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Digital twin technology provides a reliable paradigm to address the high trial-and-error costs and limited perception capabilities in satellite networking. However, the dynamic constellation topology and real-time twin applications remain significant challenges in satellite network design. This paper proposes a network topology simulation approach that dynamically analyzes the inter-satellite topology based on pre-calculated ephemeris and orbital information. Furthermore, the paper introduces a digital twin algorithm based on network virtualization, cloud platform management, and software-defined networking to validate and analyze the twin requirements at different stages. Finally, a low Earth orbit (LEO) constellation twin validation environment is constructed to verify the networking protocols at various stages. The experimental results demonstrate the performance of the proposed twin systems at different stages. [ABSTRACT FROM AUTHOR]

The article presents the discussion on meaning or semantics of the data. Topics include advancements in artificial intelligence (AI) providing a powerful tool for solving fundamental problems in semantic communications; and semantic communications in emerging wireless networks such as virtual reality and autonomous driving.

The article reports that In contrast to the Shannon paradigm whose underlying principle is to guarantee the correct reception of each single transmitted packet regardless of its meaning, semantic communication is concerned with the problem of how transmitted symbols convey a desired meaning to the destination, as well as how effectively the received meaning affects the action in a desired way.

The article reports that During the last two years of COVID-19 pandemic the importance of e-work and remote collaboration systems as key contributors to the technological and economic progress of many industries has become more and more clear. Countries and companies that were able to rapidly adapt to the changes in work settings and dynamics brought about by the pandemic were able to significantly limit the economic impact of the pandemic in those fields that don't require physical presence.

User interfaces to control networked household appliances are often inadequate. Either they are too simplistic or they are too complex and complicated relying on a PC-based interface. Therefore, a user interface that supports complex functionalities and is easy to use is required. In this article digital pen and paper are presented as a suitable interface. Digital pens are only marginally larger than their traditional counterparts, and digital paper is ordinary paper with an almost invisible unique pattern printed on it. Pen and paper are well known to users and thus an important barrier to the acceptance of networked appliances is removed. The approach is integrated with an Open Services Gateway Initiative (OSGi) gateway, thus allowing for flexibility with respect to the protocol used to communicate with the appliance. It is also shown how the approach can be applied to other appliances in and beyond the home. [ABSTRACT FROM AUTHOR]

The present research work focuses on overcoming cybersecurity problems in the Smart Grid. Smart Grids must have feasible data capture and communications infrastructure to be able to manage the huge amounts of data coming from sensors. To ensure the proper operation of next-generation electricity grids, the captured data must be reliable and protected against vulnerabilities and possible attacks. The contribution of this paper to the state of the art lies in the identification of cyberattacks that produce anomalous behaviour in network management protocols. A novel neural projectionist technique (Beta Hebbian Learning, BHL) has been employed to get a general visual representation of the traffic of a network, making it possible to identify any abnormal behaviours and patterns, indicative of a cyberattack. This novel approach has been validated on 3 different datasets, demonstrating the ability of BHL to detect different types of attacks, more effectively than other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The paper [[8]] "Dynamic Power and Bandwidth Allocation for DVB-based LEO Satellite Systems" proposes an efficient power and bandwidth allocation method employing two linear machine learning algorithms with inputs of channel conditions and TD. The fifth-generation (5G) technology has recently been consolidating its function as an infrastructure for new services, such as ultrareliable low-latency communications (URLLC) and massive machine-type communications, by further expanding the high-speed data service of the mobile communication system. In their paper titled [[1]] "Reliability-guaranteed Multi-path Allocation Algorithm in Mobile Network", Lee and Ko investigated the method of selecting higher reliable network paths for further enhancing URLLC services. [Extracted from the article]

Many applications need to read an entire database in a consistent way. This global-reading of an entire database formulated as a global-read transaction (GRT) is not a trivial issue since it will cause a high degree of interference to other concurrent transactions. Conventional concurrency control protocols are obviously inadequate in handling the long-lived GRT. Previous studies [1], [3], [4] proposed additional tests, namely, the Color Test and the Shade Test, to handle conflicts between the GRT and update transactions. However, we discovered that both algorithms can bring about nonserializable schedules of transactions. We propose an enhanced algorithm directly built on the two algorithms to guarantee the serializability of transactions. [ABSTRACT FROM AUTHOR]

This document is an editorial published in the journal Frontiers in Physics. It discusses the theory and applications of long-range dependent (LRD) processes, which are of interest to researchers in various fields, including physics and computer science. LRD processes differ from conventional random processes in terms of their autocorrelation function, power spectrum density, and probability density function. The editorial highlights two models of LRD processes: fractional Gaussian noise (fGn) and the generalized Cauchy process. It also mentions several papers included in the research topic, which explore the use of LRD processes in precision time protocol, dynamic routing algorithms, identification of cryptomining behavior, and modeling global sea surface chlorophyll concentration. [Extracted from the article]

Multipath wireless access aims to seamlessly aggregate multiple access networks to increase data rates and decrease latency. It is currently being standardized through the ATSSS architectural framework as part of the fifth-generation (5G) cellular networks. However, facilitating efficient multi-access communication in next-generation wireless networks poses several challenges due to the complex interplay between congestion control (CC) and packet scheduling. Given that enhanced ATSSS steering functions for traffic splitting advocate the utilization of multi-access tunnels using congestion-controlled multipath network protocols between user equipment and a proxy, addressing the issue of nested CC becomes imperative. In this paper, we evaluate the impact of such nested congestion control loops on throughput over multi-access tunnels using the recently introduced Multipath DCCP (MP-DCCP) tunneling framework. We evaluate different combinations of endpoint and tunnel CC algorithms, including BBR, BBRv2, CUBIC, and NewReno. Using the Cheapest Path First scheduler, we quantify and analyze the impact of the following on the performance of tunnel-based multipath: (1) the location of the multi-access proxy relative to the user; (2) the bottleneck buffer size, and (3) the choice of the congestion control algorithms. Furthermore, our findings demonstrate the superior performance of BBRv2 as a tunnel CC algorithm. [ABSTRACT FROM AUTHOR]

The intersection of cybersecurity and opportunistic networks has ushered in a new era of innovation in the realm of wireless communications. In an increasingly interconnected world, where seamless data exchange is pivotal for both individual users and organizations, the need for efficient, reliable, and sustainable networking solutions has never been more pressing. Opportunistic networks, characterized by intermittent connectivity and dynamic network conditions, present unique challenges that necessitate innovative approaches for optimal performance and sustainability. This paper introduces a groundbreaking paradigm that integrates the principles of cybersecurity with opportunistic networks. At its core, this study presents a novel routing protocol meticulously designed to significantly outperform existing solutions concerning key metrics such as delivery probability, overhead ratio, and communication delay. Leveraging cybersecurity's inherent strengths, our protocol not only fortifies the network's security posture but also provides a foundation for enhancing efficiency and sustainability in opportunistic networks. The overarching goal of this paper is to address the inherent limitations of conventional opportunistic network protocols. By proposing an innovative routing protocol, we aim to optimize data delivery, minimize overhead, and reduce communication latency. These objectives are crucial for ensuring seamless and timely information exchange, especially in scenarios where traditional networking infrastructures fall short. By large-scale simulations, the new model proves its effectiveness in the different scenarios, especially in terms of message delivery probability, while ensuring reasonable overhead and latency. [ABSTRACT FROM AUTHOR]

Efficient and reliable data routing is critical in Advanced Metering Infrastructure (AMI) within Smart Grids, dictating the overall network performance and resilience. This paper introduces Q-RPL, a novel Q-learning-based Routing Protocol designed to enhance routing decisions in AMI deployments based on wireless mesh technologies. Q-RPL leverages the principles of Reinforcement Learning (RL) to dynamically select optimal next-hop forwarding candidates, adapting to changing network conditions. The protocol operates on top of the standard IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL), integrating it with intelligent decision-making capabilities. Through extensive simulations carried out in real map scenarios, Q-RPL demonstrates a significant improvement in key performance metrics such as packet delivery ratio, end-to-end delay, and compliant factor compared to the standard RPL implementation and other benchmark algorithms found in the literature. The adaptability and robustness of Q-RPL mark a significant advancement in the evolution of routing protocols for Smart Grid AMI, promising enhanced efficiency and reliability for future intelligent energy systems. The findings of this study also underscore the potential of Reinforcement Learning to improve networking protocols. [ABSTRACT FROM AUTHOR]

Over the years, there has been a significant surge in the popularity of instant messaging applications (IMAs). However, the message notification functionality in IMAs exhibits certain limitations. Some IMAs fail to alert users about new messages after their phone restarts unless they unlock the phone. This is a consequence of end-to-end encryption (E2EE) and the app not knowing the message is in the queue until the app decrypts it. This approach using E2EE is used to prevent offline attacks, as the key is unavailable to decrypt the notification messages. In this paper, we introduce a novel design and implementation of a message notification protocol for IMAs based on the Signal protocol. The proposed protocol aims to securely display notifications on a locked device and ensures that cryptographic keys are stored in a location that is isolated from the user's device to prevent offline attacks. This approach enhances the security of private key storage, safeguarding private keys against various external threats. The innovative design strengthens the off-site key management system, rendering it resilient against offline attacks and mitigating the risk of key compromise. Additionally, the proposed protocol is highly efficient, requiring no specialized hardware for implementation. It offers confidentiality of cryptographic keys and protection against offline attacks, further enhancing the overall security of the system. We evaluate the protocol's effectiveness by analyzing multiple independent implementations that pass a suite of formal tests via ProVerif. [ABSTRACT FROM AUTHOR]

Bluetooth Low Energy Mesh (BLE Mesh) enables Bluetooth flexibility and coverage by introducing Low-Power Nodes (LPNs) and enhanced networking protocol. It is also a commonly used communication method in sensor networks. In BLE Mesh, LPNs are periodically woken to exchange messages in a stop-and-wait way, where the tradeoff between energy and efficiency is a hard problem. Related works have reduced the energy consumption of LPNs mainly in the direction of changing the bearer layer, improving time synchronization and broadcast channel utilization. These algorithms improve communication efficiency; however, they cause energy loss, especially for the LPNs. In this paper, we propose a constrained flooding algorithm based on time series prediction and lightweight GBN (Go-Back-N). On the one hand, the wake-up cycle of the LPNs is determined by the time series prediction of the surrounding load. On the other, LPNs exchange messages through lightweight GBN, which improves the window and ACK mechanisms. Simulation results validate the effectiveness of the Time series Prediction and LlightWeight GBN (TP-LW) algorithm in energy consumption and throughput. Compared with the original algorithm of BLE Mesh, when fewer packets are transmitted, the throughput is increased by 214.71%, and the energy consumption is reduced by 65.14%. [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]

Network protocol implementations, as integral components of information communication, are critically important for security. Due to its efficiency and automation, fuzzing has become a popular method for protocol security detection. However, the existing protocol-fuzzing techniques face the critical problem of generating high-quality inputs. To address the problem, in this paper, we propose MSFuzz, which is a protocol-fuzzing method with message syntax comprehension. The core observation of MSFuzz is that the source code of protocol implementations contains detailed and comprehensive knowledge of the message syntax. Specifically, we leveraged the code-understanding capabilities of large language models to extract the message syntax from the source code and construct message syntax trees. Then, using these syntax trees, we expanded the initial seed corpus and designed a novel syntax-aware mutation strategy to guide the fuzzing. To evaluate the performance of MSFuzz, we compared it with the state-of-the-art (SOTA) protocol fuzzers, namely, AFLNET and CHATAFL. Experimental results showed that compared with AFLNET and CHATAFL, MSFuzz achieved average improvements of 22.53% and 10.04% in the number of states, 60.62% and 19.52% improvements in the number of state transitions, and 29.30% and 23.13% improvements in branch coverage. Additionally, MSFuzz discovered more vulnerabilities than the SOTA fuzzers. [ABSTRACT FROM AUTHOR]

Underwater wireless sensor networks are gaining popularity since supporting a broad range of applications, both military and civilian. Wireless acoustics is the most widespread technology adopted in underwater networks, the realization of which must face several challenges induced by channel propagation like signal attenuation, multipath and latency. In order to address such issues, the attention of researchers has recently focused on the concept of cooperative communication and networking, borrowed from terrestrial systems and to be conveniently recast in the underwater scenario. In this paper, we present a comprehensive literature review about cooperative underwater wireless sensor networks, investigating how nodes cooperation can be exploited at the different levels of the network protocol stack. Specifically, we review the diversity techniques employable at the physical layer, error and medium access control link layer protocols, and routing strategies defined at the network layer. We also provide numerical results and performance comparisons among the most widespread approaches. Finally, we present the current and future trends in cooperative underwater networks, considering the use of machine learning algorithms to efficiently manage the different aspects of nodes cooperation. [ABSTRACT FROM AUTHOR]

Reflection attacks are one of the most intimidating threats organizations face. A reflection attack is a special type of distributed denial-of-service attack that amplifies the amount of malicious traffic by using reflectors and hides the identity of the attacker. Reflection attacks are known to be one of the most common causes of service disruption in large networks. Large networks perform extensive logging of NetFlow data, and parsing this data is an advocated basis for identifying network attacks. We conduct a comprehensive analysis of NetFlow data containing 1.7 billion NetFlow records and identified reflection attacks on the network time protocol (NTP) and NetBIOS servers. We set up three regression models including the Ridge, Elastic Net and LASSO. To the best of our knowledge, there is no work that studied different regression models to understand patterns of reflection attacks in a large network. In this paper, we (a) propose an approach for identifying correlations of reflection attacks, and (b) evaluate the three regression models on real NetFlow data. Our results show that (a) reflection attacks on the NTP servers are not correlated, (b) reflection attacks on the NetBIOS servers are not correlated, (c) the traffic generated by those reflection attacks did not overwhelm the NTP and NetBIOS servers, and (d) the dwell times of reflection attacks on the NTP and NetBIOS servers are too small for predicting reflection attacks on these servers. Our work on reflection attacks identification highlights recommendations that could facilitate better handling of reflection attacks in large networks. [ABSTRACT FROM AUTHOR]

This paper presents the idea of the Internet of Things (IoT) platform, which is demonstrated by creating a device that measures air quality, collects and processes realtime data, and presents it in a web application. The device comprises a microcontroller board called Wemos D1 Mini, which is powered by an ESP8266 microchip. It also consists of two sensors, CCS811, which measures air particle quantity, and BME280, which measures air temperature, pressure, and humidity. The device is wirelessly connected to an MQTT broker using a Wi-Fi network and the MQTT protocol. The paper also provides detailed information on the functioning and usage of the MQTT protocol along with guidelines for programming and configuring the MQTT broker for both Wemos D1 and Raspberry Pi. [ABSTRACT FROM AUTHOR]

With the development of society, humans are becoming more and more dependent on the Internet. And there are exploitable vulnerabilities in network sharing protocol vulnerabilities that will cause great risks to individuals and society. Therefore, vulnerability mining technology has developed into an important research problem in the field of information security. To this end, this paper uses fuzzy testing method for vulnerability mining of network protocols. The fuzzy testing technique performs vulnerability mining by sending a large amount of abnormal data to the test target and monitoring whether the software system is working properly. The vulnerability mining approach in this paper prioritizes the need to analyze and model the protocol format and generate a large number of test cases by using fuzzy test values to vary the boundaries of different parts of the protocol. These test cases are then sent to the test target, and the network state and process state of the test target are monitored in real time. Finally, if the test cases trigger a vulnerability, the system automatically records the test case information as well as the vulnerability information. The test cases evaluated by machine learning as easy to trigger vulnerabilities are sent to the test H-target, which can save the time of vulnerability mining for everyone and improve the efficiency of vulnerability mining. The vulnerability mining technology researched in this paper is of great significance to network security, which can prevent problems before they occur, discover vulnerabilities in the network in time, take effective measures to prevent them, and possibly avoid the spread of some major network vulnerability viruses. [ABSTRACT FROM AUTHOR]

In order to enhance the ability of wireless sensor networks to resist various security threats and reduce the limitations caused by the characteristics of wireless sensor networks and sensor nodes, this paper proposes a secure routing protocol for wireless sensor networks based on trust management. Combined with the relevant parameters of wireless sensor network, the simulation experiment is carried out with MATLAB. Aiming at the trust management part of the wireless sensor network security protocol proposed in this paper, the malicious attack environment such as sensor node attributes is simulated to verify the resistance of this model to relevant malicious attacks. For the trust management-based wireless sensor network security routing protocol proposed in this paper, the model included in the protocol is compared to the existing security routing model, combining the characteristics of average simulation transmission, network life, and average routing update time. Experiments show that the model has better routing performance and has improved by an average of about 20%. We offer a new solution to solve the problem of wireless routing security. [ABSTRACT FROM AUTHOR]

This paper proposes a MAC protocol for ad hoc networks using In-band Full-duplex (IBFD) wireless communications, which are named as AdHoc-FDMAC. To utilize IBFD communications in ad hoc networks, this protocol modifies a number of control frames in the IEEE 802.11 Distributed Coordination Function (DCF) MAC standard. Here, the detailed time sequences for all types of IBFD communications are shown for the data transmission and routing. In this paper, the probability and throughput equations for IBFD communications in different situations have been derived. The performance of the proposed AdHoc-FDMAC has been analysed in terms of probability, throughput, and routing time. The Maximum throughput of AdHoc-FDMAC has been found to be 48.34 Mbps, and it is compared with a recently published ad hoc MAC as well as with the conventional HD MAC. The AdHoc-FDMAC outperforms the recently published ad hoc MAC and conventional HD MAC by 16.80% and 66.50% throughput gain, respectively. AdHoc-FDMAC incorporates the existing Ad hoc On-demand Distance Vector (AODV) routing protocol, but this AODV routing is utilized here using IBFD communications. This paper also compares the routing time of the IBFD-based AODV with the conventional AODV. The result shows that the IBFD-based AODV requires 33.33% less routing time than that of the conventional AODV for 3-hop distance between the transmitter and receiver. This paper suggests that the AdHoc-FDMAC protocol provides much more throughput in ad hoc networks by utilizing IBFD communications. [ABSTRACT FROM AUTHOR]

This paper proposes a solution for ensuring the security of IoT devices in the cloud environment by protecting against distributed denial-of-service (DDoS) and false data injection attacks. The proposed solution is based on the integration of simple network management protocol (SNMP), Kullback–Leibler distance (KLD), access control rules (ACL), and moving target defense (MTD) techniques. The SNMP and KLD techniques are used to detect DDoS and false data sharing attacks, while the ACL and MTD techniques are applied to mitigate these attacks by hardening the target and reducing the attack surface. The effectiveness of the proposed framework is validated through experimental simulations on the Amazon Web Service (AWS) platform, which shows a significant reduction in attack probabilities and delays. The integration of IoT and cloud technologies is a powerful combination that can deliver customized and critical solutions to major business vendors. However, ensuring the confidentiality and security of data among IoT devices, storage, and access to the cloud is crucial to maintaining trust among internet users. This paper demonstrates the importance of implementing robust security measures to protect IoT devices in the cloud environment and highlights the potential of the proposed solution in protecting against DDoS and false data injection attacks. [ABSTRACT FROM AUTHOR]

In the process of collecting evidence of activities and events in network devices, there are problems with content and storage, and we aim to solve the problems faced by network devices in network forensics. In this paper, we propose a simple method for solving the problems with content and storage in acquiring communication logs on end hosts, implement a sniffing tool that captures raw packets with communication event control, compare it with existing tools, and conduct experiments and considerations. Through these experiments and considerations, we confirmed that the proposed communication log acquisition method can be implemented on the end host, and that the problem can be solved by using a tool that implements the proposed method. Also, we confirmed that it can be applied to real-world communication log collection scenarios, and that it can coexist with existing systems and tools that collect communication logs. [ABSTRACT FROM AUTHOR]

The adoption of reliable and real-time communication technology is an absolute necessity for the advancement of Internet of Things (IoT) applications. Messaging protocols such as MQTT, AMQP, and HTTP are frequently used for communication with resource-constrained IoT devices. However, choosing a suitable and effective messaging protocol presents a daunting challenge for organizations, as it depends on the specific characteristics and messaging requirements of the IoT system. Therefore, it is crucial to have a comprehensive understanding of three established messaging protocols, such as the Hypertext Transfer Protocol (HTTP), the Message Queuing Telemetry Transport (MQTT), and the Advanced Message Queuing Protocol (AMQP), to appropriately apply them in practical projects. In this paper, information technology solutions are provided for a chain of solar farms to improve harvest productivity, facilitate warning notifications, and enable remote control. Subsequently, a detailed comparative analysis is performed, considering various interconnected criteria, to gain valuable insight into the strengths and limitations of these protocols. The results show that MQTT and AMQP play a role in enhancing overall efficiency and speed within the framework of our suggested photovoltaic system. [ABSTRACT FROM AUTHOR]

This paper proposes a new H ∞ static output feedback control method to address the congestion control problem in transmission control protocol networks using active queue management routers. Based on linear matrix inequality optimization, this method determines a static output feedback control law to minimize the H ∞ norm of the transfer function between the controlled queue length of the buffer and the exogenous disturbance affecting the available link bandwidth. A linear matrix inequality formulation is presented as a sufficient condition to guarantee the closed-loop system's asymptotic stability while maintaining disturbance rejection within a specified level, regardless of round-trip time delays. The proposed robust static output feedback control eliminates the need to measure or estimate all system states, thus simplifying practical implementation. The effectiveness of the proposed design method is demonstrated by applying it in a practical process, as illustrated through a numerical example. [ABSTRACT FROM AUTHOR]

This paper investigates the distributed optimization problem for a class of nonlinear uncertain multi-agent systems with unmeasured states, switched parameters, and directed communication topologies changing in the control process. To achieve the goal of optimizing the global objective function, a penalty function is constructed through making up of a sum of local objective functions and integrating consensus conditions of the multi-agent systems to utilize local and neighboring information. Radial basis function neural-networks and neural-networks state observer are applied to approximate the unknown nonlinear functions and obtain the unmeasured states, respectively. To avoid "explosion of complexity" and obtain derivatives for virtual control functions continuously, dynamic surface control technology is proposed to develop a distributed adaptive backstepping neural network control protocol to ensure that all the agents' outputs asymptotically reach consensus to the optimal solution of the global objective function. Simulations demonstrate the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

FIDO2 authentication is starting to be applied in numerous web authentication services, aiming to replace passwords and their known vulnerabilities. However, this new authentication method has not been integrated yet with network authentication systems. In this paper, we introduce FIDO2CAP: FIDO2 Captive-portal Authentication Protocol. Our proposal describes a novel protocol for captive-portal network authentication using FIDO2 Authenticators as security keys and passkeys. For validating our proposal, we have developed a prototype of FIDO2CAP authentication in a mock scenario. Using this prototype, we performed a usability experiment with 15 real users. This work makes the first systematic approach for adapting network authentication to the new authentication paradigm relying on FIDO2 authentication. [ABSTRACT FROM AUTHOR]

In a world where digitization is rapidly advancing, the security and privacy of intra-domain communication within organizations are of critical concern. The imperative to secure communication channels among physical systems has led to the deployment of various security approaches aimed at fortifying networking protocols. However, these approaches have typically been designed to secure protocols individually, lacking a holistic perspective on the broader challenge of intra-domain communication security. This omission raises fundamental concerns about the safety and integrity of intra-domain environments, where all communication occurs within a single domain. As a result, this paper introduces HSM4SSL, a comprehensive solution designed to address the evolving challenges of secure data transmission in intra-domain environments. By leveraging hardware security modules (HSMs), HSM4SSL aims to utilize the Secure Socket Layer (SSL) protocol within intra-domain environments to ensure data confidentiality, authentication, and integrity. In addition, solutions proposed by academic researchers and in the industry have not addressed the issue in a holistic and integrative manner, as they only apply to specific types of environments or servers and do not utilize all cryptographic operations for robust security. Thus, HSM4SSL bridges this gap by offering a unified and comprehensive solution that includes certificate management, key management practices, and various security services. HSM4SSL comprises three layers to provide a standardized interaction between software applications and HSMs. A performance evaluation was conducted comparing HSM4SSL with a benchmark tool for cryptographic operations. The results indicate that HSM4SSL achieved 33% higher requests per second (RPS) compared to OpenSSL, along with a 13% lower latency rate. Additionally, HSM4SSL efficiently utilizes CPU and network resources, outperforming OpenSSL in various aspects. These findings highlight the effectiveness and reliability of HSM4SSL in providing secure communication within intra-domain environments, thus addressing the pressing need for enhanced security mechanisms. [ABSTRACT FROM AUTHOR]

The rapid development of mobile communication devices has brought challenges to wireless networks, where data packets are able to organize and maintain local area networks more freely without the constraints of wired devices. Scholars have developed diverse network protocols on how to ensure data transmission while maintaining its self-organizational nature. However, it is difficult for traditional network protocols to meet the needs of increasingly complex networks. In order to solve the problem that the better node set may not be selected when selecting the node set responsible for forwarding in the traditional OLSR protocol, a multi-objective optimized OLSR algorithm is proposed in this paper, which incorporating a new MPR mechanism and an improved NSGA-II algorithm. In the process of route discovery, the intermediate nodes responsible for forwarding packets are determined by the new MPR mechanism, and then the main parameters in the OLSR protocol are provided by the multi-objective optimization algorithm. Matlab was used to build a self-organizing network in this study. In addition, the conventional OLSR protocol, NSGA-II algorithm and multi-objective simulated annealing algorithm are selected to compare with the proposed algorithm. Simulation results show that the proposed algorithm can effectively reduce packet loss and end-to-end delay while obtaining better results in HV and Spacing, two multi-objective optimization result evaluation metrics. [ABSTRACT FROM AUTHOR]

This paper presents the implementation of a synchronous Structural Health Monitoring (SHM) framework utilizing wireless, low-cost, and off-the-shelf components. Vibration-based condition monitoring plays a crucial role in assessing the reliability of structural systems by detecting damage through changes in vibration parameters. The adoption of low-cost Micro-Electro-Mechanical Systems (MEMS) sensors in Wireless Sensor Networks (WSNs) has gained traction, emphasizing the need for precise time synchronization to schedule wake-up times of multiple sensor nodes for data collection. To address this challenge, our proposed method introduces a TCP/IP socket programming-based mimic broadcasting mechanism and a scalable sensing network controlled by a central gateway, leveraging the Raspberry Pi Python platform. The system operates using Internet of Things (IoT) concepts and adopts a star topology, where a packet is transmitted from the gateway to initiate measurements simultaneously on multiple sensor nodes. The sensor node comprises a MEMS accelerometer, a real time clock DS3231 module and Raspberry Pi Zero 2W (RPi0-2W), while the gateway employs a Raspberry Pi 4 (RPi4). To ensure accurate time synchronization, all Pi0-2W nodes were configured as Network Time Protocol (NTP) clients, synchronizing with an RPi4 server using chrony, the reliable implementation of the NTP. Through experimental evaluations, the system demonstrates its effectiveness and reliability in achieving initial time synchronization. This study addresses the challenge of achieving precise time alignment between sensor nodes through the utilization of the Dynamic Time Wrapping (DTW) method for Frequency Domain Decomposition (FDD) applications. The contribution of this research significantly enhances the field by improving the accuracy and reliability of time-aligned measurements, with a specific focus on utilizing low-cost sensors. By developing a practical and cost-effective SHM framework, this work advances the accessibility and scalability of structural health monitoring solutions, facilitating more widespread adoption and implementation in various engineering applications [ABSTRACT FROM AUTHOR]

This paper introduces a pioneering method for real-time image processing in electronic component assembly, revolutionizing quality control in manufacturing. By promptly capturing images from pick-and-place machines during the interval between component pick-up and mounting, defects are identified and promptly addressed in line. This proactive approach ensures that defective components are rejected before mounting, effectively preventing issues from ever occurring, thus significantly enhancing efficiency and reliability. Leveraging rapid network protocols such as gRPC and orchestration via Kubernetes, in conjunction with C++ programming and TensorFlow, this approach achieves an impressive average turnaround time of less than 5 milli-seconds. Rigorously tested on 20 operational production machines, it not only ensures adherence to IPC-A-610 and IPC-STD-J-001 standards but also optimizes production efficiency and reliability. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the author discusses various reports within the issue on topics including details of the papers presented at the 31st International Conference on Data Engineering, use of prototype system for text understanding, and the identification of network motifs.

This paper presents an in-depth study and analysis of X-ray inspection of basin insulators by wireless sensing technology. Aiming at the characteristics of low contrast and many kinds of noise in the basin insulator ray image, this paper proposes an X-ray basin insulator image denoising method based on improved 3D block matching. Using the RF microcontroller CC2530 chip as the core hardware and networked by ZigBee protocol, the sensor senses and collects various parameters and transmits this information to the monitoring end in real time through wireless. The method proposes an improved wavelet thresholding denoising method to overcome the pseudo-Gibbs phenomenon caused by the wavelet hard thresholding method in the 3D block matching algorithm cofiltering and retain more details of the image. Aiming at the ringing effect caused by the Wiener filtering method used in the three-dimensional block matching algorithm collaborative filtering, an improved Kalman filtering method based on anisotropic diffusion is proposed, which avoids the ringing effect, and has clear edges and complete details. An improved Kalman filtering method based on anisotropic diffusion is proposed to avoid the ringing effect, and the edges are clear, and the details are complete. The proposed method is a safe, efficient, accurate, and feasible method for detecting defects in basin insulators by combining X-ray and improved wireless image sensing technology to detect the internal equipment without disassembling or touching the GIS equipment. [ABSTRACT FROM AUTHOR]

Integrating the Internet of Things (IoT) and natural fiber-reinforced polymer composites (NFPCs) can revolutionize monitoring and maintaining composites. By incorporating sensors and wireless communication technology into the composites, real-time monitoring and predictive maintenance can be achieved. This review provides a comprehensive overview of the current state-of-the-art in the use of IoT for real-time monitoring and predictive maintenance of NFPCs. This paper covers the various types of sensors used, IoT networks and protocols employed, and data analysis techniques to detect potential issues and predict failures. This paper also highlights the benefits and challenges of using IoT for composite maintenance and this technology's future directions and potential applications. This review provides valuable insights for researchers, engineers, and practitioners in composites, the IoT, and predictive maintenance. [ABSTRACT FROM AUTHOR]

Stimulated by the increase in user demands and the development of intelligent driving, the automotive industry is pursuing high-bandwidth techniques, low-cost network deployment and deterministic data transmission. Time-sensitive networking (TSN) based on Ethernet provides a possible solution to these targets, which is arousing extensive attention from both academia and industry. We review TSN-related academic research papers published by major academic publishers and analyze research trends in TSN. This paper provides an up-to-date comprehensive survey of TSN-related standards, from the perspective of the physical layer, data link layer, network layer and protocol test. Then we classify intelligent driving products with TSN characteristics. With the consideration of more of the latest specified TSN protocols, we further analyze the minimum complete set of specifications and give the corresponding demo setup for the realization of TSN on automobiles. Open issues to be solved and trends of TSN are identified and analyzed, followed by possible solutions. Therefore, this paper can be an investigating basis and reference of TSN, especially for the TSN on automotive applications. [ABSTRACT FROM AUTHOR]

The rapid proliferation of Internet of Things (IoT) devices has raised significant concerns regarding the trustworthiness of IoT devices, which is becoming a crucial aspect of our daily lives. In this paper, we deal with this important aspect by taking into account Meshtastic, a dynamic mesh networking protocol that offers robustness and adaptability, important characteristics for the dynamic and heterogeneous IoT environment. LoRaWAN (Low-Range Wide Area Network), a low-power, long-range wireless communication standard, introduces energy efficiency and extends the reach of IoT networks, enabling secure communication over extended distances. To improve the trustworthiness of IoT devices, we present an integrated approach that leverages the strengths of Meshstastic's dynamic mesh networking capabilities and LoRa's low-power, long-range communication, along with the integration of a reputation model specifically designed for IoT. We evaluated the performance of the proposed solution through several simulations and real-world experiments. The results show that the devices' measured values of trust reflect the real behaviour of the devices. These findings underscore the viability and applicability of the Meshtastic protocol utilising LoRa technology as a pivotal step towards establishing resilient and trustworthy IoT infrastructures in the face of evolving security challenges. [ABSTRACT FROM AUTHOR]

Having access to the specifications of network protocols is essential for several reasons in IT security. When the specifications are not known, one may turn to protocol reverse engineering methods to reconstruct these, typically by analysing recorded network traffic or inspecting an executable that implements the protocol. First, the format and structure of the messages need to be recovered, then the state machine of the protocol itself. Over the years, several solutions have been proposed for both tasks. As a consequence, picking the right solution for a given scenario is often a complex problem that involves evaluating and comparing various solutions. In this paper, we review the current means of evaluating the performance of protocol state machine reverse engineering methods. To help alleviate the shortcomings of the current methodology, we propose two new metrics of performance to be measured: correctness and completeness of output for partial runs (when runtime is bounded). These, combined with previously used metrics should make it easier to pick the most ideal choice for a given use case. We also propose the examination of cases where the algorithms have to work with incomplete or inaccurate syntactical information. We showcase how these new metrics and related information may be useful for the evaluation and comparison of various algorithms by applying these new methods to evaluate the performance of a recent protocol state machine reverse engineering method. [ABSTRACT FROM AUTHOR]

The establishment of the Underwater Internet of Things (UIoT) and the realization of interconnection between heterogeneous underwater intelligent devices are urgent global challenges. Underwater acoustic networking is the most suitable technology to achieve UIoT for medium to long ranges. This paper presents an underwater Wi-Fi network, called uw-WiFi, that utilizes a master–slave mode architecture. uw-WiFi is dedicated to solving the problem of underwater acoustic networking with limited coverage range and number of nodes. To ensure the reliability of different types of data in the network, a reliable segmentation transmission protocol based on data type is designed. Additionally, on-demand scheduling based on the reservation MAC protocol is developed to solve the channel resource sharing problem. The uw-WiFi system has undergone shallow sea tests, and the experimental results demonstrate that the uw-WiFi network is capable of achieving a network throughput of 500 bps or higher, indicating superior network performance. [ABSTRACT FROM AUTHOR]

The objective of forensic analysis of cloud computing traffic is to identify any suspicious or malicious behavior in network activities within cloud computing settings. This includes examining network connections, data transfer patterns, and data payloads. To effectively analyze cloud computing traffic associated with IoT devices, it is imperative to understand cloud infrastructure and network protocols. The prevalence of IoT devices in cloud environments necessitates efficient forensic analysis techniques to address security incidents. Considering the distinctive attributes of IoT devices and the decentralized nature of cloud environments, this paper investigates the challenges and considerations related to forensic analysis in cloud computing systems incorporating IoT devices. The framework we present includes comprehensive guidelines about data collection, preservation, analysis, and forensic analysis in IoT-based cloud computing systems. As well as providing practical case studies, we demonstrate how our framework works in real-world scenarios involving IoT-based cloud computing. Providing tangible solutions to the forensic challenges specific to IoT-based cloud computing systems, the findings of this research contribute significantly to a better understanding of these challenges. [ABSTRACT FROM AUTHOR]

A botnet is a collection of Internet-connected computers that have been suborned and are controlled externally for malicious purposes. Concomitant with the growth of the Internet of Things (IoT), botnets have been expanding to use IoT devices as their attack vectors. IoT devices utilise specific protocols and network topologies distinct from conventional computers that may render detection techniques ineffective on compromised IoT devices. This paper describes experiments involving the acquisition of several traditional botnet detection techniques, BotMiner, BotProbe, and BotHunter, to evaluate their capabilities when applied to IoT-based botnets. Multiple simulation environments, using internally developed network traffic generation software, were created to test these techniques on traditional and IoT-based networks, with multiple scenarios differentiated by the total number of hosts, the total number of infected hosts, the botnet command and control (CnC) type, and the presence of aberrant activity. Externally acquired datasets were also used to further test and validate the capabilities of each botnet detection technique. The results indicated, contrary to expectations, that BotMiner and BotProbe were able to detect IoT-based botnets—though they exhibited certain limitations specific to their operation. The results show that traditional botnet detection techniques are capable of detecting IoT-based botnets and that the different techniques may offer capabilities that complement one another. [ABSTRACT FROM AUTHOR]

The Controller Area Network with Flexible Data-Rate (CAN-FD) bus is the predominant in-vehicle network protocol, responsible for transmitting crucial application semantic signals. Due to the absence of security measures, CAN-FD is vulnerable to numerous cyber threats, particularly those altering its authentic physical values. This paper introduces Physical Semantics-Enhanced Anomaly Detection (PSEAD) for CAN-FD networks. Our framework effectively extracts and standardizes the genuine physical meaning features present in the message data fields. The implementation involves a Long Short-Term Memory (LSTM) network augmented with a self-attention mechanism, thereby enabling the unsupervised capture of temporal information within high-dimensional data. Consequently, this approach fully exploits contextual information within the physical meaning features. In contrast to the non-physical semantics-aware whole frame combination detection method, our approach is more adept at harnessing the physical significance inherent in each segment of the message. This enhancement results in improved accuracy and interpretability of anomaly detection. Experimental results demonstrate that our method achieves a mere 0.64% misclassification rate for challenging-to-detect replay attacks and zero misclassifications for DoS, fuzzing, and spoofing attacks. The accuracy has been enhanced by over 4% in comparison to existing methods that rely on byte-level data field characterization at the data link layer. [ABSTRACT FROM AUTHOR]

The industrial control and automation sector has invested in the development and standardization of new wireless (WirelessHART, ISA 100.11a, and WIA-PA) and wired (Profibus/Profinet, Modbus, and LonWORK) solutions aimed at automating processes to support standard monitoring and control functions from the perspective of addressing critical applications, as well as those integrated within the Building Internet of Things (BIoT) concept. Distributed data acquisition and control systems allow modern installations to monitor and control devices remotely. Various network protocols have been proposed to specify communication formats between a client/gateway and server devices, with Modbus being an example that has been widely implemented in the latest industrial electrical installations. The main contribution made in this paper concerns the completion of the Modbus Extension (ModbusE) specifications for the server station in the classical Modbus communication architecture, as well as their implementation and testing in an STM32F4 kit. A general-purpose control architecture is proposed for BIoT sector, comprising both intelligent touch switches and communication protocols of which the Modbus protocol is used extensively for the monitoring and control part, especially between clients, smart switches, and devices. The specific contributions concern the presentation of a scientific and practical implementation of improved specifications and their integration as software modules on ModbusE protocol server stations. A client station with a VirtualComm USB PC connection is also implemented in the lab to test the operation of the proposed server with specific Modbus applications. [ABSTRACT FROM AUTHOR]

With the development of IoT technology and the improvement of people's living standards, smart locks have become prevalent in ordinary households. However, for enterprises and institutions requiring unified management and control capabilities for networked smart locks, the technology has been relatively lagging, and there is a lack of established brands. Existing products are mostly based on wireless low-power network protocols, leading to common issues of low stability, limited network scalability, and high maintenance efforts. This paper addresses the problems existing in current networked lock systems and leverages the centralized office characteristics of enterprises and institutions. By integrating IoT technology and Power over Ethernet- (PoE-) centralized power supply, we have developed a networked smart lock system with unified management and control capabilities. This system is primarily designed for applications in offices, collective dormitories, and hotels of enterprises, institutions, and schools. The test results demonstrate that even with a scale of over tens of thousands of networked locks under unified control, the system maintains fast response times and low packet loss rates. It also incorporates energy-saving and environmentally friendly features, as well as easy automatic updates, facilitating convenient postdeployment maintenance. Thus, it effectively meets the requirements for centralized management and control in different application scenarios with centralized use of rooms. In conclusion, this research has led to the development of a unified management and control networked smart lock system, which is well suited for enterprises, institutions, and schools. Its scalability, speed, energy efficiency, and ease of maintenance make it an excellent solution for various application scenarios requiring centralized management and control of access. [ABSTRACT FROM AUTHOR]

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As an emerging network paradigm, the space–air–ground integrated network (SAGIN) has garnered attention from academia and industry. That is because SAGIN can implement seamless global coverage and connections among electronic devices in space, air, and ground spaces. Additionally, the shortage of computing and storage resources in mobile devices greatly impacts the quality of experiences for intelligent applications. Hence, we plan to integrate SAGIN as an abundant resource pool into mobile edge computing environments (MECs). To facilitate efficient processing, we need to solve the optimal task offloading decisions. In contrast to existing MEC task offloading solutions, we have to face some new challenges, such as the fluctuation of processing capabilities for edge computing nodes, the uncertainty of transmission latency caused by heterogeneous network protocols, the uncertain amount of uploaded tasks during a period, and so on. In this paper, we first describe the task offloading decision problem in environments characterized by these new challenges. However, we cannot use standard robust optimization and stochastic optimization methods to obtain optimal results under uncertain network environments. In this paper, we propose the 'condition value at risk-aware distributionally robust optimization' algorithm for task offloading, denoted as RADROO, to solve the task offloading decision problem. RADROO combines the distributionally robust optimization and the condition value at risk model to achieve optimal results. We evaluated our approach in simulated SAGIN environments, considering confidence intervals, the number of mobile task offloading instances, and various parameters. We compare our proposed RADROO algorithm with state-of-the-art algorithms, such as the standard robust optimization algorithm, the stochastic optimization algorithm, the DRO algorithm, and the Brute algorithm. The experimental results show that RADROO can achieve a sub-optimal mobile task offloading decision. Overall, RADROO is more robust than others to the new challenges mentioned above in SAGIN. [ABSTRACT FROM AUTHOR]