Cross-layer Adaptation Research Articles

YOLO-ACT: an adaptive cross-layer integration method for apple leaf disease detection.

Apple is a significant economic crop in China, and leaf diseases represent a major challenge to its growth and yield. To enhance the efficiency of disease detection, this paper proposes an Adaptive Cross-layer Integration Method for apple leaf disease detection. This approach, built upon the YOLOv8s architecture, incorporates three novel modules specifically designed to improve detection accuracy and mitigate the impact of environmental factors. Furthermore, the proposed method addresses challenges arising from large feature discrepancies and similar disease characteristics, ultimately improving the model's overall detection performance. Experimental results show that the proposed method achieves a mean Average Precision (mAP) of 85.1% for apple leaf disease detection, outperforming the latest state-of-the-art YOLOv10s model by 2.2%. Compared to the baseline, the method yields a 2.8% increase in mAP, with improvements of 5.1%, 3.3%, and 2% in Average Precision, Recall, and mAP50-95, respectively. This method demonstrates superiority over other classic detection algorithms. Notably, the model exhibits optimal performance in detecting Alternaria leaf spot, frog eye leaf spot, gray spot, powdery mildew, and rust, achieving mAPs of 84.3%, 90.4%, 80.8%, 75.7%, and 92.0%, respectively. These results highlight the model's ability to significantly reduce false negatives and false positives, thereby enhancing both detection and localization of diseases. This research offers a new theoretical foundation and direction for future advancements in apple leaf disease detection.

Towards Full Stack Adaptivity in Permissioned Blockchains

This paper articulates our vision for a learning-based untrustworthy distributed database. We focus on permissioned blockchain systems as an emerging instance of untrustworthy distributed databases and argue that as novel smart contracts, modern hardware, and new cloud platforms arise, future-proof permissioned blockchain systems need to be designed with full-stack adaptivity in mind. At the application level, a future-proof system must adaptively learn the best-performing transaction processing paradigm and quickly adapt to new hardware and unanticipated workload changes on the fly. Likewise, the Byzantine consensus layer must dynamically adjust itself to the workloads, faulty conditions, and network configuration while maintaining compatibility with the transaction processing paradigm. At the infrastructure level, cloud providers must enable cross-layer adaptation, which identifies performance bottlenecks and possible attacks, and determines at runtime the degree of resource disaggregation that best meets application requirements. Within this vision of the future, our paper outlines several research challenges together with some preliminary approaches.

IDP-Net: Industrial defect perception network based on cross-layer semantic information guidance and context concentration enhancement

Applications in Engineering: In industry, surface defect detection is crucial for improving product quality. However, there are many challenges in industrial inspection scenarios, such as interference from background noise, complex small-target problems, significant variations in target objects, and the problem of finding a balance between inspection speed and accuracy. To address the above problems, this paper proposes an industrial defect-aware network based on cross-layer semantic information guidance and contextual attention enhancement (IDP-Net). Specifically, IDP-Net has four different new features. The contribution of artificial intelligence: Firstly, to solve the industrial surface context and defect similarity problem, this paper proposes a Lightweight Local Global Feature Extraction Network (LLG-Net), unlike other methods, the effective combination of self-attention blocks and convolution blocks ensures gradual integration of global and local features across multiple layers, to improve the detection ability of targets with significant changes in scale, this paper designs a Multiscale Perceptual Feature Aggregation Network (MPA-Net), adequately fuses the shallow fine-grained information and the deep semantic information. Then, to enhance the connection between multi-scale semantic information, an adaptive cross-layer feature fusion module (ACFF) is proposed, which is novel in integrating the characteristics of multiple adjacent levels to help the model better capture the different scale characterisation of the target. Finally, a Region Attention Module (RAM) is proposed and introduced in the detector to enhance the attention to the critical regions around the target object. In particular, this paper proposes a new localisation loss function (MEIoU) that enhances the network’s attention to objects at different scales. The experimental results show that 94.3%, 98.7% and 99.5% of mAP@.5 are obtained on steel, PCB and aluminium surface defect datasets, respectively, and 50 FPS is achieved, which is better than the current mainstream detectors and meets the demand of practical industrial production.

AFCANet: An adaptive feature concatenate attention network for multi-focus image fusion

For multi-focus image fusion, the existing deep learning based methods cannot effectively learn the texture features and semantic information of the source image to generate high-quality fused images. Thus, we develop a new adaptive feature concatenate attention network named AFCANet, which adaptively learns cross-layer features and retains the texture features and semantic information of images to generate visually appealing fully focused images. In AFCANet, the encoder-decoder network is used as the backbone network. In the unsupervised training stage, an adaptive cross-layer skip connection mode is designed, and a cross-layer adaptive coordinate attention module is built to acquire meaningful information from the image along with ignoring unimportant information to obtain a better image fusion effect. In addition, in the middle of the encoder-decoder network, we also introduce an effective channel attention module to fully learn the output of the encoder, and accelerate network convergence. In the inference stage, we apply the pixel-based spatial frequency fusion rules to fuse the adaptive features learned by the encoder, which can successfully combine the texture and semantic information of the image and produce a more precise decision map. Extensive experiments on public datasets and the HBU-CVMDSP dataset show that our AFCANet can effectively improve the accuracy of the decision map in the focus and defocus regions, as well as improve the ability to retain the abundant details and edge features of the source image.

Design of Multi-Layer Protocol Architecture using Hybrid Optimal Link State Routing (HOLSR) Protocol for CR Networks

There is a lack of spectrum due to the rising demand for sensing device communication and the inefficient use of the existing available spectrum. Through opportunistic access to licenced bands, which does not obstruct the primary sensory users (PU), it is feasible to enhance the inefficient use of the current sensor device frequency spectrum. Cognitive settings are a demanding environment in which to carry out tasks like sensor network routing and spectrum access since it is difficult to access channels due to the presence of PUs. The basic goal of the routing problem in sensor networks is to establish and maintain wireless sensor multihop paths between cognitive sensor nodes. The frequency to be used as well as the number of hops at each sensor node along the path must be determined for this assignment. In order to improve performance while using less energy, scientists suggested a unique adaptive cross-layer optimisation subcarrier distribution technique with the HOLSR protocol for wireless sensor nodes. Throughput and energy consumption parameters are used to analyse the sensor network architecture protocol that has been developed. The energy usage of the sensor nodes in the network has increased by 50%. The performance of the proposed HOLSR algorithm is assessed using the simulation results, and the results are contrasted with those of a conventional multicarrier (MC) system in terms of bit error rate and throughput.

A DRL-Based Automated Algorithm Selection Framework for Cross-Layer QoS-Aware Scheduling and Antenna Allocation in Massive MIMO Systems

The rapid development of mobile networks proliferates the demands of high data rate, low latency, and high-reliability applications for the fifth-generation (5G) and beyond (B5G) mobile networks. Concurrently, the massive multiple-input-multiple-output (MIMO) technology is essential to realize the vision and requires coordination with resource management functions for high user experiences. Though conventional cross-layer adaptation algorithms have been developed to schedule and allocate network resources, the complexity of resulting rules is high with diverse quality of service (QoS) requirements and B5G features. In this work, we consider a joint user scheduling, antenna allocation, and precoding problem in a massive MIMO system. Instead of directly assigning resources, such as the number of antennas, the allocation process is transformed into a deep reinforcement learning (DRL) based dynamic algorithm selection problem for efficient Markov decision process (MDP) modeling and policy training. Specifically, the proposed utility function integrates QoS requirements and constraints toward a long-term system-wide objective that matches the MDP return. The componentized action structure with action embedding further incorporates the resource management process into the model. Simulations show 7.2% and 12.5% more satisfied users against static algorithm selection and related works under demanding scenarios.

PISDGAN: Perceive image structure and details for laryngeal image enhancement

The introduction of optical techniques such as narrow-band imaging (NBI) during laryngeal endoscopic diagnosis can emphasize the superficial mucosal veins and increase the identification rate of lesions. To eliminate the limitation of endoscopic devices, in this paper, an image enhancement method that can perceive the information and details of the image structure (PISDGAN) is proposed. The method is based on the classical cycle consistency image translation architecture, and its generator is adjusted to a U-shaped residual structure. At the same time, a multiscale cross-layer adaptation module (CLA) is designed, which integrates features of different depths through 'feature transfer' to obtain abundant image information. A non-local feature denoising module (FD) is also introduced to enhance the quality of the translated image. In addition, the structure difference loss is proposed to constrain the image structure to ensure the integrity of the translated image. Based on the self-built white light and narrow band laryngeal image datasets, the experimental comparison of PISDGAN and other similar image translation methods shows that the proposed method not only effectively preserves the image structure, but also maintains good image quality while highlighting the details.

Adaptive cross-layer handover algorithm based on MPTCP for hybrid LiFi-and-WiFi networks

In this paper, an adaptive cross-layer handover algorithm based on multipath transmission control protocol (MPTCP) is proposed to solve the handover problem in hybrid light fidelity (LiFi) and wireless fidelity (WiFi) networks (HLWNets). According to this algorithm, the system can adapt to the user’s business type and behavioral characteristics to achieve seamless handover. By constructing nine evaluation indicators considering three dimensions of real-time network attributes, user mobility characteristics, and service demands for the network, the user mobility index (MI) is defined. By using the fuzzy analytic hierarchy process (FAHP)- the technique for order preferences by similarity to ideal solution (TOPSIS) to conduct sub-item and overall analysis of the three types of indicators, the user’s mobile score is obtained, and the required MPTCP mode is determined to realize the cross-layer assistance accordingly. By using MI and signal-to-interference-plus-noise ratio (SINR), load balancing can be achieved and the handover margin (HOM) and the time to trigger (TTT) can be dynamically set. By this, compared with the standard and location-aware algorithms, the number of handovers is significantly reduced (by 71% and 28%) and the throughput is effectively increased (by 58% and 43%). Finally, by simulation, the effectiveness of the adaptive cross-layer handover algorithm based on MPTCP is demonstrated.

Efficient models for enhancing the link adaptation performance of LTE/LTE-A networks

Link adaptation (LA) is the ability to adapt the modulation scheme (MS) and the coding rate of the error correction in accordance with the quality of the radio link. The MS plays an important role in enhancing the performance of LTE/LTE-A, which is typically dependent on the received signal to noise ratio (SNR). However, using the SNR to select the proper MSs is not enough given that adaptive MSs are sensitive to error. Meanwhile, non-optimal MS selection may seriously impair the system performance and hence degrades LA. In LTE/ LTE-A, the LA system must be designed and optimized in accordance with the characteristics of the physical (e.g., MSs) and MAC layers (e.g., Packet loss) to enhance the channel efficiency and throughput. Accordingly, this study proposes using two LA models to overcome the problem. The first model, named the cross-layer link adaptation (CLLA) model, is based on the downward cross-layer approach. This model is designed to overcome the accuracy issue of adaptive modulation in existing systems and improve the channel efficiency and throughput. The second model, named the Markov decision process over the CLLA (MDP-CLLA) model, is designed to improve on the selection of modulation levels. Besides that, our previous contribution, namely the modified alpha-Shannon capacity formula, is adopted as part of the MDP-CLLA model to enhance the link adaptation of LTE/LTE-A. The effectiveness of the proposed models is evaluated in terms of throughput and packet loss for different packet sizes using the MATLAB and Simulink environments for the single input single output (SISO) mode for transmissions over Rayleigh fading channels. In addition, phase productivity, which is defined as the multiplication of the total throughput for a specific modulation with the difference between adjacent modulation SNR threshold values, is used to determine the best model for specific packet sizes in addition to determine the optimal packet size for specific packet sizes among models. Results generally showed that the throughput improved from 87.5 to 89.6% for (QPSK rightarrow 16-QAM) and from 0 to 43.3% for (16-QAM rightarrow 64-QAM) modulation transitions, respectively, using the CLLA model when compared with the existing system. Moreover, the throughput using the MDP-CLLA model was improved by 87.5–88.6% and by 0–43.2% for the (QPSK rightarrow 16-QAM)and (16-QAM rightarrow 64-QAM) modulation transitions, respectively, when compared with the CLLA model and the existing system. Results were also validated for each model via the summation of the phase productivity for every modulation at specific packet sizes, followed by the application one-way analysis of variance (ANOVA) statistical analysis with a post hoc test, to prove that the MDP-CLLA model improves with best high efficiency than the CLLA model and the existing system.

Cross-Layer Adaptation with Safety-Assured Proactive Task Job Skipping

During the operation of many real-time safety-critical systems, there are often strong needs for adapting to a dynamic environment or evolving mission objectives, e.g., increasing sampling and control frequencies of some functions to improve their performance under certain situations. However, a system's ability to adapt is often limited by tight resource constraints and rigid periodic execution requirements. In this work, we present a cross-layer approach to improve system adaptability by allowing proactive skipping of task executions, so that the resources can be either saved directly or re-allocated to other tasks for their performance improvement. Our approach includes three novel elements: (1) formal methods for deriving the feasible skipping choices of control tasks with safety guarantees at the functional layer, (2) a schedulability analysis method for assessing system feasibility at the architectural layer under allowed task job skippings, and (3) a runtime adaptation algorithm that efficiently explores job skipping choices and task priorities for meeting system adaptation requirements while ensuring system safety and timing correctness. Experiments demonstrate the effectiveness of our approach in meeting system adaptation needs.

Adaptive Cross-Layer Routing Protocol for Optimizing Energy Harvesting Time in WSN

Adaptive Cross-Layer Routing Protocol for Optimizing Energy Harvesting Time in WSN

Adaptive virtual MIMO-based cross-layer design for wireless sensor networks via hybrid optimization model

In recent times, many MAC protocols were implemented for boosting and improving energy efficiency (EE) in WSNs. Moreover, the cooperative MIMO method is found to be much capable of enhancing the EE of WSNs if configured properly. This paper intends to propose cross-layer design for multihop virtual MIMO system to enhance the end-to-end (ETE) reliability, EE, and QoS of the adopted WSN. The protocol is set here to focus on the energy utilization for transmission of data packets by optimal selection of transmission constraints for each node of the network. Moreover, the protocol’s ETE latency and throughput are also modeled as the dependent variables of BER performance of every link. To discover the improved BER criteria of each link that meets the ETE QoS requirement in reduced energy utilization, this paper employs a new hybrid optimization algorithm named Lion Mutated Dragonfly Algorithm (LM–DA) that is a hybrid variant of both Lion Algorithm (LA) and Dragonfly Algorithm (DA). Finally, the performance of the adopted scheme is validated over other state-of-the-art models. The results state that the energy consumed by the adopted LM–DA approach is about 2.65%, 1.77%, and 1.77% reduced over LA, PSO, and DA schemes, respectively.

Multi‐objective emperor penguin handover optimisation for IEEE 802.21 in heterogeneous networks

IEEE concentrates on the development of effective media independent handover (IEEE 802.21 MIH) services. The major aim of IEEE 802.21 MIH is to optimise the handover process to make an uninterrupted handover service with less delay. The handover process is categorised into two types a horizontal and vertical process. Among them, vertical handover (VH) needs parameter optimisation for better performance. The optimised result depends on the parameter selection to avoid the rate of handover failure. Although the availability of various optimisation procedures for VH management, many existing works consider one/two parameters for VH optimisation. So that resultant optimal handover solution will not be better in terms of failure rate, latency, and accuracy. Thus, a method of multi-objective emperor penguin handover optimisation (MOEPHO) is developed in the proposed work, which includes almost overall network parameters for VH optimisation. So that, accurate handover with less delay and the minimum energy consumption is achieved in this work. Network simulator 2 working platform is used for the research evaluation. The resultant performances are compared with whale optimisation algorithm-based neural network, adaptive cross-layer design, fuzzy intelligent decision making and novel type 2-fuzzy logic controller to show the effectiveness of MOEPHO.

A Competent Ad-hoc Sensor Routing Protocol for Energy Efficiency in Mobile Wireless Sensor Networks

In wireless sensor networks (WSNs), energy utilization plays an essential part in the survival time (survivability) of the network. All the network aspects are redesigned to work in the WSN for lesser energy consumption. In this regard, routing protocol and dynamic topology are very crucial aspects to be noted. In this work, we present a competent ad-hoc sensor routing (CASeR) protocol for delay reduction, reliable data communication, and efficient energy usage in mobile WSNs (MWSNs). It is aimed to cope up with challenging requirements of the rising limited battery powered technologies, which requires low energy consumption and end-to-end delay (EED). Further, the CASeR uses reservation based channel allocation using reservation time division multiple access mobility adaptive cross-layer in dynamic networks and cost based multi-hop communication for packet forwarding and gradient maintenance. The MWSN routing protocols, robust ad-hoc sensor routing, mobility adaptive cross-layer routing, and proactive highly ambulatory sensor routing as well as the mobile ad-hoc network protocols, optimized link state routing and ad-hoc on demand distance vector, measure up with the CASeR. The simulation results show improvements over other routing protocols in energy consumption, EED, queuing delay, and reliable data communication. The competence of this protocol makes it highly suitable to minimize time delay in target applications.

A Cross Layer Protocol for Fast Identification of Blocked Tags in Large-scale RFID Systems

Blocker tag attack is one of the denial-of-service (DoS) attacks that threatens the privacy and security of RFID systems. The attacker interferes with the blocked tag by simulating a fake tag with the same ID, thus causing a collision of message replies. In many practical scenarios, the number of blocked tags may vary, or even be small. For example, the attacker may only block the important customers or high-value items. To avoid the disclosure of privacy and economic losses, it is of great importance to fast pinpoint these blocked ones. However, existing works do not take into account the impact of the number of blocked tags on the execution time and suffer from incomplete identification of blocked tags, long identification time or privacy leakage. To overcome these limits, we propose a cross layer blocked tag identification protocol (CLBI). CLBI consists of multiple rounds, in which it enables multiple unblocked tags to select one time slot and concurrently verify them by using tag estimation in physical layer. Benefiting from the utilization of most collision slots, the execution time can be greatly reduced. Furthermore, for efficient identification of blocked tags under different proportions, we propose a hybrid protocol named adaptive cross layer blocked tag identification protocol (A-CLBI), which estimates the remaining blocked tag in each round and adjusts the identification strategy accordingly. Extensive simulations show that our protocol outperforms state-of-the-art blocked tags identification protocol.

A Simulation Study for Long-Range Underwater Acoustic Networks in the High North

In stark contrast to a typical underwater acoustic network (UAN) deployed in mid-latitudes, ice-covered environments make network deployment difficult and expensive. A limited number of nodes must cover ranges of hundreds of kilometers. We tackle the network design in three layers: engineering, physical, and networking. At the engineering layer, we investigate hardware and bandwidth limitations for real-world implementation. Based on the proposed bandwidth, we design a software modem equipped with three waveforms achieving 1.8, 21.4, and 96.2 b/s. The packet error rate performance is computed with a channel simulator that takes realistic environmental parameters. Our simulations show that ranges of more than 100 km can be achieved in two High North areas during summer months provided that the point-to-point links exploit the ducted sound propagation. However, during winter months, this performance may not be always possible and multiple hops may be needed to cover the same range. Finally, based on the outcomes of the physical layer, an adaptive cross-layer routing protocol, termed network-aware adaptive routing (NADIR), is simulated. Link quality, energy consumption, and topological data are used to select the best coded modulation scheme and relay node in the next transmission slot. Our results show that the use of an adaptive strategy offers higher packet delivery and lower energy consumption than a nonadaptive strategy.

Coordinated Handover Signaling and Cross-Layer Adaptation in Heterogeneous Wireless Networking

The key idea of this paper is the usage of Handover (HO) control message to decrease connection dropping in accordance. Motivated by addressing challenging problems, developing an effective Multi-layer parallel handover optimization scheme is critical to enhance network performance in heterogeneous network environment. This paper proposes a cross-layer-based adaptive vertical handoff algorithm to reduce the handover latency and signaling overhead. The used mobility protocol manage multiple physical paths over heterogeneous networks. This architecture is mainly composed of three parts, (i) Collecting network conditions, (ii) signaling between entities, and (iii) adaptive algorithm based on network conditions. The cross-layer scheme provides the sender with prediction of the available bandwidth. Numerical results highlight the enhancement brought by the capability of the IS to support dynamic information. The proposed architecture can be deployed on any Internet protocol layer such as application layer, transport layer and network layer. Evaluation results demonstrate the achieved improvements over the reference schemes.

Video data wireless transmission method based on cross-layer bitrate adaptation and error control

Aiming at the difficulty of video data transmission in wireless transmission, the idea of cross-layer design is taken as the main line, and the wireless transmission method of video data based on cross-layer code rate adaptation and error control is studied. The MAC layer rate adaptation technology, application layer video codec technology and its rate control technology, transport layer data transmission protocol and its packet encapsulation strategy are studied. The design of video transmission system based on cross-layer design is realized. Aiming at the RTP transmission of H.264 video stream, this paper studies the RTP packet encapsulation mode of H.264 data, and implements an adaptive packet encapsulation strategy based on MAC layer channel quality estimation. Aiming at the RTP transmission of H.264 video stream, the RTP packet encapsulation mode of H.264 data is studied. The adaptive packet encapsulation strategy is implemented based on the MAC layer channel quality estimation. The performance test of the proposed rate control strategy and packet encapsulation strategy is carried out in the actual test environment, and compared with the existing methods. The results show that the proposed strategy is in the case of long communication distance or node movement. The proposed method can reduce the header overhead by 50% and barely 50% of the throughput, and the performance in terms of video playback quality and image PSNR is better than the existing strategy.

Enhanced adaptive cross-layer scheme for low latency HEVC streaming over Vehicular Ad-hoc Networks (VANETs)

Vehicular communication has become a reality guided by various applications. Among those, high video quality delivery with low latency constraints required by real-time applications constitutes a very challenging task. By dint of its never-before-achieved compression level, the new High-Efficiency Video Coding (HEVC) is very promising for real-time video streaming through Vehicular Ad-hoc Networks (VANET). However, these networks have variable channel quality and limited bandwidth. Therefore, ensuring satisfactory video quality on such networks is a major challenge. In this work, a low complexity cross-layer mechanism is proposed to improve end-to-end performances of HEVC video streaming in VANET under low delay constraints. The idea is to assign to each packet of the transmitted video the most appropriate Access Category (AC) queue on the Medium Access Control (MAC) layer, considering the temporal prediction structure of the video encoding process, the importance of the frame and the state of the network traffic load. Simulation results demonstrate that for different targeted low-delay video communication scenarios, the proposed mechanism offers significant improvements regarding video quality at the reception and end-to-end delay compared to the Enhanced Distributed Channel Access (EDCA) adopted in the 802.11p. Both Quality of Service (QoS) and Quality of Experience (QoE) evaluations have been also carried out to validate the proposed approach.

Adaptive Cross-Layer-Based Packet Scheduling and Dynamic Resource Allocation for LTE-Advanced Relaying Cellular Communications

In 4G, LTE-Advanced specifies the key objective that the gigabit data rate for real-time video streaming can be transmitted under a high mobility scenario; thus, packet scheduling and radio resource management become the critical techniques that should be addressed effectively. Additionally, the mechanisms of relaying and the OFDMA coding have been certainly adopted in LTE-Advanced and IEEE 802.16 j/m for the purposes of increasing the wireless service coverage and improving signal quality of UEs nearing the cell boundaries. Several studies propose some improvements for the relaying-based packet scheduling, but suffer from long packet delay, high packet dropping probability moderate system capacity, and degrading QoS of real-time packet transmissions. This paper thus proposes an Adaptive LTE-Advanced cross-layer packet Scheduling (ALS) to guarantee real-time high-speed packet service for LTE-Advanced. ALS consists of two mechanisms: (1) Adaptive Reward Priority Scheduling: the cross layer-based adaptive packet scheduling at the MAC layer and (2) Dynamic Resource Allocation algorithm: the efficient radio resource allocation at the PHY layer. Numerical results demonstrate ALS outperforms the compared approaches in system capacity, packet dropping probability, average packet delay, etc.