Network Transmission Research Articles

Application of optical network transmission based on machine learning and wireless sensor networks in artificial intelligence online education system

Application of optical network transmission based on machine learning and wireless sensor networks in artificial intelligence online education system

Intraspinal microstimulation of the ventral horn has therapeutically relevant cross-modal effects on nociception.

Electrical stimulation of spinal networks below a spinal cord injury is a promising approach to restore functions compromised by inadequate and/or inappropriate neural drive. The most translationally successful examples are paradigms intended to increase neural transmission in weakened yet spared descending motor pathways and spinal motoneurons rendered dormant after being severed from their inputs by lesion. Less well understood is whether spinal stimulation is also capable of reducing neural transmission in pathways made pathologically overactive by spinal cord injury. Debilitating spasms, spasticity and neuropathic pain are all common manifestations of hyperexcitable spinal responses to sensory feedback. Whereas spasms and spasticity can often be managed pharmacologically, spinal cord injury-related neuropathic pain is notoriously medically refractory. Interestingly, however, spinal stimulation is a clinically available option for ameliorating neuropathic pain arising from aetiologies other than spinal cord injury, and the limited evidence available to date suggests that it holds considerable promise for reducing spinal cord injury-related neuropathic pain, as well. Spinal stimulation for pain amelioration has traditionally been assumed to modulate sensorimotor networks overlapping with those engaged by spinal stimulation for rehabilitation of movement impairments. Thus, we hypothesize that spinal stimulation intended to increase the ability to move voluntarily may simultaneously reduce transmission in spinal pain pathways. To test this hypothesis, we coupled a rat model of incomplete thoracic spinal cord injury, which results in moderate to severe bilateral movement impairments and spinal cord injury-related neuropathic pain, with in vivo electrophysiological measures of neural transmission in networks of spinal neurons integral to the development and persistence of the neuropathic pain state. We find that when intraspinal microstimulation is delivered to the ventral horn with the intent of enhancing voluntary movement, transmission through nociceptive specific and wide dynamic range neurons is significantly depressed in response to pain-related sensory feedback. By comparison, spinal responsiveness to non-pain-related sensory feedback is largely preserved. These results suggest that spinal stimulation paradigms could be intentionally designed to afford multi-modal therapeutic benefits, directly addressing the diverse, intersectional rehabilitation goals of people living with spinal cord injury.

Nonlinear equalization based on feature crosses neural networks for High-speed PAM4 transmission

In this paper, a low complexity feature crosses nonlinear equalization(FC-NLE) scheme using an additional feature crosses layer based on deep neural network is proposed. A 120 Gb/s quadratic pulse amplitude modulation (PAM4) transmission system based on intensity modulation and direct detection (IM/DD) is experimentally demonstrated. After a 5 km transmission, the FC-NLE scheme achieves a bit error rate (BER) below the hard-decision forward-error-correction threshold of 7% when the received optical power exceeds 4.5 dBm. This performance surpasses that of the Volterra equalizer(VE) and network-based equalization schemes. k-means clustering and model compression techniques were adopted to effectively diminish the complexity of the FC-NLE. Experimental results show that the performance of the FC-NLE is about 0.5–1.0 dB better than that of the VE, DNN, LSTM equalizers and its complexity is less than 50% of them. Compared with other low complexity network schemes in recent years, FC-NLE still exhibits a performance improvement of more than 1 dB at lower complexity.

IRS-Enhanced Spectrum Sensing and Secure Transmission in Cognitive Radio Networks

IRS-Enhanced Spectrum Sensing and Secure Transmission in Cognitive Radio Networks

Stability analysis of power system under n-1 contingency condition

Several voltage stability indices (VSIs) have been developed to assess the potential for voltage collapse. However, certain indexes are computationally costly. Meanwhile, some have been noted to underperform across various conditions. This work proposes a novel line index called the super voltage stability index (SVSI) to calculate the system's voltage stability margin (VSM). The suggested approach is based on the transmission system's two bus systems. The reactive power loss and N-1 contingency conditions to voltage sensitivity is a unique calculation approach used in this study to identify voltage instability. Day to day, the demand for electric power is being increased due to incessant increments in technology and population growth. Therefore, the power system networks are under pressure. The operational conditions of transmission system networks are affected at this point, which may result in voltage collapse. Regular monitoring of power supply is essential to avert voltage collapse. The effectiveness of the suggested index has been assessed using the IEEE 5 and 30-bus systems across diverse operating scenarios, including variations in active and reactive power loading as well as single line losses. The findings indicate that SVSI provides a more reliable indication of the proximity to voltage collapse when compared to conventional line VSIs.

Dynamic Fault Tree Model of Civil Aircraft Avionics Network Transmission Failure Based on Optimized Extended Fuzzy Algorithm

Dynamic Fault Tree Model of Civil Aircraft Avionics Network Transmission Failure Based on Optimized Extended Fuzzy Algorithm

HCFAIUN: A novel hyperelliptic curve and fuzzy extractor-based authentication for secure data transmission in IoT-based UAV networks

IoT-based UAV networks comprise interconnected UAVs outfitted with sensors and microcontrollers to simplify data exchange in environments such as smart cities. In light of open-access communication landscapes, IoT-based UAV networks could pose security challenges, encompassing authentication vulnerabilities and the inadvertent disclosure of location and other confidential information to unauthorised parties. Henceforth, we have proposed a lightweight and secure authentication protocol: Hyperelliptic Curve and Fuzzy Extractor based Authentication in IoT-based UAV networks (HCFAIUN) leveraging Hyperelliptic Curve Cryptography(HCC), Fuzzy Extractor (FE), XOR operations and hash functions. HCC's maximum key size is 80 bits, differing from the 160-bit requirement of the elliptic curve, making it apt for UAVs with limited resources. The proposed scheme utilises biometrics traits of users to avoid exposing data from stealing smart devices using FE. This protocol facilitates the mutual authentication of users and UAVs, allowing them to exchange a session key for secure communication. The Hyperelliptic Curve (HC) scalar multiplication protects the user's private key from attackers, even in public channels. The obfuscation identity of the user and UAVs generated through the hash function and timestamp makes the external user and UAV anonymous. The efficacy of this proposed framework is examined using the Scyther verification tool and Random oracle model-based formal analysis, and informal analysis is also discussed, which validates its robustness against well-known potential physical and logical attacks. The performance analysis shows that the HCFAIUN scheme has lower computation, communication, and storage costs, i.e., 3.832 ms and 1456 bits and 1128 bits, respectively, compared to existing schemes.

Energy-efficient quad tree-based clustering using edge-assisted UAV-relay to enhance network lifetime in WSN

Wireless sensor networks' most prominent concern is energy optimization. It faces significant problems like high energy consumption, data loss, delay, and low network lifetime. To improve, it uses clustering. However, during clustering, coverage holes are most likely to appear near the network's edge, within the cluster, and between clusters. As a result, there are more energy holes and dead nodes; therefore, the goal of this work is to maximize node network lifetime and minimize energy consumption during data transmission in the wireless sensor network (WSN). The proposed work includes three entities: sensor nodes, an edge-assisted unmanned aerial vehicle (UAV), and a base station. It uses an edge-assisted unmanned aerial vehicle to provide additional resources to the UAV, which helps reduce energy consumption during data transmission. This research proposes using communication to enhance the speed and bandwidth of data transmission and reduce transmission latency. This work attempts to improve performance by increasing throughput.

Aquila Optimized Fuzzy Deep Belief Network for Secure Data Transmission in WSN

A wireless sensor network (WSN) is made up of several independent sensor nodes that are able to interpret, analyze, and work with data. It is generally recognized that security and limited energy are the two challenging tasks with WSNs. To address these challenges, a novel Aquila-optimized fuzzy deep belief network (AO-FDBN) model has been proposed in this paper. The suggested AO-FDBN framework consists of three stages. Initially, the Aggregator has been selected by using the Aquila optimization algorithm. Secondly, the data from the aggregator are encrypted by using the blowfish algorithm. Finally, the optimal route has been selected by using the fuzzy-deep belief network (DBN). Packet delivery ratio (PDR), transport delay, energy usage, and network lifetime are evaluated between the suggested framework and current methods. Experimental results specify that the suggested AO-FDBN approach achieves higher performance of 22.617%, 14.22%, and 15.64% than TEEFCA, HESC, and SEPC methods. This system is more effective and secure for real-time applications.

Joint optimization of communication and mission performance for multi-UAV collaboration network: A multi-agent reinforcement learning method

In emergency rescue, target search and other mission scenarios with Unmanned Aerial Vehicles (UAVs), the Relay UAVs (RUs) and Mission UAVs (MUs) can collaborate to accomplish tasks in unknown environments. In this paper, we investigate the problem of trajectory planning and power control for the MU and RU collaboration. Firstly, considering the characteristics of multi-hop data transmission between the MU and Ground Control Station, a multi-UAV collaborative coverage model is designed. Meanwhile, a UAV control algorithm named MUTTO is proposed based on multi-agent reinforcement learning. In order to solve the problem of the unknown information about the number and locations of targets, the geographic coverage rate is used to replace the target coverage rate for decision making. Then, the reward functions of two types of UAVs are designed separately for the purpose of better cooperation. By simultaneously planning the trajectory and transmission power of the RU and MU, the mission target coverage rate and network transmission rate are maximized while the energy consumption of the UAV is minimized. Finally, numerical simulations results show that MUTTO can solve the UAV network control problem in an efficient way and has better performance than the benchmark method.

GSHFA-HCP: a novel intelligent high-performance clustering protocol for agricultural IoT in fragrant pear production monitoring

The agriculture Internet of Things (IoT) has been widely applied in assisting pear farmers with pest and disease prediction, as well as precise crop management, by providing real-time monitoring and alerting capabilities. To enhance the effectiveness of agriculture IoT monitoring applications, clustering protocols are utilized in the data transmission of agricultural wireless sensor networks (AWSNs). However, the selection of cluster heads is a NP-hard problem, which cannot be solved effectively by conventional algorithms. Based on this, This paper proposes a novel AWSNs clustering model that comprehensively considers multiple factors, including node energy, node degree, average distance and delay. Furthermore, a novel high-performance cluster protocol based on Gaussian mutation and sine cosine firefly algorithm (GSHFA-HCP) is proposed to meet the practical requirements of different scenarios. The innovative Gaussian mutation strategy and sine–cosine hybrid strategy are introduced to optimize the clustering scheme effectively. Additionally, an efficient inter-cluster data transmission mechanism is designed based on distance between nodes, residual energy, and load. The experimental results show that compared with other four popular schemes, the proposed GSHFA-HCP protocol has significant performance improvement in reducing network energy consumption, extending network life and reducing transmission delay. In comparison with other protocols, GSHFA-HCP achieves optimization rates of 63.69%, 17.2%, 19.56%, and 35.78% for network lifespan, throughput, transmission delay, and packet loss rate, respectively.

Pulse Shape and Voltage-Dependent Synchronization in Spiking Neuron Networks.

Pulse-coupled spiking neural networks are a powerful tool to gain mechanistic insights into how neurons self-organize to produce coherent collective behavior. These networks use simple spiking neuron models, such as the θ-neuron or the quadratic integrate-and-fire (QIF) neuron, that replicate the essential features of real neural dynamics. Interactions between neurons are modeled with infinitely narrow pulses, or spikes, rather than the more complex dynamics of real synapses. To make these networks biologically more plausible, it has been proposed that they must also account for the finite width of the pulses, which can have a significant impact on the network dynamics. However, the derivation and interpretation of these pulses are contradictory, and the impact of the pulse shape on the network dynamics is largely unexplored. Here, I take a comprehensive approach to pulse coupling in networks of QIF and θ-neurons. I argue that narrow pulses activate voltage-dependent synaptic conductances and show how to implement them in QIF neurons such that their effect can last through the phase after the spike. Using an exact low-dimensional description for networks of globally coupled spiking neurons, I prove for instantaneous interactions that collective oscillations emerge due to an effective coupling through the mean voltage. I analyze the impact of the pulse shape by means of a family of smooth pulse functions with arbitrary finite width and symmetric or asymmetric shapes. For symmetric pulses, the resulting voltage coupling is not very effective in synchronizing neurons, but pulses that are slightly skewed to the phase after the spike readily generate collective oscillations. The results unveil a voltage-dependent spike synchronization mechanism at the heart of emergent collective behavior, which is facilitated by pulses of finite width and complementary to traditional synaptic transmission in spiking neuron networks.

Unveiling intrusions: explainable SVM approaches for addressing encrypted Wi-Fi traffic in UAV networks

AbstractUnmanned aerial vehicles (UAVs), also known as drones, have become instrumental in various domains, including agriculture, geographic information systems, media, logistics, security, and defense. These UAVs often rely on wireless communication networks for data transmission, making them vulnerable to cyberattacks. To address these challenges, it is necessary to detect potential threats by analyzing the encrypted Wi-Fi traffic data generated by UAVs. This study aimed to develop a linear SVM model that is enhanced with explainable artificial intelligence (XAI) techniques and fine-tuned using Bayesian optimization for intrusion detection systems (IDSs); the model is specifically designed to identify malware threats targeting UAVs. This research utilized encrypted Wi-Fi traffic data derived from three different UAV networks, namely, Parrot Bebop 1, DBPower UDI, and DJI Spark, while considering unidirectional and bidirectional communication flow modes. SVM-based intrusion detection models have been modeled on these datasets, identified their key features using the local interpretable model-agnostic explanations (LIME) technique, and conducted a cost analysis of the proposed modeling approach. The incorporation of the LIME method enabled to highlight the features that are highly indicative of cyberattacks and provided valuable insights into the importance of each feature in the context of intrusion detection. In conclusion, this interpretable IDS model, fine-tuned with Bayesian optimization, demonstrated its superiority over the state-of-the-art methods, proving its efficacy in detecting and mitigating threats to UAVs while offering a cost-effective solution.

Partial-MIMO Based Mode-Group Transmission and Routing in a Field-Deployed 15-Mode Network: Throughput, DSP Resources and Network Flexibility

Partial-MIMO Based Mode-Group Transmission and Routing in a Field-Deployed 15-Mode Network: Throughput, DSP Resources and Network Flexibility

Secure state estimation of memristive neural networks with dynamic self-triggered strategy subject to deception attacks

This paper is dedicated to addressing state estimation for memristive neural networks (MNNs) featuring dynamic self-triggered mechanisms (DSTM) subject to deception attacks (DA). Taking into account the constrained channel bandwidth, the data sampling controller by dynamic self-triggering is proposed for measurement output. The network transmission of data among sensor and estimator is susceptible to deception attacks, and a corresponding state estimator is developed. Utilizing Lyapunov stability theory, it is demonstrated that the state error system is exponentially ultimately bounded in the mean square, and the dynamic self-triggered strategy avoids Zeno behavior. Furthermore, the estimation gains are obtained using a linear matrix inequality (LMI) approach. Lastly, simulated examples are provided to demonstrate the efficacy of the proposed approach.

A reliable cluster-based opportunistic routing protocol for underwater wireless sensor networks

Underwater Acoustic Sensor Networks (UASNs) have gained significant popularity and application in various marine engineering exploration scenarios, driven by the global evolution of the ocean ecosystem. However, UASNs face numerous challenges arising from the unique characteristics of the underwater environment and acoustic channels, such as limited energy resources, unreliable communication, and dynamic network topology in uncertain environments, all of which impact the network lifetime and transmission reliability. To address these challenges and enable efficient underwater data packet transmissions, this paper proposes a reliable cluster-based routing method called RCRP. The RCRP organizes sensor nodes into clusters, where cluster heads transmit the fused data to the sink node through multi-hop forwarding. To overcome routing voids caused by node failures during packet transmission, a prediction model based on the Markov chain is introduced to identify void nodes and inform neighboring nodes about the routing holes. Furthermore, a connection prediction mechanism is developed to tackle changes in the uncertain network topology, taking into account node mobility and the received SNR. For packet transmissions, a waiting mechanism inspired by the opportunistic routing (OR) paradigm is proposed, which determines the optimal forwarding route based on the calculated probability of successful connection between nodes. Simulation results demonstrate that RCRP outperforms existing routing protocols in terms of packet delivery ratio, surviving time, and energy consumption, highlighting its effectiveness in enhancing the performance of UASNs.

Innovative landslide disaster monitoring: unmanned aerial vehicle-deployed GNSS technology

Real-time monitoring technologies of surface deformation represented by the global navigation satellite system (GNSS) are essential for landslides early warning. Monitoring methods such as GNSS require on-site manual installation. Therefore, it becomes nearly impossible to deploy surface monitoring equipment when landslides are located in high mountain valleys that are challenging for personnel to access, and/or in hazardous situations. We propose an intelligent real-time monitoring and early-warning technology that employs an unmanned aerial vehicle (UAV) as a carrier to deploy GNSS equipment. A system was initially designed with five components: an adaptive sampling GNSS receiver, an intelligent cooperative network transmission module, UAV-dropped GNSS equipment, special delivery UAV, and an intelligent monitoring and early warning cloud platform. This new technology was applied to a real landslide in Gansu Province, China, in 2020. The mean absolute error of 30 days in the east, north, and upward directions were 1.2, 1.3, and 2.9 mm/d, respectively, comparing the deformation velocity between the traditional monitoring station and the UAV-dropped monitoring station. This observation facilitated the successful prediction of a landslide hazard on 27 January 2021, allowing for a timely alert to be issued. Collectively, the results of this study established that UAV-deployed GNSS technology can accomplish unmanned deployment of GNSS equipment for landslide monitoring to ensure early warning issuance in inaccessible and/or high-risk areas.

Dynamic‐memory event‐based asynchronous security control for positive singular semi‐Markov jumping CPSs against multiple cyber‐attacks

AbstractThis paper mainly studies the design of asynchronous controllers for positive singular semi‐Markov jump systems with performance under the dynamic‐memory event‐triggered. The positive singular semi‐Markov jump system model is established for the first time. And the dynamic‐memory event‐triggered transmission protocol is used to reduce network transmission pressure. Then, a unified cyber‐attack framework that includes DoS attacks and spoofing attacks is established. A simple hidden Markovian model (HMM) model is used to describe the asynchronous phenomena between the controller and the system. Considering the situation where all data cannot be accurately obtained in reality, a simple HMM is set as a partially unknown conditional probability matrix. Based on stochastic Lyapunov functional, the stochastically admissible conditions for the closed‐loop systems are obtained. Finally, a numerical example is given to verify the effectiveness of the proposed method.

Resource allocation algorithm for downlink secure transmission in wireless EH cooperative networks with idle relay-assisted jamming

In wireless Energy Harvesting (EH) cooperative networks, we investigate the problem of secure energy-saving resource allocation for downlink physical layer security transmission. Initially, we establish a model for a multi-relay cooperative network incorporating wireless energy harvesting, spectrum sharing, and system power constraints, focusing on physical layer security transmission in the presence of eavesdropping nodes. In this model, the source node transmits signals while injecting Artificial Noise (AN) to mitigate eavesdropping risks, and an idle relay can act as a jamming node to assist in this process. Based on this model, we formulate an optimization problem for maximizing system secure harvesting energy efficiency, this problem integrates constraints on total power, bandwidth, and AN allocation. We proceed by conducting a mathematical analysis of the optimization problem, deriving optimal solutions for secure energy-saving resource allocation, this includes strategies for power allocation at the source and relay nodes, bandwidth allocation among relays, and power splitting for the energy harvesting node. Thus, we propose a secure resource allocation algorithm designed to maximize secure harvesting energy efficiency. Finally, We validate the correctness of the theoretical derivation through Monte Carlo simulations, discussing the impact of parameters such as legitimate channel gain, power splitting factor, and the number of relays on secure harvesting energy efficiency of the system. The simulation results show that the proposed secure energy-saving resource allocation algorithm effectively enhances the security performance of the system.

DEVELOPMENT OF A HYBRID NETWORK CONGESTION MONITORING SYSTEM

Constantly growing requirements for the information systems service quality require monitoring of network congestion. The experimental results proved that when using hybrid networks, it is difficult to estimate the network load based on the average time of information delivery due to the presence of a significant scatter of time data. A mathematical model of information transmission in wireless computer networks in the form of a queuing system has been developed. The system identifies the main elements: a data transmission channel, a data frame and a device that transmits data. Analysis of transmission standards allows us to identify the following main transmission modes for modeling: one frame successful transmission; transmitting a frame with an error and repeating the transmission (possibly several times); frame transmission using performance-enhancing technology (Bursting technology is used as an example). Events associated with the transmission of information over the wireless network of one device are considered as monitoring system states under consideration. Packet exchange process was considered to be a homogeneous Markov process with discrete states, continuous time and constant intensities. A mathematical model has been developed based on the state graph of the device-frame-transmission channel system, which allows one to determine the timing characteristics for the previously identified modes. The methods for assessing network congestion based on statistical information during data transmission is proposed. Delivery times are broken down into ranges derived from the simulation. The proportion of packets whose delivery time lies within the selected test range is estimated. To evaluate the results, experimental studies were carried out on a fragment of the hybrid network. Delivery time results for each experiment were divided into four ranges: corresponding to accelerated delivery of fragments, successful delivery on the first, second and third attempts, respectively. Results analysis showed that it is preferable to use the fourth range to assess network congestion. Approximation reliability in this case is 0.85, the correlation coefficient is 0.92. The proposed methodology allows us to estimate network congestion based on testing data with a fairly high degree of reliability.