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In modern telecommunications and computer networks, efficient and reliable information collection is essential for effective decision-making and control task resolution. Current methods, such as periodic data transmission, event-driven data collection, and on-demand requests, have distinct advantages and limitations. The object of the paper: The study focuses on developing a comprehensive model to optimize information collection processes in network environments. Subject of the paper: This paper investigates various information collection methods, including periodic data transmission, event-driven data collection, and on-demand requests, and evaluates their efficiency under different network conditions. This study proposes a flexible and accurate model that can optimize information support technologies for network control tasks. The key tasksinclude 1. Developing a probabilistictime graph model to evaluate the efficiency of different information collection methods. 2. Analyzing model performance through mathematical relationships and simulations. 3. Comparing the proposed model with existing methodologies. Results. The proposed model demonstrated significant variations in the efficiency of the information collection methods. Periodic data transmission increased network load, while event-driven data collection was more responsive but could miss infrequent changes. On-demand requests balanced timely data needs with resource constraints but faced delays due to packet loss. The probabilistic time graph effectivel y captured these dynamics, providing a detailed understanding of the trade-offs. Conclusions. This study developed a flexible and accurate model for optimizing information support technologies during network control tasks. The model's adaptability to varying network conditions has significant practical implications for improving network efficiency and performance. Future research should explore the integration of machine learning techniques and extend the model to more complex network environments. [ABSTRACT FROM AUTHOR]

The internet of medical things (IoMT) has transformed the healthcare sector by facilitating real-time monitoring, remote patient care, and tailored healthcare solutions. However, the challenge of upholding a high standard quality of service (QoS) in IoMT implementations remains a pressing issue. This article delves into the possibilities of utilizing 5G networks and smart techniques to optimize QoS within IoMT systems. By capitalizing on the capabilities of 5G networks, including substantial bandwidth, minimal latency, and extensive connectivity, in conjunction with intelligent methods such as machine learning and predictive analytics, this paper introduces novel strategies to enhance QoS in IoMT environments. The summary underscores the advantages of these methods in elevating network dependability, diminishing latency, enhancing data transmission efficiency, and enabling resource allocation efficiency in IoMT deployments. Additionally, it explores the potential ramifications of these developments on healthcare outcomes, patient contentment, and the overall effectiveness of the healthcare system. The conclusions propose that by maximizing QoS through 5G networks and intelligent techniques, IoMT holds the potential to significantly enhance the delivery of healthcare services, fostering a more interconnected and efficient healthcare ecosystem in the process innovation. [ABSTRACT FROM AUTHOR]

In the recent era of computational technologies, the internet is needed daily. The data generated is enormous and primarily stored on dedicated servers or clouds. Data migration and transfer are significant tasks for maintaining consistency and updating data. The data is the most critical component in any cloud service. There are various methods to protect data, like secure transfer, encryption, and authentication. These techniques are used as per need and transmission of the data. As data grows on a server or cloud, it must be migrated securely. Here, the exhaustive survey is provided for building a framework for migrating and transmitting cloud data. The framework should be sustainable and adaptable for load-balancing recovery and secure transmission. Various security load balancing parameters must be considered to obtain these state-of-the-art functionalities in the framework. The existing similar frameworks are studied, and findings are proposed in the paper to develop the framework. [ABSTRACT FROM AUTHOR]

How to select the transmitting path in MPTCP scheduling is an important but open problem. This paper proposes an intelligent data scheduling algorithm using spatiotemporal synchronous graph attention neural networks to improve MPTCP scheduling. By exploiting the spatiotemporal correlations in the data transmission process and incorporating graph self-attention mechanisms, the algorithm can quickly select the optimal transmission path and ensure fairness among similar links. Through simulations in NS3, the algorithm achieves a throughput gain of 7.9% compared to the PDAA3C algorithm and demonstrates improved packet transmission performance. [ABSTRACT FROM AUTHOR]

In the field of data transmission and storage, serialization formats play a crucial role by converting complex data structures into a byte stream that can be easily stored, transmitted, and reconstructed. Despite the myriad available serialization formats, ranging from JSON to Protobuf, each has limitations, particularly in balancing schema flexibility, performance, and data copying overhead. This paper introduces Lite2, a novel data serialization format that addresses these challenges by combining schemaless flexibility with the efficiency of zero-copy operations for flat or key–value pair data types. Unlike traditional formats that often require a predefined schema and involve significant data copying during serialization and deserialization, Lite2 offers a dynamic schemaless approach that eliminates unnecessary data copying, optimizing system performance and efficiency. Built upon a contiguously stored B-tree structure, Lite2 enables efficient data lookup and modification without deserialization, thereby achieving zero-copy operations. [ABSTRACT FROM AUTHOR]

This paper investigates the secure state estimation problem of the cyber-physical systems (CPSs) in the presence of malicious attacks. Considering the destructiveness and stealthiness of the attacker, a novel stochastic transmission scheme, in which the repeater generates transmission data by randomly combining some measurement values, is proposed to ensure the effectiveness of the attack detector. A filter equipped with the $ \chi ^2 $ χ 2 detector and the stochastic transmission scheme is proposed to conduct the secure state estimation. The attack probability condition of the estimation error being mean-square bounded is given, which indicates that stochastic transmission scheme has a limited effect on the filter. Some numerical simulations are given to illustrate the effectiveness of the proposed transmission scheme. [ABSTRACT FROM AUTHOR]

The problem which is concerned with filter-based neural control for switched uncertain nonlinear systems with incomplete measurement is studied in this paper. Filters are used to estimate unknown states, while neural networks approximate unknown functions. Data loss, saturation and other problems often occur during data transmission. An appropriate control law is designed by a backstepping method to solve this problem. Through using the average dwell time, it is proved that the switching system tends to be stable in a certain time. The analysis of probabilistic stability is also carried out to ensure that the system can achieve uniformly ultimately boundedness. Finally, the effectiveness of the previous design is proved by simulation. [ABSTRACT FROM AUTHOR]

Emerging computing paradigms provide field‐level service responses for users, for example, edge computing, fog computing, and MEC. Edge virtualization technologies represented by Docker can provide a platform‐independent, low‐resource‐consumption operating environment for edge service. The image‐pulling time of Docker is a crucial factor affecting the start‐up speed of edge services. The layer reuse mechanism of native Docker cannot fully utilize the duplicate data of node local images. In this paper, we propose a chunk reuse mechanism (CRM), which effectively targets node‐local duplicate data during container updates and reduces the volume of data transmission required for image building. We orchestrate the CRM process for cloud and remote‐cloud nodes to ensure that the resource overhead from container update data preparation and image reconstruction is within an acceptable range. The experimental results show that the CRM proposed in this paper can effectively utilize the node local duplicate data in the synchronous update of containers in multiple nodes, reduce the volume of data transmission, and significantly improve container update efficiency. [ABSTRACT FROM AUTHOR]

In the near future, there will be a greater emphasis on sharing network resources between roads and railways to improve transportation efficiency and reduce infrastructure costs. This could enable the development of global Cooperative Intelligent Transport Systems (C-ITSs). In this paper, a software-defined networking (SDN)-based common emergency service is developed and validated for a railway and road telecommunication shared infrastructure. Along with this, the developed application is capable of reducing the chances of distributed denial-of-service (DDoS) situations. A level-crossing scenario is considered to demonstrate the developed solution where railway tracks are perpendicular to the roads. Two cases are considered to validate and analyze the developed SDN application for common emergency scenarios. In case 1, no cross-communication is available between the road and railway domains. In this case, emergency message distribution is carried out by the assigned emergency servers with the help of the SDN controller. In case 2, nodes (cars and trains) are defined with two wireless interfaces, and one interface is reserved for emergency data communication. To add the DDoS resiliency to the developed system the messaging behavior of each node is observed and if an abnormality is detected, packets are dropped to avoid malicious activity. [ABSTRACT FROM AUTHOR]

The named data vehicular sensor network (NDVSN) has become an increasingly important area of research because of the increasing demand for data transmission in vehicular networks. In such networks, ensuring the quality of service (QoS) of data transmission is essential. The NDVSN is a mobile ad hoc network that uses vehicles equipped with sensors to collect and disseminate data. QoS is critical in vehicular networks, as the data transmission must be reliable, efficient, and timely to support various applications. This paper proposes a QoS-aware forwarding and caching algorithm for NDVSNs, called QWLCPM (QoS-aware Forwarding and Caching using Weighted Linear Combination and Proximity Method). QWLCPM utilizes a weighted linear combination and proximity method to determine stable nodes and the best next-hop forwarding path based on various metrics, including priority, signal strength, vehicle speed, global positioning system data, and vehicle ID. Additionally, it incorporates a weighted linear combination method for the caching mechanism to store frequently accessed data based on zone ID, stability, and priority. The performance of QWLCPM is evaluated through simulations and compared with other forwarding and caching algorithms. QWLCPM's efficacy stems from its holistic decision-making process, incorporating spatial and temporal elements for efficient cache management. Zone-based caching, showcased in different scenarios, enhances content delivery by utilizing stable nodes. QWLCPM's proximity considerations significantly improve cache hits, reduce delay, and optimize hop count, especially in scenarios with sparse traffic. Additionally, its priority-based caching mechanism enhances hit ratios and content diversity, emphasizing QWLCPM's substantial network-improvement potential in vehicular environments. These findings suggest that QWLCPM has the potential to greatly enhance QoS in NDVSNs and serve as a promising solution for future vehicular sensor networks. Future research could focus on refining the details of its implementation, scalability in larger networks, and conducting real-world trials to validate its performance under dynamic conditions. [ABSTRACT FROM AUTHOR]

The practical employment of network coding (NC) has shown major improvements when it comes to the transmission reliability of sender data and bandwidth utilization. Moreover, network coding has been employed recently to secure the transmission of data and prevent unauthorized recovery of sender packets. In this paper, we employ network coding (NC) in a practical way in networks with constrained resources with the goal of improving the reliability and security of data transfer. More specifically, we apply NC on the recent options of block-wise transfer (BWT) of the constrained application protocol (CoAP). The goal is to enhance the reliability of CoAP when used to transfer larger data blocks using BWT. Also, we employ an innovative homomorphic encryption approach to secure the BWT of CoAP. [ABSTRACT FROM AUTHOR]

Data collection and energy consumption are critical concerns in Wireless sensor networks (WSNs). To address these issues, both clustering and routing algorithms are utilized. Therefore, this paper proposes an intelligent energy-efficient data routing scheme for WSNs utilizing a mobile sink (MS) to save energy and prolong network lifetime. The proposed scheme operates in two major modes: configure and operational modes. During the configure mode, a novel clustering mechanism is applied once, and a prescheduling cluster head (CH) selection is introduced to ensure uniform energy expenditure among sensor nodes (SNs). The scheduling technique selects successive CHs for each cluster throughout the WSNs' lifetime rounds, managed at the base station (BS) to minimize SN energy consumption. In the operational mode, two main objectives are achieved: sensing and gathering data by each CH with minimal message overhead, and establishing an optimal path for the MS using the genetic algorithm. Finally, the MS uploads the gathered data to the BS. Extensive simulations are conducted to verify the efficiency of the proposed scheme in terms of stability period, network lifetime, average energy consumption, data transmission latency, message overhead, and throughput. The results demonstrate that the proposed scheme outperforms the most recent state-of-the-art methods significantly. The results are substantiated through statistical validation via hypothesis testing utilizing ANOVA, as well as post hoc analysis. [ABSTRACT FROM AUTHOR]

Data caching is a promising technique to alleviate the data traffic burden from the backhaul and minimize data access delay. However, the cache capacity constraint poses a significant challenge to obtaining content through the cache resource that degrades the caching performance. In this paper, we propose a novel two-tier caching mechanism for data caching on mobile user equipment (UE) and the small base station (SBS) level in ultra-dense 6G heterogeneous networks for reducing data access failure via cache resources. The two-tier caching enables users to retrieve their desired content from cache resources through device-to-device (D2D) communications from neighboring users or the serving SBS. The cache-enabled UE exploits millimeter-wave (mmWave)-based D2D communications, utilizing line-of-sight (LoS) links for high-speed data transmission to content-demanding mobile UE within a limited connection time. In the event of D2D communication failures, a dual-mode hybrid system, combining mmWave and microwave μ Wave technologies, is utilized to ensure effective data transmission between the SBS and UE to fulfill users' data demands. In the proposed framework. the data transmission speed is optimized through mmWave signals in line-of-sight (LoS) conditions. In non-LoS scenarios, the system switches to μ Wave mode for obstacle-penetrating signal transmission. Subsequently, we propose a reinforcement learning (RL) approach to optimize cache decisions through the approximation of the Q action-value function. The proposed technique undergoes iterative learning, adapting to dynamic network conditions to enhance the content placement policy and minimize delay. Extensive simulations demonstrate the efficiency of our proposed approach in significantly reducing network delay compared with benchmark schemes. [ABSTRACT FROM AUTHOR]

At present, in order to conduct non‐destructive testing on the grounding grid of substations under the condition of continuous power supply and no excavation, researchers have applied wireless technology based on electrochemical methods to remotely monitor the corrosion state of grounding conductors online. Nevertheless, wireless signals are affected by the environment when they are transmitted underground. In the field of grounding gird wireless monitoring, how to plan the information transmission path of wireless sensor network (WSN) with high accuracy of data transfer and low energy consumption earns growing research attention. To address the problem of WSN path planning in grounding grid area, a path planning method for WSN based on the hybrid algorithm of map‐matching algorithm and double‐pole search algorithm (MM‐DPS) is proposed in this paper. The map‐matching algorithm is employed to calculate the optimal sampling node number of the data transmission path. On the basis of the optimal sampling node number, the double‐pole search algorithm is employed in seeking out each sensor node of the path, and two groups of path plans are obtained. In the simulation experiment, compared with the A‐star algorithm, the MM‐DPS algorithm shortens the data transmission path length by about 39% and reduces the energy consumption by about 57%. The research work brings a method to alleviate the problem of data transmission underground of WSN in grounding grid area. The method not only ensures the accuracy of data transmission, but also shorts the transmission distance and reduces energy consumption. [ABSTRACT FROM AUTHOR]

The security of patient data is a critical problem for health networks due to the rising popularity of telehealth services and the requirements for clinical data sharing between surgeons, consultants, and medical groups. In contrast to protecting medical data, which includes the cover (i.e., clinical picture), the requirements for protecting other pertinent data are more general. The paper advocates for data encryption before concealment as a robust strategy to uphold patient privacy during medical information exchange. The primary focus of this analysis revolves around a comprehensive exploration of different steganography methods. Through a meticulous examination of various steganographic techniques, including LSB, PVD, and transform domain approaches, this paper provides a detailed analysis of their strengths and limitations. Objective metrics like PSNR and SSIM are employed to dissect the trade-offs between data security and visual fidelity. The research leads to the conclusion that the diagonal queue-based steganog-raphy methodology, supported by chaotic methods, the Linear Feedback Shift Register (LFSR), and the durable Rabin encryption system, is the best course of action. This method enables autonomous data transmission among numerous contacts while ensuring the highest level of confidentiality and integrity of patient data inside e-health networks. In summary, this study provides a comprehensive solution that protects patient data and speeds data transmission inside the expanding framework of e-health networks in order to meet urgent data security concerns in telemedicine and healthcare data exchange. [ABSTRACT FROM AUTHOR]

Mobile smart devices, such as mobile phones, wearable devices, and in-vehicle navigation systems, bring us convenience and have become necessities in modern daily life. The built-in global positioning system (GPS) of these mobile devices collects the users' mobility data to support path planning, navigation and other location-related applications, which also inevitably causes privacy issues. Previous research has shown that employing count-min sketch (CMS) to aggregate mobility datasets is a valid privacy-preserving method for resisting the reconstruction attack on population distributions. However, as the utility/accessibility of the protected datasets is excessively correlated with the size of CMS, decreasing the data transmission cost has become an unsolved issue of that approach. In this paper, we propose an efficient scheme with differential privacy to protect mobility datasets, which releases the privacy-preserving population distributions and achieves better utility as well as a much smaller data transmission cost compared to the CMS-based method. Our proposed scheme is comprised of two collaborative components, global sketch and temporal sketch. The global sketch is responsible for aggregating the raw mobility data and decreasing the data transmission cost, while the temporal sketch is in charge of guaranteeing the utility of the population distributions aggregated by the global sketch. Besides, to enhance the privacy preservation, we employ the Laplace mechanism to make the transmitted data satisfy ϵ -differential privacy. Through our analysis and empirical experiments, compared to the other three state-of-the-art privacy-preserving methods on mobility datasets, our scheme could preserve the privacy of the mobility datasets with much less data transmission cost under the same utility loss. [ABSTRACT FROM AUTHOR]

With the development of mobile Internet, more and more readers search for book information through mobile phones and other devices, so how to optimize the efficiency and accuracy of book information search has become an important issue of intelligent library system management. This paper presents a book information search algorithm based on cellular network, which can effectively improve the efficiency and accuracy of book information search. Based on the existing problems of book information search in the intelligent library system, this paper proposes a book information search algorithm based on cellular network. By assigning the book information to multiple search nodes, the algorithm makes use of the advantages of cellular network to realize efficient search and transmission. At the same time, the improved text similarity algorithm is introduced, which can improve the search efficiency and accuracy by analyzing and processing the keywords searched by users, and can match the readers' search requests and book information records more accurately. The experimental results show that this scheme can improve the search efficiency and data transmission rate of intelligent library system, and provide a new idea and method for the management optimization of intelligent library system. [ABSTRACT FROM AUTHOR]

As China's electricity market continues to evolve, the increasing number and diversity of market participants have led to a rapid increase in data volume. An immediate challenge is designing a robust blockchain system that ensures not only the transparency, credibility, tamper-resistance, traceability, privacy, and security of substantial market data in a complex environment, but also provides trusted data support for intelligent dispatching within the power market. This paper proposes a solution to this pressing issue through Blockchain as a Service (BaaS). BaaS leverages the capabilities of cloud computing platforms to enhance the ease and efficiency of deploying and operating blockchain systems. Specifically, the paper introduces a BaaS system built upon an integrated intelligent scheduling and operation cloud-edge platform (CEP). It incorporates technological solutions such as data on-chain, trusted comparison, edge cluster data transmission, optimized transaction authentication, and lightweight storage nodes. Rigorous testing has conclusively demonstrated that the proposed BaaS system meets the requirements of the digital transformation of the China Southern Power Grid, and provides robust technical support for its future practical applications. [ABSTRACT FROM AUTHOR]

By the analysis of vulnerabilities of Android native system services, we find that some vulnerabilities are caused by inconsistent data transmission and inconsistent data processing logic between client and server. The existing research cannot find the above two types of vulnerabilities and the test cases of them face the problem of low coverage. In this paper, we propose an extraction method of test cases based on the native system services of the client and design a case construction method that supports multi-parameter mutation based on genetic algorithm and priority strategy. Based on the above method, we implement a detection tool-BArcherFuzzer to detect vulnerabilities of Android native system services. The experiment results show that BArcherFuzzer found four vulnerabilities of hundreds of exception messages, all of them were confirmed by Google and one was assigned a Common Vulnerabilities and Exposures (CVE) number (CVE-2020-0363). [ABSTRACT FROM AUTHOR]

In recent years, experimental economics has seen a rise in the collection and analysis of choice-process data, such as team communication transcripts. The main purpose of this paper is to understand whether the collection of team communication data influences how individuals reason and behave as they enter the team deliberation process, i.e. before any communication exchange. Such an influence would imply that team setups have limited validity to speak to individual reasoning processes. Our treatment manipulations allow us to isolate the effects of (1) belonging to a team, (2) actively suggesting an action to the team partner, and (3) justifying the suggestion in a written text to the team partner. Across three different tasks, we find no systematic evidence of changed suggestions and altered individual sophistication due to changes in aspects (1)–(3) of our experimental design. We thus find no threat to said validity of team setups. In addition to investigating how the team setup affects individual behavior before communication, we also investigate the sophistication of decisions after the communication. We find that sophisticated strategies are more persuasive than unsophisticated strategies, especially when communication includes written justifications, thereby explaining why teams are more sophisticated and proving rich communication to be fruitful. [ABSTRACT FROM AUTHOR]

One of the key elements of the internet of things (IoT) nowadays are wireless sensor networks (WSNs). Any IoT application uses a variety of sensor-based devices to collect data from real-world objects and send it to the base station (BS). In accordance with the sensor position, the BS evaluates this data. Any IoT-based application needs a high-performance intelligent WSN. IoT-enabled WSNs are high-performance intelligent WSNs, which are used throughout this article. Fault incidence likelihood in IoT-enabled WSNs is substantially higher than in conventional networks. The dependability of the IoT-enabled WSNs is affected by faulty nodes and broken connections. Incorporating multipath transmission, relay node location, and backup node selection, various faulttolerance techniques improve the network's dependability. These methods, however, have significant packet overhead, worse detection accuracy, and lengthy data transmission delays. For fault tolerance in IoT-enabled WSNs, a multi-objective red panda optimization method incorporating deep reinforcement learning (DRL) is suggested in this paper. This study's primary goal is to identify defective nodes using starting energy, transmission of packets rate, communication overhead, and packet delay measurements. In order to capture data in an energy-efficient manner and extend the network lifespan, a mobile sink (MS) is deployed. The suggested approach beat state-of-the-art techniques in terms of energy efficiency (EE), latency(L), packet delivery ratio (PDR) and network lifetime (NL) according to thorough simulations and theoretical research. [ABSTRACT FROM AUTHOR]

Multivariate time series forecasting is essential in various fields, including healthcare and traffic management, but it is a challenging task due to the strong dynamics in both intra-channel relations (temporal patterns within individual variables) and inter-channel relations (the relationships between variables), which can evolve over time with abrupt changes. This paper proposes ERAN (Evolving Relational Attention Network), a framework for multivariate time series forecasting, that is capable to capture such dynamics of these relations. On the one hand, ERAN represents inter-channel relations with a graph which evolves over time, modeled using a recurrent neural network. On the other hand, ERAN represents the intra-channel relations using a temporal attentional convolution, which captures the local temporal dependencies adaptively with the input data. The elvoving graph structure and the temporal attentional convolution are intergrated in a unified model to capture both types of relations. The model is experimented on a large number of real-life datasets including traffic flows, energy consumption, and COVID-19 transmission data. The experimental results show a significant improvement over the state-of-the-art methods in multivariate time series forecasting particularly for non-stationary data. [ABSTRACT FROM AUTHOR]

The rapid development of information and communication technology has driven the demand for higher data transmission rates. Multi-core optical fiber, with its ability to transmit multiple signals simultaneously, has emerged as a promising solution to meet this demand. Additionally, due to its characteristics such as multi-channel transmission, high integration, spatial flexibility, and versatility, multi-core optical fibers hold vast potential in sensing applications. However, the manufacturing technology of multi-core fiber is still in its early stages, facing challenges such as the design and fabrication of high-quality cores, efficient coupling between cores, and the reduction of crosstalk. In this paper, an overview of the current status and future prospects of multi-core fiber manufacturing technology has been presented, and their limitations will be discussed. Some potential solutions to overcome these challenges will be proposed. Their potential applications in optical fiber sensing will also be summarized. [ABSTRACT FROM AUTHOR]

The growing ecosystem of Internet of Things (IoT) devices in the automotive industry demands innovative solutions for secure communication and data exchange. In this context, we present a novel lightweight symmetric key exchange protocol based on matrix power functions, employing the supertropical semiring as a platform. This protocol offers a unique and robust approach to ensure the confidentiality and integrity of data transmission in Vehicle IoT (VIoT) devices. In this paper. we give the theoretical foundations of our protocol. explaining the mathematics behind supertropical semirings. We also describe the algorithm of key exchange process, highlighting its efficiency and security features. Furthermore, we demonstrate the feasibility and performance of our protocol through extensive simulations and experimentation analysis. [ABSTRACT FROM AUTHOR]

This paper introduces a highperformance multiple - input - multiple - output (MIMO) antenna featuring a compact circular shape with slots, fed through co-planar wave-guide (CPW), emphasizing low mutual coupling. The proposed antenna has been physically realized using an FR4 substrate. Enhancing the antenna characteristics involved creating slits positioned at a 120-degree angle to each other, alongside a central circular-shaped slot within the patch. The arrangement of antenna elements involves an edge-to-edge distance of 0.18λ (4mm) to ensure isolation surpassing 15dB across the entire wideband range. The compact MIMO antenna overall dimension is 0.81λ x 1.45λ x 0.073λ (18 x 32 x 1.61 mm3). The CPW-fed circular patch MIMO antenna proposed in this study resonates at 12.611 GHz, and 13.852 GHz, exhibiting an impedance bandwidth of 2.65 GHz (12.116 GHz to 14.772 GHz). It showcases a reflection coefficient of -31.833dB and achieves an observed gain of 5.81 dBi. The observed results include a low envelope correlation coefficient (<0.025) and a high diversity gain (>9.87) at the resonating frequency. The measured outcomes closely align with the simulated results, indicating the efficacy of the designed wideband antenna. The designed wideband antenna is suitable for satellite downlink communications to transmit data at a higher rate. [ABSTRACT FROM AUTHOR]

In this article, we present new research on the impact of various optical filters on the functionality of an Orthogonal optical Frequency Division Duplexing (OFDM) system for the first time. This paper focused on long-haul optical systems using four Quadrature Amplitude Modulation (4-QAM) methods for OFDM systems. The analysis demonstrates the impact of choosing a Gaussian, Rectangular, or Trapezoidal optical Filter on the effectiveness of the optical system used for long-distance high data rate transmission. Compared with other systems for long-distance transmission using various filter types, 4-QAM OFDM showed a high Bit Error Rate (BER) performance. Significant findings like transmitting Power received Power, and Error Vector Magnitude (EVM) is compared with the system’s propagation length. [ABSTRACT FROM AUTHOR]

In recent years, wireless sensors have progressively supplanted conventional limited sensors owing to their attributes of small size, low cost, and high accuracy. Consequently, there has been a growing interest in leveraging wireless sensor networks for bridge structural health monitoring applications. By employing wireless sensor nodes to gather data from various segments of the bridge, information is relayed to a signal-receiving base station. Subsequently, the health status of the bridge is inferred through specific data processing and analysis, aiding monitoring personnel in making informed decisions. Nonetheless, there are limitations in this research, particularly pertaining to power consumption and efficiency issues in data acquisition and transmission, as well as in determining the appropriate wireless sensor types and deployment locations for different bridge configurations. This study aims to comprehensively examine research on the utilization of wireless sensor networks in the realm of bridge structural health monitoring. Employing a systematic evaluation methodology, more than one hundred relevant papers were assessed, leading to the identification of prevalent sensing techniques, data methodologies, and modal evaluation protocols in current use within the field. The findings indicate a heightened focus among contemporary scholars on challenges arising during the data acquisition and transmission processes, along with the development of optimal deployment strategies for wireless sensor networks. In continuing, the corresponding technical challenges are provided to address these concerns. [ABSTRACT FROM AUTHOR]

BIM collaborative design involves numerous participants from various specialties who create and share vast amounts of design data at different design stages to ensure the efficient transmission of design data between these specialties. It is imperative for the BIM collaborative design platform to guarantee the security of design data and effectively trace any instances of malicious leakage or tampering. Therefore, this paper proposes a traceable ciphertext-policy attribute-based encryption scheme (TCP-ABE) that formulates a dynamic data access control mechanism based on different participants and effectively tracks malicious users in the event of risks such as tampering, theft, and unauthorized access to BIM data. In this scheme, the user's identity information is embedded into their private key as a key component, ensuring that only users who satisfy the access policy can decrypt it. The compromised private key allows for tracing of the user's identity. Additionally, Linear Secret Sharing Scheme (LSSS) is employed as the access structure with the user's attribute set divided into an attribute name set and an attribute value set to protect privacy by concealing the latter within the access policy. Furthermore, the scheme integrates blockchain with cloud storage as a trusted third-party storage mechanism to ensure data integrity. Finally, the TCP-ABE scheme is comprehensively evaluated by comparing its strengths and weaknesses with other algorithms. This evaluation includes a theoretical analysis of functional and computational time overhead aspects, as well as an experimental analysis of initialization time, data encryption time, and data decryption time. The scheme exhibits excellent performance across all stages and encompasses the most comprehensive functionalities, as demonstrated by the comparative analysis and experimental results. [ABSTRACT FROM AUTHOR]

With the booming development of the Internet of Things (IoT) industry, millions of data are generated every day. How to use and manage these data safely and efficiently has become a hot issue of concern to people. Due to the accumulation of IoT data, the isolated data island phenomenon makes it difficult to connect and interact with each data owner, and the security and privacy of IoT data also become a challenge. Blockchain is a decentralized database technology that uses distributed accounting to ensure reliable data transmission and access, along with smart contracts that can be executed automatically to program and manipulate data. At the same time, blockchain techniques have stronger security and privacy, which can better meet the needs of users. In this paper, we analyze the current management mode and challenges of IoT data and propose an IoT data platform based on blockchain. The proposed platform takes into account important factors including IoT data traceability, IoT data trusted transactions, etc. Our IoT data platform achieves the trusted management and transaction of IoT data. We also propose follow-up optimization solutions to expand the application scope of our platform and achieve more efficient management of IoT data. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) is vulnerable to data-tampering (DT) attacks. Due to resource limitations, many anomaly detection systems (ADSs) for IoT have high false positive rates when detecting DT attacks. This leads to the misreporting of normal data, which will impact the normal operation of IoT. To mitigate the impact caused by the high false positive rate of ADS, this paper proposes an ADS management scheme for clustered IoT. First, we model the data transmission and anomaly detection in clustered IoT. Then, the operation strategy of the clustered IoT is formulated as the running probabilities of all ADSs deployed on every IoT device. In the presence of a high false positive rate in ADSs, to deal with the trade-off between the security and availability of data, we develop a linear programming model referred to as a security trade-off (ST) model. Next, we develop an analysis framework for the ST model, and solve the ST model on an IoT simulation platform. Last, we reveal the effect of some factors on the maximum combined detection rate through theoretical analysis. Simulations show that the ADS management scheme can mitigate the data unavailability loss caused by the high false positive rates in ADS. [ABSTRACT FROM AUTHOR]

Edge data analytics refers to processing near data sources at the edge of the network to reduce delays in data transmission and, consequently, enable real-time interactions. However, data analytics at the edge introduces numerous security risks that can impact the data being processed. Thus, safeguarding sensitive data from being exposed to illegitimate users is crucial to avoiding uncertainties and maintaining the overall quality of the service offered. Most existing edge security models have considered attacks during data analysis as an afterthought. In this paper, an overview of edge data analytics in healthcare, traffic management, and smart city use cases is provided, including the possible attacks and their impacts on edge data analytics. Further, existing models are investigated to understand how these attacks are handled and research gaps are identified. Finally, research directions to enhance data analytics at the edge are presented. [ABSTRACT FROM AUTHOR]

This paper presents an innovative high-gain multiple-input-multiple-output (MIMO) antenna featuring a compact circular shape, enhanced by strategically positioned slots, slits, and defected grounds created by etching multiple iterations of a circle inserted with a triangle shape. The investigation thoroughly explores the various traits and properties exhibited by the antenna. The antenna design harmoniously incorporates two radiating elements shaped in circles, positioned 16 mm apart from their centers, and has been physically constructed using an FR4 substrate. Enhancing the antenna's bandwidth and gain requires the implementation of slots with fractal patterns on the ground with a precise edge-to-edge separation of 2.5 mm. The placement of the antenna elements at a 16 mm distance guarantees an isolation level exceeding 15 dB consistently throughout the entire wideband frequency range. The dimensions of the compact MIMO antenna are tailored to be 1.12λ × 1.8λ × 0.091λ (20 × 32 × 1.62 mm³). In this study, the circular patch MIMO antenna with a fractal defected ground structure (DGS) resonates precisely at 16.903 GHz. It showcases an impressive impedance bandwidth spanning 2.027 GHz, ranging from 15.946 GHz to 17.973 GHz. It exhibits a reflection coefficient of -43.82 dB and achieves an observed gain of 6.25 dBi. The observed results include a minimal envelope correlation coefficient (< 0.025) and a substantial diversity gain (> 9.89). The measured results mirror the simulated outcomes, affirming the effectiveness of the wideband, high-gain antenna design. Its bandwidth and gain are well-suited for satellite communications, particularly in downlink applications, enabling faster data transmission rates. [ABSTRACT FROM AUTHOR]

The dynamic characteristics of vehicular ad hoc networks (VANETs) demand reliable and secure communication over wireless media. However, there are significant contradictions in autonomous vehicular systems related to security and privacy. Furthermore, VANETs require seamless data transmission to exchange time-critical data about accidents and traffic. Therefore, VANETs require secure seamless data transmission mechanisms to provide secure reliable communication without communication interruption to exchange time-critical data between vehicles. This paper proposes an Enhanced Location-Aided Ant colony Routing (ELAACR) for VANETs to achieve seamless secure data transmission. The proposed ELAACR is a two-fold scheme in which Phase 1 implements Location-Aided Key Management (LAKM) and Phase 2 implements Ant Colony Routing (ACR). An ACR combined with an LAKM pattern for offering secure and reliable communication. The LAKM scheme works based on the group signature concept to construct a secure network, and an ACR is used to find an efficient secure shortest path for seamless data transmission. The proposed work is simulated in Network Simulator (NS 2.35). By using an efficient and secure shortest path with secure seamless data transmission, the proposed ELAACR achieves an average throughput (T) of 640 Kbps, packet loss rate (PLR) of 0.018%, packet delivery ratio (PDR) of 98.5%, overhead (OH) of 0.157%, and 0.16 s end-to-end delay (EED) compared to Enhanced Hybrid Ant Colony Optimization Routing Protocol (EHACORP) and Fuzzy Based Ant Colony Optimization (F-ANT). The simulation results show that the proposed ELAACR registers better performance in all aspects compared to the existing EHACORP and F-ANT. [ABSTRACT FROM AUTHOR]

A novel magnetic coupling resonance wireless power transfer (MCR‐WPT) system, which has constant‐current (CC) and constant‐voltage (CV) output characteristics suiting for batteries charging, is proposed in this paper. The hybrid compensation topology is adopted; in detail, the LCL‐P circuit topology is used in the CC mode, while the LCL‐S topology is used in the CV mode. Since the switchable components are only located at the receiving side of WPT, the switching can be controlled by the receiving side based on the batteries' state without data communication between transmitting side and receiving side. Furthermore, an adjustable frequency tuning method which is based on proportion integration (PI) closed‐loop controlled by the transmitting side is applied to maximize the current of the transmitting coil. This method also can solve the frequency splitting problem, improving the stability and power of the system while the distance of the two sides changes. The performance of the proposed WPT system is verified by simulation and experiments. The experiment results show that the load current variation is less than 3% in the CC mode and the load voltage changes less than 5% in the CV mode, as the load resister changes from 5 to 20 Ω. [ABSTRACT FROM AUTHOR]

In this paper, an innovative event-triggered optimal tracking control algorithm is proposed for input saturated strict-feedback nonlinear systems with unknown dynamics. In order to reduce the requirement of configuring a complete suit of sensors and enhance the reliability of the controlled system, a neural networks (NNs) based adaptive state observer is developed firstly to reconstruct the system states. Subsequently, based on the state estimation information, a hybrid-triggered feedforward controller is designed to transform the original tracking control problem into an equivalent regulation issue, which is then solved by developing an event-triggered optimal controller. Therefore, the final controller consists of a hybrid-triggered feedforward controller and an event-triggered optimal controller. In order to make the actual input signals of the two controllers be updated simultaneously, a synchronization-oriented triggering rule is established by using multiple triggering errors. By virtue of this unique framework, the proposed control scheme can not only minimize the predefined cost function, but also greatly reduce the data transmission. What is more, the convergence properties of the proposed control strategy are achieved by using Lyapunov theory. It is important to note that unlike the widely adopted observer-controller framework, where the separation principle holds for the design of the state observer, there is a considerable coupling relationship between the error dynamics of the state observer and the event-triggered optimal controller in this paper. The distinguishing feature of the proposed method is its ability to ensure a satisfactory level of precision in both state estimation and tracking control, even in the presence of control saturation issues. At last, the proposed control strategy is applied to the tracking control problem of a high-order robot system and marine surface vehicle to demonstrate its effectiveness. • A novel event-triggered optimal tracking control framework is proposed. • A phenomenon we defined as the "jump of error surfaces" is noticed and tackled via filter technique. • A multi-error based adaptive triggering rule is established to improve the control performance. • The proposed method not only achieves the optimality but also lowers data transmission. [ABSTRACT FROM AUTHOR]

The integration of numerous embedded cores onto a single die is made possible by the Network-on-chip (NoC), which serves as a crucial technology. The utilization of planar metal interconnects for implementing a NoC through existing methods is inadequate because of the use of multihop channels in data exchange leads to significant power consumption and increased latency. Scalability challenges are anticipated to affect communication systems within many-core architectures expected in the future. As a solution, the Wireless Network-on-Chip or WiNoC design paradigm has been proposed to address these issues. In this paper, we evaluate how subnet clustering in the radio hub affects the 100-core Wireless Network-on-Chip architecture. This study investigates the overall delay in data transmission, the throughput of the network and energy usage in the 100-core WiNoC architecture with equipped with four radio hubs by analyzing single, four, and nine-tile radio subnet clustering. The validation of the results involves simulating the 100-core mesh WiNoC architectures being tested using the cycle-accurate Noxim simulator, with both random and transpose synthetic traffic workloads. Based on the simulation results, the architecture with a four-tile radio hub subnet clustering provides the most optimal performance for both random and transpose traffic distributions at PIR 0.013 flit/cycle/tile when compared to the other analyzed subnet clusterings. [ABSTRACT FROM AUTHOR]

Objective In recent years, there has been an explosive growth in data communication traffic. To address the demands for high-speed, high-capacity data transmission and the diversification of network application scenarios, 100 Gbit/s and Beyond (B100 Gbit/s) Dense Wavelength Division Multiplexing (DWDM) hybrid networking has gradually gained attention as an efficient solution. This paper conducts an analysis of the requirements, key technologies, and practical case studies of B100 Gbit/s DWDM hybrid networking to provide technical support and guidance for constructing high-capacity and efficient communication networks. Methods This paper first elucidates the requirements for the development of B100 Gbit/s DWDM hybrid networks, including the need for network capacity expansion and support for complex network architectures. Next, it focuses on introducing the key technologies of B100 Gbit/s DWDM hybrid networking, including constellation shaping, spectrum shaping, and flexible grid techniques. To support bitrate design in hybrid networking, a method for Optical Signal-to-Noise Ratio (OSNR) calculation in cascaded Erbium-Doped Fiber Amplifier (EDFA) communication systems is presented, utilizing parameters such as channel configuration information, signal optical power after transmitter, gain and noise parameters of EDFAs, to progressively calculate the output OSNR for each wavelength along the entire link. Finally, in conjunction with a foreign network case study and based on actual OSNR evaluation findings, a rational hybrid rate network design is performed, demonstrating the practical application of B100 Gbit/s DWDM hybrid networking in engineering projects. Results By employing B100 Gbit/s DWDM hybrid network, after flexibly configuring transmission rates and bandwidths according to OSNR evaluations, the deployment of hybrid rate networks at 200, 600 and 800 Gbit/s is achieved. This approach fulfills the high-capacity requirements of core sites while accommodating the long-distance, extensive span requirements of edge sites. Furthermore, network upgrades and expansion were smoothly accomplished within a three-year period. Conclusion Empirical evidence demonstrates that B100 Gbit/s DWDM networking effectively enhances network capacity, flexibility, and spectrum resource utilization. Moreover, it provides room for the continuous evolution of the networks, serving as a vital methods to advance the development of high-capacity optical transmission networks. [ABSTRACT FROM AUTHOR]

Wireless power and data transmission (WPDT) solutions for medical implants are highly desired. However, achieving a high-power transmission efficiency and data rate simultaneously over an inductive link remains a significant challenge. This paper presents an innovative WPDT circuit that incorporates additional MOSFETs with an inductor in a Class-E power amplifier (PA), achieving amplitude-shift keying (ASK) modulation to address this issue. Firstly, the efficiency of the inductive power transmission link and Class-E PA was analyzed, providing design insights. Then, leveraging the insights, the proposed circuit was designed in such a way that it could effectively switch between two load networks to maintain high transfer efficiency for ASK modulation. Based on the load networks, the relationship between introducing the inductor's value and the data modulation index (MI) was derived to help achieve the desired high-power transmission efficiency. Additionally, the design and calculation of the proposed circuit are also presented. Finally, the proposed circuit was validated through simulations and experiments, demonstrating a power delivery to a load of 84.1 mW with a power transmission efficiency of 70.8% at a data rate and carrier frequency of 3 Mbps and 16 MHz, respectively. Furthermore, the bit error rate (BER) is less than 10−6 with an MI of 10%. [ABSTRACT FROM AUTHOR]

Medical data are increasing drastically due to the vast development of medical sciences. The security of this immense data is also a challenge of the present era. Image watermarking is a technique to secure medical data from alteration. Authentication of patients records is also necessary in case of medical data transmission. In this paper, an optimized electroencephalogram watermarking technique are proposed with dual authentication using Advanced encryption standards (AES) and speeded-up robust features is proposed. Scaling factor plays an important role to balance the properties of watermarking algorithm. The cuckoo search optimization is used to get the optimized scaling factor. The Henon encryption (HE) is used to enhance the security of sub-band obtained from identity of the patient image used as the watermark. The diagonalized Hessenberg decomposition (HD) is used for embedding watermark while secured hash algorithm (SHA-256) is used to protect watermark against malicious attacks. For the proposed technique, detailed security analysis has been performed for AES encryption technique. Various performance metrics are computed for the proposed technique to estimate the effectiveness of the watermarking system. [ABSTRACT FROM AUTHOR]

The development of new telecommunication services requires the implementation of advanced technologies and the next generations of networks. Currently, the Long-Term Evolution (LTE) is a widely used standard. On the other hand, more and more mobile network operators (MNOs) are implementing the fifth-generation (5G) New Radio standard in their networks. It allows for increasing throughput, spectral, and energy efficiency and maximizing coverage, while reducing latency. The effectiveness of the introduced changes is measured by assessing the quality of service (QoS) in mobile networks. The paper presents the result evaluation of the QoS measurement campaign carried out using the drive test method in an urban area for four MNOs. We analyze the data transmission scenario, which is the basis of most modern telecommunications services. The result comparison provides an assessment of the 5G service implementation advancement by MNOs. In this analysis, we consider many QoS metrics (e.g., session time, throughput, and round-trip time) and parameters defining the radio signal quality (i.e., reference signal received power, signal-to-interference-plus-noise ratio). Our work also included searching for relationships between these parameters, using a correlation analysis. It allows for the selection of uncorrelated parameters to assess the quality of network, i.e., MNO evaluation, in terms of the provided QoS. [ABSTRACT FROM AUTHOR]

The real-time transmission of ship status data from vessels to shore is crucial for live status monitoring and guidance. Traditional reliance on expensive maritime satellite systems for this purpose is being reconsidered with the emergence of the global short message communication service offered by the BeiDou-3 navigation satellite system. While this system presents a more cost-effective solution, its bandwidth is notably insufficient for handling real-time ship status data. This inadequacy necessitates the compression of such data. Therefore, this paper introduces an algorithm tailored for real-time compression of sequential ship status data. The algorithm is engineered to ensure both accuracy and the preservation of valid data range integrity. Our methodology integrates quantization, predictive coding employing an attention-averaging-based predictor, and arithmetic coding. This combined approach facilitates the transmission of succinct messages through the BeiDou Navigation System, enabling the live monitoring of ocean-going vessels. Experimental trials conducted with authentic data obtained from ship monitoring systems validate the efficiency of our approach. The achieved compression rates closely approximate theoretical minimum values. Consequently, this method exhibits substantial promise for the real-time transmission of parameters across various systems. [ABSTRACT FROM AUTHOR]

The current trend of people having their multimedia-capable wireless and mobile devices roam within highly urbanized areas (i.e., smart cities) has led to the wide deployment of overlapping heterogeneous wireless network technologies. In this regard, various challenges have emerged, such as the number of mobile devices that connect or disconnect a wireless network domain, the diversity of time of their connections, frequent handovers as mobile devices frequently change their locations, the heterogeneity of wireless network technologies, cooperation challenges between the overlapping heterogeneous wireless network technologies, the increasing volume of multimedia traffic, security and privacy issues, and many more. This paper focuses on the deployment of a routing-based distributed mobility management (DMM) scheme to address the constraints and limitations of centralized wireless architectures for smart cities. The comparative analysis with centralized mobility management solutions shows significant alleviation in performance as to handover latency and packet losses, thus providing seamless handovers to maintain quality of service (QoS) for multimedia services. [ABSTRACT FROM AUTHOR]

Traditional internet protocol (IP) networks, adhering to a "best-effort" service model, typically utilize shortest-path routing for data transmission. Nevertheless, this methodology encounters limitations, especially considering the increasing demands for both high reliability and high bandwidth. These demands reveal shortcomings in this routing strategy, notably its inefficient bandwidth utilization and fault recovery capabilities. The method of multipath transmission has been extensively researched as a solution to these challenges. With the emergence of innovative Internet architectures, notably information-centric networking (ICN), network nodes have gained enhanced capabilities, opening new avenues for multipath transmission design. This paper introduces a multipath scheduling approach for network nodes, capitalizing on the advanced features of these modern nodes. It reimagines the conventional next-hop node as a group of potential next-hop nodes based on both global and local routing strategies and assigns traffic shares to each node within this group for balanced traffic distribution. Network nodes are configured to periodically review and adjust traffic shares according to the link statuses. If scheduling cannot be completed within the set, feedback is sent to upstream nodes. Simulations demonstrate that this approach effectively leverages network path variety, improves bandwidth usage and throughput, and minimizes average data transmission time. [ABSTRACT FROM AUTHOR]

This paper introduces the novel IRS-based Optimal Relay Selection (ORS-IRS) method, aimed at analyzing the performance of wireless communication systems with an emphasis on symmetry. The ORS-IRS approach presents an innovative communication algorithm that seamlessly integrates Intelligent Reflecting Surfaces (IRS) with relay selection techniques. Through adaptive adjustments of reflection coefficients, IRS elements efficiently manipulate incoming signals, fostering symmetry in signal strength enhancement and latency reduction for improved signal delivery to the intended destination. This symmetrical optimization in channel capacity and transmission power ensures reliable data transmission with low latency, achieved through the seamless integration of IRS and relay selection techniques. In contrast, the Cell-Free Massive MIMO (CF-M-MIMO), with its decentralized architecture, excels in serving a larger user base and attaining remarkable capacity gains, showcasing a different dimension of symmetry. The Decode-and-Forward (DF) relaying approach demonstrates its potential in enhancing signal reliability across extended distances, contributing to the overall symmetry of the comparative analysis. This comprehensive evaluation provides valuable insights into selecting appropriate transmission strategies, particularly for applications that demand high capacity and reliability in the design of modern wireless communication systems with a symmetrical focus. [ABSTRACT FROM AUTHOR]

This paper introduces a model employing Wavelength Division Multiplexed-Radio - over-Fiber (WDM-RoF) and Fiber Bragg Grating (FBG) technologies for high-speed data transmission over 340 km, meeting modern user needs. The setup includes an efficient RoF network with an Optical Phase Conjugator (OPC) and FBG combination to enhance extended, accurate transmission with a wide bandwidth. Operating at a 50 GHz carrier frequency and 10 Gbps data rate, the proposed 16-channel WDM-RoF system aims to optimize Optical Signal to Noise Ratio (OSNR) and Signal to Noise Ratio (SNR) for efficient fiber optic cable data transmission. This model achieves a minimum OSNR of 21.01 dB and an SNR of 18.88 dB when channel spacing is kept at 0.5 nm. A comparative exploration highlights that the proposed model outperforms existing systems, demonstrating higher transmission efficiency up to 340 km, making it a versatile solution for a wide range of communication applications. [ABSTRACT FROM AUTHOR]

Wire and cable are important industrial products involving the national economy and people's livelihood, which are hailed as the "blood vessel" and "nerve" of the national economy, providing the basic guarantee for the normal operation of modern economy and society. The data traceability of their production and circulation process is a key factor in ensuring their quality and safety management. We aim to solve the problems of unsafe data transmission, weak quality control, and information islands in the process of wire and cable quality traceability in order to improve the production management efficiency of wire and cable manufacturing enterprises and to reduce the cost of consumer quality traceability of wire and cable products. We analyzed the technical characteristics and advantages of Industrial Internet of Things (IIoT) identity resolution and the blockchain. Key technologies are introduced, a traceability method that integrates the two is proposed, and a quality traceability framework based on the IIoT identity resolution system and blockchain technology is constructed. By analyzing the quality information composition of the wire and cable supply chain, a new quality traceability model based on the wire and cable supply chain is established. Finally, through the verification of the developed quality traceability system, the quality traceability function and quality information of each production link of wire and cable are successfully realized. This paper fills a gap in the field of cable product quality traceability using the combination of IIoT and blockchain technology. According to this model, it also has some potential for the traceability of other industrial products. [ABSTRACT FROM AUTHOR]

Federated learning (FL) is a privacy-preserving collective machine learning paradigm. Vertical federated learning (VFL) deals with the case where participants share the same sample ID space but have different feature spaces, while label information is owned by one participant. Early studies of VFL supported two participants and focused on binary-class logistic regression problems, while recent studies have put more attention on specific aspects such as communication efficiency and data security. In this paper, we propose the multi-participant multi-class vertical federated learning (MMVFL) framework for multi-class VFL problems involving multiple parties. By extending the idea of multi-view learning (MVL), MMVFL enables label sharing from its owner to other VFL participants in a privacy-preserving manner. To demonstrate the effectiveness of MMVFL, a feature selection scheme is incorporated into MMVFL to compare its performance against supervised feature selection and MVL-based approaches. The proposed framework is capable of quantifying feature importance and measuring participant contributions. It is also simple and easy to combine with other communication and security techniques. The experiment results on feature selection for classification tasks on real-world datasets show that MMVFL can effectively share label information among multiple VFL participants and match the multi-class classification performance of existing approaches. [ABSTRACT FROM AUTHOR]

Underwater acoustic sensor networks (UASNs) are critical to a range of applications from oceanographic data collection to submarine surveillance. In these networks, efficient energy management is critical due to the limited power resources of underwater sensors. The LEACH protocol, a popular cluster-based protocol, has been widely used in UASNs to minimize energy consumption. Despite its widespread use, the conventional LEACH protocol faces challenges such as an unoptimized cluster number and low transmission efficiency, which hinder its performance. This paper proposes an improved LEACH protocol for cluster-based UASNs, where the cluster number is optimized with an underwater energy propagation model to reduce energy consumption, and a transmission scheduling algorithm is also employed to achieve conflict-free parallel data transmission. Replication computing is introduced to the LEACH protocol to reduce the signaling in the clustering and data transmission phases. The simulation results show that the proposed protocol outperforms several conventional methods in terms of normalized average residual energy, average number of surviving nodes, average round when the first death node occurs, and the number of packets received by the base station. [ABSTRACT FROM AUTHOR]

In the process of data transmission in mobile ad hoc networks, it is essential to establish optimal routes from source nodes to destination nodes. However, as network density increases, this process is often accompanied by a significant rise in network overhead. To address this issue, the ND-AODV (neighborhood density AODV) protocol has been introduced, which reduces the probability of transmitting control information in high-density node environments to mitigate network overhead. Nevertheless, this may come at the cost of reduced routing accuracy, potentially leading to unnecessary resource wastage in certain scenarios. Furthermore, ND-AODV does not comprehensively consider the location of the receiving nodes, which limits its ability to reduce network overhead effectively. To overcome these limitations, this paper introduces a novel routing approach, known as CND-AODV (common neighborhood density AODV). In comparison to ND-AODV, CND-AODV offers a more comprehensive solution to the challenges posed by high-density network environments. It intelligently processes control information based on the special positioning of the receiving nodes, thereby significantly reducing unnecessary network overhead. Through simulation experiments comparing performance metrics such as throughput, packet delivery rate, and latency, the results clearly indicate that CND-AODV substantially decreases network overhead, enhancing network performance. Compared to ND-AODV, this innovative routing approach exhibits significant advantages. It provides a more efficient and reliable solution for ad hoc networks in high-density environments. [ABSTRACT FROM AUTHOR]