following areas and draws the following conclusions: In recent years, the pharmaceutical industry has leveraged technologies such as big data, cloud computing, Internet+, and artificial intelligence to enhance data transmission services. These advancements are considered crucial strategic resources for the pharmaceutical industry and ongoing healthcare system reforms in our country. Their goal is to improve the efficiency and quality of medical services, establish new models of healthcare delivery, and drive economic growth. Given this, our research aims to further explore these advancements. There is an increasing demand for an in-depth exploration of the utilization of logic in the fields of artificial intelligence and computer science. To address this need, the Manual of Logic in Artificial Intelligence and Logic Programming, along with its companion, the Manual of Logic in Computer Science, were developed. These resources combine comprehensive investigations and fundamental research to delve into various underlying topics across multiple disciplines. A foundational understanding of mathematical complexity is assumed as background knowledge, making the material of interest to logicians and mathematicians [1]. However, existing works primarily focus on core concepts and technologies, leaving out extensions, applications, and languages. The handbook aims to provide a comprehensive coverage of constraint programming, offering readers an accurate overview of the entire field and its potential. While each topic is presented in a survey-like manner, some details may be omitted in favor of broader coverage [2].Among various types of uncertainties, randomness and ambiguity are considered crucial and fundamental. The relationship between randomness and ambiguity is discussed, exploring uncertain states and their changes through measures like entropy and hyperentropy. The study investigates uncertainties in chaotic, fractal, and complex networks' evolutions and differentiations. A simple and effective method for simulating uncertainty through knowledge representation is proposed, providing a foundation for automating logical and visual thinking in the presence of uncertainty [4]. The appropriate timing for monitoring implementation remains unclear. Although there is room for improvement, if a data processing algorithm functions correctly, it can be employed separately from the original algorithm. However, there are limitations to its usage [5] Keywords: Big Data, biomedicine, cluster, electronic health cards, healthcare.

Multi-mobile vehicles task offloading for vehicle-edge-cloud collaboration: A dependency-aware and deep reinforcement learning approach

The wide application of edge cloud computing in the Internet of Vehicles (IoV) provides lower latency, more efficient computing power, and more reliable data transmission services for vehicle applications. Realistic vehicle applications frequently consist of multiple tasks with dependencies. Efficiently and quickly scheduling individual tasks for multiple vehicle applications to reduce latency and energy consumption is challenging. Our proposed approach leverages Deep Reinforcement Learning (DRL) to develop a task scheduling strategy that ensures real-time and efficient operations. We maximize the utilization of available resources by harnessing the computational capabilities of vehicles, multiple MEC servers, and a cloud server. Specifically, we model the dependencies of tasks using a Directed Acyclic Graph (DAG) and design dynamically adjustable weights for delay and energy consumption. Transforming the task offloading problem in a vehicle-edge-cloud environment, which considers dependencies, into a Markov Decision Process (MDP) enables us to tackle it effectively. To obtain optimized offloading decisions quickly, we employ Double Deep Q-Network (DDQN) along with specially designed mobility management strategies. A penalty mechanism is introduced in DDQN to impose penalties when the vehicle application is delayed beyond its deadline. Simulation results show that the proposed scheme can significantly decrease the latency and energy consumption of multi-applications compared to the other three schemes and ensure the successful execution of tasks.

Field demonstration of novel architecture supporting DWDM data transmission and fiber path services in metro/access-integrated All-photonics network

Field demonstration of novel architecture supporting DWDM data transmission and fiber path services in metro/access-integrated All-photonics network

Elastically accelerating lookup on virtual SDN flow tables for software-defined cloud gateways

In recent years, Software-Defined Networking has been gradually applied to cloud gateways to provide efficient, reliable and flexible data transmission services for various large-scale cloud platforms. However, massive concurrent accessing produces huge network traffic from tenants to cloud platforms, which aggregates at cloud gateways and brings serious performance bottlenecks regarding packet classification. To solve this problem, this paper proposes an elastically accelerated lookup method of virtual SDN flow tables for software-defined cloud gateways. The method caches active exact flows in virtue of network traffic locality, enabling most packets to directly hit the cache and bypass tuple space search, which significantly accelerates flow table lookup. Focusing on network traffic jitters, the cache adaptively adjusts its capacity according to the dynamic changes of the number of active exact flows to maintain high cache hit rates, aiming to achieve elastic acceleration of flow table lookup. Furthermore, we theoretically derive the performance metrics of our proposed method such as cache hit rates, cache yield rates and average search length, based on the Zipf distribution model of network traffic. Eventually, we evaluate the performance of our proposed elastically accelerated lookup method by experiments with real network traffic traces. Experimental results indicate that our proposed method outperforms existing cache-accelerated methods with stable cache hit rates around 80% and the speedup of average search length up to 2.84 even under network traffic jitters.

An on‐chain trading model of real world asset backed digital assets

AbstractMetaverse is a digital value interaction network based on blockchain technology, with an important economic system component. While both traditional financial industries and crypto‐native industries have made significant progress by leveraging blockchain, the value stream of each remains limited to separate ecosystems. To bridge this gap between off‐chain and on‐chain economic systems, an on‐chain trading model was proposed using HD key derivation technique for direct uploading onto chains without going through centralized services for IoT data transmission. To improve the current status of NFTs as static assets, a token protocol binding each NFT with a unique account address was proposed. Additionally, oracle technique was leveraged with a decentralized and distributed trust model spanning across on‐chain and off‐chain components which securely pushes data between smart contracts and Web‐APIs. A decentralized trading model was developed based on smart contracts implementing automated market makers according to CFMM algorithm. Parallel transaction computing was executed based on the DAG model to ensure high operational performance and security standards of underlying blockchain. Finally, the on‐chain trading system of real world asset backed digital assets was developed integrating all the above key techniques that correspond to crucial functions of a complete economic system in Metaverse.

Encrypted optical fiber tag based on encoded fiber Bragg grating array

Optical fibers are typically used in telecommunications services for data transmission, where the use of fiber tags is essential to distinguish between the different transmission fibers or channels and thus ensure the working functionality of the communication system. Traditional physical entity marking methods for fiber labeling are bulky, easily confused, and, most importantly, the label information can be accessed easily by all potential users. This work proposes an encrypted optical fiber tag based on an encoded fiber Bragg grating (FBG) array that is fabricated using a point-by-point femtosecond laser pulse chain inscription method. Gratings with different resonant wavelengths and reflectivities are realized by adjusting the grating period and the refractive index modulations. It is demonstrated that a binary data sequence carried by a fiber tag can be inscribed into the fiber core in the form of an FBG array, and the tag data can be encrypted through appropriate design of the spatial distributions of the FBGs with various reflection wavelengths and reflectivities. The proposed fiber tag technology can be used for applications in port identification, encrypted data storage, and transmission in fiber networks.

Terahertz Meets AI: The State of the Art.

Terahertz (THz) is a promising technology for future wireless communication networks, particularly for 6G and beyond. The ultra-wide THz band, ranging from 0.1 to 10 THz, can potentially address the limited capacity and scarcity of spectrum in current wireless systems such as 4G-LTE and 5G. Furthermore, it is expected to support advanced wireless applications requiring high data transmission and quality services, i.e., terabit-per-second backhaul systems, ultra-high-definition streaming, virtual/augmented reality, and high-bandwidth wireless communications. In recent years, artificial intelligence (AI) has been used mainly for resource management, spectrum allocation, modulation and bandwidth classification, interference mitigation, beamforming, and medium access control layer protocols to improve THz performance. This survey paper examines the use of AI in state-of-the-art THz communications, discussing the challenges, potentials, and shortcomings. Additionally, this survey discusses the available platforms, including commercial, testbeds, and publicly available simulators for THz communications. Finally, this survey provides future strategies for improving the existing THz simulators and using AI methods, including deep learning, federated learning, and reinforcement learning, to improve THz communications.

Multi-panel extra-large scale MIMO based joint activity detection and channel estimation for near-field massive IoT access

The extra-large scale multiple-input multiple-output (XL-MIMO) for the beyond fifth/sixth generation mobile communications is a promising technology to provide Tbps data transmission and stable access service. However, the extremely large antenna array aperture arouses the channel near-field effect, resulting in the deteriorated data rate and other challenges in the practice communication systems. Meanwhile, multi-panel MIMO technology has attracted extensive attention due to its flexible configuration, low hardware cost, and wider coverage. By combining the XL-MIMO and multi-panel array structure, we construct multi-panel XL-MIMO and apply it to massive Internet of Things (IoT) access. First, we model the multi-panel XL-MIMO-based near-field channels for massive IoT access scenarios, where the electromagnetic waves corresponding to different panels have different angles of arrival/departure (AoAs/AoDs). Then, by exploiting the sparsity of the near-field massive IoT access channels, we formulate a compressed sensing based joint active user detection (AUD) and channel estimation (CE) problem which is solved by AMP-EM-MMV algorithm. The simulation results exhibit the superiority of the AMP-EM-MMV based joint AUD and CE scheme over the baseline algorithms.

Recognition Of Chicken Species Through Sound Using Weierstrass Transform

Voice recognition is an applied technique in the field of digital signal processing that has been widely used, such as technology in the field of telecommunications which is now able to provide data transmission services not only text but can also serve data transmission using voice. Speech recognition studies to date have only focused on human speech recognition, so it is important to develop research on speech recognition in animals. In this research a speech recognition system will be developed for balenggek, pelung and bekisar ornamental chickens using the Delphi 7 programming language and the weiertrass transform method. The performance evaluation measurement of the speech recognition system in this study uses two parameters, namely the detection rate and the false positive rate. The results showed that the speech recognition system for ornamental chickens using the weierstrass transform method had an average detection rate of 83.00% and an average false positive rate of 16%.

METHOD OF ANALYSIS OF QUALITY INDICATORS OF COMPUTER NETWORK OF INFORMATION SYSTEM OF CRITICAL APPLICATION

Topicality. Our time is characterized by rapid growth in the number and quality of various computer systems. An increasing number of users, both in their daily and professional activities, need data transmission and Internet access services in addition to traditional communication services. The network infrastructure created as a result of convergence should provide the needs of preferably all service users. But in order to ensure the necessary quality of service, it is necessary not only to have information about the state of the telecommunications network, but also to be able to forecast it. This fact requires improving the analysis of quality indicators characterizing the state of the telecommunications network. The purpose of this work is to develop a method of analyzing quality indicators of a computer network of a critical application information system. The object of the research is the process of functioning of the computer network of the information system of critical application. The subject of the study is the methods of analyzing the quality indicators of the computer network of the information system. The results. In this work, an analysis of the main indicators of the quality of the telecommunications network, which are basic in the provision of services. Based on the received information, 6 states of the telecommunication network were formalized and classified, in which it is possible to provide non-essential quality of service for various types of traffic. The developed method of analyzing the quality indicators of the computer network of the information system of critical application is planned to be used in the future to ensure the quality and reliability of the functioning of computer systems of critical application.

Orthogonal Variable Spreading Factor Codes Suppressing Signal-Envelope Fluctuation

Recently, complex orthogonal variable spreading factor (OVSF) codes based on polyphase orthogonal codes have been proposed to support multi-user/multi-rate data transmission services in synchronous direct-sequence code-division multiple access (DS-CDMA) systems. This study investigates the low signal-envelope fluctuation property of the complex OVSF codes in terms of transmission signal trajectories. In addition, a new method is proposed to suppress the envelope fluctuation more strongly at the expense of reducing the number of spreading sequences of the codes.

Data and Service Security of GNSS Sensors Integrated with Cryptographic Module.

Navigation and positioning are of increasing importance because they are becoming a new form of infrastructure. To ensure both development and security, this study designed a technical innovation structure to upgrade the GNSS (Global Navigation Satellite System) data transmission and real-time differential correction service system and proposed a new multiple cryptographic fusion algorithm to achieve the encryption and decryption of GNSS data and services. First, a GNSS station encrypts GNSS data with an encryption key and obtains a public key from a GNSS data center to encrypt the GNSS data encryption key. After that, identity authentication of a GNSS station is carried out, and an SSL VPN is established between the GNSS station and a GNSS data center before GNSS data are transmitted to the GNSS data center. Then, the GNSS data center decrypts the received GNSS data. The process of an intelligent terminal for real-time differential corrections is similar to that of the GNSS station and the GNSS data center. A GNSS sensor integrated with a cryptographic module was developed to validate the structure in an open environment. The results showed that the developed GNSS sensor was successful in encrypting the data, and the GNSS data center was able to decrypt the data correctly. For the performance test, a cryptography server was able support the requirements of GNSS applications. However, a cryptography server was optimal in supporting 40~50 GNSS stations simultaneously, whereas a cluster was suggested to be configured if the number of GNSS stations was more than 60. In conclusion, the method was able to ensure the validity, confidentiality, integrity, and non-repudiation of GNSS data and services. The proposed upgrading technology was suitable for coordinating GNSS development and security.

Management of National eID Infrastructure as a State-Critical Asset and Public-private Partnership: Learning from the Case of Estonia

In the management of national electronic identity (eID) infrastructure, cooperation between public and private parties becomes more and more important, as the mutual dependencies between the provision of e-services and the provision of the national public key infrastructure (PKI) continuously increases. Yet, it is not clear which key factors affect the public-private collaboration in the eID field, as existing studies do not provide insight into this particular matter. Therefore, we aim to identify the factors that affect public-private partnership (PPP) in the field of eID. We also describe feasible formats that help to improve the cooperation between the two sectors, based on insights from the case of Estonia. In service of that study, we conducted twelve qualitative interviews with high-level experts representing several parties from the public and the private sector. By conducting a thematic analysis of the interviews, we identified five key factors for successful PPP in the eID field, i.e., engagement, joint understanding, two-way communication, clear role division, and process orientation. Furthermore, we generalize our results by discussing, in how far the found cooperation formats can be used by stakeholders to manage state-critical information technology (IT) infrastructure components similar to eID such as mobile phone services, data transmission services and digital signature services.

A Study of Licklider Transmission Protocol in Deep-Space Communications in Presence of Link Disruptions

Delay/disruption-tolerant networking (DTN) is widely recognized as a key internetworking technology for implementing a space system of systems (SoS). Aimed at being the main data transport protocol of DTN, Licklider transmission protocol (LTP) is expected to provide reliable data transmission service in a challenging space SoS networking environment regardless of random link disruptions. In this paper, a study of LTP in deep-space communication networks in presence of link disruptions is presented. An analytical framework is developed for the effect of multiple random link disruptions on LTP's reliable data delivery in deep-space networks. The study indicates that the models accurately predict the transmission performance of LTP over a deep-space communication channel in presence of multiple link disruptions. A threshold interval value, obtained from the analytical modeling results, is proposed as a means of determining the reducibility of two successive link disruptions to a single disruption; that threshold value is verified by the experiments.