Redundant Transmission Research Articles

Service migration with edge collaboration: Multi-agent deep reinforcement learning approach combined with user preference adaptation

Multi-access edge computing provides proximate intelligent services for distributed users. Due to the user’s mobility and highly dynamic network, edge servers with limited coverage cannot ensure continuity of running services and maintain high-level Quality of Service. To tackle this issue, an effective service migration strategy is of paramount importance. However, the current approach ignores the cooperation between multiple edge servers and independent users. In this article, we study service migration with edge collaboration to realize lightweight migration by layer-sharing framework of containers, saving redundant transmissions of migration. Then, we formalize the migration decision problem as maximizing the migration utility problem. To obtain efficient online decisions, we proposed a dynamic service migration strategy (MA-DSM) based on multi-agent proximal policy optimization (MAPPO) algorithm, which leverages a flexible multi-policy framework to achieve user preference adaptation. Specifically, we improve the basic MAPPO by devising a context-aware grouping method to cluster agents with user’s mobility patterns and service preferences. Parameter sharing is introduced into the actor–critic network to learn customized policies for different clusters, facilitating cooperation among users in the same cluster. Extensive experiments demonstrate that our proposed approach outperforms baselines in terms of convergence, latency and migration utility.

SSPRD: A Shared-Storage-Based Hardware Packet Reordering and Deduplication System for Multipath Transmission in Wide Area Networks.

To increase bandwidth and overcome packet loss in Wide Area Networks (WANs), per-packet multipath transmission and redundant transmission are increasingly being used as Software-Defined Wide Area Network (SD-WAN) solutions. However, this results in out-of-order and duplicate packets in the destination network. To restore sequential and unique data streams for multiple connections, hardware packet buffers with significant depth are required due to the large delay difference between WAN paths. To address this issue, SSPRD, a shared-storage-based packet reordering and deduplication system using a Field-Programmable Gate Array (FPGA), is proposed. The storage space for packets and sub-buffers is shared by all sessions with dynamic allocation. Packets are stored in the DDR and are sorted by their descriptors in the buffers. We also develop a sub-buffer-based timeout event handling algorithm. While supporting four sessions, SSPRD achieves a deep reorder buffer on hardware, with a depth of up to 15,360 packets per session. Compared with other solutions, SSPRD reduces buffer space usage by 62.5%, and reaches a packet reordering and deduplicating performance of 10 Gbps for 1500-byte packets.

Energy Efficient Secured PSO Optimized Clustering and Data Aggregation Routing Protocol for Wireless Sensor Networks

In Wireless Sensor Networks (WSNs), sensor nodes are placed to sense and collect data. Due to the energy constraint nature of WSN, optimising the energy during the data dissemination is a major concern. To solve this problem, data aggregation may be used to bring down the redundant transmission of packets in WSN. In most of the previously available techniques, security is also a major concern during data aggregation and routing process with optimized energy. Many data aggregation-based routing systems are subject to security attacks during the data transfer from sensors to source to Clustered Heads (CHs) and then to sink with data aggregation process. Moreover, the existing data aggregation-based routing protocols suffer from data redundancy with less accuracy in aggregated data. For handling such issues order to overcome these issues, an Energy Efficient Secured Clustered Particle Swarm Optimization (PSO) oriented Data Aggregation Routing Protocol (EESCPSO-DARP) that can provide efficient authentication during data aggregation-based routing is introduced in this paper. Moreover, the proposed protocol enhances the rate of the data transmission by efficient prevention of false data injection and other attacks through node authentication and data encryption. This proposed protocol minimizes the energy usage by minimizing the retransmissions by eliminating the possible redundant transmissions of data during data aggregation-based routing. Moreover, the introduced protocol minimizes both communication and computational overhead through optimal clustering and routing with PSO and provides and efficient routing system. This proposed EESCPSO-DARP protocol has been developed by using the NS3 simulator. The results of this protocol showed improved security, higher packet delivery ratio and enhanced network throughput with reduced energy and delay.

GCP: A multi-strategy improved wireless sensor network model for environmental monitoring

Nowadays, smart environmental monitoring devices are widely used in various fields, and one of the most representative tools is the wireless sensor network (WSN). WSNs are easy to deploy and provide real-time information feedback, which is very suitable for environmental monitoring. As we all know, the environmental monitoring network because of the special nature of its work requirements, the need for uninterrupted and real-time transmission of monitoring data, which leads to energy consumption is extremely large, and this cannot meet the needs of its long-term work. Existing traditional routing has problems such as unscientific cluster head election and high redundancy in data transmission, which usually lead to a large amount of energy consumption, which is not conducive to the long-term stable operation of sensor networks. In this paper, we improve the traditional routing protocol and design a cluster head election method based on the genetic algorithm, which proposes a new fitness function in terms of energy, distance, and the number of nodes in the cluster, and performs the selection of cluster head nodes based on this method. In addition, we propose a new grey prediction model, which can realize the real-time update of data queues, and optimize the data transmission process of traditional WSNs based on this prediction model to reduce the amount of intra-cluster data transmission. Combining these improvements, a grey cluster prediction (GCP) model is proposed, and the performance of the model is tested based on real mine and soil data sets. The simulation results show that the model significantly reduces energy loss and extends the network life cycle while ensuring the integrity of data transmission. It can also meet the requirements of long-term stable operation of environmental monitoring equipment.

Event‐triggered tracking control for networked control systems under diversified attacks

AbstractThis paper mainly concerns on the tracking control problem for event‐triggered networked control systems (NCSs) with diversified cyber‐attacks. An event‐triggered mechanism (ETM) is exploited to decrease the amount of redundant transmissions, thus saving communication resources. In addition, a novel tracking error model for NCSs is built with consideration of ETM and diversified cyber‐attacks, which involves deception attacks and Denial‐of‐Service (DoS) attacks. On the basis of Lyapunov stability theory and linear matrix inequalities (LMIs), the stability criterion of the tracking error system is constructed, and the controller is designed to ensure the tracking performance of the system. In the end, simulation examples are provided to testify the validity of the proposed method.

A neural network-guided correlation thresholding technique for wireless sensor networks

Wireless sensor networks comprise autonomous nodes monitoring environments while managing energy consumption. Extending network lifespan involves minimizing redundant transmissions by filtering identical information from correlated sensor nodes. Correlation depends on spatial locations and it quantifies sensor relationships. It allows nodes classification as correlated or uncorrelated using a threshold value between 0 and 1 which enhances network lifespan by 60–65 %. Selecting the optimal threshold considering network metrics remains a challenge. This study proposes a neural network-guided correlation thresholding technique (NNCT). NNCT aims to identify the optimal threshold for filtering redundant reports by training a neural network on varied parameters like node density, threshold, coverage, and events using different clustering methods. The process involves implementing spatial correlation models, virtual clustering, event detection, and iterative threshold-based node selection. The neural network achieves a high accuracy of R = 0.97813, offering a promising adaptive solution for automated threshold selection in correlation-based filtering.

Dynamic sum-based event-triggered H∞ filtering for networked T-S fuzzy wind turbine systems with deception attacks

This article is concerned with the dynamic sum-based event-triggered H∞ filtering issue for networked wind turbine systems subject to deception attacks and communication delays. By considering the time-varying wind power case rather than the existing maximum power case, a more general T-S fuzzy system with two premise variables is modeled for nonlinear wind turbine system. In order to save the communication cost, a novel dynamic sum-based event-triggered scheme is proposed, which has the following three merits. First, some past sampled measurements are utilized to reduce redundant transmissions. Second, an auxiliary dynamic variable based on the past sampled measurements is introduced in the triggering condition to further enlarge the triggering intervals. Third, a dynamic triggering threshold is designed, which can be regulated adaptively along with the system evolution. With the help of Lyapunov method and linear matrix inequality technique, some sufficient co-design conditions of H∞ filter and triggering matrices are derived for the T-S fuzzy filtering error system with deception attacks and communication delays. Lastly, some simulations are carried out to illustrate the advantages of the proposed strategy.

DCP-Net: A Distributed Collaborative Perception Network for Remote Sensing Semantic Segmentation

Collaborative perception enhances onboard perceptual capability by integrating features from other platforms, effectively mitigating the compromised accuracy caused by a restricted observational range and vulnerability to interference. However, current implementations of collaborative perception overlook the prevalent issues of both limited and low-reliability communication, as well as misaligned observations in remote sensing. To address this problem, this article presents an innovative distributed collaborative perception network (DCP-Net) specifically designed for remote sensing applications. Firstly, a self-mutual information match module is proposed to identify collaboration opportunities and select suitable partners. This module prioritizes critical collaborative features and reduces redundant transmission for better adaptation to weak communication in remote sensing. Secondly, a related feature fusion module is devised to tackle the misalignment between local and collaborative features due to the multiangle observations, improving the quality of fused features for the downstream task. We conduct extensive experiments and visualization analyses using three semantic segmentation datasets, namely Potsdam, iSAID, and DFC23. The results demonstrate that DCP-Net outperforms the existing collaborative perception methods comprehensively, improving mIoU by 2.61% to 16.89% at the highest collaboration efficiency and achieving state-of-the-art performance.

RL-ANC: Reinforcement Learning-Based Adaptive Network Coding in the Ocean Mobile Internet of Things

As the demand for sensing and monitoring the marine environment increases, the Ocean Mobile Internet of Things (OM-IoT) has gradually attracted the interest of researchers. However, the unreliability of communication links represents a significant challenge to data transmission in the OM-IoT, given the complex and dynamic nature of the marine environment, the mobility of nodes, and other factors. Consequently, it is necessary to enhance the reliability of underwater data transmission. To address this issue, this paper proposes a reinforcement learning-based adaptive network coding (RL-ANC) approach. Firstly, the channel conditions are estimated based on the reception acknowledgment, and a feedback-independent decoding state estimation method is proposed. Secondly, the sliding coding window is dynamically adjusted based on the estimates of the channel erasure probability and decoding probability, and the sliding rule is adaptively determined using a reinforcement learning algorithm and an enhanced greedy strategy. Subsequently, an adaptive optimization method for coding coefficients based on reinforcement learning is proposed to enhance the reliability of the underwater data transmission and underwater network coding while reducing the redundancy in the coding. Finally, the sampling period and time slot table are updated using the enhanced simulated annealing algorithm to optimize the accuracy and timeliness of the channel estimation. Simulation experiments demonstrate that the proposed method effectively enhances the data transmission reliability in unreliable communication links, improves the performance of underwater network coding in terms of the packet delivery rate, retransmission, and redundancy transmission ratios, and accelerates the convergence speed of the decoding probability.

Resilient Event-Based Fuzzy Fault Detection for DC Microgrids in Finite-Frequency Domain against DoS Attacks.

This paper addresses the problem of fault detection in DC microgrids in the presence of denial-of-service (DoS) attacks. To deal with the nonlinear term in DC microgrids, a Takagi-Sugeno (T-S) model is employed. In contrast to the conventional approach of utilizing current sampling data in the traditional event-triggered mechanism (ETM), a novel integrated ETM employs historical information from measured data. This innovative strategy mitigates the generation of additional triggering packets resulting from random perturbations, thus reducing redundant transmission data. Under the assumption of faults occurring within a finite-frequency domain, a resilient event-based H-/H∞ fault detection filter (FDF) is designed to withstand DoS attacks. The exponential stability conditions are derived in the form of linear matrix inequalities to ensure the performance of fault detected systems. Finally, the simulation results are presented, demonstrating that the designed FDF effectively detects finite-frequency faults in time even under DoS attacks. Furthermore, the FDF exhibits superior fault detection sensitivity compared to the conventional H∞ method, thus confirming the efficacy of the proposed approach. Additionally, it is observed that a trade-off exists between fault detection performance and the data releasing rate (DRR).

Optimal redundant transmission scheduling for remote state estimation via reinforcement learning approach

This paper studies the optimal redundant transmission scheduling for remote state estimation. Multiple smart sensors observe some systems, and transmit the local state estimates via independent channels to a remote estimator (RE), where packet losses may occur with some particular probabilities. To improve the estimation performance, some redundant channels are adopted for the data transmission. Since the number of redundant channels is fixed, the optimal redundant scheduling for multiple sensors is worth investigating to determine how to allocate the redundant channels. To address this problem, the redundant transmission scheduling is modeled as a Markov decision process (MDP) to minimize the estimation error for all systems. By constructing a sufficient condition, one ensures that the MDP has an optimal deterministic and stationary policy. Meanwhile, the threshold structure of the redundant transmission scheduling policy is verified to further decrease the complexity of the calculation. Reinforcement learning (RL) is used for this problem, and a near-optimal policy is obtained by dueling double-deep Q-networks (D3QN) algorithm. Finally, an illustrative simulation is presented to demonstrate its effectiveness.

Covert Communication through Robust Fragment Hiding in a Large Number of Images.

For covert communication in lossy channels, it is necessary to consider that the carrier of the hidden watermark will undergo multiple image-processing attacks. In order to ensure that secret information can be extracted without distortion from the watermarked images that have undergone attacks, in this paper, we design a novel fragmented secure communication system. The sender will fragment the secret data to be transmitted and redundantly hide it in a large number of multimodal carriers of messenger accounts on multiple social platforms. The receiver receives enough covert carriers, extracts each fragment, and concatenates the transmitted secret data. This article uses the image carrier as an example to fragment the text file intended for transmission and embeds it into a large number of images, with each fragment being redundant and embedded into multiple images. In this way, at the receiving end, only enough stego images need to be received to extract the information in each image, and then concatenate the final secret file. In order to resist various possible attacks during image transmission, we propose a strong robust image watermarking method. This method adopts a watermark layer based on DFT, which has high embedding and detection efficiency and good invisibility. Secondly, a watermark layer based on DCT is adopted, which can resist translation attacks, JPEG attacks, and other common attacks. Experiments have shown that our watermarking method is very fast; both the embedding time and the extraction time are less than 0.15 s for images not larger than 2000×2000. Our watermarking method has very good invisibility with 41dB PSNR on average. And our watermarking method is more robust than existing schemes and robust to nearly all kinds of attacks. Based on this strong robust image watermarking method, the scheme of fragmenting and hiding redundant transmission content into a large number of images is effective and practical. Our scheme can 100% restore the secret file completely under different RST or hybrid attacks, such as rotation by 1 degree and 5 degrees, scaling by 1.25 and 0.8, and cropping by 10% and 25%. Our scheme can successfully restore the secret file completely even if 30% of received images are lost. When 80% of received images are lost, our scheme can still restore 61.1% of the secret file. If all stego images can be obtained, the original text file can be completely restored.

Dealing With Jamming Attacks in Uplink Pairwise NOMA Using Outage Analysis, Smart Relaying, and Redundant Transmissions

Dealing With Jamming Attacks in Uplink Pairwise NOMA Using Outage Analysis, Smart Relaying, and Redundant Transmissions

Dynamic Threshold Finite-Time Prescribed Performance Control for Nonlinear Systems With Dead-Zone Output.

This article investigates the tracking control problem for nonlinear systems. An adaptive model is proposed to represent the dead-zone phenomenon and solve its control challenge with a Nussbaum function in conjunction. Drawing inspiration from the existing prescribed performance control schemes, a novel dynamic threshold scheme is developed that fuses a proposed continuous function with a finite-time performance function. A dynamic event-triggered strategy is applied to reduce the redundant transmission. The proposed time-varying threshold control strategy has fewer updates than the traditional fixed threshold and improves the efficiency of resource utilization. A command filter backstepping approach is employed to prevent the complexity explosion faced by the computation. The suggested control strategy ensures that all system signals are bounded. The validity of the simulation results has been verified.

Performance of Unslotted ALOHA With Capture and Multiple Collisions in LoRaWAN

This paper explores the capacity limits and the tradeoff between the antagonistic means of enabling reliable data delivery in a loaded LoRaWAN cell. In fact, channel attenuation and variability call for robust transmission settings but the associated load increase causes more collisions between frames. In addition to the physical layer parameters of the LoRa modulation, this paper considers the benefits and tuning of inter-packet Error Correction Codes (ECC), which also trades transmission redundancy for reliability. We thus start by proposing a refined Packet Delivery Ratio (PDR) model that improves the ones found in the literature in that, first, it takes into account the dependency between overcoming ambient noise and dominating colliding frames, and second, it considers the sum of interference powers when multiple colliding frames are present, even if the interference preexists. Moreover, the model extends to the case of a gateway with receiver diversity. In a second step, the model allows to set out the level of redundancy at which ECC is the most effective without hindering capacity: a coding rate of one third. When this is fixed, it allows to define the transmission parameters allocation within a cell and thus the size of the cell. We finally develop an ad-hoc Python discrete-event simulator, freely accessible, to complement the model for assessing the effect of inter-SF and near-far interference and to show the benefits of power control in this respect.

Saving Energy and Spectrum in Enabling URLLC Services: A Scalable RL Solution

Communication systems supporting cyber-physical production applications should satisfy stringent delay and reliability requirements. Diversity techniques and power control are the main approaches to reduce latency and enhance the reliability of wireless communications at the expense of redundant transmissions and excessive resource usage. Focusing on the application layer reliability key performance indicators (KPIs), we design a deep reinforcement learning orchestrator for power control and hybrid automatic repeat request retransmissions to optimize these KPIs. Furthermore, to address the scalability issue that emerges in the per-device orchestration problem, we develop a new branching soft actor-critic framework in which a separate branch represents the action space of each industrial device. Our orchestrator enables near real-time control and can be implemented in the edge cloud. We test our solution with a 3GPP-compliant and realistic simulator for factory automation scenarios. Compared to the state-of-the-art, our solution offers significant scalability gains in terms of computational time and memory requirements. Our extensive experiments show significant improvements in our target KPIs, over the state-of-the-art, especially for 5th percentile user availability. To achieve these targets, our framework requires substantially less total energy or spectrum, thanks to our scalable RL solution.

HAND EXOSKELETON FOR REHABILITATION AND FUNCTIONALIZATION

Many diseases and injuries of the hand require rehabilitation to restore function. However, the high human, financial, spatial, and temporal costs associated with rehabilitation often mean that the population in need does not have access to optimal rehabilitative care. Therefore, devices that complement the therapist are a possible solution, as they make rehabilitation more independent and frequent, and save healthcare facilities the aforementioned resources. Nevertheless, these devices are not widely distributed in the market, mainly due to their poor accessibility. The newly designed exoskeleton has four motors and a redundant transmission system that allows independent flexion and extension of each finger, except the thumb. Kinematics were analyzed with motion studies and loads were evaluated with static studies and structural analysis using motion loads. In the simulations, both flexion and extension were achieved in four seconds. A prototype transmission system was built and its kinematics matched that of the simulation and corresponded to the biomechanics of the fingers. At maximum flexion, the exoskeleton would be able to hold small objects and exert a normal force of up to 20 N with structural integrity. Keywords: Engineering design, exoskeleton, hand rehabilitation, hand functionalization.

A Dynamic Opportunistic Routing Protocol for Asynchronous Duty-Cycled WSNs

Opportunistic routing (OR) is widely adopted in Wireless Sensor Networks (WSNs) running asynchronous duty-cycled MAC protocols. In conventional routing, where packets are forwarded along predetermined routes, the sender may wait for the receiver to wake up for a long time. To reduce the sender waiting time, the OR protocols allow nodes to select multiple neighbors as the forwarders so that the packets could be forwarded by multi-path. Thus, the forwarders selection algorithm affects network performance seriously. However, an excessive number of forwarders increases the probability that more than one forwarders wake up simultaneously. This will consume more energy since each of them will receive the packet. To address the two issues, a Dynamic Opportunistic Routing protocol using Analytical Hierarchy Process (AHP) and Fuzzy Inference System (FIS) called DORAF is proposed in this paper. DORAF is implemented in three steps. First, multiple criteria (i.e., residual energy, distance, and angle) at the network layer are defined to evaluate the nodes where the importance of these criteria is determined by AHP. Second, the pairwise comparison matrices in AHP are generated by using mathematical functions (i.e., Boltzmann function and Logistic function) and FIS. Third, each node uses AHP and FIS to prioritize its neighbors based on the criteria and selects appropriate ones as the forwarders dynamically in a distributed manner. The experimental results demonstrate that our protocol performs better than other state-of-the-art in terms of network lifetime, energy consumption, and average redundant transmissions.

Handover-Aware Downlink Beamforming Design for LEO Multibeam Satellite Communications

Because of their capability of providing seamless connectivity, low-Earth-orbit (LEO) satellites will play a vital role in integrated satellite–terrestrial paradigms. However, high-speed satellite motion brings frequent handovers, during which redundant transmission and even loss of data easily occur. In this letter, we propose a handover-aware downlink beamforming design. To mitigate the impact of inter-satellite handover, we reduce the blocklength of coverage-edge users, aiming at completing transmission within the remaining visible time (RVT). Hence, the capacity of these users should be evaluated in the finite-block length (FBL) regime. After obtaining a surrogate of the FBL capacity function with a low-complexity method, we formulate the beamforming optimization model as a semidefinite programming (SDP) problem and solve it with CVX. The simulation results show that the proposed scheme can reach a 99.35% transmission success rate before inter-satellite handover.

Graph dropout self-learning hierarchical graph convolution network for traffic prediction

Traffic prediction is a challenging topic in urban traffic construction and management due to its complex dynamic spatial–temporal correlations. Currently, graph neural network achieves good results in traffic prediction, but the existing methods usually do not take into account the multi-layer information of the graph and the redundant transmission of information. Moreover, they do not consider the over-smoothing of the graph. In this paper, we propose a novel graph dropout self-learning hierarchical graph convolution network (DHGCN). Firstly, we design a self-learning hierarchical graph convolution network, which captures spatial features at different layers through multiple self-learning dynamic graphs and avoids redundant information transmission. Secondly, a novel graph dropout structure is proposed to sparsify the graph and avoid the over-smoothing of the graph. Meanwhile, an encoder–decoder architecture with gated residuals is designed to capture dynamic temporal features. In addition, this paper addresses two important traffic forecasting tasks: traffic flow and traffic speed. Extensive experiments with six real-world datasets verify that our method achieves state-of-the-art performance in both traffic flow and traffic speed and consistently outperforms baselines.