Data Link Research Articles

A survey of intelligent multimedia forensics for internet of things communications: Approaches, strategies, perspectives, and challenges for a sustainable future

Digital forensics is a proven method for collecting, preserving, reporting, analyzing, identifying, and presenting digital evidence from the original data, and it helps find evidence of cybercrimes. Intelligent multimedia forensics is a type of digital forensics and is essential because it is used to identify fake multimedia, including images, videos, audio, and text. This paper conducted a comprehensive survey for intelligent multimedia forensics, categorized into 3 classes: deep learning forensics, multimedia forensics, and network and Internet of Things forensics. In the first class, we provided a survey of the multiple attacks breaking the DL models, such as attacks in the training step, and the testing step, and crafted. In the craft attacks, we survey the three attacks: white box, gray box, and black box. We also provided some defense methods against DL attacks, training step attacks, and testing step attacks. In the second class, we offered a survey of multimedia forensics, including passive and active manipulation. In the third class, we provide a survey of network/IoT forensics, including the attacks in five layers: physical, data link, transport, application, and network. We also provided network/IoT attack detection in the third class using deep learning models. We applied AI models in various datasets and obtained higher accuracy and performance on the datasets from various used models. Finally, we offered the challenges and future direction for the researcher in this scope.

A synthesis of machine learning and internet of things in developing autonomous fleets of heterogeneous unmanned aerial vehicles for enhancing the regenerative farming cycle

AbstractThe use of Unmanned Aerial Vehicles (UAVs) for agricultural monitoring and management offers additional advantages over traditional methods, ranging from cost reduction to environmental protection, especially when they utilize Machine Learning (ML) methods, and Internet of Things (IoT). This article presents an autonomous fleet of heterogeneous UAVs for use in regenerative farming the result of a synthesis of Deep Reinforcement Learning (DRL), Ant Colony Optimization (ACO) and IoT. The resulting aerial framework uses DRL for fleet autonomy and ACO for fleet synchronization and task scheduling inflight. A 5G Multiple Input Multiple Output-Long Range (MIMO-LoRa) antenna enhances data rate transmission and link reliability. The aerial framework, which has been originally prototyped as a simulation to test the concept, is now developed into a functional proof-of-concept of autonomous fleets of heterogeneous UAVs. For assessing performance, the paper uses Normalized Difference Vegetation Index (NDVI), Mean Squared Error (MSE) and Received Signal Strength Index (RSSI). The 5G MIMO-LoRa antenna produces improved results with four key performance indicators: Reflection Coefficient (S11), Cumulative Distribution Functions (CDF), Power Spectral Density Ratio (Eb/No), and Bit Error Rate (BER).

A Real-time Associative Feature-based CRM and ERP Integration Model for SMEs

Abstract Customer relationship management (CRM) and enterprise resource planning (ERP) have been exponentially discussed in the research literature. However, existing studies do not reach a complete agreement on the CRM/ERP integration method, especially for small and medium-sized enterprises (SMEs). Generally, this paper proposed a new method of CRM/ERP integration approach, i.e. consistently using associative feature technology. A new feature type, Real-time Data Link Board (RDLB) is suggested and detailed as a generic information carrier solution for multi-system integration between CRM and ERP packages. Useful data elements are mapped into such data boards (sheets) dynamically and synchronized across different data sources, structures and databases. The ultimate contribution of this work is to provide a well-defined and generically reusable data carrier definition and related methods for consistent data modeling during the system integration phase of different digitalization engineering implementation projects. With this proposed approach, data integration among complex systems can be managed consistently throughout application lifecycles supported by the object-oriented software engineering foundation.

Mobility-Assisted Digital Twin Network Optimization over Industrial Internet of Things

Many real-world networks for the Industrial Internet of Things (IIoT) have diverse connectivity characteristics and real-time constraints imposed by industrial processing. In the context of digital twin networks (DTNs), a large number of IIoT devices may access the network and have a tremendous volume of data. A crucial element of these IIoT devices is mobility, which cannot be effectively solved because the number of IIoT devices connected is extremely large. IIoT devices in DTNs suffer from poor data transmission and link quality because of their mobility. In this paper, device-to-device (D2D) communication-based mobility-assisted digital twin networks are proposed, where edge computing is introduced to design an efficient mapping between the physical and virtual space. Then, we propose the architecture of data transmission for the D2D network to maximize the data rate for reliable connectivity among multiple mobile nodes based on IIoT. A Markov decision process (MDP) is formulated to maximize the data rate for multiple mobile nodes while maintaining the D2D communication link quality. The simulation results demonstrate the superiority of the proposed scheme over other comparable models.

Acceptability of linking individual credit, financial, and public records data to healthcare records for suicide risk machine learning models.

Individual-level information about negative life events (NLE) such as bankruptcy, foreclosure, divorce, and criminal arrest might improve the accuracy of machine learning models for suicide risk prediction. Individual-level NLE data is routinely collected by vendors such as Equifax. However, little is known about the acceptability of linking this NLE data to healthcare data. Our objective was to assess preferences for linking external NLE data to healthcare records for suicide prevention. We conducted a discrete choice experiment (DCE) among Kaiser Permanente Washington (KPWA) members. Patient partners assisted in the design and pretesting of the DCE survey. The DCE included 12 choice tasks involving 4 data linking program attributes and 3 levels within each attribute. We estimated latent class conditional logit models to derive preference weights. There were 743 participants. Willingness to link data varied by type of information to be linked, demographic characteristics, and experience with NLE. Overall, 65.1% of people were willing to link data and 34.9% were more private. Trust in KPWA to safeguard data was the strongest predictor of willingness to link data. Most respondents supported linking NLE data for suicide prevention. Contrary to expectations, People of Color and people who reported experience with NLEs were more likely to be willing to link their data. A majority of participants were willing to have their credit and public records data linked to healthcare records provided that conditions are in place to protect privacy and autonomy.

Broadband Integrated SiN Optical Phased Array for Near-Visible Infrared LiDAR and FSO Data Link Applications

Broadband Integrated SiN Optical Phased Array for Near-Visible Infrared LiDAR and FSO Data Link Applications

A Credibility Monitoring Approach and Software Monitoring System for VHF Data Exchange System Data Link Based on a Combined Detection Method

Due to VDES’s higher data transmission speed and complex communication protocols, vulnerabilities within its data link infrastructure are more pronounced. To ensure the reliability of VDES data transmission, this manuscript proposes a credibility monitoring approach based on the combined detection method of radio interference detection and spoofing source identification and localization, focusing on key data link vulnerabilities outlined in the IALA G1181 VDES VDL Integrity Guide. Automated monitoring is achieved through VDES data link monitoring software (VDES(AIS 2.0)), which is based on a three-tier architecture and a Client/Server (C/S) model. The software validates monitoring techniques and software against various interference scenarios. Visualization of monitoring results, alarm notifications, and relevant data through the front-end interface enhances understanding of VDES data link credibility. This framework supports effective surveillance and detection of vulnerabilities, such as radio interference and spoofing sources.

Cryo-VCSELs Operated at 2.8 K and 40 K With Record Bandwidth, Power, and Linearity for Optical Data Links in Quantum Computing

Cryo-VCSELs Operated at 2.8 K and 40 K With Record Bandwidth, Power, and Linearity for Optical Data Links in Quantum Computing

Residual multiscale attention based modulated convolutional neural network for radio link failure prediction in 5G

In the realm of the 5 G environment, Radio Access Networks (RANs) are integral components, comprising radio base stations communicating through wireless radio links. However, this communication is susceptible to environmental variations, particularly weather conditions, leading to potential radio link failures that disrupt services. Addressing this, proactive failure prediction and resource allocation adjustments become crucial. Existing approaches neglect the relationship between weather changes and radio communication, lacking a holistic view despite their effectiveness in predicting radio link failures for one day. Therefore, the Dynamic Arithmetic Residual Multiscale attention-based Modulated Convolutional Neural Network (DARMMCNN) is proposed. This innovative model considers radio link data and weather changes as key metrics for predicting link failures. Notably, the proposed approach extends the prediction span to 5 days, surpassing the limitations of existing one-day prediction methods. In this, input data is collected from the Radio Link Failure (RLF) prediction dataset. Then, the distance correlation and noise elimination are used to improve the quality and relevance of the data. Following that, the sooty tern optimization algorithm is used for feature selection, which contributes to link failures. Next, a multiscale residual attention modulated convolutional neural network is applied for RLF prediction, and a dynamic arithmetic optimization algorithm is accomplished to tune the weight parameter of the network. The proposed work obtains 79.03 %, 65.93 %, and 67.51 % of precision, recall, and F1-score, which are better than existing techniques. The analysis shows that the proposed scheme is appropriate for RLF prediction.

Analisis Efektivitas Keamanan Jaringan Layer 2: Port Security, VLAN Hopping, DHCP Snooping

In the industrial era 4.0, which is marked by the rapid progress of computer networks and the internet, network security at Layer 2 is part of the Data Link Layer in the OSI model, which is very important considering the increasing number of cyber threats. This research aims to compare the effectiveness of various security algorithms applied to Layer 2 switches, namely MAC Address Filtering with Port Security, VLAN Hopping Mitigation, and DHCP Snooping. Some of these methods have the function or purpose of creating MAC address filtering, port limitations, VLAN protection, ARP, etc. The device used to conduct this research is a Cisco Switch. This research uses an experimental approach by implementing each method in different network scenarios and comparing it with external data, measuring effectiveness based on attack detection and prevention, as well as its impact on network performance, then combining several of these methods into one scope. The results show that each method has its own advantages and disadvantages, which function to block various types of attacks such as Flooding, Snooping, and Rogue DHCP. It is highly recommended to combine all security methods into one integrated system.

Evaluating the Impact of Mid-Link Bandwidth Constraints on Video Streaming Performance Using NS2

A detailed simulation study of video streaming over a simple best-effort network topology is conducted using Network Simulator 2 (NS2). The simulation involves transmitting a video stream across a simple network topology where the central link's data rate varies to simulate different cases. The network topology consists of : node 0 (sender), node 3 (receiver), and nodes 1 and 2 as intermediate nodes. The study aims to examine the impact of mid-link bandwidth variations on packet loss during video transmission. Specifically, the mid-link connecting nodes experience bandwidth reductions to 10, 8, and 5 Mbps. By adjusting the mid-link bandwidth, we simulate scenarios that typically cause packet drops and measure the resulting packet loss for each bandwidth configuration. The simulation results provide insights into how bandwidth constraints affect video streaming performance, emphasizing the correlation between reduced mid-link capacity and increased packet drop rates. The results indicate that maintaining the video encoding constant while reducing the mid-link bandwidth to half of its required capacity results in an approximately 30% rise in dropped packets and a fivefold increase in end-to-end delay. This significant packet loss severely degrades the video quality, highlighting the importance of adequate mid-link bandwidth for maintaining high-quality transmission.

An Assessment of HF Radio Wave Propagation in Antarctica for a Radio Link Between McMurdo and South Pole Station

AbstractIn this work, we analyze data collected by an HF transmitter/receiver radio link, operating as an oblique ionosonde between the McMurdo Station (transmitter) and South Pole Station (receiver) at 4.1, 5.1, 6.0, 6.4, and 7.2 MHz between 28 February and 14 March 2019. To help contextualize the link's data we have performed numerical raytrace simulations to help understand the observations. By considering both the data and simulations, we have identified both single‐ and two‐hop E‐ and F‐region propagation modes in the data, where the multi‐hop modes were observed in the hours around sunrise and sunset in the 4.1 and 5.1 MHz channels. This is an unexpected result given the accepted wisdom that multi‐hop modes, which require a ground scatter component, cannot be supported in Antarctica because of the highly absorptive ice covering much of the continent. Our results show that multi‐hop propagation modes can be supported in the region under specific ionospheric conditions—around sunrise and sunset—if the mode's ground scatter component is collocated with the Transantarctic Mountains. The mountains are located along the great‐circle path between the link's transmitter and receiver. However, the combination of favorable ionospheric and ground scattering conditions makes the detection of the multi‐hop mode a rare occurrence in the data set analyzed here. These findings are critical to data analysis efforts of any current or future oblique ionosonde systems operating in Antarctica and other regions such as the Arctic.

Cost-Sensitive Rainfall Intensity Prediction with High-Noise Commercial Microwave Link Data

Rainfall intensity prediction based on commercial microwave link data has received significant attention in recent years due to the higher spatial resolution and lower energy consumption. However, the predictive performance is inferior to the model based on meteorological data by reason of the high noise in commercial microwave link data, further exacerbated by the imbalance in the number of samples across different rainfall intensities. Hence, a cost-sensitive rainfall intensity prediction model (CSRFP) is proposed to achieve better predictive performance in high-noise commercial microwave link data. First, the spatiotemporal scene information is encoded, and its weights are trained to provide the model with correlations between signal data from different stations, which helps the model to better capture potential patterns between the data and thus reduce the effect of noise. Next, the rainfall cross-entropy loss based on the rainfall distribution provides the model with the probability of different rainfall intensities occurring and back-calculates the signal attenuation at a specific rainfall intensity, assigning more reasonable weights to different samples considering signal attenuation, which makes the model cost-sensitive and can address the class imbalance problem. Extensive experiments are carried out on high-noise communication data and imbalanced rainfall data in Fuzhou. Compared to typical prediction methods such as RNN applied to rainfall and communication data, CSRFP improves Recall, Precision, AUCROC, AUCPR and F1 and Accuracy by approximately 19%, 37%, 8%, 22%, 30%, and 17%, respectively. Significantly, the model’s prediction accuracy for heavy rain with the smallest number of samples improves by about 13%.

Collaborative Channel Perception of UAV Data Link Network Based on Data Fusion

Collaborative Channel Perception of UAV Data Link Network Based on Data Fusion

Cell identity revealed by precise cell cycle state mapping links data modalities.

Several methods for cell cycle inference from sequencing data exist and are widely adopted. In contrast, methods for classification of cell cycle state from imaging data are scarce. We have for the first time integrated sequencing and imaging derived cell cycle pseudo-times for assigning 449 imaged cells to 693 sequenced cells at an average resolution of 3.4 and 2.4 cells for sequencing and imaging data respectively. Data integration revealed thousands of pathways and organelle features that are correlated with each other, including several previously known interactions and novel associations. The ability to assign the transcriptome state of a profiled cell to its closest living relative, which is still actively growing and expanding opens the door for genotype-phenotype mapping at single cell resolution forward in time.

UAV data link anti-interference via SLHS-SVM-AdaBoost algorithm: Classification prediction and route planning

The ability to predict the anti-interference communications performance of unmanned aerial vehicle (UAV) data links is critical for intelligent route planning of UAVs in real combat scenarios. Previous research in this area has encountered several limitations: Classifiers exhibit low training efficiency, their precision is notably reduced when dealing with imbalanced samples, and they cannot be applied to the condition where the UAV’s flight altitude and the antenna bearing vary. This paper proposes the sequential Latin hypercube sampling (SLHS)-support vector machine (SVM)-AdaBoost algorithm, which enhances the training efficiency of the base classifier and circumvents local optima during the search process through SLHS optimization. Additionally, it mitigates the bottleneck of sample imbalance by adjusting the sample weight distribution using the AdaBoost algorithm. Through comparison, the modeling efficiency, prediction accuracy on the test set, and macro-averaged values of Precision, Recall, and F1-score for SLHS-SVM-AdaBoost are improved by 22.7%, 5.7%, 36.0%, 25.0%, and 34.2%, respectively, compared with Grid-SVM. Additionally, these values are improved by 22.2%, 2.1%, 11.3%, 2.8%, and 7.4%, respectively, compared with particle swarm optimization (PSO)-SVM-AdaBoost. Combining Latin hypercube sampling with the SLHS-SVM-AdaBoost algorithm, the classification prediction model of anti-interference performance of UAV data links, which took factors like three-dimensional space position of UAV and antenna bearing into consideration, is established and used to assess the safety of the classical flying path and optimize the flying route. It was found that the risk of loss of communications could not be completely avoided by adjusting the flying altitude based on the classical path, and that intelligent path planning based on the classification prediction model of anti-interference performance can realize complete avoidance of being interfered while reducing the route length by at least 2.3%, thus benefiting both safety and operation efficiency.

Precipitation measurements experiment using microwave links in Eastern China from October 2020 to March 2022

Precipitation is an important piece of information needed in many areas such as transportation, military and agriculture, and microwave links have proven to be an effective means of acquiring it and can be used as a complementary means for professional precipitation measurement instruments such as rain gauges, weather radar, rain measuring satellites, etc. In this paper, two microwave links (at 26 GHz, both vertically polarised) located across a river in eastern China and a nearby OTT PARSIVEL disdrometer were used to carry out a precipitation observation experiment from October 2020 to March 2022 (March to November 2021 for liquid precipitation, others for possible non-liquid precipitation). The applicability of the dry and rainy period identification methods (standard deviation method and correlation coefficient method) based on this link data is analyzed first. In addition, a wet antenna attenuation correction model based on a Long Short-Term Memory (LSTM) neural network was applied to non-winter precipitation inversion. The results show that the rain rate and cumulative rainfall calculated from the two links are in good agreement with that calculated by the disdrometer data. Furthermore, quantitative inversion of winter precipitation using two microwave links is performed, and the variation characteristics of the link levels during non-liquid precipitation periods are also analyzed.

Optimizing High-Speed Serial Links for Multicore Processors and Network Interfaces

In modern computing environments, high-speed serial links have become a critical component for ensuring efficient data transfer between multicore processors and network interfaces. These links, characterized by their ability to transmit data at very high rates over single or multiple lanes, are essential for meeting the increasing bandwidth demands of contemporary applications. The optimization of these serial links is crucial for maintaining performance, reliability, and energy efficiency in systems that leverage multicore processors and advanced network interfaces. This paper explores the key strategies for optimizing high-speed serial links in the context of multicore processors and network interfaces. We begin by examining the architectural considerations and design principles that influence the performance of serial links, including signal integrity, power consumption, and thermal management. We also discuss the impact of link speed and data encoding techniques on overall system efficiency. One of the central challenges in optimizing high-speed serial links is managing signal integrity across different operating conditions. Techniques such as equalization and adaptive filtering are essential for mitigating the effects of signal degradation due to channel impairments and crosstalk. The paper provides a detailed analysis of these techniques, including their implementation in both hardware and software.

Optimizing High-Speed Serial Links for Multicore Processors and Network Interfaces

In modern computing environments, high-speed serial links have become a critical component for ensuring efficient data transfer between multicore processors and network interfaces. These links, characterized by their ability to transmit data at very high rates over single or multiple lanes, are essential for meeting the increasing bandwidth demands of contemporary applications. The optimization of these serial links is crucial for maintaining performance, reliability, and energy efficiency in systems that leverage multicore processors and advanced network interfaces. This paper explores the key strategies for optimizing high-speed serial links in the context of multicore processors and network interfaces. We begin by examining the architectural considerations and design principles that influence the performance of serial links, including signal integrity, power consumption, and thermal management. We also discuss the impact of link speed and data encoding techniques on overall system efficiency.

A dynamic analysis of UAV -based IoT networks for efficient communication using falcon optimization approach

Unmanned Aerial Vehicles (UAVs) play a pivotal role in Internet of Things (IoT) applications by sensing data, conducting continuous surveillance, and maintaining connectivity among users. However, their high mobility and continuous surveillance for data transmission result in high energy consumption, possibly resulting in lesser network performance, including link failure, node depletion, data loss, and high delays. To address these challenges, a novel scheme termed as 'On-Demand Cross-Layered Falcon Optimization Approach (OCLFO)' has been proposed to enhance connectivity and optimize network performance in UAV-based IoT systems. The on- demand Cross-Layer Optimization (CLO) scheme facilitates data transmission across each layer of the OSI model. CLO is applied to the design of UAVs with the integration of on-demand, based on the queuing size. Falcon Optimization Approach (FOA) has been integrated with the CLO to overcome the energy consumption drawback, and provide successful data transmission between UAV users with the help of fitness value. The fitness value depends maximum on the energy and minimum on the queuing size, and this provides the energy efficient routing path for an efficient data transmission. The proposed OCLFO aims to optimize energy consumption while ensuring successful and expedited data transmission among UAV users. Simulations have been conducted using a network simulator to analyse Quality of Service (QoS) parameters, considering high mobility scenarios by varying energy levels such as 100 J and 200 J. Compared to the existing optimization algorithms, the proposed OCLFO achieves high delivery ratio, high throughput, less delay, lesser energy consumption and enhanced link efficiency.