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

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

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

Wireless Sensor Networks (WSNs) have emerged as an efficient solution for numerous real-time applications, attributable to their compactness, cost-effectiveness, and ease of deployment. The rapid advancement of 5G technology and mobile edge computing (MEC) in recent years has catalyzed the transition towards large-scale deployment of WSN devices. However, the resulting data proliferation and the dynamics of communication environments introduce new challenges for WSN communication: (1) ensuring robust communication in adverse environments and (2) effectively alleviating bandwidth pressure from massive data transmission. In response to the aforementioned challenges, this paper proposes a semantic communication solution. Specifically, considering the limited computational and storage resources of WSN devices, we propose a flexible Attention-based Adaptive Coding (AAC) module. This module integrates window and channel attention mechanisms, dynamically adjusts semantic information in response to the current channel state, and facilitates adaptation of a single model across various Signal-to-Noise Ratio (SNR) environments. Furthermore, to validate the effectiveness of this approach, the paper introduces an end-to-end Joint Source Channel Coding (JSCC) scheme for image semantic communication, employing the AAC module. Experimental results demonstrate that the proposed scheme surpasses existing deep JSCC schemes across datasets of varying resolutions; furthermore, they validate the efficacy of the proposed AAC module, which is capable of dynamically adjusting critical information according to the current channel state. This enables the model to be trained over a range of SNRs and obtain better results. [ABSTRACT FROM AUTHOR]

With the advent of 6G Narrowband IoT (NB-IoT) technology, IoT security faces inevitable challenges due to the application requirements of Massive Machine-Type Communications (mMTCs). In response, a 6G base station (gNB) and User Equipment (UE) necessitate increased capacities to handle a larger number of connections while maintaining reasonable performance during operations. To address this developmental trend and overcome associated technological hurdles, this paper proposes a hardware-accelerated and software co-designed mechanism to support streaming data transmissions and secure zero-trust inter-endpoint communications. The proposed implementations aim to offload processing efforts from micro-processors and enhance global system operation performance by hardware and software co-design in endpoint communications. Experimental results demonstrate that the proposed secure mechanism based on the use of non-repeating keys and implemented in FPGA, can save 85.61%, 99.71%, and 95.68% of the micro-processor's processing time in key block generations, non-repeating checks, and data block transfers, respectively. [ABSTRACT FROM AUTHOR]

This paper begins by exploring the challenge of event-triggered state estimations in nonlinear systems, grappling with packet dropout and correlated noise. A communication mechanism is introduced that determines whether to transmit measurement values based on whether event-triggered conditions are violated, thereby minimizing redundant communication data. In designing the filter, noise decorrelation is initially conducted, followed by the integration of the event-triggered mechanism and the unreliable network transmission system for state estimator development. Subsequently, by combining the three-degree spherical–radial cubature rule, the numerical implementation steps of the proposed state estimation framework are outlined. The performance estimation analysis highlights that by adjusting the event-triggered threshold appropriately, the estimation performance and transmission rate can be effectively balanced. It is established that when there is a lower bound on the packet dropout rate, the covariance matrix of the state estimation error remains bounded, and the stochastic stability of the state estimation error is also confirmed. Ultimately, the algorithm and conclusions that are proposed in this paper are validated through a simulation example of a target tracking system. [ABSTRACT FROM AUTHOR]

A digitizer is considered one of the fundamental components of an earthquake monitoring system. In this paper, we design and implement a high accuracy seismic digitizer. The implemented digitizer consists of several blocks, i.e., the analog-to-digital converter (ADC), GPS receiver, and microprocessor. Three finite impulse response (FIR) filters are used to decimate the sampling rate of the input seismic data according to user needs. A graphical user interface (GUI) has been designed for enabling the user to monitor the seismic waveform in real time, and process and adjust the parameters of the acquisition unit. The system casing is designed to resist harsh conditions of the environment. The prototype can represent the three component sensors data in the standard MiniSEED format. The digitizer stream seismic data from the remote station to the main center is based on TCP/IP connection. This protocol ensures data transmission without any losses as long as the data still exist in the ring buffer. The prototype was calibrated by real field testing. The prototype digitizer is integrated with the Egyptian National Seismic Network (ENSN), where a commercial instrument is already installed. Case studies shows that, for the same event, the prototype station improves the solution of the ENSN by giving accurate timing and seismic event parameters. Field test results shows that the event arrival time and the amplitude are approximately the same between the prototype digitizer and the calibrated digitizer. Furthermore, the frequency contents are similar between the two digitizers. Therefore, the prototype digitizer captures the main seismic parameters accurately, irrespective of noise existence. [ABSTRACT FROM AUTHOR]

Survivability is a critical concern in WSNs, heavily influenced by energy efficiency. Addressing severe energy constraints in WSNs requires solutions that meet application goals while prolonging network life. This paper presents an Energy Optimization Approach (EOAMRCL) for WSNs, integrating the Grey Wolf Optimization (GWO) for enhanced performance. EOAMRCL aims to enhance energy efficiency by selecting the optimal duty-cycle schedule, transmission power, and routing paths. The proposed approach employs a centralized strategy using a hierarchical network architecture. During the cluster formation phase, an objective function, augmented with GWO, determines the ideal cluster heads (CHs). The routing protocol then selects routes with minimal energy consumption for data transmission to CHs, using transmission power as a metric. In the transmission phase, the MAC layer forms a duty-cycle schedule based on cross-layer routing information, enabling nodes to switch between active and sleep modes according to their network allocation vectors (NAVs). This process is further optimized by an enhanced CSMA/CA mechanism, which incorporates sleep/activate modes and pairing nodes to alternate between active and sleep states. This integration reduces collisions, improves channel assessment accuracy, and lowers energy consumption, thereby enhancing overall network performance. EOAMRCL was evaluated in a MATLAB environment, demonstrating superior performance compared with EEUC, DWEHC, and CGA-GWO protocols, particularly in terms of network lifetime and energy consumption. This highlights the effectiveness of integrating GWO and the updated CSMA/CA mechanism in achieving optimal energy efficiency and network performance. [ABSTRACT FROM AUTHOR]

Due to the ability of optical modulators to achieve rapid modulation of optical signals, meeting the demands of high-speed data transmission, modulators based on different novel nanomaterials have become one of the research hotspots over the past dacade. Recently, TiN/Ti3C2 heterojunction exhibits highly efficient thermo-optic performance and extremely strong stability. Therefore, we have demonstrated an all-optical modulator based on the principle of Michelson interference and the thermo-optic effect in this paper. The modulator employs a TiN/Ti3C2 heterojunction-coated microfiber (THM) and further demonstrates its ability to generate phase shifts through an ASE light source. The modulator, with a phase shift slope of 0.025π/mW, can also convert the phase shifts of signal light into amplitude modulation through Michelson interference. The fixed signal light wavelength is 1552.09 nm, and the modulation depth is stable at about 26.4 dB within a wavelength detuning range of −10 to 6 nm; The waveforms of signal light at modulation rates of 500 Hz, 1000 Hz, 2000 Hz, and 3000 Hz were tested, and a 3 dB modulation bandwidth of 2 kHz was measured. The all-optical modulator based on THM has the advantages of high efficiency and stability and has broad application prospects in the fields of all-optical signal processing and high-speed optical communication. [ABSTRACT FROM AUTHOR]

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

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

Encryption is a fundamental security measure to safeguard data during transmission to ensure confidentiality while at the same time posing a great challenge for traditional packet and traffic inspection. In response to the proliferation of diverse network traffic patterns from Internet-of-Things devices, websites, and mobile applications, understanding and classifying encrypted traffic are crucial for network administrators, cybersecurity professionals, and policy enforcement entities. This paper presents a comprehensive survey of recent advancements in machine-learning-driven encrypted traffic analysis and classification. The primary goals of our survey are two-fold: First, we present the overall procedure and provide a detailed explanation of utilizing machine learning in analyzing and classifying encrypted network traffic. Second, we review state-of-the-art techniques and methodologies in traffic analysis. Our aim is to provide insights into current practices and future directions in encrypted traffic analysis and classification, especially machine-learning-based analysis. [ABSTRACT FROM AUTHOR]

Energy efficiency and data reliability are important indicators to measure network performance in wireless sensor networks. In existing research schemes of routing protocols, the impact of node coverage on the network is often ignored, and the possibility that multiple sensor nodes may sense the same spatial point is not taken into account, which results in a waste of network resources, especially in large-scale networks. Apart from that, the blindness of geographic routing in data transmission has been troubling researchers, which means that the nodes are unable to determine the validity of data transmission. In order to solve the above problems, this paper innovatively combines the routing protocol with the coverage control technique and proposes the node collaborative scheduling algorithm, which fully considers the correlation characteristics between sensor nodes to reduce the number of active working nodes and the number of packets generated, to further reduce energy consumption and network delay and improve packet delivery rate. In order to solve the problem of unreliability of geographic routing, a highly reliable link detection and repair scheme is proposed to check the communication link status and repair the invalid link, which can greatly improve the packet delivery rate and throughput of the network, and has good robustness. A large number of experiments demonstrate the effectiveness and superiority of our proposed scheme and algorithm. [ABSTRACT FROM AUTHOR]

In today's health-monitoring applications, there is a growing demand for wireless and wearable acquisition platforms capable of simultaneously gathering multiple bio-signals from multiple body areas. These systems require well-structured software architectures, both to keep different wireless sensing nodes synchronized each other and to flush collected data towards an external gateway. This paper presents a quantitative analysis aimed at validating both the wireless synchronization task (implemented with a custom protocol) and the data transmission task (implemented with the BLE protocol) in a prototype wearable monitoring platform. We evaluated seven frequencies for exchanging synchronization packets (10 Hz, 20 Hz, 30 Hz, 40 Hz, 50 Hz, 60 Hz, 70 Hz) as well as two different BLE configurations (with and without the implementation of a dynamic adaptation of the BLE Connection Interval parameter). Additionally, we tested BLE data transmission performance in five different use case scenarios. As a result, we achieved the optimal performance in the synchronization task (1.18 ticks as median synchronization delay with a Min-Max range of 1.60 ticks and an Interquartile range (IQR) of 0.42 ticks) when exploiting a synchronization frequency of 40 Hz and the dynamic adaptation of the Connection Interval. Moreover, BLE data transmission proved to be significantly more efficient with shorter distances between the communicating nodes, growing worse by 30.5% beyond 8 m. In summary, this study suggests the best-performing network configurations to enhance the synchronization task of the prototype platform under analysis, as well as quantitative details on the best placement of data collectors. [ABSTRACT FROM AUTHOR]

Intelligent compaction (IC) has emerged as a breakthrough technology that utilizes advanced sensing, data transmission, and control systems to optimize asphalt pavement compaction quality and efficiency. However, accurate assessment of compaction status remains challenging under real construction conditions. This paper reviewed recent progress and applications of smart sensors and machine learning (ML) to address existing limitations in IC. The principles and components of various advanced sensors deployed in IC systems were introduced, including SmartRock, fiber Bragg grating, and integrated circuit piezoelectric acceleration sensors. Case studies on utilizing these sensors for particle behavior monitoring, strain measurement, and impact data collection were reviewed. Meanwhile, common ML algorithms including regression, classification, clustering, and artificial neural networks were discussed. Practical examples of applying ML to estimate mechanical properties, evaluate overall compaction quality, and predict soil firmness through supervised and unsupervised models were examined. Results indicated smart sensors have enhanced compaction monitoring capabilities but require robustness improvements. ML provides a data-driven approach to complement traditional empirical methods but necessitates extensive field validation. Potential integration with digital construction technologies such as building information modeling and augmented reality was also explored. In conclusion, leveraging emerging sensing and artificial intelligence presents opportunities to optimize the IC process and address key challenges. However, cooperation across disciplines will be vital to test and refine technologies under real-world conditions. This study serves to advance understanding and highlight priority areas for future research toward the realization of IC's full potential. [ABSTRACT FROM AUTHOR]

Subsurface exploration of ice-covered planets and moons presents communications challenges because of the need to communicate through kilometers of ice. The objective of this task is to develop the capability to wirelessly communicate through kilometers of ice and thus complement the potentially failure-prone tethers deployed behind an ice-penetrating probe on Ocean Worlds. In this paper, the preliminary work on the development of wireless deep-ice communication is presented and discussed. The communication test and acoustic attenuation measurements in ice have been made by embedding acoustic transceivers in glacial ice at the Matanuska Glacier, Anchorage, Alaska. Field test results show that acoustic communication is viable through ice, demonstrating the transmission of data and image files in the 13–18 kHz band over 100 m. The results suggest that communication over many kilometers of ice thickness could be feasible by employing reduced transmitting frequencies around 1 kHz, though future work is needed to better constrain the likely acoustic attenuation properties through a refrozen borehole. [ABSTRACT FROM AUTHOR]

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

In the evolving landscape of Industry 4.0, the integration of advanced wireless technologies into manufacturing processes holds the promise of unprecedented connectivity and efficiency. In particular, the data transmission in a heavy industry environment needs stable connectivity with mobile operators. This paper deals with the performance study of 4G and 5G mobile signal coverage within a complex factory environment. For this purpose, a cost-effective and portable measurement setup was realized and used to provide long-term measurement campaigns monitoring and recording several key parameter indicators (KPIs) in 4G/5G downlink and upload. To support the reproducibility of the provided study and other research activities, the measured dataset is publicly available for download. Among others findings, the obtained results show how the performance of 4G/5G is influenced by a heavy industry environment and of the time of day on the network load. [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]

This article presents the hardware and software architectures used to implement the Modbus Extension (ModbusE) IIoT gateway, the performance of the acquisition cycle at the PRU real-time programmable core level, the acquisition cycle communication flow—dispatcher—OPC UA server (Linux)—OPC UA client (Windows) as well as the performance analysis of data communications between the IIoT ModbusE gateway and the OPC UA client (Windows). In order to be able to implement both the ModbusE acquisition cycle and the OPC UA server, the BeagleBone Black Board was chosen as the hardware platform. This board uses the Sitara AM335x processor (Texas Instruments (TI), Dallas, TX, USA) from Texas Instruments. Thus, the acquisition cycle was implemented on the PRU0 real-time core, and the OPC UA server, running under the Linux operating system, was implemented on the ARM Cortex A8 processor. From the analysis of the communication speed of the messages between the OPC UA client and the ModbusE servers, it was found that the ModbusE acquisition cycle speed was higher than the acquisition speed of the OPC UA client. [ABSTRACT FROM AUTHOR]

This study presents a novel approach to enhancing indoor navigation in crowded multi-terminal airports using visible light communication (VLC) technology. By leveraging existing luminaires as transmission points, encoded messages are conveyed through modulated light signals to provide location-specific guidance. The objectives are to facilitate navigation, optimize routes, and improve system performance through Edge/Fog integration. The methodology includes the use of tetrachromatic LED-equipped luminaires with On–Off Keying (OOK) modulation and a mesh cellular hybrid structure. Detailed airport modeling and user analysis (pedestrians and luggage/passenger carriers) equipped with PINPIN optical sensors are conducted. A VLC-specific communication protocol with coding and decoding techniques ensures reliable data transmission, while wayfinding algorithms offer real-time guidance. The results show effective data transmission and localization, enabling self-localization, travel direction inference, and route optimization. Agent-based simulations demonstrate improved traffic control, with analyses of user halting and average speed. This approach provides reliable indoor navigation independent of GPS signals, enhancing accessibility and convenience for airport users. The integration of VLC with Edge/Fog architecture ensures efficient movement through complex airport layouts. [ABSTRACT FROM AUTHOR]

The Internet of Things faces significant security challenges, particularly in device authentication. Traditional methods of PUF-based authentication protocols do not fully address IoT's unique security needs and resource constraints. Existing solutions like Identity-Based Encryption with Physically Unclonable Functions enhance security but still struggle with protecting data during transmission. We show a new protocol that leverages PUFs for device authentication by utilizing Paillier homomorphic encryption or the plaintext equality test to enhance security. Our approach involves encrypting both the challenge–response pairs (CRPs) using Paillier homomorphic encryption scheme or ElGamal encryption for plaintext equality testing scheme. The verifier does not need access to the plaintext CRPs to ensure that sensitive data remain encrypted at all times and our approach reduces the computational load on IoT devices. The encryption ensures that neither the challenge nor the response can be deciphered by potential adversaries who obtain them during the transmission. The homomorphic property of the Paillier scheme or plaintext equality testing scheme allows a verifier to verify device authenticity without decrypting the CRPs, preserving privacy and reducing the computational load on IoT devices. Such an approach to encrypting both elements of the CRP provides resistance against CRP disclosure, machine learning attacks, and impersonation attacks. We validate the scheme through security analysis against various attacks and evaluate its performance by analyzing the computational overhead and the communication overhead. Comparison of average computational and communication time demonstrates Paillier scheme achieves approximately 99% reduction while the plaintext equality test achieves approximately 94% reduction between them. [ABSTRACT FROM AUTHOR]

Quantum mechanical phenomena are revolutionizing classical engineering fields such as signal processing or cryptography. When randomness plays an important role, like in cryptography where random bit sequences guarantee certain levels of security, quantum mechanical phenomena allow new ways of generating random bit sequences. Such sequences have a lot of applications in the communication sector, e.g., regarding data transmission, simulation, sensors or radars, and beyond. They can be generated deterministically (e.g., by using polynomials, resulting in pseudo-random sequences) or in a non-deterministic way (e.g., by using physical noise sources like external devices or sensors, resulting in random sequences). Important characteristics of such binary sequences can be modelled by gap processes in conjunction with the probability theory. Recently, all-optical approaches have attracted a lot of research interest. In this work, an adaptation of the quantum key distribution setup is utilized for generating randomised bit sequences. The simulation results show that all-optically generated sequences very well resemble the theoretically ideal probability density characteristic. Additionally, an experimental optical setup is developed that confirms the simulation results. Furthermore, m-sequences show very promising results as well as Gold sequences. Additionally, the level of burstiness, i.e., the distribution of ones and zeros throughout the sequence, is studied for the different sequences. The results enable the finding that generator polynomials with concentrated non-zero coefficients lead to more bursty bit sequences. [ABSTRACT FROM AUTHOR]

Autonomous systems are becoming increasingly relevant in our everyday life. The transportation field is no exception and the smart cities concept raises new tasks and challenges for the development of autonomous systems development which has been progressively researched in literature. One of the main challenges is communication between different traffic objects. For instance, a mobile robot system can work as a standalone autonomous system reacting to a static environment and avoiding obstacles to reach a target. Nevertheless, more intensive communication and decision making is needed when additional dynamic objects and other autonomous systems are present in the same working environment. Traffic is a complicated environment consisting of vehicles, pedestrians, and various infrastructure elements. To apply autonomous systems in this kind of environment it is important to integrate object localization and to guarantee functional and trustworthy communication between each element. To achieve this, various sensors, communication standards, and equipment are integrated via the application of sensor fusion and AI machine learning methods. In this work review of vehicular communication systems is presented. The main focus is the researched sensors, communication standards, devices, machine learning methods, and vehicular-related data to find existing gaps for future vehicular communication system development. In the end, discussion and conclusions are presented. [ABSTRACT FROM AUTHOR]

The sensors used in the Internet of Medical Things (IoMT) network run on batteries and need to be replaced, replenished or should use energy harvesting for continuous power needs. Additionally, there are mechanisms for better utilization of battery power for network longevity. IoMT networks pose a unique challenge with respect to sensor power replenishment as the sensors could be embedded inside the subject. A possible solution could be to reduce the amount of sensor data transmission and recreate the signal at the receiving end. This article builds upon previous physiological monitoring studies by applying new decision tree-based regression models to calculate the accuracy of reproducing data from two sets of physiological signals transmitted over cellular networks. These regression analyses are then executed over three different iteration varieties to assess the effect that the number of decision trees has on the efficiency of the regression model in question. The results indicate much lower errors as compared to other approaches indicating significant saving on the battery power and improvement in network longevity. [ABSTRACT FROM AUTHOR]

The healthcare industry went through reformation by integrating the Internet of Medical Things (IoMT) to enable data harnessing by transmission mediums from different devices, about patients to healthcare staff devices, for further analysis through cloud-based servers for proper diagnosis of patients, yielding efficient and accurate results. However, IoMT technology is accompanied by a set of drawbacks in terms of security risks and vulnerabilities, such as violating and exposing patients' sensitive and confidential data. Further, the network traffic data is prone to interception attacks caused by a wireless type of communication and alteration of data, which could cause unwanted outcomes. The advocated scheme provides insight into a robust Intrusion Detection System (IDS) for IoMT networks. It leverages a honeypot to divert attackers away from critical systems, reducing the attack surface. Additionally, the IDS employs an ensemble method combining Logistic Regression and K-Nearest Neighbor algorithms. This approach harnesses the strengths of both algorithms to improve attack detection accuracy and robustness. This work analyzes the impact, performance, accuracy, and precision outcomes of the used model on two IoMT-related datasets which contain multiple attack types such as Man-In-The-Middle (MITM), Data Injection, and Distributed Denial of Services (DDOS). The yielded results showed that the proposed ensemble method was effective in detecting intrusion attempts and classifying them as attacks or normal network traffic, with a high accuracy of 92.5% for the first dataset and 99.54% for the second dataset and a precision of 96.74% for the first dataset and 99.228% for the second dataset. [ABSTRACT FROM AUTHOR]

The large amount of sampled data in coherent phase-sensitive optical time-domain reflectometry (Φ-OTDR) brings heavy data transmission, processing, and storage burdens. By using the comparator combined with undersampling, we achieve simultaneous reduction of sampling rate and sampling resolution in hardware, thus greatly decreasing the sampled data volume. But this way will inevitably cause the deterioration of detection signal-to-noise ratio (SNR) due to the quantization noise's dramatic increase. To address this problem, denoising the demodulated phase signals using compressed sensing, which exploits the sparsity of spectrally sparse vibration, is proposed, thereby effectively enhancing the detection SNR. In experiments, the comparator with a sampling parameter of 62.5 MS/s and 1 bit successfully captures the 80 MHz beat signal, where the sampled data volume per second is only 7.45 MB. Then, when the piezoelectric transducer's driving voltage is 1 Vpp, 300 mVpp, and 100 mVpp respectively, the SNRs of the reconstructed 200 Hz sinusoidal signals are respectively enhanced by 23.7 dB, 26.1 dB, and 28.7 dB by using compressed sensing. Moreover, multi-frequency vibrations can also be accurately reconstructed with a high SNR. Therefore, the proposed technique can effectively enhance the system's performance while greatly reducing its hardware burden. [ABSTRACT FROM AUTHOR]

A design method for an ultra-wideband coplanar-stripline-based vertical transition that can be used for ultra-high-speed digital interfaces is proposed. A conventional via structure, based on a differential line (DL), inherently possesses performance limitations (<10 GHz) due to difficulties in maintaining constant line impedance and smooth electric field transformation, in addition to the effects of signal skews, FR4 fiber weave, and unbalanced EM interferences. DL-based digital interfaces may not meet the demands of ultra-high-speed digital data transmission required for the upcoming 6G communications. The use of a coplanar stripline (CPS), a type of planar balanced line (BL), for the vertical transition, along with the ultra-wideband DL-to-CPS transition, mostly removes the inherent and unfavorable issues of the DL and enables ultra-high-speed digital data transmission. The design process of the transition is simplified using the analytical design formulas, derived using the conformal mapping method, of the transition. The characteristic line impedances of the transition are calculated and found to be in close agreement with the results obtained from EM simulations. Utilizing these results, the CPS-based vertical transition, maintaining the characteristic line impedance of 100 Ω, is designed and fabricated. The measured results confirm its ultra-wideband characteristics, with a maximum of 1.6 dB insertion loss and more than 10 dB return loss in the frequency range of DC to 30 GHz. Therefore, the proposed CPS-based vertical transition offers a significantly wider frequency bandwidth, i.e., more than three times that of conventional DL-based via structures. [ABSTRACT FROM AUTHOR]

In visible light communication (VLC), the precise latency evaluation of wireless access networks and the efficient forwarding strategy of core networks are the crux for end-to-end reliability provisioning. Leveraging martingale theory, an elegant latency-bounded reliability analysis framework is studied for the VLC network. Considering the characteristic that VLC links are easy to block, the martingale of the service process is constructed. Based on the time shift features, the martingale process related to latency is proposed for the VLC system, which models the influence of entanglement between aggregate arrivals and random service on latency. A stopping time event about latency is defined. Renting the stopping time theory, a tight upper bound of the unreliability with regard to latency is derived. In the core network, a dynamic forward backhaul framework is proposed, which uses the relay selection algorithm based on back-pressure theory to improve data transmission quality. The theoretical latency-bounded reliability matches the simulation results well, which verifies the effectiveness of the proposed analysis framework, and the proposed relay selection algorithm can also improve network performance under data-intensive transmission. [ABSTRACT FROM AUTHOR]

Vehicular ad hoc networks (VANETs) use multiple channels to communicate using wireless access in vehicular environment (WAVE) standards to provide a variety of vehicle-related applications. The current IEEE 802.11p WAVE communication channel structure is composed of one control channel (CCH) and several service channels (SCHs). SCHs are used for non-safety data transmission, while the CCH is used for broadcasting beacons, control, and safety. WAVE devices transmit data that alternate between CCHs and SCHs, and each channel is active for a duration called the CCH interval (CCHI) and SCH interval (SCHI), respectively. Currently, both intervals are fixed at 50 ms. However, fixed-length intervals cannot effectively respond to dynamically changing traffic loads. Additionally, when many vehicles are simultaneously using the limited channel resources for data transmission, the network performance significantly degrades due to numerous packet collisions. Herein, we propose an adaptive resource allocation technique for efficient data transmission. The technique dynamically adjusts the SCHI and CCHI to improve network performance. Moreover, to reduce data collisions and optimize the network's backoff distribution, the proposed scheme applies reinforcement learning (RL) to provide an intelligent channel access algorithm. The simulation results demonstrate that the proposed scheme can ensure high throughputs and low transmission delays. [ABSTRACT FROM AUTHOR]

As the Internet of Things (IoT) becomes more widespread, wearable smart systems will begin to be used in a variety of applications in people's daily lives, not only requiring the devices to have excellent flexibility and biocompatibility, but also taking into account redundant data and communication delays due to the use of a large number of sensors. Fortunately, the emerging paradigms of near-sensor and in-sensor computing, together with the proposal of flexible neuromorphic devices, provides a viable solution for the application of intelligent low-power wearable devices. Therefore, wearable smart systems based on new computing paradigms are of great research value. This review discusses the research status of a flexible five-sense sensing system based on near-sensor and in-sensor architectures, considering material design, structural design and circuit design. Furthermore, we summarize challenging problems that need to be solved and provide an outlook on the potential applications of intelligent wearable devices. [ABSTRACT FROM AUTHOR]

This study enhances infrared communication security in nuclear power plants' secondary systems, addressing the risk of mechanical and cyber failures. A novel random address generator, employing an innovative S-box, was developed to secure IoT sensor data transmissions to gateway nodes, mitigating eavesdropping, interference, and replay attacks. We introduced a structured IR communication protocol, generating unique, encrypted addresses to prevent unauthorized access. Key-dependent S-boxes, based on a compound chaotic map system, significantly improved encryption, increasing data transmission randomness and uniqueness. Entropy analysis and reduced duplicated addresses confirmed the effectiveness of our method, with the Hash-CCM algorithm showing the highest entropy and fewest duplicates. Integrating advanced cryptographic techniques into IR systems significantly enhances nuclear power plants' security, contributing to the protection of critical infrastructure from cyber threats and ensuring operational integrity. [ABSTRACT FROM AUTHOR]

A Hybrid LiFi and WiFi network (HLWNet) integrates the rapid data transmission capabilities of Light Fidelity (LiFi) with the extensive connectivity provided by Wireless Fidelity (WiFi), resulting in significant benefits for wireless data transmissions in the designated area. However, the challenge of decision-making during the handover process in HLWNet is made more complex due to the specific characteristics of electromagnetic signals' line-of-sight transmission, resulting in a greater level of intricacy compared to previous heterogeneous networks. This research work addresses the problem of handover decisions in the Hybrid LiFi and WiFi networks and treats it as a binary classification problem. Consequently, it proposes a handover method based on a deep neural network (DNN). The comprehensive handover scheme incorporates two sets of neural networks (ANN and DNN) that utilize input factors such as channel quality and the mobility of users to enable informed decisions during handovers. Following training with labeled datasets, the neural-network-based handover approach achieves an accuracy rate exceeding 95%. A comparative analysis of the proposed scheme against the benchmark reveals that the proposed method considerably increases user throughput by approximately 18.58% to 38.5% while reducing the handover rate by approximately 55.21% to 67.15% compared to the benchmark artificial neural network (ANN); moreover, the proposed method demonstrates robustness in the face of variations in user mobility and channel conditions. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is playing a pivotal role in transforming various industries, and Wireless Sensor Networks (WSNs) are emerging as the key drivers of this innovation. This research explores the utilization of a heterogeneous network model to optimize the deployment of sensors in agricultural settings. The primary objective is to strategically position sensor nodes for efficient energy consumption, prolonged network lifetime, and dependable data transmission. The proposed strategy incorporates an offline model for placing sensor nodes within the target region, taking into account the coverage requirements and network connectivity. We propose a two-stage centralized control model that ensures cohesive decision making, grouping sensor nodes into protective boxes. This grouping facilitates shared resource utilization, including batteries and bandwidth, while minimizing box number for cost-effectiveness. Noteworthy contributions of this research encompass addressing connectivity and coverage challenges through an offline deployment model in the first stage, and resolving real-time adaptability concerns using an online energy optimization model in the second stage. Emphasis is placed on the energy efficiency, achieved through the sensor consolidation within boxes, minimizing data transmission hops, and considering energy expenditures in sensing, transmitting, and active/sleep modes. Our simulations on an agricultural farmland highlights its practicality, particularly focusing on the sensor placement for measuring soil temperature and humidity. Hardware tests validate the proposed model, incorporating parameters from the real-world implementation to enhance calculation accuracy. This study provides not only theoretical insights but also extends its relevance to smart farming practices, illustrating the potential of WSNs in revolutionizing sustainable agriculture. [ABSTRACT FROM AUTHOR]

An Internet of Things (IoT) system for managing and coordinating unmanned aerial vehicles (UAVs) has revolutionized the industrial sector. The largest issue with the design of the Internet of UAVs (IoUAV) is security. Conspicuously, the novel contribution of the proposed work is to develop a layered authentication approach to facilitate safe IoUAV communication. Specifically, four modules, including the pre-deployment module, user registration module, login module, and authentication module, form the basis of security analysis. In the proposed technique, UAVs are added to the IoUAV registry. The next step is the user registration module, where people are registered with the UAV so they may access the information in real time. In the login module, the user connects with the server for data transmission. Finally, in the authentication module, all entities, including users, servers, and UAVs, are authenticated to ensure secure data communication. The proposed method achieves peak performance as compared to the state-of-the-art techniques in terms of statistical parameters of latency (3.255s), throughput (90.15%), and packet loss (8.854%). [ABSTRACT FROM AUTHOR]

A vehicular ad hoc network (VANET) is a sophisticated wireless communication infrastructure incorporating centralized and decentralized control mechanisms, orchestrating seamless data exchange among vehicles. This intricate communication system relies on the advanced capabilities of 5G connectivity, employing specialized topological arrangements to enhance data packet transmission. These vehicles communicate amongst themselves and establish connections with roadside units (RSUs). In the dynamic landscape of vehicular communication, disruptions, especially in scenarios involving high-speed vehicles, pose challenges. A notable concern is the emergence of black hole attacks, where a vehicle acts maliciously, obstructing the forwarding of data packets to subsequent vehicles, thereby compromising the secure dissemination of content within the VANET. We present an intelligent cluster-based routing protocol to mitigate these challenges in VANET routing. The system operates through two pivotal phases: first, utilizing an artificial neural network (ANN) model to detect malicious nodes, and second, establishing clusters via enhanced clustering algorithms with appointed cluster heads (CH) for each cluster. Subsequently, an optimal path for data transmission is predicted, aiming to minimize packet transmission delays. Our approach integrates a modified ad hoc on-demand distance vector (AODV) protocol for on-demand route discovery and optimal path selection, enhancing request and reply (RREQ and RREP) protocols. Evaluation of routing performance involves the BHT dataset, leveraging the ANN classifier to compute accuracy, precision, recall, F1 score, and loss. The NS-2.33 simulator facilitates the assessment of end-to-end delay, network throughput, and hop count during the path prediction phase. Remarkably, our methodology achieves 98.97% accuracy in detecting black hole attacks through the ANN classification model, outperforming existing techniques across various network routing parameters. [ABSTRACT FROM AUTHOR]

Real-time monitoring and timely risk warnings for the safety, health, and fatigue of underground miners are essential for establishing intelligent mines, enhancing the safety of production, and safeguarding the well-being of miners. This concerns the collection, transmission, and processing of relevant data. To minimize physical strain on miners, data collection functions are consolidated into two wearable terminals: an electronic bracelet equipped with reliable, low-power components for gathering vital sign data and transmitting them via Bluetooth and a miner lamp that integrates multi-gas detection, personnel positioning, and wireless communication capabilities. The gas sensors within the miner lamp undergo regular calibration to maintain accuracy, while the positioning tag supports round-trip polling to ensure a deviation of less than 0.3 m. Data transmission is facilitated through the co-deployment of 5G communication and UWB positioning base stations, with distributed MIMO networking to minimize frequent cell handovers and ensure a low latency of no more than 20 ms. In terms of data processing, a backpropagation mapping model was developed to estimate miners' fatigue, leveraging the strong correlation between saliva pH and fatigue, with vital signs as the input layer and saliva pH as the output layer. Furthermore, a unified visualization platform was established to facilitate the management of all miners' states and enable prompt emergency response. Through these optimizations, a monitoring system for underground miners' status based on mine IoT technology can be constructed, meeting the requirements of practical operations. [ABSTRACT FROM AUTHOR]

5G cellular networks are already more than six times faster than 4G networks, and their packet loss rate, especially in the Internet of Vehicles (IoV), can reach 0.5% in many cases, such as when there is high-speed movement or obstacles nearby. In such high bandwidth and high packet loss network environments, traditional congestion control algorithms, such as CUBIC and bottleneck bandwidth and round-trip propagation time (BBR), have been unable to balance flow fairness and high performance, and their flow rate often takes a long time to converge. We propose a congestion control algorithm based on bottleneck routing feedback using an in-network control mode called bottleneck routing feedback (BRF). We use SDN technology (OpenFlow protocol) to collect network bandwidth information, and BRF controls the data transmission rate of the sender. By adding the bandwidth information of the bottleneck in the option field in the ACK packet, considering the flow fairness and the flow convergence rate, a bandwidth allocation scheme compatible with multiple congestion control algorithms is proposed to ensure the fairness of all flows and make them converge faster. The performance of BRF is evaluated via Mininet. The experimental results show that BRF provides higher bandwidth utilization, faster convergence rate, and fairer bandwidth allocation than existing congestion control algorithms in 5G cellular networks. [ABSTRACT FROM AUTHOR]

In the realm of IoT sensor data security, particularly in areas like agricultural product traceability, the challenges of ensuring product origin and quality are paramount. This research presents a novel blockchain oracle solution integrating an enhanced MTAS signature algorithm derived from the Schnorr signature algorithm. The key improvement lies in the automatic adaptation of flexible threshold values based on the current scenario, catering to diverse security and efficiency requirements. Utilizing the continuously increasing block height of the blockchain as a pivotal blinding parameter, our approach strengthens signature verifiability and security. By combining the block height with signature parameters, we devise a distinctive signing scheme reliant on a globally immutable timestamp. Additionally, this study introduces a reliable oracle reputation mechanism for monitoring and assessing oracle node performance, maintaining both local and global reputations. This mechanism leverages smart contracts to evaluate each oracle's historical service, penalizing or removing nodes engaged in inappropriate behaviors. Experimental results highlight the innovative contributions of our approach to enhancing on-chain efficiency and fortifying security during the on-chain process, offering promising advancements for secure and efficient IoT sensor data transmission. [ABSTRACT FROM AUTHOR]

Air pollution has become a global issue due to rapid urbanization and industrialization. Bad air quality is Europe's most significant environmental health risk, causing serious health problems. External air pollution is not the only issue; internal air pollution is just as severe and can also lead to adverse health outcomes. IoT is a practical approach for monitoring and publishing real-time air quality information. Numerous IoT-based air quality monitoring systems have been proposed using micro-sensors for data collection. These systems are designed for outdoor air quality monitoring. They use sensors to measure air quality parameters such as C O 2 , C O , P M 10 , N O 2 , temperature, and humidity. The data are acquired with a set of sensors placed on an electric car. They are then sent to the server. Users can subscribe to the list and receive information about local pollution. This system allows real-time localized air quality monitoring and sending data to customers. The work additionally presents a secure data transmission protocol ensuring system security. This protocol provides system-wide attack resiliency and interception, which is what existing solutions do not offer. [ABSTRACT FROM AUTHOR]

Telemonitoring and telerehabilitation have shown promise in delivering individualized healthcare remotely. We introduce STASISM, a sensor-based telerehabilitation and telemonitoring system, in this work. This platform has been created to facilitate individualized telerehabilitation and telemonitoring for those who need rehabilitation or ongoing monitoring. To gather and analyze pertinent and validated physiological, kinematic, and environmental data, the system combines a variety of sensors and data analytic methodologies. The platform facilitates customized rehabilitation activities based on individual needs, allows for the remote monitoring of a patient's progress, and offers real-time feedback. To protect the security of patient data and to safeguard patient privacy, STASISM also provides secure data transmission and storage. The platform has the potential to significantly improve the accessibility and efficacy of telerehabilitation and telemonitoring programs, enhancing patients' quality of life and allowing healthcare professionals to provide individualized care outside of traditional clinical settings. [ABSTRACT FROM AUTHOR]