Data Transmission Delay Research Articles

Spiking Quantum Fire Hawk Network Based Reliable Scheduling for Lifetime Maximization of Wireless Sensor Network

ABSTRACTManaging energy consumption poses a substantial challenge within Wireless Sensor Networks (WSN) due to frequent communication among sensor nodes. A reliable scheduling framework presents a promising solution for maximizing WSN lifetime, minimizing energy consumption, and ensuring robust communication. Despite various proposed methods for reliable scheduling, energy consumption remains high. To address this, a spiking quantum fire hawk network‐based reliable scheduling in WSN is introduced. This research incorporates clustering, duty‐cycle management, and reliable routing to enhance energy efficiency. An improved Nutcracker Optimization‐based Cluster Head (CH) election and Spiking Quantum Fire Hawk Network duty cycling contribute to optimal CH selection and increased network lifetime. Additionally, a Link Quality‐based Energy Aware Proficient Trusted Routing Protocol (LQEAP‐TRP) minimizes data transmission delay, offering reliable routing. Finally, the data communication is done in the reliable route. The developed approach is executed in Network Simulator and validated with the existing protocols. The results of the simulations indicate that the proposed approach achieves a network lifetime of 99.34% and a packet delivery ratio of 99.95%.

Adaptive Congestion Control in IoT Networks: Leveraging One-Way Delay for Enhanced Performance

With the exploding number of IoT devices generating vast data volumes, there is a growing risk of significant performance degradation without efficient congestion management. To tackle this challenge, efficient regulation and supervision are essential for managing congestion in IoT networks. This research work introduces a One-Way-Delay (OWD)-based congestion control (CC) method that estimates data transmission delays of the communication path and adjusts network traffic accordingly. The proposed method enhances IoT device performance by continuously monitoring OWD along the transmission path to identify and mitigate congestion. Comparative analysis with existing methods demonstrates that the proposed approach more effectively utilizes network resources, reduces congestion, and improves throughput while ensuring fairness and reliability within the IoT infrastructure. The experimental simulations show that the proposed OWD-based method outperforms well-established TCP variants such as BBR, TCP Cubic, HTCP, and New Reno, achieving average throughput improvements ranging from 4.1% to 22.7%. The proposed method also maintains fairness in mixed-traffic environments and effectively manages congestion in complex network topologies.

Spatial evaluation rainfall estimation on weather radar using Marshall–Palmer reflectivity–rainfall rate in Banten

Based on data from the National Disaster Management Agency (Ind.: Badan Nasional Penanggulangan Bencana – BNPB), throughout 2022, more than 91% of disaster events were hydrometeorological disasters, with floods at 43% and landslides at 17%. One of the factors for floods and landslides is high rainfall intensity. Automatic rain gauge (ARG) is a rainfall observation instrument that can accurately measure rainfall at observation points. However, it has problems such as communication systems that cause delays in data transmission, low instrument density, and inability to cover a wide spatial area, which can affect the accuracy of rainfall information. Weather radar is a remote sensing instrument that can estimate rainfall spatially so that weather radar observations can reach areas of the region that do not have ARG. However, before being used as rainfall information, estimation rainfall needs to be evaluated or calibrated. Evaluation of rainfall estimation on weather radar to ARG in Banten at a 30– 120 km distance range, shows a coefficient of determination above 0.8. Based on the studies that have been conducted, increase of root mean square error (RMSE) is due to influence of radar observation range and observation distance on ARG. Adjustment of rainfall estimation improves the accuracy of rainfall estimation. Adjusting rainfall estimation can reduce RMSE by 50%.

Enhanced Hybrid Congestion Mitigation Strategy for ‘6LoWPAN-RPL based patient-centric IoHT’

The Internet of Healthcare Things (IoHT) is rapidly evolving, providing new opportunities to enhance healthcare delivery. However, the resource limitation of connected medical sensing devices leads to congestion, resulting in reduced network performance, delayed data transmission, and loss of critical medical information, which can have significant consequences in healthcare. To address this issue, this paper proposes an Enhanced Hybrid Congestion Mitigation Strategy (EHCMS) for IPv6 over low-power wireless personal area networks (6LoWPAN) and routing protocol for low-Power and lossy networks (RPL) based patient-centric IoHT (PC-IoHT). The EHCMS combines several techniques, including traffic management, network topology optimization, and load balancing, to enhance network performance and reduce congestion. The proposed framework is a hybrid strategy that utilizes resource- and traffic-control mechanisms to alleviate congestion in the 6LoWPAN-RPL-based patient-centric IoHT network. For the resource-control-based approach, a congestion-aware composite objective function is designed using a few congestion-specific routing metrics and formulated as a multi-attribute decision-making problem solved using Grey relational analysis (GRA). In addition, a non-linear multi-criteria optimization problem-based transmission rate adaptation mechanism is contrived as a traffic-control scheme for congestion mitigation. The effectiveness of the proposed EHCMS is evaluated using simulations on the Cooja simulator in the Contiki-3.0 OS and compared with existing congestion-alleviating strategies. The results demonstrate that the proposed framework can significantly reduce congestion in the IoHT network, perform better than existing works, and improve the quality of service. This research paper contributes to the field of IoHT by proposing an effective congestion mitigation strategy that enhances the reliability and performance of the PC-IoHT network, ultimately improving the quality of healthcare delivery.

Energy efficient and reliable data transmission in WBAN using multivariate gradient divergence African buffalo optimization

SummaryWireless body area network (WBAN), a popular radio communication technique, tracks patients' health conditions remotely using small, low‐power, portable sensors positioned on the body to sense vital signs and send data to a control node. To sustain long‐term health monitoring, WBANs require significant energy from the nodes. This research proposes an energy efficient and reliable data transmission using multivariate gradient divergence African buffalo optimization (EEMGDABO) to minimize the delay of critical node data transmission by identifying the optimal path to the control node. The protocol comprises two key processes: winsorized correlative segmented symbolic regression with reinforcement learning (WCS‐SRRL) and the walrus optimization algorithm (WaOA) for accurate priority‐based classification of health data (normal, abnormal, and critical). The second process involves transferring these priority‐based data with reduced energy consumption and delay while increasing throughput by determining the best path from the critical node to the destination (hospital/doctor) using EEMGDABO, which calculates node distance and energy fitness value. Implemented in MATLAB with a health monitoring WBAN dataset, the proposed protocol demonstrates 23% less energy consumption, 12.33% less delay, and 23.44% higher network lifetime compared to existing optimization approaches.

Precision encoder grating mounting: a near-sensor computing approach.

The rotary motor plays a pivotal role in various motion execution mechanisms. However, an inherent issue arises during the initial installation of the encoder grating, namely, eccentricity between the centers of the encoder grating and motor shaft. This eccentricity substantially affects the accuracy of motor angle measurements. To address this challenge, we proposed a precision encoder grating mounting system that automates the encoder grating mounting process. The proposed system mainly comprises a near-sensor detector and a push rod. With the use of a near-sensor approach, the detector captures rotating encoder grating images, and the eccentricity is computed in real-time. This approach substantially reduces the time delays in image data transmission, thereby enhancing the speed and accuracy of eccentricity calculation. The major contribution of this article is a method for real-time eccentricity calculation that leverages an edge processor within the detector and an edge-vision baseline detection algorithm. This method enables real-time determination of the eccentricity and eccentricity angle of the encoder grating. Leveraging the obtained eccentricity and eccentricity angle data, the position of the encoder grating can be automatically adjusted by the push rod. In the experimental results, the detector can obtain the eccentricity and eccentricity angle of the encoder grating within 2.8 s. The system efficiently and precisely completes a encoder grating mounting task in average 25.1 s, and the average eccentricity after encoder grating mounting is 3.8 µm.

Modeling impacts of different data transmission delays on traffic jam, fuel consumption and emissions on curved road

Under the intelligent transportation system, the use of autonomous vehicles can ease traffic jam, and reduce fuel consumption and emissions. The effects of different data transmission delays on traffic jam, fuel consumption and emissions on curved road are studied by constructing an extended multi-vehicle curve following model considering different data transmission delays to describe the curve following behavior of multiple autonomous vehicles. Subsequently, the stability of the novel model is derived by using linear stability theory. And then, numerical simulation is executed. Finally, some influence factors including curvature radius, backward looking effect, position signal time delay, and velocity signal time delay are discussed. Numerical simulation results show that reducing curvature radius and increasing backward looking effect can reduce fuel consumption and emissions, decrease density fluctuations, thereby gradually eliminating traffic jam. In addition, as difference between position signal time delay and velocity signal time delay decreases, the improvement degree in fuel consumption and emissions is limited. Meanwhile, it also makes density fluctuation decreases gradually, enhances traffic stream stability, and alleviates traffic jam, which is keeping with theoretical analysis. The finding has theoretical implications for designing autonomous vehicle control algorithms to reduce the negative effects of data transmission delays in traffic flow.

Application of edge computing and IoT technology in supply chain finance

This study proposes an IoT data management framework based on blockchain and edge computing and conducts detailed simulation experiment design and performance evaluation for the field of supply chain finance. Previous methods often struggled with high latency, limited scalability, and inadequate risk management, and to address these issues, the experimental platform includes traditional centralized systems, distributed systems, blockchain-based systems, and edge computing + blockchain systems. By monitoring indicators such as data processing delay, data transmission delay, data retrieval time, system throughput, and data integrity of each platform, and introducing evaluation formulas for credit risk, operational risk, and market risk, the performance of different systems in processing supply chain financial data is comprehensively analyzed. The experimental results show that the edge computing + blockchain system performs well in data processing efficiency, security, real-time, and system throughput, especially under high load conditions. Our market risk value is reduced to 0.015. This framework improves the efficiency and security of data transmission and effectively reduces credit risk and operational risk, providing an efficient and reliable solution for data management in supply chain finance.

A computationally intelligent framework for traffic engineering and congestion management in software-defined network (SDN)

Software-defined networking (SDN) revolutionizes network administration by centralizing control and decoupling the data plane from the control plane. Despite its advantages, the escalating volume of network traffic induces congestion at nodes, adversely affecting routing quality and overall performance. Addressing congestion has become imperative due to its emergence as a fundamental challenge in network management. Previous strategies often faced drawbacks in handling congestion, with issues arising from the inability to efficiently manage heavy packet surges in specific network regions. In response, this research introduces a novel approach integrating a multiplicative gated recurrent neural network with a congestion-aware hunter prey optimization (HPO) algorithm for effective traffic management in SDN. The framework leverages machine learning and deep learning techniques, acknowledged for their proficiency in processing traffic data. Comparative simulations showcase the congestion-aware HPO algorithm's superiority, achieving a normalized throughput 3.4–7.6% higher than genetic algorithm (GA) and particle swarm optimization (PSO) alternatives. Notably, the proposed framework significantly reduces data transmission delays by 58–65% compared to the GA and PSO algorithms. This research not only contributes a state-of-the-art solution but also addresses drawbacks observed in existing methodologies, thereby advancing the field of traffic engineering and congestion management in SDN. The proposed framework demonstrates notable enhancements in both throughput and latency, providing a more robust foundation for future SDN implementations.

Digital Twin of Space Environment: Development, Challenges, Applications, and Future Outlook

This paper explores and discusses the revolutionary applications of digital twin technology in space environments and its profound impact on future space exploration activities. Originating from a proposal by the National Aeronautics and Space Administration (NASA) in 2002, digital twin technology aims to enhance the safety and reliability of space missions by creating precise virtual models. As the technology has evolved, its applications have successfully expanded beyond aerospace to include Industry 4.0, healthcare, and urban management, demonstrating remarkable cross-industry adaptability and broad impact. In space applications, digital twin technology can not only improve spacecraft design and maintenance processes but also enhance the efficiency of mission planning and execution. It plays a crucial role in astronaut training and emergency response as well. Particularly in extreme space conditions, this technology provides real-time monitoring and fault prediction, significantly enhancing mission safety and success rates. However, despite its recognized potential, the implementation of digital twins in space environments faces numerous challenges, including data transmission delays, model accuracy, and the design of user–system interactions. In the future, as artificial intelligence (AI) and machine learning (ML) technologies become mature and integrated, the digital twin will play a more central role in space missions, especially in remote operations, complex system management, and deep space exploration. This article is to overview key technical features, application examples, and challenges of digital twin technology, aiming to provide a comprehensive reference framework for researchers and developers while inspiring further in-depth studies and innovative applications.

A machine learning-based multiclass classification model for bee colony anomaly identification using an IoT-based audio monitoring system with an edge computing framework

Approximately one-third of human food is derived from flowering plants, and 80% of those plants require honeybees for pollination. However, climate change is affecting bees as well as the beekeeping industry. Having continuous information on the condition of honey bee colonies would be helpful in studying new diseases such as colony collapse disorder, as well as in developing novel beekeeping tools to improve the health management of bee colonies. In this study, an audio monitoring system is established based on Internet of Thing (IoT) technology to record the sounds produced by bees. A colony status classification model coupled with a machine learning algorithm is used to analyze the sound data of bee colonies under various treatments to classify the colony conditions, such as queenless, virus-infected, and pesticide-contaminated. With an edge computing framework, the classification is done by the monitoring system, which greatly reduces data transmission delay and cloud computing burden. The field test results show that the accuracy andF1-score for the classification model for the beehive under various treatments reach 98.8% and 0.98, respectively.The proposed IoT-based audio monitoring system for beehives can assist beekeepers and users in managing bee colonies more effectively by providing automatic, accurate, and real-time monitoring information regarding the current colony conditions.

FETR: Feature Transformer for vehicle-infrastructure cooperative 3D object detection

3D object detection plays a crucial role in the perception system of autonomous vehicles, however, the vehicle’s field of view is restricted due to obstructions from nearby vehicles and buildings. Vehicle-infrastructure cooperation can compensate for the issue of visibility, but due to discrepancies in timestamps between vehicle and infrastructure sensors as well as data transmission delays, there is typically a time asynchrony between vehicle and infrastructure data. Therefore, Feature Transformer (FETR) has been introduced, which is a vehicle-infrastructure cooperative 3D object detection model utilizing Transformer as a Feature Predictor. The Transformer Predictor is capable of predicting features of future frame based on the current frame features, efficiently addressing the problem of time asynchrony. Additionally, to enhance the precision of 3D object detection, we have introduced a plug-and-play module named Mask Feature Enhancement (MFE), MFE employs a mask to amplify the features in the object region while simultaneously diminishing the features of the surrounding environment, enlarging the difference between object features and environmental features, thereby improving the detection effect. Experimental results show that FETR attains a 68.15 BEV-mAP (IoU=0.5) on the DAIR-V2X dataset, with a 200ms latency, and the data transmission is merely 6.0×104 bytes, constituting just 4.2% of the original point cloud data, outperforming current vehicle-infrastructure cooperative models in terms of both precision and data transmission.

Network Latency in Teleoperation of Connected and Autonomous Vehicles: A Review of Trends, Challenges, and Mitigation Strategies

With remarkable advancements in the development of connected and autonomous vehicles (CAVs), the integration of teleoperation has become crucial for improving safety and operational efficiency. However, teleoperation faces substantial challenges, with network latency being a critical factor influencing its performance. This survey paper explores the impact of network latency along with state-of-the-art mitigation/compensation approaches. It examines cascading effects on teleoperation communication links (i.e., uplink and downlink) and how delays in data transmission affect the real-time perception and decision-making of operators. By elucidating the challenges and available mitigation strategies, the paper offers valuable insights for researchers, engineers, and practitioners working towards the seamless integration of teleoperation in the evolving landscape of CAVs.

A Mobile AD Hoc Network Relay Method based on OLSR Protocol

Aiming at the reliability and performance problems of data transmission in the complex mobile AD hoc network environment, a relay method is designed and implemented in the unstable network environment with complex dynamics such as mobile, multi-hop and no center according to practical needs. By introducing a prior link-state routing protocol, OLSR is introduced. Using range value judgment, sliding window statistics and improved multi-point relay selection technology, it can effectively reduce the redundancy of relay node set, reduce the forwarding frequency of service data in the network, save the network overhead, and ensure the data transmission delay and quality in the large scale and dense mobile AD hoc network.

RANCANG BANGUN REPEATER UNTUK KOMUNIKASI LORA

Not all situations allow for direct observation of the object being observed. This is due to several factors, such as distances that are too far or terrains that are too dangerous to reach, as well as weather and temperature conditions that are not supportive. Therefore, observation or measurement cannot always be done directly and continuously in these difficult-to-reach locations. To overcome these constraints, telemetry is used for long-distance measurement. However, telemetry requires an adequate transmission range. One efficient method for long-distance transmission is using Long Range (LoRa) technology, which has deficient power consumption. LoRa repeaters are required to forward data from the Node to the Gateway to extend the transmission range. In this study, a LoRa repeater was developed for LoRa communication using the LoRa TTGO ESP32 module. The results of this repeater development demonstrate its ability to forward sensor data from the node to the gateway with a range of up to 2.8 km and a data transmission delay of 0.7 seconds.

Data-driven traffic signal adaptive control algorithm integrating vehicle perception and traffic flow data

Due to the equipment error, environmental interference and data transmission delay of vehicle flow detection, the accuracy and real-time performance of vehicle perception and traffic flow data will be affected to some extent, resulting in poor traffic signal control effect. Therefore, a data-driven traffic signal adaptive control algorithm is designed by integrating vehicle perception and traffic flow data. To complete the modeling of urban traffic, the discrete distribution and continuous distribution of traffic are obtained. Based on this research environment, the DV-hop localization algorithm is improved to sense the vehicle position. Based on the phase space reconstruction of traffic flow time series and vehicle location information, traffic flow data is predicted. Based on the driving of traffic data, the vehicle types are divided into small, medium and large three categories, and the impact weights are assigned respectively, and the weight values affecting the final allocation of green time are obtained to realize the allocation of green time. The experimental results show that: The research algorithm can not only predict the traffic flow intensity effectively, but also the predicted results are highly coincident with the actual traffic flow intensity. Vehicle arrival rates are higher, vehicle delays are shorter, and vehicles stop fewer times on average.

Edge Caching Data Distribution Strategy with Minimum Energy Consumption.

In the context of the rapid development of the Internet of Vehicles, virtual reality, automatic driving and the industrial Internet, the terminal devices in the network show explosive growth. As a result, more and more information is generated from the edge of the network, which makes the data throughput increase dramatically in the mobile communication network. As the key technology of the fifth-generation mobile communication network, mobile edge caching technology which caches popular data to the edge server deployed at the edge of the network avoids the data transmission delay of the backhaul link and the occurrence of network congestion. With the growing scale of the network, distributing hot data from cloud servers to edge servers will generate huge energy consumption. To realize the green and sustainable development of the communication industry and reduce the energy consumption of distribution of data that needs to be cached in edge servers, we make the first attempt to propose and solve the problem of edge caching data distribution with minimum energy consumption (ECDDMEC) in this paper. First, we model and formulate the problem as a constrained optimization problem and then prove its NP-hardness. Subsequently, we design a greedy algorithm with computational complexity of O(n2) to solve the problem approximately. Experimental results show that compared with the distribution strategy of each edge server directly requesting data from the cloud server, the strategy obtained by the algorithm can significantly reduce the energy consumption of data distribution.

Latency and Residual Energy Analysis of MIMO Heterogeneous Wireless Sensor Networks

Energy-constrained Wireless Sensor Networks (WSNs) have garnered significant research interest in recent years. Multiple-Input Multiple-Output (MIMO), or Cooperative MIMO, represents a specialized application of MIMO technology within WSNs. This approach operates effectively, especially in challenging and resource-constrained environments. By facilitating collaboration among sensor nodes, Cooperative MIMO enhances reliability, coverage, and energy efficiency in WSN deployments. Consequently, MIMO finds application in diverse WSN scenarios, spanning environmental monitoring, industrial automation, and healthcare applications. This research paper presents a comparative performance analysis of MIMO wireless sensor networks and traditional wireless sensor networks without MIMO using Network Simulator NS2.35 for analysis of End to End Delay for packet transmission and Residual energy of nodes. The research work shows application of MIMO in Wireless Sensor Networks with considerable improvements in Quality of Service parameters which is achieved through Spatial Multiplexing and Diversity Gain. MIMO enables multiple spatial streams, allowing several data streams to be transmitted simultaneously on the same channel. This increases the overall throughput as multiple sensors can transmit their data concurrently without interference. MIMO systems also provide diversity gain by transmitting multiple copies of the same data over different antennas which helps in mitigating the effects of fading and interference, resulting in a more reliable and higher-throughput communication link as compared to a SISO channel. Another advantage of employing MIMO in WSN is reduction in End-to-End delays in data transmission. Last but not the least, MIMO can be configured to optimize the power consumption of individual sensors by adjusting the number of antennas used and transmission power levels based on channel conditions. Hence, MIMO can help to extend the network's lifetime by conserving energy in resource-constrained sensor nodes by preservation of Residual Energy.

Improvement and optimization of coal dust concentration detection technology: Based on the 3σ criterion and the kalman filtering composite algorithm

Due to the inherent time delay in dust concentration data transmission, dust reduction equipment is unable to respond according to the current moment's dust concentration. Hence, the focus of mine dust concentration detection technology extends beyond the current dust concentration to accurately predicting the next moment's dust concentration, allowing a response window for dust reduction equipment, leading to more optimal dust reduction effects. This necessitates the introduction of the Kalman filtering algorithm. Despite the traditional single Kalman filtering algorithm's ability to make certain dust concentration predictions, it is constrained by high-temperature, high-humidity underground environments and electromagnetic pulse signal interference from fan and coal mining machine operations, with an error rate reaching up to 40 %, seriously disrupting underground dust reduction equipment. However, the composite Kalman filtering algorithm incorporating the 3σ rule effectively eliminates bad values and removes instability from the underground environment and noise, rendering it applicable to most complex environments, providing accurate predicted concentrations for dust reduction equipment, and boosting overall universality. This study designed a fusion algorithm based on the Pauta criterion and Kalman filter to further optimize the dust concentration detection technology based on electrostatic induction. The sensor's model and measuring principle were first analyzed, and the initial calibration and precision testing of the sensor using the experimental system were completed. The dust concentration fluctuation characteristics were then modeled with Matlab, and the noise variance was estimated to process and filter the dust concentration data with the Pauta criterion, Kalman filter, and fusion algorithm respectively. The accuracy of the dust concentration sensor was tested again after applying the fusion algorithm, which proved to significantly enhance the stability and accuracy of the electrostatic induction-based dust concentration sensor. This study has critical implications for coal mine dust concentration monitoring and coal miner occupational health protection.

Analisis Quality of Service (QoS) Menggunakan Standar Parameter Tiphon pada Jaringan Internet Berbasis Wi-Fi Kampus 1 Unjaya

Internet is a very important need in the life of people in the world. Universities can exchange information and share data and communicate with the internet network. General Achmad Yani University Yogyakarta (Unjaya) uses the internet network for activities that occur on campus. QoS testing or internet network performance aims to maintain the stability of network access on the Unjaya campus and reduce the risk of problems such as delays in data transmission. In addition, this research also aims to determine the results of signal quality measurements in the Unjaya real-time area. In this research, QoS measurement is done by using wireshark application. The QoS parameters measured are throughput, delay, jitter, and packet loss. Based on the measurements taken, the results obtained for QOS measurements show that WiFi network performance based on throughput, delay, jitter, and packet loss parameters is in the good category. The final result obtained after measuring QoS is that the Internet network at Campus 1 Unjaya is in the medium category based on TIPHON standardization with an index value of 3.