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Colourimetric quantification of analytes on paper analytical devices (PADs) using smartphones has been gaining popularity. However, the accuracy of these devices is prone to be affected by different environmental conditions, including the intensity and colour of the ambient light. This work explains in depth the design and fabrication of a handheld colourimeter whose accuracy is unaffected by ambient light conditions using the commercially available spectral sensor AS7262. The device works on the principle of light reflection from the sample and can quantify as well as display the concentration of the analyte on an organic light-emitting-diode (OLED) display. The data acquisition, processing, and transmission of the data to a smartphone using Bluetooth in less than 7 seconds is also a salient feature. The accuracy of the device was validated by testing different concentrations of alkaline phosphatase (ALP) and total protein on PADs explicitly developed for this study. The colourimeter exhibited linearity with R² values of 0.98 for ALP and 0.97 for total protein. The LOD and LOQ obtained for ALP and total protein are 1.1, 3.89 U/L and 0.037, 0.12 g/dL, respectively. The relative standard deviation (RSD) calculated was 0.006 for ALP and 0.004 for total protein. The validation of the colourimeter was carried out with the clinical samples to obtain a relative error of less than 10 % for both the analytes. [Display omitted] • A low-power, handheld, reflection-based colourimeter was developed. • The device employed 0°/45° optical configuration for analyte quantification. • The device exhibited excellent linearity for alkaline phosphatase and total protein on PADs. • The developed colourimeter was validated with real and laboratory samples. • By reconfiguring the calibration equation, the device can quantify multiple analytes. [ABSTRACT FROM AUTHOR]

In this advanced and forward-thinking era of science, data transmission is an extremely common practice. Up to this point, the majority of our data transfer has been accomplished through the use of Wi-Fi technology. But light itself is another option for us to employ as a source for the data transfer. The technique of exchanging data by the use of light is referred to as Li-Fi, which is short for "light Fidelity." The Visible Light serves as the channel of communication in the Li-Fi protocol. This is the newest technology that has been created in the field of data exchange in recent years. Additionally, this technology is in the process of being developed as a result of the increasing number of studies that are being conducted on this particular subject for the purpose of facilitating its improvement. From a scientific standpoint, this optical communication technology is now the most cutting-edge issue of discussion. Because Li-Fi uses VLC, which stands for visible light communication, the rate of data transfer is quite fast with this technology (VLC). In terms of the data transmission, it adheres to the standards established by the IEEE (Institute of Electrical and Electronics Engineers). In this study article, we will be pointing out some particular elements such as the characteristics, techniques, applications, pros and downsides, and comparison with ongoing related technologies such as Wi-Fi. [ABSTRACT FROM AUTHOR]

In this paper, we present the MU (multi-user) MIMO (multiple-input & multiple-output) system which is capable to serve the maximum number of users without additional bandwidth or power budget. MIMO systems maximize the channel capability as per the DPC scheme (Dirty Paper Coding). According to DPC, several antennas distributed over the base station are used for serving multiple users simultaneously. Nevertheless, DPC is a comprehensive search algorithm employing sequences of encoded user data for transmitting information to multiple users. Due to increased user density and subsequent transmit antennas in the MU-MIMO system, the exhaustive search (i.e. DPC) becomes cumbersome with the expanded search space domain. This search task would not be completed within a coherence time frame of the packet data communication system. The required objective is to consider this as a cost function for optimization to maximize the realizable sum-rate (throughput) of the MU-MIMO systems. This paper illuminates the joint antenna and user scheduling (JAAU) process with binary artificial bee colony (binary ABC) accomplishing nearly 98–99% of system throughput with the suggestively decreased figure in computation complexity and run time. Binary ABC is observed to have the global optimum value swiftly within the stipulated packet time frame of next-generation wireless data communications. Moreover, binary ABC achieves an appreciable system capacity for MU-MIMO systems as compared with DPC (which attains the maximum system throughput with the highest complexity). These findings are corroborated with exhaustive simulation results presented in this article. [ABSTRACT FROM AUTHOR]

Background Aims Methods Main Contribution Conclusions WHAT THIS PAPER ADDS What is already known on the subject What this paper adds to existing knowledge What are the clinical implications of this work? Communication impairments are a leading concern for parent caregivers of individuals with rare neurodevelopmental disorders (RNDDs). Clinical trials of disease modifying therapies require valid and responsive outcome measures that are relevant to individuals with RNDDs. Identifying and evaluating current psychometric properties for communication measures is a critical step towards the selection and use of appropriate instruments.This systematic review offers (1) a description of parent‐reported communication measures and (2) evidence for their psychometric properties, in RNDDs.The systematic review protocol was registered in the International Prospective Register of Systematic Reviews (PROSPERO; CRD42022334649). MEDLINE (Ovid), Embase, PsychINFO, Web of Science, CINAHL Plus, Cochrane Library, ClinicalTrials.gov, the Australian New Zealand Clinical Trials Registry were searched from inception to August 2023. Methodological assessment of quality was completed using the COnsensus‐based Standards for the selection of health status Measurement INstruments (COSMIN) checklist. Parent‐reported measures used in observational studies and clinical trials were identified. Data on utility, reliability and validity for RNDDs were extracted.Sixteen parent‐reported communication measures were used in RNDD research, the Vineland Adaptive Behavior Scales being most commonly used. Validation data in RNDDs were identified for six of these measures. Limitations related to sample size or the scope of psychometric testing.Many communication measures have been used for RNDDs but there are few data validating their use. Valid and reliable methods of measuring communication in persons with RNDDs is a priority for future high‐quality clinical trials. Communication is a critical domain for families with a child with a rare neurodevelopmental disorder (RNDD). Validated outcome measures are essential for accurate evaluation and interpretation of responses to treatments in clinical trials. We identified 16 parent‐reported communication measures that have been used with RNDDs, but only six measures had validation data for at least one RNDD. High quality evidence is accumulating, with all validation studies in this review published between 2020 to 2023. Modifications of existing measures may be required to assess communication for RNDDs. This systematic review catalogues the available psychometric data for communication measures and indicates an ongoing need for new validation studies to ensure they are fit‐for‐purpose for upcoming clinical trials in RNDDs. This review will inform the selection of communication measures for clinical trials and research studies. [ABSTRACT FROM AUTHOR]

Wireless Power Transmission (WPT) is widely used. In practical engineering applications, the transmitter and receiver of a stable and reliable WPT system need to establish not only a power transmission channel but also a data transmission channel to realize closed-loop control and information interaction. In this paper, a novel simultaneous wireless power and data transfer system based on a series–parallel (SP) resonant network is proposed. The system integrates an SP higher-order resonant network with series carrier injection, which makes power transmission and data transmission share a magnetic coupling channel. The isolation between power and data is realized by exploiting the frequency characteristics of the compensation network, thus realizing efficient power transmission and data transmission. In this paper, accurate and detailed mathematical modeling of the system is conducted, and the gain characteristics of the power transmission channel and the data transmission channel are demonstrated. Meanwhile, the mutual interference between the two channels is analyzed, and a feasible and effective parameter design method is developed. Finally, an experimental prototype with an output power of 16 W and a backward data rate of 100 Kbps is constructed to verify the feasibility of the proposed system and the correctness of the parameter design. [ABSTRACT FROM AUTHOR]

To send and receive data simultaneously, reduce network interference, and effectively improve energy efficiency, this paper analyzes the network characteristics of Multi-Radio Multi-Channel Wireless Sensor Network and optimizes the power and channel of each radio frequency interface. Based on the novel energy efficiency model and energy consumption efficiency model, this paper establishes a distributed optimization game model using Game Theory to maximize network energy efficiency, minimize energy consumption, and ensure network connectivity. Based on the game model, an efficient distributed resource allocation game algorithm is designed, in which a joint power control and channel allocation can quickly converge to Nash Equilibrium with low complexity. At the same time, the simulation results show that the distributed resource allocation game algorithm can effectively improve the energy efficiency, reduce network interference and bit error rate to ensure the successful data transmission. [ABSTRACT FROM AUTHOR]

This paper discusses the design and implementation of a portable IoT station. Communication and data synchronization issues in several installations are addressed here, making possible a detailed analysis of the entire system during its operation. The system operator requires a synchronized data stream, combining multiple communication protocols into one single time stamp. The hardware selected for the portable IoT station complies with the International Electrotechnical Commission (IEC) industrial standards. A short discussion regarding interface customization shows how easily the hardware can be modified so that it is integrated with almost any system. A programmable logic controller enables the Node-RED to be utilized. This open-source middleware defines operations for each global variable nominated in the Modbus register. Two applications are presented and discussed in this paper; each application has a distinct methodology utilized to publish and visualize the acquired data. The portable IoT station is highly customizable, consisting of a modular structure and providing the best platform for future research and development of dedicated algorithms. This paper also demonstrates how the portable IoT station can be implemented in systems where time-based data synchronization is essential while introducing a seamless implementation and operation. [ABSTRACT FROM AUTHOR]

This paper studies the problem of scheduling control data transmissions from embedded processors to physical systems. For this problem, we propose novel state feedback and output feedback control architectures that are predicated on energy function-based and norm-free event-triggering conditions, where the embedded processor broadcasts a sampled data of its control signal value through a zero-order-hold operator to the physical system when the left side of the event-triggering condition equals to its right side. In this context, the energy function-based feature means that the right sides of the proposed event-triggering conditions involve an energy function as well as its time-derivative to make the selection of these right sides user-adjustable. Furthermore, the norm-free feature means that the left sides of the proposed event-triggering conditions do not depend on signal norms to allow for better control data transmission reduction. System-theoretical analyses of our event-triggered state feedback and output feedback control architectures are shown using the same energy functions and their time-derivatives used in the proposed event-triggering conditions, where illustrative numerical examples are also presented to demonstrate the efficacy of our contributions. To the best of our knowledge, the results given in this paper explore how event-triggering conditions are linked not only to energy functions but also to their time-derivatives for the first time. [ABSTRACT FROM AUTHOR]

Safety helmet wearing detection based on video surveillance is an important means of safety monitoring in many industrial scenes. The training of safety helmet wearing detection models requires large and well-labeled dataset. However, the incidence of security violations is relatively low, which results in insufficient samples for training deep detection models. Safety helmet wearing detection is a common requirement in many scenarios such as construction sites, substations, and factory workshops. Aggregating data from multiple companies for model training would improve the performance of the detection model. Traditional centralized training methods are not feasible because aggregating data in centralized locations (such as the cloud) can raise concerns about data privacy and the high cost of data communication and storage. This paper proposes FedSH, a novel cloud-edge-based federated learning framework, which learns a shared global safety helmet wearing detection model in the cloud from multiple companies at the network edges and achieves data privacy protection by keeping company data locally. In addition, this paper designs reweighting mechanisms and applies transfer learning to address class imbalance and non-IID problems in the training data, so as to obtain an accurate and personalized detection model. Extensive experiments have been conducted on real surveillance video datasets. The experimental results demonstrate that FedSH outperforms the existing widely used federated learning methods with an accuracy improvement of at least 3.4%; the reduction in accuracy is within the range of 5% compared with centralized learning methods. FedSH effectively achieves a good balance between model performance, privacy protection, and communication efficiency. [ABSTRACT FROM AUTHOR]

With remarkable advancements in the development of connected and autonomous vehicles (CAVs), the integration of teleoperation has become crucial for improving safety and operational efficiency. However, teleoperation faces substantial challenges, with network latency being a critical factor influencing its performance. This survey paper explores the impact of network latency along with state-of-the-art mitigation/compensation approaches. It examines cascading effects on teleoperation communication links (i.e., uplink and downlink) and how delays in data transmission affect the real-time perception and decision-making of operators. By elucidating the challenges and available mitigation strategies, the paper offers valuable insights for researchers, engineers, and practitioners working towards the seamless integration of teleoperation in the evolving landscape of CAVs. [ABSTRACT FROM AUTHOR]

Intrusion detection has become a research focus in internet information security, with deep learning algorithms playing a crucial role in its development. Typically, intrusion detection data are transformed into a two-dimensional matrix by segmenting, stacking and padding them with zeros for input into deep learning models. However, this method consumes computational resources and fails to consider the correlation between features. In this paper, we transform the data into images through visualization operations and propose an information entropy weighted scheme to optimize the collision element problem during the transformation process. This method enhances the correlation between pixel frame features, leading to approximately 2% improvement in accuracy of the classification model when using the generated image samples for detection in experiments. To address the issues of insensitivity to target feature locations and incomplete feature extraction in traditional neural networks, this paper introduces a new network model called CBAM-CapsNet, which combines the advantages of the lightweight Convolutional Block Attention Module and capsule networks. Experimental results on the UNSW-NB15 and IDS-2017 datasets demonstrate that the proposed model achieves accuracies of 92.94% and 99.72%, respectively. The F1 scores obtained are 91.83% and 99.56%, indicating a high level of detection. [ABSTRACT FROM AUTHOR]

Internet of vehicles (IoV) technology connects various types of vehicles through the Internet of Things to achieve mutual communication and data sharing, thereby promoting the development of intelligent transportation systems. Present data sharing in IoV mainly relies on complicated centralized certificate management and modern public-key cryptosystems, which are insecure and inefficient because the rapid development of quantum computing has put the security of modern public-key cryptosystems under serious threat. Considering the potential quantum adversaries, this paper proposes a quantum blockchain-based data sharing scheme called quantum blockchain for IoV (QBIoV) to realize secure and efficient data sharing in IoV. Specifically, QBIoV discards vulnerable modern cryptographic components, such as classical hash functions, public key signatures, and voting protocols, and innovatively introduces quantum replacement items, such as a quantum hash function (QHF), a quantum public key signature, and a quantum binary voting (QBV) protocol, to eliminate potential quantum cyberspace attack hazards. Furthermore, QBIoV improves the conventional proof of authority (PoA) consensus algorithm and introduces a more secure and fair consensus mechanism called quantum proof of authority (QPoA). QPoA enhances the efficiency of the blockchain system, enabling the secure and efficient sharing of data in IoV. Additionally, a security analysis of QBIoV is conducted, demonstrating that QBIoV is secure against both classic and quantum adversaries, with performance comparisons proving that QBIoV outperforms peer work regarding resource efficiency. In conclusion, this paper provides an effective solution for enhancing data security protection in IoV, assisting in designing blockchain systems in the post-quantum era. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel discrete-time consensus algorithm based on the new min–max criterion for the discrete-time second-order linear agents. To boost the convergence speed, the proposed algorithm allocates three different operating modes to the network agents. Unlike most of the existing consensus algorithms in which the links of the communication graph have specific weights and these weights are required to have positive lower bounds to achieve asymptotic convergence, the communication graph in this paper has unspecific link weights. Accordingly, because of data transmission errors and faults, the weights of the communication links can even go from a positive to a negative value. Thus, the proposed algorithm can bring the agents to consensus under more general conditions. To demonstrate the efficacy of the proposed algorithm, numerical simulations are performed, and their results are compared with those of the minimum consensus algorithm, which shows a significantly faster convergence speed. [ABSTRACT FROM AUTHOR]

This paper focuses on the characterization of radio propagation, and data communication in a marine environment. More specifically, we consider signal propagation when three different sub-gigahertz industrial, scientific, and medical (ISM) bands, i.e., 169 MHz, 434 MHz, and 868 MHz, are used. The main focus of the paper is to evaluate the path loss (PL), i.e., the power loss that a propagation radio wave would experience when communication occurs between a sail boat and a buoy. We describe the measurement results obtained performing three different radio power measurement campaigns, at the three different aforementioned ISM sub-gigahertz bands. We also want to correlate the radio propagation quality with the weather conditions present in the measurement areas. The obtained results show that higher distances are achieved by transmitting at lower frequencies, i.e., 169 MHz, and, on average, the propagation is directly dependent from the dew point index. [ABSTRACT FROM AUTHOR]

Today, there is a greater demand to produce more timely official statistics at a more granular level. National Statistical Institutes (NSIs) are more and more looking to novel data sources to meet this demand. This paper focuses on the use of one such source to compile more timely and detailed official statistics on port visits. The data source used is sourced from the Automatic Identification System (AIS) used by ships to transmit their position at sea. The primary purpose of AIS is maritime safety. While some experimental statistics have been compiled using this data, this paper evaluates the potential of AIS as a data source to compile official statistics with respect to port visits. The paper presents a novel method called "Stationary Marine Broadcast Method" (SMBM) to estimate the number of port visits using AIS data. The paper also describes how the H3 Index, a spatial index originally developed by Uber, is added to each transmission in the data source. While the paper concludes that the AIS based estimates won't immediately replace the official statistics, it does recommend a pathway to using AIS-based estimates as the basis for official port statistics in the future. [ABSTRACT FROM AUTHOR]

Recently, wireless body area network (WBAN) becomes a hot research topic in the advanced healthcare system. The WBAN plays a vital role in monitoring the physiological parameters of the human body with sensors. The sensors are small in size, and it has a small-sized battery with limited life. Hence, the energy is limited in the multi-hop routing process. The patient data is collected by the sensor, and the data are transmitted with high energy consumption. It causes failure in the data transmission path. To avoid this, the data transmission process should be optimized. This paper presents an advanced authentication and energy-efficient routing protocol (AAERP) for optimal routing paths in WBAN. Patients' data are aggregated from the WBAN through the IoMT devices in the initial stage. To secure the patient's private data, a hybrid mechanism of the elliptic curve cryptosystem (ECC) and Paillier cryptosystem is proposed for the data encryption process. Data security is improved by authenticating the data before transmission using an encryption algorithm. Before the routing process, the data encryption approach converts the original plain text data into ciphertext data. This encryption approach assists in avoiding intrusions in the network system. The encrypted data are optimally routed with the help of the teamwork optimization algorithm (TOA) approach. The optimal path selection using this optimization technique improves the effectiveness and robustness of the system. The experimental setup is performed by using Python software. The efficacy of the proposed model is evaluated by solving parameters like network lifetime, network throughput, residual energy, success rate, number of packets received, number of packets sent, and number of packets dropped. The performance of the proposed model is measured by comparing the obtained results with several existing models. [ABSTRACT FROM AUTHOR]

Reducing network energy consumption and balancing workload are two key optimization goals for data transmission in edge computing field. However, these two goals are likely to be conflicting in some cases and fail to achieve the optimum simultaneously. In this paper, we design a new data transmission optimization algorithm using multi-objective reinforcement learning. We design the vector of rewards for the two objectives, and update Pareto approximate set by multiple state steps to approach the optimal solution. In every step, we classify the candidate links into four different levels for path selection. We aggregate network traffic to construct minimum topology subset, minimizing the number of occupied device to reduce energy consumption. We optimize the load distribution on those selected links, minimizing maximum congestion factor to balance workload. For action selection, we leverage roulette-based Chebyshev scalarization function to solve the weight selection problem for multi-objectives and enforce exploration to avoid falling into local optimum. To improve the convergence rate, we design heuristic factor to control the search of solution space and enhance the guiding effect of the existing optimal solution. Simulation result shows that the proposed algorithm achieves good performance in energy-saving and load balance at the same time. [ABSTRACT FROM AUTHOR]

Hash algorithms are among the most crucial algorithms in cryptography. The SM3 algorithm is a hash cryptographic standard of China. Because of the strong collision resistance and irreversibility of hash algorithms, they are widely used as a basic function in various fields such as digital signatures and random number generation. With the increasing real-time applications of automation in the fields of finance and office, the network puts forward higher demands for the implementing efficiency of the SM3 algorithm. We present a CUDA-based parallel optimized method for SM3 algorithm by four different ways: They are Single data stream with Single thread (SS), Multiple data streams with Single thread (MS), Single data stream with Multi-thread (SM), and Multiple data streams with Multi-thread (MM). The experimental result shows MM is the best of the four. When considering the data transmission between CPU and GPU, the proposed optimized algorithm achieves a peak performance of 166.42 Gb/s, which is 1.96 times of the best-known implementation of the SM3 algorithm on GPU platforms. Without transmission time counting, the peak performance is near 8500 Gb/s. Compared with other SM3 GPU algorithms, the algorithm proposed in this paper significantly enhances the efficiency of digest generation. Furthermore, the results show a new conclusion that the optimization of logical operations in the SM3 algorithm has reached a very high extent and the data transmission of PCIE becomes the bottleneck in the CPU+GPU data processing mode. Therefore, future work on the optimization of the SM3 algorithm should pay more attention to the PCIE data transfer efficiency. [ABSTRACT FROM AUTHOR]

Summary: In wireless sensor networks (WSNs), routing based on cluster construction is highly preferred as it greatly supports reliable data communication, load balancing, and fault tolerance with extended network lifetime. In specific, metaheuristic approach‐based dynamic cluster heads (CHs) selection has the possibility of enhancing the lifespan of network and at the same time is capable in reducing the energy consumption. In this paper, hybrid grasshopper and Harris hawk optimization algorithm‐based energy efficient routing protocol (HGHHOA) is propounded for optimal CH selection. This proposed HGHHOA approach adopted a fitness function that incorporated the factors of residual energy, distance between CH and cluster members, distance between selected CHs and the sink, node centrality, and node degree into account. The fitness function values of optimality facilitate a potential CH selection with significant cost‐effective routing. It is proposed with primary objective of improving the network lifespan through optimized selection of CHs that balances the available energy in a predominant way. It is proposed with significance of handling premature convergence with minimized energy consumption and network lifetime through the possibility of establishing an ideal balance between number of alive and dead nodes. The results of this proposed HGHHOA approach with varying rounds of implementation exhibited better results in throughput and residual energy which is 23.98% and 29.21%, better than the bassline CH selection mechanisms. [ABSTRACT FROM AUTHOR]

Summary: In the past few years, restricted wireless sensor networks (WSNs) enabled the Internet of Things (IoT) have attracted significant attention and expansion to enhance service delivery and resource efficiency. Dissemination is a service offered by WSN that uses radio transmission and over‐the‐air programming for updating the deployed sensor nodes through online. The centralized data dissemination methods are replaced by the distributed approaches because they affect the drawbacks of a single point of failure, no scalability, and insecurity. Therefore, an Energy Efficient Protocol for Data Dissemination in Wireless Sensor network‐enabled IoT using Domain‐Adaptive Message Passing Graph Neural Network (EEP‐WSN‐IoT‐DMPGNN) is proposed in this paper. The nodes are formed as clusters utilizing the Deep Fuzzy Curriculum Clustering (DFCC) technique that rewards nodes belonging to a given cluster. By using the Crayfish Optimization Algorithm (COA), the Cluster Head (CH) selection optimally chose the ideal CH and satisfies the multiple objective functions, such as energy, delay, traffic density, and distance. Afterward, domain‐adaptive Message Passing Graph Neural Network (DMPGNN) based routing protocol is developed, the input given to the routing protocol includes a sink, action history, future node, and maximum‐distance node, which attains enhanced data transfer in the chosen path. The proposed technique attains a lower no. of dead nodes, lower energy consumption, and higher Network Lifetime while analyzed with existing techniques, such as routing technique depending on deep learning for effectual data transmission in 5G WSN communication (DL‐RPDT‐WSN), Reinforcement‐Learning base energy effectual optimized routing protocol in WSN (RL‐EERP‐WSN), and Energy‐efficient intellectual routing method for IoT‐enabled WSN (EIR‐IoT‐WSN), respectively. [ABSTRACT FROM AUTHOR]

In the present paper, we mainly consider the direct solution of cyclic tridiagonal linear systems. By using the specific low-rank and Toeplitz-like structure, we derive a structure-preserving factorization of the coefficient matrix. Based on the combination of such matrix factorization and Sherman–Morrison–Woodbury formula, we then propose a cost-efficient algorithm for numerically solving cyclic tridiagonal linear systems, which requires less memory storage and data transmission. Furthermore, we show that the structure-preserving matrix factorization can provide us with an explicit formula for n-th order cyclic tridiagonal determinants. Numerical examples are given to demonstrate the performance and efficiency of our algorithm. All of the experiments are performed on a computer with the aid of programs written in MATLAB. [ABSTRACT FROM AUTHOR]

The Direct Simulation Monte Carlo (DSMC) algorithm is widely employed for simulating rarefied gas flows and is increasingly applied in near-continuum regimes for research and engineering purposes. However, its computational demands, notably load imbalance and extended simulation time, hinder widespread adoption. Addressing these challenges, this paper introduces the Two-Level parallel load balancing strategy. This novel approach combines thread-level and multi-process parallelism to enhance load balancing and reduce simulation time. Key features include a thread-level load-decoupling strategy implemented via OpenMP and a multi-process load balancing mechanism employing distributed memory via MPI. Building upon our previous PartPlusColl [L. Li, W. Ren and B. Zhang, J. Aeronaut. Astronaut. Aviat. Ser. A 46, 88 (2014)] approach, the load balancing mechanism utilizes Stop At Risk (SAR) criteria for repartitioning with METIS. Additionally, a specialized data transmission mechanism utilizing MPI nonblocking communication minimizes global communication between processes. Validation and evaluation are performed using four hypersonic flow cases around a cylinder and sphere, demonstrating significant improvements. Notably, the proposed strategy achieves 30% enhancement over the PartPlusColl strategy under 512 CPU cores compared to 16 CPU cores, and reduces between-process communication time with 33.57%. These advancements contribute to enhancing the effectiveness of the DSMC algorithm in near-continuum aerodynamic simulations. [ABSTRACT FROM AUTHOR]

Summary In this paper, we delve into the intricate problem of lateral control in autonomous vehicles, utilizing adaptive event triggering, dynamic quantizers, and incorporating stochastic sampling. By integrating the Adaptive Event‐Triggering Scheme (AETS) and dynamic quantizer in dual channels—namely the sensor‐to‐controller and controller‐to‐observer channels—we aptly cater to the multifaceted road conditions faced by autonomous vehicles. Moreover, in light of Denial of Service (DoS) attacks, our controllers ensure system stability amidst stochastic sampling. While ensuring an effective reduction in the amount of network communication data, the efficiency of the output feedback controllers is also significantly improved, thus enabling the closed‐loop system to be strictly dissipative performance stabilized. To substantiate the efficacy of our proposed method, simulation experiments were rigorously conducted using the Carsim‐Simulink platform, highlighting the enhanced safety of autonomous vehicles in real‐world operations. [ABSTRACT FROM AUTHOR]

In the multi‐paralleled converter microgrid system, the traditional hierarchical control strategy can eliminate the bus voltage amplitude and frequency deviation from the rated value. However, isolated AC microgrids may face extreme scenarios such as communication delays and interruptions in data transmission due to the use of low‐bandwidth communication (LBC) lines. Additionally, the inconsistent line impedance of each distributed generation (DG) unit may result in the inaccurate division of reactive power in multi‐paralleled converter systems, thus affecting system stability. To address these issues, this paper presents a distributed coordinated control strategy for isolated AC microgrids based on the consensus algorithm. The proposed strategy first replaces LBC lines with a filter to alleviate the effects of communication delays. A small‐signal model is established in its state space, and stability of the microgrid system under the proposed control strategy is verified through eigenvalue analysis. Furthermore, based on the above theoretical analysis, a consensus algorithm is introduced, and a distributed control strategy for isolated AC microgrids based on the consensus algorithm is proposed to solve the issue of inaccurate equalization of the system's reactive power. Finally, factors that influence the dynamic convergence performance of the consensus algorithm are analyzed through simulation in PLECS software. Also, the maximum tolerable communication delays of the microgrid system under different communication topologies are also compared, and the system's robustness is evaluated under the condition of sudden communication interruption in a DG unit and sudden weather variation. These analyses confirmed the robustness of the proposed strategy against communication delays, output power fluctuation, and communication interruptions. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to optical communication systems by introducing a hybrid MGDM–FSO (Mode Group Diversity Multiplexing—Free Space Optical) system. The primary objective of this innovative system is to address the limitations of traditional communication systems, particularly in challenging environmental conditions. By combining the advantages of MGDM, which enhances transmission capacity in optical fibers, with the flexibility and high data rates offered by FSO technology, the hybrid MGDM–FSO system aims to overcome obstacles encountered during data transmission. In this study, we provide a detailed analysis of the proposed system's architecture and performance characteristics under various operating conditions. Specifically, we investigate both single-input single-output (SISO) and multiple-input multiple-output (MIMO) configurations of the MGDM–FSO system. Performance metrics such as received power levels, bit error rate (BER), and quality factor (Q-factor) are evaluated to assess the system's efficiency across different weather conditions. Numerical simulations are conducted to analyze the system's performance under a range of scenarios. Our findings indicate that under clear weather conditions, both SISO and MIMO configurations exhibit optimal performance, characterized by high Q-factor and low BER. However, as weather conditions deteriorate to heavy rain and heavy fog, the Q-factor of the received signal decreases, highlighting the impact of environmental factors on system performance. This study contributes valuable insights into the behavior of hybrid MGDM–FSO systems and emphasizes the importance of robust communication techniques to mitigate environmental challenges. The findings presented here have implications for the design and implementation of optical communication systems in real-world scenarios. [ABSTRACT FROM AUTHOR]

Vehicular Ad Hoc Networks (VANETs) play a critical role in ensuring safety and welfare applications for drivers and passengers amidst the escalating vehicular population in urban environments. The efficient functioning of VANETs hinges on addressing the challenge of load balancing among Road Side Units (RSUs). This paper introduces a groundbreaking approach aimed at enhancing real-time data transmission services within VANETs. The key contribution lies in the development of a multicast routing algorithm utilizing a geo-targeting protocol, facilitating simultaneous delivery of source data packets to multiple destinations. This innovative strategy aims to alleviate RSU congestion, thereby significantly enhancing the quality of real-time data transmission services. Moreover, this study presents advancements in the Statistical Match and Queuing algorithm, refining it over time to substantially mitigate network congestion and redundancy. Additionally, a Multi-Protocol Label Switching based algorithm is implemented to elevate service quality parameters, including end-to-end latency, packet loss, and overall network efficiency within in-vehicle networks. Importantly, this approach remains adaptable across various Layer two technologies, ensuring compatibility and scalability. Simulation results validate the efficacy of the proposed methodology, showcasing its superiority over existing methods. The findings underscore the innovative algorithms' prowess in addressing load balancing challenges across diverse scenarios, affirming their potential to significantly enhance VANET service quality. [ABSTRACT FROM AUTHOR]

With the vigorous development of cloud computing, most organizations have shifted their data and applications to the cloud environment for storage, computation, and sharing purposes. During storage and data sharing across the participating entities, a malicious agent may gain access to outsourced data from the cloud environment. A malicious agent is an entity that deliberately breaches the data. This information accessed might be misused or revealed to unauthorized parties. Therefore, data protection and prediction of malicious agents have become a demanding task that needs to be addressed appropriately. To deal with this crucial and challenging issue, this paper presents a Malicious Agent Identification-based Data Security (MAIDS) Model which utilizes XGBoost machine learning classification algorithm for securing data allocation and communication among different participating entities in the cloud system. The proposed model explores and computes intended multiple security parameters associated with online data communication or transactions. Correspondingly, a security-focused knowledge database is produced for developing the XGBoost Classifier-based Malicious Agent Prediction (XC-MAP) unit. Unlike the existing approaches, which only identify malicious agents after data leaks, MAIDS proactively identifies malicious agents by examining their eligibility for respective data access. In this way, the model provides a comprehensive solution to safeguard crucial data from both intentional and non-intentional breaches, by granting data to authorized agents only by evaluating the agent's behavior and predicting the malicious agent before granting data. The performance of the proposed model is thoroughly evaluated by accomplishing extensive experiments, and the results signify that the MAIDS model predicts the malicious agents with high accuracy, precision, recall, and F1-scores up to 95.55%, 95.30%, 95.50%, and 95.20%, respectively. This enormously enhances the system's security in terms of authorized data access accuracy up to 55.49%, precision up to 43.15%, recall up to 55.49%, and F1-score up to 39.96%, respectively, as compared to state-of-the-art work. [ABSTRACT FROM AUTHOR]

The assessment of energy consumption of data traffic for Internet services usually relies on energy intensity figures (in Wh/GB). In this paper, we argue against using these indicators for evaluating the evolution of energy consumption of data transmission induced by changes in Internet usage. We describe a model that estimates global impacts for different scenarios of Internet usages and technological hypothesises, and show that it can overcome some limitations of intensity indicators. We experiment the model on four use‐cases: basic usage, video streaming, large downloads, and video conferencing. Results show that increasing the resolution of videos does increase the total energy consumption while misleadingly decreasing the power intensity indicator at the same time. In other words, a more efficient network does not necessarily mean less energy consumption. [ABSTRACT FROM AUTHOR]

The frequency of natural disasters has increased recently, posing a huge threat to human society. Rapid, accurate, authentic, and comprehensive acquisition and transmission of disaster information are crucial in emergency response. In this paper, we propose a design scheme for an aerial remote sensing platform based on real-time satellite communication. This platform mainly includes a civilian heavy-duty unmanned aerial vehicle, ground observation system with the self-developed orthographic image stabilization device, wireless communication system with an airborne mobile communication device using Ku band, ground satellite information receiving station, and data processing and application analysis system. The image stabilization capability of the ground observation system and the communication capability of the wireless communication system were verified through ground and flight tests respectively. The results showed that the stability accuracy of the platform was better than the theoretical threshold, the system transmission rate was not less than 2 M bandwidth, the data packet loss rate was low, and the time delay was not more than 2 s. The images captured in the experiment were clear, with a resolution of less than 1cm and an overlap rate of more than 70%. These all results meet the emergency observation requirement, which indicates that the aerial remote sensing platform based on real-time satellite communication has great potential for application in natural disaster emergency response. [ABSTRACT FROM AUTHOR]

In the framework of the space-air-ground-ocean integrated network, the underwater acoustic sensor network (UASN) plays a pivotal role. The design of media access control (MAC) protocols is essential for the UASN to ensure efficient and reliable data transmission. From the perspective of differentiated services in the UASN, a service-aware and scheduling-based hybrid MAC protocol, named the SSH-MAC protocol, is proposed in this paper. In the SSH-MAC protocol, the centralized scheduling strategy is adopted by sensor nodes with environmental monitoring service, and the distributed scheduling strategy is adopted by sensor nodes with target detection service. Considering the time-varying data generation rate of sensor nodes, the sink node will switch the scheduling mode of sensor nodes based on the specific control packet and adjust the resource allocation ratio between centralized scheduling and distributed scheduling. Simulation results show that the performance of the SSH-MAC protocol, in terms of utilization, end-to-end delay, packet delivery ratio, energy consumption, and payload efficiency, is good. [ABSTRACT FROM AUTHOR]

One of the networking technologies is called a wireless sensor network, and what it does is link sensor nodes by using wireless communication as the channel of connection. A haphazard and random distribution of nodes is carried out throughout the deployment process within the network area. As a result, the distances between the nodes do not remain constant. The nodes have a high sensing capacity, but their range of perception and battery life are also severely limited. The wireless data may be sensed by all of the nodes inside their respective sensing regions. Consequently, there are a variety of new applications that make use of WSN applications, particularly in surveillance monitoring applications such as those used in hospitals, buildings, forests, and airspace monitoring. The transmission of the data to the appropriate server or data centre that is linked with the cloud or the internet is experiencing greater difficulties as a result of the growing amount of monitoring data. This indicates that a significant quantity of data is lost as a result of the fact that wireless transmission is affected by environmental factors such as precipitation, humidity, and wind storms. It is vital to create and execute an innovative routing protocol to deliver greater throughput and raise the overall Quality of Service of the WSN applications in order to solve the difficulties that have been outlined above. This study paper presents a metaheuristic algorithm based optimal routing protocol for WSN application, which boosts the overall quality of service of any surveillance monitoring applications. To achieve this goal, the protocol was designed for wireless sensor networks. It does so in an effective manner, resulting in decreased delay and energy consumption as well as enhanced throughput and PDR. Based on the findings of the experiments, it has been hypothesised that the proposed metaheuristic algorithm-based routing protocol is capable of performing better routing for any type of surveillance monitoring and remote monitoring, as well as successfully transmitting any type of data, including numerical, video, and image data. [ABSTRACT FROM AUTHOR]

With the rapid development of wireless technology and Mobile communication technology worldwide, Mobile Learning represents a new learning trend in the field of higher education, and has gradually become the development trend and research hotspot of independent learning and lifelong learning. Based on modern wireless network mobile communication terminal technology, this paper proposes a Granott model-driven mobile learning system. Firstly, the Granott model is studied and analyzed, and the ubiquitous computing with physical integration and spontaneous interoperation is used to control the Android mobile terminal, which greatly improves the interactivity of M-Learning. Then JSON is selected to optimize the data transmission format in Ajax, and j Query is used to parse JSON data. Ajax + JSON + j Query can shield the complexity of DOM parsing XML files, greatly shorten the data transmission time and improve the efficiency of Granott model. Finally, the Fast kernel density estimation (Fast KDE) algorithm is introduced to compress the data transmitted by Ajax + JSON + j Query, so as to obtain a reasonable number of training samples. Through experiments and relevant data analysis, the proposed mobile learning system based on modern wireless network mobile communication terminal technology, optimized by JSON and Fast KDE algorithm, driven by Granott model, can efficiently complete the learning task, and improve the ubiquity, interactivity, timeliness of mobile learning. [ABSTRACT FROM AUTHOR]

This paper studies the problem of joint deployment, user association, channel, and resource allocation in unmanned aerial vehicle‐enabled access network. Since different user equipments performing different tasks and have different data rate requirements, the priority‐based traffic fairness problem is investigated. This problem, however, is a mixed integer nonlinear programming problem with NP‐hard complexity, making it challenging to be solved. To address this issue, a self‐organized and distributed framework "sense‐as‐you‐fly" based on the decomposition process, which divides the original problem into several subproblems, is proposed. Assuming without central controller, we derive the closed‐form resource allocation scheme and propose distributed many‐to‐one matching to optimize user association subproblem. Considering the coupled characteristics, the multi‐unmanned aerial vehicle deployment and channel allocation subproblems are modelled as a local altruistic game. The existence of Nash equilibrium is proved with the aid of exact potential game and efficient best response learning‐based algorithm is proposed. The original problem is finally addressed by solving the sub‐problems alternately and iteratively. Simulation results verify its effectiveness. By jointly optimizing multidimensional variables, the proposed algorithm unlocks network performance gains, especially in resource‐limited regimes. [ABSTRACT FROM AUTHOR]

Let = { T 1 , T 2 , ... , T k } be a set of k ≥ 2 spanning trees in a graph G. The k spanning trees are called completely independent spanning trees (CISTs for short) if the paths joining every pair of vertices x and y in any two trees have neither vertex nor edge in common except for x and y. Particularly, is called a dual-CIST provided k = 2. For data transmission applications in reliable networks, the existence of a dual-CIST can provide a configuration of fault-tolerant routing called protection routing. This paper investigates the problem of constructing a dual-CIST in the n -dimensional hierarchical folded cubic network H F Q n . The network is a two-level network using folded hypercube F Q n as clusters to reduce the diameter, hardware overhead and improve the fault tolerance ability. We propose a recursive algorithm to construct a dual-CIST of H F Q n in (2 2 n) time for n ≥ 2 , where the time required is the same scale as the number of vertices of H F Q n . Also, the diameter of each constructed CIST is 4 n + 1. [ABSTRACT FROM AUTHOR]

In the colloquy concerning human rights, equality, and human health, mental illness and therapy regarding mental health have been condoned. Mental disorder is a behavioral motif that catalyzes the significant anguish or affliction of personal functioning. The symptoms of a mental disorder may be tenacious, degenerative, or transpire as a single episode. Brain sickness is often interpreted as a combination of how a person thinks, perceives, contemplates and reacts. This may be analogous to a specific region or workings of the brain frequently in a social context. Anxiety disorders, psychotic disorders, personality disorders, mood disorders, eating disorders, and many more are examples of mental disorders, while complications include social problems, suicides, and cognitive impairment. These days, mental disorders are quotidian worldwide, and clinically consequential levels of derangement rise adversely. The purpose of this paper is to aid in prognosis of the type of mental disorder by analyzing the brainwaves such as Alpha (α), Beta (β), Gamma (γ), Theta (), Delta (δ) with the help of big data analysis and the Internet of Medical Things (IoMT). IoMT helps in gathering the required data and data transmission, while big data analysis helps in predicting the type of disorder. [ABSTRACT FROM AUTHOR]

Summary: Underwater wireless sensor network (UWSN) connects the real world to the hidden resources available deep into the sea using sensor nodes deployed sparsely and are interconnected to gather information for any movement/change in the ocean. The computed information on nodes obtained from concurrently running UWSN's applications will be meaningful, only if the location of this change will be identified. Nevertheless, it is always difficult to obtain the exact location coordinates of any misadventure like tectonic plate's movement but using localization algorithms in UWSN helps to obtain the coordinates. Since localization algorithms for terrestrial networks are not feasible for UWSN because of environmental challenges. Moreover, GPS systems do not work after 15 m of depth and the radio frequency gets suppressed. So, the requirement arises to analyze existing localization approaches in the deep sea where acoustic signals are used for communication and data transfer. In this paper, along with describing the UWSN's applications and challenges, the underwater localization schemes are reviewed to present, summarize, and mention the scope of improvement. Further, classification into range‐based and range‐free categories of these schemes is depicted with implementation in the NS2.30 simulation environment of some of the recent techniques to showcase the reasons for better performance. [ABSTRACT FROM AUTHOR]

Summary: The main goal of this strategy is to detect the occurrence of wild animals in farmsteads and human habitats using an improved deep learning in order to avoid loss of life and damage to agricultural crops. This paper formulates a technique for recognizing wild animals in an Internet of Things (IoT) environment utilizing deep batch normalized exponential linear unit AlexNet (DbneAlexnet) and the Gazelle Hunting Optimization Algorithm (GHOA). The IoT‐Multimedia Sensor Networks (WMSN) network is first simulated, with the IoT nodes capturing the images needed for wild animal identification. The suggested GHOA is used for routing, which sends the captured images to the base station (BS). The input wild animal image is transmitted to image preprocessing at the BS, where it is processed using the Weiner filter (WF) to remove undesirable noise from the image. The denoised output is sent into salient map extraction, which extracts salient map to determine the regions that are conspicuous or noticeable at every place in the field of vision and guides the selection of attended location. Finally, the saliency map is forwarded to DbneAlexnet for detecting the wild animals, where the DbneAlexnet is trained using proposed GHOA. The suggested GHOA algorithm is created by combining the Gazelle Optimization Algorithm (GOA) and the deer hunting optimization algorithm (DHOA). Furthermore, detection is appraised by precision, recall, and f1‐score, which provide values of 90.2%, 89.1%, and 89.6%, respectively. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper optimizes a ground replenishment strategy for large-scale LEO satellite constellations, aiming for ensuring high economic efficiency and performance requirements. Firstly, the uncertainties of influential elements are analyzed and satellite lifetime distributions are estimated to determine satellite operating statuses. Secondly, satellite statuses are used for obtaining real-time constellation status and simulating data package transmission paths. Performance degradation function is then derived by selecting time delay as the key performance. To confront with the performance degradation, an optimization model of ground replenishment strategy is established with the objective of minimizing the overall replenishment cost under the time delay and loading capacity constraints. The model is solved by a Dichotomous-domain algorithm. Finally, a case study of a large-scale STARLINK constellation is adopted to validate the effectiveness of the model and sensitivity analyses are conducted to verify its robustness. [ABSTRACT FROM AUTHOR]

This paper proposes a novel cache replacement technique based on the notion of combining periodic popularity prediction with size caching. The popularity, size, and time updates characteristics are used to calculate the value of each cache item. When it comes to content replacement, the information with the least value is first eliminated. Simulation results show that the proposed method outperforms the current algorithms in terms of cache hit rate and delay. The hit rate of the proposed scheme is 15.3% higher than GDS, 17.3% higher than MPC, 20.1% higher than LRU, 22.3% higher than FIFO, and 24.8% higher than LFU when 350 different categories of information are present. In real-world industrial applications such as including supply chain management, smart manufacturing, automation energy optimization, intelligent logistics transportation, and e-healthcare applications, it offers a foundation for the selection of caching algorithms. [ABSTRACT FROM AUTHOR]

Summary Wireless sensor networks (WSNs) are becoming increasingly important and well liked for delivering pervasive computing environments for a range of applications. Extending the networking life lifetime in WSNs is an important issue that must be addressed. Effective techniques for conserving the WSN's limited energy resources must be developed. Cross‐layer protocols are employed in WSNs to solve network lifespan difficulties. This paper proposes a new cross‐layer–cross‐domain routing scheme with stages such as “(1) network association stage, (2) nearer node detection phase, and (3) consistent state phase.” In the consistent stage, the optimal cluster head selection (CHS) is carried out by taking into account risk, delay, energy, trust, and distance. A new model called manta ray collided dwarf mongoose optimization (MRC‐DMO) is introduced to help with this. Furthermore, the routing is accomplished by dependable data communication. The results obtained establish the effectiveness of the MRC‐DMO scheme for SEACRCLCD in WSN over varied methods. [ABSTRACT FROM AUTHOR]

Enhancing spectral efficiency in non-line-of-sight (NLoS) environments is essential as 5G networks evolve, surpassing 4G systems with high information rates and minimal interference. Instead of relying on traditional Orthogonal Multiple Access (OMA) systems to tackle issues caused by NLoS, advanced wireless networks adopt innovative models like Non-Orthogonal Multiple Access (NOMA), cooperative relaying, Multiple Input Multiple Output (MIMO), and intelligent reflective surfaces (IRSs). Therefore, this study comprehensively analyzes these techniques for their potential to improve communication reliability and spectral efficiency in NLoS scenarios. Specifically, it encompasses an analysis of cooperative relaying strategies for their potential to improve reliability and spectral efficiency in NLoS environments through user cooperation. It also examines various MIMO configurations to address NLoS challenges via spatial diversity. Additionally, it investigates IRS settings, which can alter signal paths to enhance coverage and reduce interference and analyze the role of Unmanned Aerial Vehicles (UAVs) in establishing flexible communication infrastructure in difficult environments. This paper also surveys effective energy harvesting (EH) strategies that can be integrated with NOMA for efficient and reliable energy-communication networks. Our findings show that incorporating these technologies with NOMA not only enhances connectivity and spectral efficiency but also promotes a stable and environmentally sustainable data communication system. [ABSTRACT FROM AUTHOR]

The rapid development of the Industrial Internet of Things (IIoT) and its application across various sectors has led to increased interconnectivity and data sharing between devices and sensors. While this has brought convenience to users, it has also raised concerns about information security, including data security and identity authentication. IIoT devices are particularly vulnerable to attacks due to their lack of robust key management systems, efficient authentication processes, high fault tolerance, and other issues. To address these challenges, technologies such as blockchain and the formal analysis of security protocols can be utilized. And blockchain-based Industrial Internet of Things (BIIoT) is the new direction. These technologies leverage the strengths of cryptography and logical reasoning to provide secure data communication and ensure reliable identity authentication and verification, thereby becoming a crucial support for maintaining the security of the Industrial Internet. In this paper, based on the theory of the strand space attack model, we improved the Fiber Channel Password Authentication Protocol (FACP) security protocol in the network environment based on symmetric cryptography and asymmetric cryptography. Specifically, in view of the problem that the challenge value cannot reach a consensus under the symmetric cryptography system, and the subject identity cannot reach a consensus under the asymmetric cryptography system, an improved protocol is designed and implemented to meet the authentication requirements, and the corresponding attack examples are shown. Finally, the effectiveness and security of the protocol were verified by simulating different networking environments. The improved protocol has shown an increase in efficiency compared with the original protocol across three different network configurations. There was a 6.43% increase in efficiency when centralized devices were connected to centralized devices, a 5.81% increase in efficiency when centralized devices were connected to distributed devices, and a 6.32% increase in efficiency when distributed devices were connected to distributed devices. Experimental results show that this protocol can enhance the security and efficiency of communication between devices and between devices and nodes (servers, disks) in commonly used Ethernet passive optical network (EPON) environments without affecting the identity authentication function. [ABSTRACT FROM AUTHOR]

When the COMS camera of optical camera communication (OCC) system acquired the information image, the grayscale value of pixels fluctuated due to the influence of blooming effect and light noise, so that the data logic could not be correctly judged by the decision threshold, which affected the bit error rate (BER) performance transmission distance and data rate, and degraded the system performance. In this paper, the adaptive exponential weighted moving average algorithm is proposed, and the optimal grayscale value matrix of signal demodulation is verified by experiment, so as to reduce the fluctuation of grayscale value and improve the performance of OCC system. The experimental results show that for data processing and analysis based on ZYNQ7020 experimental platform, a 7% forward error correction（BER = 3.8 × 10−3）requirements can be satisfied at 150 cm when the data rate is 3 Kbps, and a 7% forward error correction（BER = 3.8 × 10−3）requirements can be satisfied at 100 cm when the data rate is 5 Kbps. [ABSTRACT FROM AUTHOR]

Due to the uniqueness of the underwater environment, traditional data aggregation schemes face many challenges. Most existing data aggregation solutions do not fully consider node trustworthiness, which may result in the inclusion of falsified data sent by malicious nodes during the aggregation process, thereby affecting the accuracy of the aggregated results. Additionally, because of the dynamically changing nature of the underwater environment, current solutions often lack sufficient flexibility to handle situations such as node movement and network topology changes, significantly impacting the stability and reliability of data transmission. To address the aforementioned issues, this paper proposes a secure data aggregation algorithm based on a trust mechanism. By dynamically adjusting the number and size of node slices based on node trust values and transmission distances, the proposed algorithm effectively reduces network communication overhead and improves the accuracy of data aggregation. Due to the variability in the number of node slices, even if attackers intercept some slices, it is difficult for them to reconstruct the complete data, thereby ensuring data security. [ABSTRACT FROM AUTHOR]

Software-defined vehicular networks (SDVN) is a promising technology for wireless data transmissions between vehicles. SDVN inherits software-defined networking principles and aims to improve the typical performance of safety and non-safety applications of vehicular adhoc networks. Consequently, enhancing the performance of Intelligent Transportation System (ITS). However, the performance of these ITS applications largely depends on the computational capability of the controller node, which involves creating or destroying a data path from the source vehicle to the destination vehicle and generating flow rules for the requests coming from the data plane elements. As a result, SDVN often suffers from the problems of overburdening the controller node with route requests under heavy traffic generation at vehicles and single-point controller failure. To counter these problems, solutions based on multiple controllers are proposed. In fact, the load-balancing problem remains an important issue. So, routing of data with multiple controllers and load-balancing, both topics in SDVN, go hand in hand. In this paper, we survey this state-of-the-art that discusses the above-mentioned challenges, starting with the SDVN preliminaries. We scrutinize the existing routing methodologies and also discuss load-balancing techniques. Furthermore, we provide real-time applications and services of SDVN, discuss trending research, potential future research directions, and the real-life applicability of SDVN that have not been addressed previously. [ABSTRACT FROM AUTHOR]

A large number of interconnected smart devices or objects interact with the physical environment known as the Internet of Things (IoT). These networks efficiently perform complex tasks with a high level of intelligence without human intervention and require run-time processing and computation. Edge computing is introduced where devices are placed at the edge between the data source and the cloud. These devices can provide more powerful computational and storage capabilities to IoT users. With various benefits, one of the significant requirements is secure and trustworthy data communication. Various trust management schemes are introduced to guarantee the trustworthiness and isolation of the malicious nodes and give preference to the most trustworthy ones. The existing schemes selected trustworthy nodes again and again from the path without considering the load factor which leads them to be more vulnerable for the Denial of Services (DoS) attacks. In this paper, we propose a Link-based Penalized Trust Management scheme to provide trust management and consider load factors for the selection of most trustworthy nodes and provide a preemptive measure to secure the network from DoS attacks based on its link's association. Simulation results indicate the better performance of the proposed scheme as compared with existing schemes. [ABSTRACT FROM AUTHOR]

In 5G, users can easily enjoy services by accessing the edge devices (EDs) deployed around the base stations. Generally, due to the uneven distribution and mobility of users, one ED is required to serve multiple users simultaneously, that results in the ED overload, seriously affecting the quality of experience (QoE) of the users. By responding to users' requests, service migration realizes the cross edge device migrations, that dynamically provides services for mobile users, which effectively improves the resource utilization rate of edge servers and the QoE of users. However, during the service migrations, malicious users may deploy pseudo base stations, gateways and other devices to illegally eavesdrop or tamper with user service data, that causes user information loss or disclosure. To prevent these security issues, a fountain-coding based cooperative jamming strategy is proposed in this paper. Specifically, the fountain coding technology is introduced to construct a three-node transmission model, including an original node, a target node and a malicious node, to realize the secure data transmission process in service migration. Besides, a group of relay nodes are employed to carry out cooperative jamming, that deteriorate the illegal eavesdropping quality of malicious nodes on service data. Then secrecy rate and outage probability are utilized to evaluate the security and reliability of the whole service migration. Finally, the theoretical analysis is shown by simulation results. [ABSTRACT FROM AUTHOR]