Cross Layer Research Articles

Enhanced Cross Layer Refinement Network for robust lane detection across diverse lighting and road conditions

With the rapid development of autonomous driving technology, lane detection, a key component of intelligent vehicle systems, is crucial for ensuring road safety and efficient vehicle navigation. In this paper, a new lane detection method is proposed to address the problem of degraded performance of existing lane detection methods when dealing with complex road environments. The proposed method evolves from the original Cross Layer Refinement Network (CLRNet) by incorporating two of our carefully designed core components: the Global Feature Optimizer (GFO) and the Adaptive Lane Geometry Aggregator (ALGA). The GFO is a multi-scale attention mechanism that mimics the human visual focusing ability, effectively filtering out unimportant information and focusing on the image regions most relevant to the task. The ALGA is a shape feature-aware aggregation module that utilizes the shape prior of lanes to enhance the correlation of anchor points in an image, better fusing global and local information. By integrating both components into CLRNet, an enhanced version called Enhanced CLRNet (E-CLRNet) is presented, which exhibits higher performance stability in complex roadway scenarios. Experiments on the CULane dataset reveal that E-CLRNet demonstrates superior performance stability over the original CLRNet in complex scenarios, including curves, shadows, missing lines, and dazzling light conditions. In particular, in the curves, the F1 score of E-CLRNet is improved by almost 3% over the original CLRNet. This study not only improves the accuracy and performance stability of lane detection but also provides a new solution for the application of autonomous driving technology in complex environments, which promotes the development of intelligent vehicle systems.

A Cross-Layer Approach to Analyzing Energy Consumption and Lifetime of a Wireless Sensor Node

Several wireless communication technologies, including Wireless Sensor Networks (WSNs), are essential for Internet of Things (IoT) applications. WSNs employ a layered framework to govern data exchanges between sender and recipient, which facilitates the establishment of rules and standards. However, in this conventional framework, network data sharing is limited to directly stacked layers, allowing manufacturers to develop proprietary protocols while impeding WSN optimization, such as energy consumption minimization, due to non-directly stacked layer effects on network performance. A Cross-Layer (CL) framework addresses implementation, modeling, and design challenges in IoT systems by allowing unrestricted data and parameter sharing between non-stacked layers. This holistic approach captures system dynamics, enabling network design optimization to address IoT network challenges. This paper introduces a novel CL modeling methodology for wireless communication systems, which is applied in two case studies to develop models for estimating energy consumption metrics, including node and network lifetime. Each case study validates the resulting model through experimental tests, demonstrating high accuracy with less than 3% error.

AI-based tree modeling for multi-point dioxin concentrations in municipal solid waste incineration

Numerous investigations have shown that the municipal solid waste incineration (MSWI) has become one of the major sources of dioxin (DXN) emissions. Currently, the primary issue that needs to be addressed for DXN emission reduction control is the online measurement of DXN. Data-driven AI algorithms enable real-time DXN concentration measurement, facilitating its control. However, researchers mainly focus on building models for DXN emissions at the stack. This approach does not allow for the construction of models that online measurement of DXN generation and absorption throughout the whole process. To achieve optimal pollution control, models that encompass the whole process are necessary, not just models focused on the stack. Therefore, this article focuses on modeling the whole process of DXN concentrations, including generation, adsorption, and emission. It uses machine learning techniques based on advanced tree-based data-driven deep and broad learning algorithms. The determination of data characteristics at different phases is grounded in the understanding of the DXN mechanism, offering a novel framework for DXN modeling. State-of-the-art tree-based models, including adaptive deep forest regression algorithm based on cross layer full connection, tree broad learning system, fuzzy forest regression, and aid modeling technologies, are applied to handle diverse data characteristics. These characteristics encompass high-dimensional small samples, low-dimensional ultra-small size samples, and medium-dimensional small samples across different phases related to DXN. The most interesting is the robust validation where the proposed a whole process tree-based model for DXN is validated using nearly one year of authentic data on DXN generation, adsorption, and emission phases in an MSWI plant of Beijing. The proposed modeling framework can be used to explore the mechanism characterization and support the pollution reduction optimal control.

Node and edge centrality based failures in multi-layer complex networks

Multi-layer complex networks (MLCN) appears in various domains, such as, transportation, supply chains, etc. Failures in MLCN can lead to major disruptions in systems. Several research have focussed on different kinds of failures, such as, cascades, their reasons and ways to avoid them. This paper considers failures in a specific type of MLCN where the lower layer provides services to the higher layer without cross layer interaction, typical of a computer network. A three layer MLCN is constructed with the same set of nodes where each layer has different characteristics, the bottom most layer is Erdos–Renyi (ER) random graph with shortest path hop count among the nodes as gaussian, the middle layer is ER graph with higher number of edges from the previous, and the top most layer is preferential attachment graph with even higher number of edges. Both edge and node failures are considered. Failures happen with decreasing order of centralities of edges and nodes in static batch mode and when the centralities change dynamically with progressive failures. Emergent pattern of three key parameters, namely, average shortest path length (ASPL), total shortest path count (TSPC) and total number of edges (TNE) for all the three layers after node or edge failures are studied. Extensive simulations show that all but one parameters show definite degrading patterns. Surprising, ASPL for the middle layer starts showing a chaotic behaviour beyond a certain point for all types of failures.

Hybrid Clustering Approach (SG-MFOA) using Multipath Cross-Layer Design in MANET Network

Mobile Ad-hoc Networks (MANETs) are used in a wide range of applications because of their unique capabilities. The routing in MANET is regarded as a main challenge on account of its permanent movement and nodes' randomness that can cause a continual change of network topology, thus finding correct paths among the nodes is the fundamental task of routing protocols. There is a rise in protocols that depend on cross-layer communication across several layers to increase the MANET performance. In this manuscript, a Hybrid Clustering Approach (SG-MFOA) using a Multipath Cross-Layer Design in a MANET Network is proposed. Here, multiple routes are selected for data packet transmission with the help of hybrid routing. Furthermore, a cross-layer metric is acquired depending upon Expected Transmission Time (ETT), Residual Energy and Load Balancing Factor. It is necessary to select a Cluster Head (CH) depending on this trade for proficient routing. So, the cluster head is optimally chosen to utilize SG-MFOA. Thus, the multipath route selection is performed by the multi-objective functions containing bandwidth, congestion delay, transmission delay and to ensure the prolonging of network lifetime, queue delay at each connection Access Category from the available routes to a destination. When compared with the existing methods, like a proficient self-attention-based conditional variational auto-encoder generative adversarial networks-based multipath cross-layer design routing paradigm for MANET (SCVAGAN-MCDR-MANET), Energy Aware Cross Layer Routing Protocol to Increase Route Reliability in MANETs (EACLRP-RR-MANET), Cross-layer Adaptive Fuzzy-based Ad hoc On-Demand Distance Vector Routing Protocol for MANET (CLAF-DVRP-MANET), respectively.

GroupTransNet: Group transformer network for RGB-D salient object detection

As an active topic in computer vision, RGB-D salient object detection has witnessed substantial progress. Although the existing methods have achieved appreciable performance, there are still some challenges. The locality of convolutional neural networks requires that the model has a sufficiently deep global receptive field, while the local characteristic represented by transformer with strong globality is always not enough. Besides, the shared information of contextual features tends to be usually overlooked. To address these bottlenecks, we propose a novel group transformer network (GroupTransNet), which is good at learning the long-range dependencies of cross layer features to promote more perfect feature expression between high-level and low-level features. Importantly, we soft group the features of the middle and latter three levels to absorb the semantic information of slightly former level features. Firstly, the input features are adaptively purified by the element-wise operation and sequential attention mechanism. Afterwards, the intermediate features are uniformly fused at different layers, and then processed by several transformers in multiple groups. Finally, the output features are clustered within different classifications and combined with underlying features. Extensive experiments demonstrate the proposed GroupTransNet outperforms the competitors and achieves new state-of-the-art performance.

Research on Bidirectional Multi-Span Feature Pyramid and Key Feature Capture Object Detection Network

UAV remote sensing (RS) image object detection is a very valuable and challenging technology. This article discusses the importance of key features and proposes an object detection network (URSNet) based on a bidirectional multi-span feature pyramid and key feature capture mechanism. Firstly, a bidirectional multi-span feature pyramid (BMSFPN) is constructed. In the process of bidirectional sampling, bicubic interpolation and cross layer fusion are used to filter out image noise and enhance the details of object features. Secondly, the designed feature polarization module (FPM) uses the internal polarization attention mechanism to build a powerful feature representation for classification and regression tasks, making it easier for the network to capture the key object features with more semantic discrimination. In addition, the anchor rotation alignment module (ARAM) further refines the preset anchor frame based on the key regression features extracted by FPM to obtain high-quality rotation anchors with a high matching degree and rich positioning visual information. Finally, the dynamic anchor optimization module (DAOM) is used to improve the ability of feature alignment and positive and negative sample discrimination of the model so that the model can dynamically select the candidate anchor to capture the key regression features so as to further eliminate the deviation between the classification and regression. URSNet has conducted comprehensive ablation and SOTA comparative experiments on challenging RS datasets such as DOTA-V2.0, DIOR and RSOD. The optimal experimental results (87.19% mAP, 108.2 FPS) show that URSNet has efficient and reliable detection performance.

A cross layer graphical neural network based convolutional neural network framework for image dehazing

The current version of imaging equipment cannot quickly and effectively make up for the reduction of visibility triggered by bad weather. Traditional strategies minimize hazy impacts by employing an image depth model and a physical model. Following experts, erroneous depth data reduces the efficacy of the dehazing algorithm. Dehazing methods based on CNN are imperfect to handle region which is bright or similar to atmospheric light and thus leads to oversaturation of pixels. These challenges can be addressed by proposing a novel model that incorporates the idea of a Graphical Neural Network. The amount of light coming from the atmosphere is estimated using normalization where the contrast of the image gets adjusted using Bias Contrast stretch Histogram Equalization. An enhanced Transmission map estimator is used to render the hazy scene. Finally, the cross-layer graphical neural network-based CNN model is applied to produce a haze-free image and eliminate the over-saturation of pixels. Extensive evaluation findings show that the proposed approach can significantly recuperate misty imagery, even if the images have a substantial amount of haze.

Cross layered adaptive rate optimized irregular LDPC for WSN

This paper proposes a cross layered adaptive rate optimized technique that uses irregular low-density parity-check (LDPC) codes over an additive white Gaussian noise (AWGN) channel in wireless sensor networks (WSNs). Irregular LDPC is used as error control code. Cross layer design along with error control code gives the energy balance in the network. This new algorithm uses the physical layer parameters such as coherence time of the channel, BER and SNR and the routing layer parameter such as demanded data rate to determine the rate of the LDPC coder. The algorithm adaptively changes the code rates as ¾, ½, and 1/3. We have conducted the simulations to verify our methods using MATLAB and find that the proposed scheme increases the network performance in terms of energy balance and quality of data by using irregular LDPC codes in the cross-layer environment.

A data and knowledge driven autonomous intelligent manufacturing system for intelligent factories

The non-ferrous metal industry is encountering several challenges, including production efficiency, manufacturing information fragmentation, and human health problems, which highlights the importance of implementing autonomous intelligent manufacturing systems (AIMS). Recently, the foundation model like GPT-4, has garnered attentions due to its exceptional capabilities and proficiency in diverse domains and tasks, facilitating the realization of AIMS. However, the existing foundation models can only address basic general-purpose tasks and are difficult to use for industrial applications. In this paper, we propose a data and knowledge driven AIMS with industrial-generative pretrained Transformer (Industrial-GPT) for intelligent factories. The paradigms and architecture of autonomous intelligent factories are firstly defined. Then, we explore the mechanism with knowledge graph, digital twin, and Industrial-GPT, including multi-level autonomous perception, cross layer and domain cognition, and event-driven collaborative decision-making. Finally, the detailed case study is based on the cooperation with a zinc smelting intelligent factory to achieve networked collaborative manufacturing, and explores the theory and realization mechanism of AIMS on a small scale. We explore the experimental analyses, evaluation mechanisms and platform applications of AIMS at the workshop level. We believe this will help to realize larger scale AIMS in the future.

ECNet: Network Based on Encoder With Local Convolutional Attention and Cross Layer Information Fusion for Natural Gas Pipeline Leakage Detection

ECNet: Network Based on Encoder With Local Convolutional Attention and Cross Layer Information Fusion for Natural Gas Pipeline Leakage Detection

A Review of Blockchain-Rooted Energy Administration in Networking

Energy Administration (EA) in networking involves improving energy efficiency by managing energy. The blockchain framework involves a chain of associated blocks that obviously protects the genuineness, upholds accountability, and upholds disguised-anonymity of its transactions/entries with the help of peer-to-peer consensus techniques and cryptographic mechanisms. Driven by the fact that existing surveys do not focus on the EA in the broad scope of networking, we review diverse blockchain-rooted EA solutions, where we recognize 7 roles of blockchain in EA and explore them in detail with regard to EA techniques, EA approaches, blockchain-linked factors, network-linked factors, and such. We assembled a first-stage sample of 80 document citations by appraising the articles for qualification criteria hunted from E-libraries operating a detailed and prolonged process. Considering the review, in blockchain-rooted EA, blockchain can facilitate storing and exchanging data in a trustworthy manner, operate energy-efficient consensus approaches, act as an energy manager, provide authentication and access control for EA, facilitate secure offloading for EA, and provide automated EA tasks operating smart contracts. Detailed exploration shows that from blockchain-rooted EA, 32.5% operate blockchain to store and exchange data for an EA task, 95% operate uniform blockchain, 30% operate PoW consensus, 82.5% operate fully decentralized EA, 57.5% operate cross-layer EA, and 10% operate in IoT networks. Finally, we debate the possibilities and barriers to the conception of blockchain-rooted EA and then present guidance to vanquish them.

Cooperative Sensing Assisted Cross layer QoS Assured Routing in Cognitive Radio Adhoc Networks: Ensuring Security and Privacy

Cooperative Sensing Assisted Cross layer QoS Assured Routing in Cognitive Radio Adhoc Networks: Ensuring Security and Privacy

Exploring key modules in lane detection through comparing two updated models

Lane detection is significant in the digital navigation system of modern vehicles. It is vita in lane switching and route planning. We want to explore what modules are critical in lane detection. In this work, we choose two representative models, Cross Layer Refinement Network (CLRNet) and Ultra Fast Lane Detector version 2 (UFLD-v2), for comparison, aiming at evaluating their performance and finding out what determines their performance or why they perform well. The first one is a top-down model while another is a bottom-top model, which represents they are built through different ideas. We use two common datasets, CULane and TuSimple, to train, validate, and test them. The experiment results are divided into quantitative results and qualitative results, by which we can conclude that global learning capability and immunity to interference are indispensable.

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.

Intelligent RAN Automation for 5G and Beyond

5G is becoming more agile and inevitably more complicated for supporting various deployment scenarios and diversified applications. Artificial Intelligence (AI) is becoming one of the most essential driving forces for 5G evolution to handle the complexity and further improve the network spectrum efficiency, energy efficiency and user experience. In this article, we share the latest standardization progress of the industry on AI in 5G. Evolving directions for the intelligent radio access networks (RAN) are analyzed and further enhancements on both AI for RAN and RAN for AI are proposed based on a hierarchical and distributed intelligent RAN architecture. Case studies are presented along with field trial results. An innovative AI-empowered RAN and service-aware cross layer optimization framework is illustrated for the quality of experience (QoE) optimization of cloud virtual reality (VR) with converged communication and computing services provided by RAN. The extension of the solution to support the AI/ML services is also discussed to shed some light on how to evolve network for AI towards the 6G vision.

Study on Drone Handover Methods Suitable for Multipath Interference Due to Obstacles

Networks constructed in the sky are known as non-terrestrial networks (NTNs). As an example of an NTN, relay transmission using drones as radio stations enables flexible network construction in the air by performing handovers with ground stations. However, the presence of structures or obstacles in the flight path causes multipath interference; consequently, the propagation environment fluctuates significantly based on the flight. In such a communication environment, it is difficult for a drone to select an optimal ground station for a handover. Moreover, unlike a terrestrial network, the propagation environment of a flying drone is affected by structures and other factors that cause multipaths based on the flight speed and altitude, making the conditions of the propagation environment even more complex. To solve these problems, we propose handover schemes between drones and the ground that consider the multipath interference caused by obstacles. The proposed methods are used to perform handovers based on an optimal threshold of received power considering interference and avoid unnecessary handovers based on the moving speed, which makes the handover seamless. Finally, we develop a simulator that evaluates the cross layer from propagation to upper network protocols in a virtual space, including buildings, evaluate the communication quality of a drone flying in a three-dimensional space, and confirm the effectiveness of the proposed methods as well as the evaluation of the real environment.

Optimization Techniques in Cooperative and Distributed MAC Protocols

The tremendous increase in wireless network application finds distributed allocation of resources allocation very useful in the network. Packet delivery ratio and delay can be improved by concentrating on payload size, mobility, and density of nodes in the network. In this article, a survey is carried out on different cooperative and distributed MAC protocols for communication and optimization algorithms for various applications and the mathematical issues related to game theory optimizations in MAC protocol. Spatial reuse of channel improved by (3-29) % and multi-channel improves throughput by 8% using distributed MAC protocol. The energy utility of individual players can be focused to get better network performance with NASH equilibrium. Fuzzy logic improves channel selection by 17% and secondary users' involvement by 8%. Jamming, interference problems can be addressed using cross layer approach in the MAC and simultaneous data, voice transmissions in IoT; WSN applications can be attained using hybrid distributed MAC protocol.

Hybrid Relaying Based Cross Layer MAC Protocol Using Variable Beacon for Cooperative Vehicles

Hybrid Relaying Based Cross Layer MAC Protocol Using Variable Beacon for Cooperative Vehicles

Enhancing secrecy using hybrid elliptic curve cryptography and Diffie Hellman key exchange approach and Young's double slit experiment optimizer based optimized cross layer in multihop wireless network

Wireless Network is usually a non-infrastructure multi-hop network, in which the communication between the nodes are carried implicitly or explicitly with the help of intermediate nodes. The two primary considerations when creating a wireless network are energy efficiency and secrecy, which are currently hot topics in study. Power-related issues make the conventional tiered approach ineffective. We suggest a novel solution to address those problems: the Young's double slit experiment optimizer (YDEO) based optimized cross layer routing protocol. This method computes parameters like data success rate, average energy loads, and traffic indices to determine consistency and energy efficacious routing paths. The secrecy of the network can be enhanced with the proposed innovative approach known as Hybrid Elliptic Curve Cryptography and Diffie Hellman Key Exchange (HECC-DH). This greatly enhances data integrity and security by offering dual authentications with key exchange techniques. In addition to this HECC-DH, bilinear maps are used for secure communication. To verify the effectiveness of the suggested work, experiments are performed with the NS2 simulator and contrasted with state-of-the-art works. When it comes to communication, our method performs better than the others and with greater satisfaction.