Transmission Success Rate Research Articles

X-ray communication system with high-repetition-rate pulsed X-ray source and LYSO(Ce) and YAP(Ce) scintillators

X-ray communication offers significant advantages over traditional microwave methods due to its shorter wavelength and higher theoretical bandwidth, enabling efficient space communication and penetration through complex electromagnetic environments. However, current systems face limitations in X-ray emission modulation and high-precision timing detection. To meet the high-frequency transmission demands of space missions, we developed a pulsed X-ray emission source capable of high-frequency modulation. Additionally, we identified specific scintillators with distinct advantages for different transmission frequency ranges, allowing for performance optimization. Experimental results demonstrated a successful transmission rate of 10 MHz, validating the feasibility of MHz-frequency X-ray communication.

Switching to a 100% remote follow-up of implantable cardiac electronic devices: Organizational model and results of a single center experience.

During the SARS-CoV-2 COVID-19 pandemic, the global health system needed to review important processes involved in daily routines such as outpatient activities within the hospital, including follow-up visits of implantable cardiac electronic devices (CIEDs) carried out in office. The aim of this study is to describe our 3.5 years of real-world experience of a full remote CIED follow-up, evaluate the success rate of remote transmissions, and verify the adopted organizational model. From April 2020 to November 2023, all patients with an activated and well-functioning remote monitoring (RM) system and automatic algorithms, like autocapture and autosensing, underwent exclusive RM follow-up. Unscheduled in-office visits were only prompted by remote yellow or red alerts. Patients were divided into two groups, based on available technology: Manual Transmission System (MTS) and Automatic Transmission System (ATS). The ATS group, in addition to ensuring a daily transmission of any yellow or red alerts, was checked at least every 15 days to ensure a valid connection. An automatic transmission was scheduled once a year, irrespective of alerts occurred. The MTS group provided a manual transmission every 6 months. One thousand nine hundred thirty-seven consecutive patients were included in the study. By the end of November 2023, a total of 1409 patients (1192 in the ATS and 217 in the MTS group) were still actively followed by our remote clinic (384 expired, 137 dismissed, 7 transferred). The overall success rate of transmissions with the adopted organizational model was 96.6% in the ATS group (connection index) and 87% in the MTS group. Conventional in-hospital follow-up visits decreased by 44%. Total clinic working time, resulting from the sum of the time spent during in-hospital and remote follow-up, after an initial increase, was progressively reduced to the actual -25%. Mortality rate for any cause was 7.5% per year in remote follow-up patients and 8.3% (p=NS) in in-office patients. In the ATS group, no device malfunctions were notified to our remote clinic, before we had already realized it through appropriate alerts. The available technology makes moving to a 100% remote clinic possible, without overwhelming clinic workflow, safely. Adopting an appropriate organizational model, it is possible to maintain high transmission success rates. The automatic transmissions allow a more frequent control of patients with CIED.

An Energy-Balance Clustering Routing Protocol for Intra-Body Wireless Nanosensor Networks

Aims and Background: Numerous sensor nodes spread out across the surveillance region form the Wireless-Sensor Network (WSN), a smart, self-organizing network. Since the lumps can typically only be motorized by batteries, creating a WSN while maintaining an optimal energy balance and extending the network's lifetime is the biggest issue. Methods: A novel network architecture that integrates nanotechnology with sensor networks is known as a Wireless-NanoSensor-Network(WNSN). A new area of focus in research is intra-bodyiWNSNs, which are WNSNs with promising potential applications in biomedicine, damage detection, and intra-body health monitoring. We suggest an energy-balance-clustering-routing protocol (EBCR) for iSN nodes that have limited energy storage, short communication range, and low computation and processing capabilities. The protocol uses a novel hierarchical clustering approach to lessen the communication burden on nano-nodes. Results: Cluster nano-nodes can use one-hop routing to send data directly to the Cluster-Head(CH) nodes, and the CH-nodes can utilize multi-hop routing to send data to the nano control node. In addition, selecting the next hop node to minimize energy usage while guaranteeing successful data packet delivery involves balancing distance and channel capacity. The protocol's strengths in energy efficiency, network-lifetime extension, and data-packet transmission success rate were highlighted by the simulation results. Conclusion: It is clear that the EBCR protocol is a viable option for iWNSNs' routing system.

Multi-Cell Cooperative Resource Allocation and Performance Evaluation for Roadside-Assisted Automated Driving

The proliferation of wireless technologies, particularly the advent of 5G networks, has ushered in transformative possibilities for enhancing vehicular communication systems, particularly in the context of autonomous driving. Leveraging sensory data and mapping information downloaded from base stations using I2V links, autonomous vehicles in these networks present the promise of enabling distant perceptual abilities essential to completing various tasks in a dynamic environment. However, the efficient down-link transmission of vehicular network data via base stations, often relying on spectrum sharing, presents a multifaceted challenge. This paper addresses the intricacies of spectrum allocation in vehicular networks, aiming to resolve the thorny issues of cross-station interference and coupling while adapting to the dynamic and evolving characteristics of the vehicular environment. A novel approach is suggested involving the utilization of a multi-agent option-critic reinforcement learning algorithm. This algorithm serves a dual purpose: firstly, it learns the most efficient way to allocate spectrum resources optimally. Secondly, it adapts to the ever-changing dynamics of the environment by learning various policy options tailored to different situations. Moreover, it identifies the conditions under which a switch between these policy options is warranted as the situation evolves. The proposed algorithm is structured in two layers, with the upper layer consisting of policy options that are shared across all agents, and the lower layer comprising intra-option policies executed in a distributed manner. Through experimentation, we showcase the superior spectrum efficiency and communication quality achieved by our approach. Specifically, our approach outperforms the baseline methods in terms of training average reward convergence stability and the transmission success rate. Control-variable experiments also reflect the better adaptability of the proposed method as the environmental conditions change, underscoring the significant potential of the proposed method in aiding successful down-link transmissions in vehicular networks.

RON‐based cross‐chain routing optimization strategy in metaverse

AbstractMetaverse is a new ecology that integrates the digital world with the physical world, generates a mirror image of the real world based on digital twin technology, and guarantees the fairness of the virtual world through the trusted mechanism of blockchain. Cross‐chain communication technology is the key to realizing data circulation and collaborative processing between blockchains, which provides the communication foundation for the massive connection of blockchains in the metaverse. The current cross‐chain communication mode is dominated by direct‐connect routing, leading to network congestion and high propagation delay once the direct‐connect link fails and cannot be recovered quickly. To optimize direct‐connect routing, this paper proposed a cross‐chain routing optimization strategy based on RON (Resilient Overlay Network), that is, Cross‐Chain_RON, which firstly applies RON to reconstruct the direct‐connect routing model, and then selects the optimal link through the shortest‐path algorithm and policy routing, and combines with the RON performance database to improve the data transmission efficiency. Finally, the two communication modes were simulated by simulation tools. The experiments show that Cross‐Chain_RON outperforms the direct‐connect routing in terms of latency, rate of successful data transmission, and throughput in poor network environments, but at the expense of a certain amount of system overhead.

Clustered DTN routing based on sensing node relationship strength

AbstractDelay tolerant networks (DTNs) is a network evolved from mobile networks. Differing from the traditional network, which has a stable end‐to‐end transmission path, DTNs are sparse and intermittently connected mobile ad hoc network, which are widely used in harsh environments, such as battlefields, seabed, space communication networks, and so on. In DTNs, intermittent connectivity, partitioned network, long delays and node mobility characteristics make the network fail to communicate frequently, therefore, how to successfully forward the message is of extreme importance. Up to now, almost all the traditional models in DTNs use the store‐carry‐forward method. This paper proposes a novel clustered DTN routing model based on sensing node relationship strength. The routing mechanism takes advantage of the number of other nodes encountered by the nodes in the process of movement and the changes in the number of nodes to calculate the strength of the relationship between nodes, and clusters DTN routing according to the strength of the relationship between nodes. Moreover, the relationship between nodes in a cluster and other clusters is used to transmit messages between clusters, and messages are transmitted within clusters according to the strength of the relationship between nodes. Simulation results show that the routing mechanism not only increases the success rate of message transmission, but also reduces the transmission delay of messages and improves network performance.

The communication and measurement architecture of BDS-3 global operations and services

This study introduces the third-generation BeiDou Navigation Satellite System (BDS-3) system’s creative global operation and service architecture based on regional stations and Ka-band inter-satellite links (ISLs). The architecture’s contributions to BDS-3 include not only combined orbit determination and time synchronization but also autonomous orbit determination and time synchronization, global operations and services. The BDS-3 on-orbit operation results are being presented in the paper to verify the designed capabilities. Compared with BDS-2, the orbit determination accuracy is improving from 0.6 m to 0.15 m, and the time synchronization accuracy is improving from 6.67 ns to 1.16 ns. Under autonomous orbit determination and time synchronization, the 30-day orbit maintenance accuracy reaches 16 m, and the time synchronization accuracy reaches 3.74 ns. The success rate of data transmission reaches 99%, and the delay is better than 12.9 s. In the future, laser ISLs will be used to enhance the system structure and further improve global operation and service capabilities.

Research on Secure Community Opportunity Network Based on Trust Model

With the innovation of wireless communication technology and the surge of data in mobile networks, traditional routing strategies need to be improved. Given the shortcomings of existing opportunistic routing strategies in transmission performance and security, this paper proposes a community opportunistic routing decision-making method based on the trust model. This algorithm calculates the node’s trust value through the node’s historical forwarding behavior and then calculates the node’s trust value based on the trust model. Thresholds and trust attenuation divide dynamic security communities. For message forwarding, nodes in the security community are prioritized as next-hop relay nodes, thus ensuring that message delivery is always in a safe and reliable environment. On this basis, better relay nodes are further selected for message forwarding based on the node centrality, remaining cache space, and remaining energy, effectively improving the message forwarding efficiency. Through node trust value and community cooperation, safe and efficient data transmission is achieved, thereby improving the transmission performance and security of the network. Through comparison of simulation and opportunistic network routing algorithms, compared with traditional methods, this strategy has the highest transmission success rate of 81% with slightly increased routing overhead, and this algorithm has the lowest average transmission delay.

Against Jamming Attack in Wireless Communication Networks: A Reinforcement Learning Approach

When wireless communication networks encounter jamming attacks, they experience spectrum resource occupation and data communication failures. In order to address this issue, an anti-jamming algorithm based on distributed multi-agent reinforcement learning is proposed. Each terminal observes the spectrum state of the environment and takes it as an input. The algorithm then employs Q-learning, along with the primary and backup channel allocation rules, to finalize the selection of the communication channel. The proposed algorithm designs primary and backup channel allocation rules for sweep jamming and smart jamming strategies. It can predict the behavior of jammers while reducing decision conflicts among terminals. The simulation results demonstrate that, in comparison to existing methods, the proposed algorithm not only enhances data transmission success rates across multiple scenarios but also exhibits superior operational efficiency when confronted with jamming attacks. Overall, the anti-jamming performance of the proposed algorithm outperforms the comparison methods.

Cooperative communication and relay node selection algorithm based on SWIPT

The development of nanotechnology and the emergence of new materials such as graphene make wireless nanosensor networks (WNSN) based on electromagnetic communication possible. Due to the energy, computing power and transmission distance carried by nano-nodes are extremely limited, nanonetworks are usually ultra-dense, so nanonetworks are usually cluster-managed. Aiming at the problem of data transmission failure caused by poor link quality or insufficient energy in inter-cluster transmission, a relay node selection algorithm (RNS-AHP) based on simultaneous wireless information and power transfer (SWIPT) and analytics hierarchy process (AHP) is proposed. The algorithm is based on the SWIPT cooperative communication model, and uses the AHP to calculate the weights of four factors: the distance of the relay node, the channel capacity, the remaining energy of the relay node and the signal-to-noise ratio of the relay node receiver in the process of relay node selection. According to the weight, the weight of the candidate relay node is obtained as the selection criterion of the relay node. The simulation results show that the RNS-AHP algorithm can improve the average throughput, the success rate of data packet transmission and the energy efficiency.

Analysis of multimedia network data security based on routing algorithm

In today’s society, the application of multimedia data is increasing, and the accompanying data security is also an important issue. This paper will analyze the data security of multimedia network based on routing algorithm. The advantages of wireless local area networks (WLAN) and ad hoc networks are combined in wireless mesh networks (WMNs), which have steadily grown to be a reliable method of wireless broadband access. The application of multi RF and multi-channel technology improves the performance of WMN. However, the limitation that includes the number of wireless network interface cards and channels in the network, results in the great challenges ofrouting and channel allocation in multi RF and multi-channel WMN. Based on AODV algorithm, this paper proposes a new cross layer routing algorithm which is JRCA-AODV algorithm. The simulation results demonstrate that the JRCA-AODValgorithm can successfully increase multiRF multi-channel WMN throughput while lowering packet loss rate and network delay. Take the packet rate of 8 Mbps as an example, the network throughput based on JRCA-AODV algorithm is 1.14 times that of MRMC-AODV algorithm, the packet loss rate based on JRCA-AODV algorithm is 14.06% lower than that of MRMC-AODV algorithm, and the network time delay based on JRCA-AODV algorithm is 14.27% lower than that of MRMC-AODV algorithm. At the same time, JRCA-AODV algorithm can effectively reduce the probability of data loss caused by the competitive and shared access mechanism of the channel, improve the success rate of data transmission, thus reduce the probability of data eavesdropping and tampering in the process of repeated transmission, and improve the security of multimedia network data.This will provide a new direction for promoting the idea of multimedia data security.

A Multi-Path QKD Algorithm with Multiple Segments

Quantum Key Distribution (QKD) provides unconditional peer-to-peer security based on the principles of quantum physics. By utilizing relay nodes, the security of QKD can be extended over longer distances. However, the introduction of relay nodes brings both security and communication success rates issues. To tackle those issues we propose an enhanced multi-path scheme. The key features of our proposal are as follows: 1. By taking the reliability of relay nodes as one of the algorithm inputs,making the scheme more suitable for partially trusted QKD (PTQKD) networks. 2. By using Multi-Segment Multi-Path approach increases the difficulty for attackers to obtain complete key information and improves the security of PTQKD. 3. The adaptive routing algorithm generates a sufficient number of diverse paths based on node contribution rate, key freshness, and reliability. We conducted a theoretical analysis of the algorithm,and simulation results on PTQKD demonstrate that our method outperforms traditional QKD methods in terms of security and transmission success rate. This advancement has the potential to enhance the adoption of QKD networks.

Laser communications system with drones as relay medium for healthcare applications.

This article introduces a prototype laser communication system integrated with uncrewed aerial vehicles (UAVs), aimed at enhancing data connectivity in remote healthcare applications. Traditional radio frequency systems are limited by their range and reliability, particularly in challenging environments. By leveraging UAVs as relay points, the proposed system seeks to address these limitations, offering a novel solution for real-time, high-speed data transmission. The system has been empirically tested, showcasing its ability to maintain data transmission integrity under various conditions. Results indicate a substantial improvement in connectivity, with high data transmission success rate (DTSR) scores, even amidst environmental disturbances. This study underscores the system's potential for critical applications such as emergency response, public health monitoring, and extending services to remote or underserved areas.

IoT-Enabled Real-Time Monitoring System for Electricity Consumption in Maritime SMEs: Design and Evaluation

The growing demand for electrical energy in Small and Medium Enterprises (SMEs) within the maritime sector, necessitates efficient and cost-effective monitoring systems. This paper presents the design and implementation of an Internet of Things (IoT)-based real-time monitoring system for electricity consumption, specifically aimed at maritime SMEs. Using SCT-013-000 and ZMPT101B sensors for current and voltage measurements, the system enables remote monitoring through the Cayenne IoT platform. Experimental results demonstrate an average accuracy rate of 93.94% for current measurement and 98.50% for voltage measurement. Data transmission success rates were 74.00% and 99.02% in two consecutive tests. The proposed system provides an affordable and practical solution for maritime SMEs, contributing to improved energy management, reduced operational costs, and the promotion of sustainable practices. Future work includes the integration of predictive analytics and smart grid technology to further enhance energy efficiency and sustainability in maritime operations.

Exploring the Diversified Development of Vocal Music Teaching Methods in Colleges and Universities Combined with Multi-Priority Dynamic Threshold Algorithm

Abstract Enhancing the integration of modern information technology with educational practices, particularly in the vocal classroom, is crucial for improving academic quality. This paper presents an optimization method aimed at addressing the limitations of the existing multi-priority dynamic threshold algorithm. The specific performance is as follows: first, on the original basis of the PDT-RED algorithm, the expected cache utilization is set to ensure the utilization of the buffer. The optimized OPDT-RED algorithm should be used to ensure that the data frame is received with a high probability. Then, according to the channel environment, the dynamic fullness threshold was adjusted, which improved the performance of the highest priority queue with a high transmission success rate. Lastly, the measurements of vocal characteristic parameters like pitch, rhythm, beat, melody, and others. The goal was to create a complete evaluation network model for vocal teaching, which was applied to actual cases. The model’s performance and its effectiveness were evaluated. After testing, the subjects’ vocal rhythmic tempo was below 60, while the range of the sample repertoire was between 60 and 90, presenting two different forms of expression. The model proposed in this paper has the optimal effect on the enhancement of expressive movements while singing; 15.33% of the students improved by two notches, and the vocal teaching model designed in this paper has a very good effect on vocal enhancement.

Analysis of the change path of news dissemination under the trend of short-video convergence - Based on multi-priority dynamic thresholding algorithm

Abstract This paper proposes a multi-priority dynamic threshold algorithm for short video news transmission. The average queue threshold is dynamically adjusted according to the packet priority and unused cache space, and the data frames of short video news nodes are processed into multiple pulse data segments after the encoding process. The success rate and confidentiality of data transmission are improved by scrambling each segment of pulse data. Finally, after updating the thresholds at all levels, the overall thresholds of short video news are kept full to meet the performance requirements of the highest priority queue of short video news. The results show that 95.03% of the short video news with a high dissemination degree has a file size of less than 5 MB, and its video length is most popular among users in the range of 8~15 seconds. Compared to the traditional news dissemination mode, short video news is not limited by time and space, which greatly improves the efficiency of information dissemination.

WLQRP: A Weighting Link-Quality-Based Routing Protocol for Underwater Sensor Networks

Due to challenges posed by long propagation distances, high mobility, limited bandwidth, multipath propagation, and Doppler effects in underwater acoustic sensor networks (UASNs), the design of routing protocols faces numerous complexities. To enhance reliability in sparse node regions and reduce network energy consumption, this paper proposes a dependable and energy-efficient routing protocol termed WLQRP for weighting link-quality-based routing protocol. WLQRP leverages link quality information between nodes for data forwarding. When computing the link quality between nodes, it considers not only the historical transmission success ratio between nodes and their immediate relay but also incorporates the link quality between the relay and the relay’s next-hop nodes. This approach effectively reduces the likelihood of encountering void regions. Moreover, the protocol judiciously selects the next-hop node to improve transmission success rates, thereby minimizing data packet retransmissions and reducing energy consumption. We conduct MATLAB simulations to evaluate the WLQRP protocol, comparing it against the VBF (vector-based forwarding) and the HH-VBF (hop-by-hop vector-based forwarding) underwater routing protocol. Experimental results demonstrate that WLQRP enhances network performance in terms of data transmission rate, energy consumption, and end-to-end delay. These findings validate the efficacy of the proposed protocol.

Implementation of LoRa Wireless Communication in Smart Diabetic Shoes Design

Diabetes is a widespread medical condition affecting a substantial portion of the global population. It is a metabolic disorder known as diabetes mellitus, characterized by severe fluctuations in blood glucose levels due to inadequate insulin production in the human body. The effective monitoring of diabetes is of paramount importance to researchers as it holds the potential to enhance the quality of healthcare services. Among the challenges faced by individuals with diabetes, one prevalent issue is the development of ulcers, which can be challenging to detect promptly. Technological advancements offer a promising avenue for cost-effective and continuous monitoring of chronic diseases like diabetes. This study centers on the development of IoT-based intelligent diabetic footwear that incorporates pressure sensors and temperature monitoring for individuals with diabetic feet. In the evaluation of SX1278 (Ra-02) Lora Module communication, deployed at eight different locations, it was observed that two of these points achieved a flawless transmission success rate of 100%. Conversely, the communication failed at points 6 and 7, with a success rate falling below 50%. Some of these failures can be attributed to signal obstructions, including natural elements such as trees and terrain, as well as man-made structures like buildings and machinery workshops, which hinder the efficient transmission of signals from the end node to the gateway. The results of this research provide positive implications in the form of developing IoT-based diabetes shoes that can be applied with alternative Lora communication connections in areas with poor internet signal.

Multi-Agent Reinforcement Learning Resources Allocation Method Using Dueling Double Deep Q-Network in Vehicular Networks

The communication between vehicle-to-vehicle (V2V) with high frequency, group sending, group receiving and periodic lead to serious collision of wireless resources and limited system capacity, and the rapid channel changes in high mobility vehicular environments preclude the possibility of collecting accurate instantaneous channel state information at the base station for centralized resource management. In addition, road and traffic safety have very strict requirements for low-latency, high-reliability communication, and information transmission is only valuable within a certain range, and requires timeliness. For the Internet of Vehicles (IoV), it is a fundamental challenge to achieve low latency and high reliability communication for real-time data interaction over short distances in a complex wireless propagation environment, as well as to attenuate and avoid inter-vehicle interference through a reasonable spectrum allocation. To solve the above problems, this paper proposes a resource allocation (RA) method using dueling double deep-Q network reinforcement learning (RL) with low-latitude fingerprints and soft-update architecture (D3QN-LS) while constructing a multi-agent model based on a Manhattan grid layout urban virtual environment, with communication links between V2V links acting as agents to reuse vehicle-to-infrastructure (V2I) spectrum resources. These agents work in concert, interact with the environment, receive appropriate observations, rewards, and ultimately learn to improve power and spectrum allocation to provide a better entertainment experience and safer driving environment for users. We also address the shortcomings in existing literature studies. Firstly, the low upper limit of transmitted data volume; Secondly, the artificial assumption that the spectrum is consistent with the number of V2V links, which is insufficient for future applications in spectrum shortage sections. We accordingly extend the amount of transmitted data while adding a scenario where the spectrum resources are relatively short, i.e., the number of V2V links is significantly larger than the amount of spectrum. Experimental results show that with proper training mechanism and reward function construction, multiple intelligence can cooperate effectively. Therefore, the total link capacity of the V2I link and the success rate of periodic security information transmission are further improved.

Deep Reinforcement Learning for Multi-Objective Resource Allocation in Multi-Platoon Cooperative Vehicular Networks

Grouping vehicles into platoons is a promising cooperative driving scenario to enhance the traffic safety and capacity of future vehicular networks. However, fast changing channel conditions in multi-platoon vehicular networks cause tremendous uncertainty for resource allocation. In addition, the unprecedented proliferation of various emerging vehicle-to-infrastructure (V2I) applications may result in some service demands with conflicting quality of experience. In this paper, we formulate a multi-objective resource allocation problem, which maximizes the transmission success rate of intra-platoon communications and the mean opinion score (MOS) of V2I communication links. To efficiently solve this multi-objective optimization problem, we resort to a deep reinforcement learning (DRL) framework. Specifically, we divide it into a set of scalar optimization subproblems based on the weighted sum approach and model each one as a partially observable stochastic game (P-OSG), where each platoon acts as an agent and the actions taken by all platoons correspond to the resource allocation solution. We further propose a Contribution-based Dual-Clip Proximal Policy Optimization (CD-PPO) algorithm to deal with each subproblem, which is a DRL algorithm based on the actor-critic framework. The network parameters of all subproblems are then optimized collaboratively by using the proposed training algorithm and the neighborhood parameter transfer strategy. The desired Pareto front is obtained when all the subproblems are solved. Simulation results reveal that proposed algorithm can outperform the other algorithms in terms of the MOS and transmission success rate.