The adoption of blockchain technology across financial, industrial, and IoT ecosystems has increased the demand for reliable and scalable consensus mechanisms. Traditional Practical Byzantine Fault Tolerance (PBFT) offers strong consistency and finality but scales poorly due to high message complexity and communication overhead. To address this, we propose Reliable Practical Byzantine Fault Tolerance (rPBFT), a consensus mechanism that improves performance and fault tolerance in large-scale distributed systems. rPBFT evaluates node reliability through message exchanges during consensus and dynamically forms a committee based on reliability scores. This reduces communication load, improves scalability, and prevents performance degradation caused by faulty nodes while maintaining decentralization without representative election. Experiments evaluated rPBFT against PBFT and HotStuff. In fault-free environments, PBFT achieved better performance due to the absence of reliability computation overhead. Under faulty conditions, rPBFT outperformed PBFT and HotStuff, achieving 30%–40% lower latency, 50%–80% higher throughput, and a 33% improvement in consensus success rate. In fault-tolerance tests, rPBFT maintained stable throughput as the Byzantine ratio and network fault ratio increased, demonstrating resilience via a reliability-based recovery model. These results confirm that rPBFT enhances scalability and robustness while preserving the core properties of Byzantine fault-tolerant consensus, making it well-suited for large-scale blockchain and IoT environments.

• Next generation wifi (WiFi 8) is set to prioritize an ultra-high reliability (UHR). • WiFi's evolution has primarily focused on enhancing performance, neglecting security. • High-reliability and low-latency adaptive relay transmission (ART) scheme was proposed. • ART scheme aims to optimize security and performance by recognizing latency and security requirements of packet. • ART scheme improved link throughput, network throughput, energy efficiency and information leakage compared to conventional models. The ongoing evolution of Wi-Fi technology has spurred the development of high-performance and highly efficient wireless networks. The recently introduced Institute of Electrical and Electronics Engineers (IEEE) 802.11bn standard aims to foster next-generation Wi-Fi technology, thereby ensuring ultra-high reliability by significantly enhancing data throughput, reducing latency, and minimizing power consumption. However, despite the expanding market for information technology products equipped with Wi-Fi and the escalating security risks associated with wireless local area networks, current standardization efforts fall short in bolstering security. This study proposes a transmission scheme in which a station discerns the security and latency requirements of packets and executes Adaptive Relay Transmission (ART). When low-latency transmission is imperative, the ART scheme resorts to a direct transmission approach using single-hop transmission. Conversely, when stringent security measures are warranted, it adopts a relay transmission scheme to minimize the risk of eavesdropping by transmitting packets with short hops. In scenarios necessitating both low latency and a high level of security, a transmission scheme is selected based on a comprehensive evaluation of data throughput, energy efficiency, and security, thereby enhancing the network reliability. Experimental results indicate that ART enhances the network energy efficiency by 13.1 times compared to the direct transmission scheme, which consistently employs direct packet transmission in a delay-tolerant network environment. Furthermore, it limits information leakage to the level of the relay packet transmission scheme, which utilizes multiple hops in a high-security threat environment. ART also boosts the network throughput environment by 212.4 % compared to the direct transmission scheme in a densely populated network and maintains link throughput even in scenarios affected by significant interference, thereby demonstrating effective interference mitigation. The experimental results underscore the efficacy of the ART model in mitigating strong interference and addressing security threats in high-density wireless sensor networks.

With the arrival of the information age, the amount of usable high-dimensional data is rapidly increasing; however, the vast amount of unrefined data may deteriorate the performance of machine learning algorithms. Furthermore, lightweight devices, such as the secure Internet of Things (IoT), have small memory capacity and limited computing power for large data processing; thus, it is difficult to introduce machine learning algorithms to IoT applications. Therefore, it is necessary to develop an efficient learning method that can be used on lightweight devices. This paper proposes a feature selection model consisting of two steps to efficiently reduce the computational complexity of conventional models in the learning process. The proposed two-step feature selection model (TFSM), which performs an exhaustive search for all features by selecting features based on variable importance and then deriving the optimal feature set, significantly reduced complexity, memory usage, and latency with similar accuracy to the greedy model. Particularly, TFSM decreased the complexity by 99.73%, memory usage by 99.97%, and latency by 72.4% compared to the greedy model that uses all features, and improved accuracy by 15.89% compared to random models that randomly perform feature selection.

With the tremendous increase in the number of Internet of Things (IoT) devices connected to networks, attacks on vulnerable IoT targets are also on the rise. Lightweight devices like IoT have limited battery lifetimes and memory capacities; thus, efficient resource utilization is essential. Existing security solutions require significant memory space and energy to process complete log data, which can lead to network service availability issues. Therefore, a new lightweight security solution is needed for such environments. This study presents a lightweight detection method for random permutation attacks in IoT environments. This method achieves detection by employing long detection intervals when a system is in a normal state. When there is suspicion of a potential attack, it adjusts the detection interval to be shorter, thereby conserving energy and reducing memory requirements for operation in the normal state. We experimentally verified the proposed approach; the results revealed that, compared to conventional methods, it exhibited almost no degradation in detection performance for brute-force attacks, while significantly decreasing the amount of data and detection time.

Selective video encryption schemes for battery-powered video devices have been proposed to prevent illegal acquisition of recorded video during transmission. However, the transmission security of conventional schemes is lower than the fully encrypted transmission schemes because a substantial part of the recorded video remains unencrypted including significant metadata for decoding recorded video. In order to address this limitation, we propose a secure video transmission framework that selectively encrypts video metadata and frames on a per packet basis with a consideration of currently available computing power and transmission distance. Evaluation on a testbed demonstrates that the proposed framework provides enhanced video transmission security with less than half of the battery power consumption required by the fully encrypted transmission scheme.

Various biometric technologies are applied to secure authentication, and use Internet of things and smart devices; among them, speech recognition is used as an interface for artificial intelligence (AI) devices as it can conveniently operate and control AI devices through interactive commands. Voice interface, developed with AI technology, is an innovative method to actively control devices. However, the system's vulnerabilities are exploited by malicious attempts on the system, such as dolphin and adversarial attacks on speech recognition devices, and frequent unintended errors occur, such as malfunctioning in response to unauthorized voice signals. In this study, we attempt to effectively find vulnerabilities in these AI speech recognition devices using fuzzing techniques that are generally used to find vulnerabilities in software. In addition, for effective testing, an optimization method for voice fuzzing is proposed to reduce testing time from test coverage perspective. Experimental results demonstrate that the optimization scheme can improve the testing time by approximately 29% by adopting a two-stage fuzzing method.

The demand for frequency is continuously increasing owing to the growth of wireless communication devices and the development of network technology. To share frequency effectively in a limited frequency resource environment, studies to develop spectrum-sharing technology should be conducted. In this study, a blockchain-based spectrum- sharing system, in which various types of users can efficiently share spectrum in dense networks, is proposed. Furthermore, a method to apply game theory to obtain cooperation from users who do not participate in the spectrum-sharing system is studied. When the simulation was conducted based on game theory, the proposed blockchain-based spectrum-sharing technique was simulated by using the tit-for-tat (TFT) strategy, in which the system cooperates with the users if the users collaborate and the system does not cooperate with the users if the users do not collaborate. Simulations show that there are more than a certain percentage of users who use the TFT strategy, and if users are encouraged to change their sharing strategy on a regular basis, they can provide more efficient spectrum sharing than traditional centralized methods. It was confirmed that this technique can improve spectrum sharing by 55.1% or more through optimization.

Wireless local area networks (WLANs) can adopt dynamic channel access technologies such as dynamic bandwidth or channel hopping schemes in order to avoid interference for better link quality. However, in dense networks, the dynamic channel access leads to a higher probability of adjacent channel int

The characterization for the coverage and performance of RF transceiver is an important step in wireless network design. We present that the enhanced receiver sensitivity utilizing an external LNA and improved transmit power can extend the coverage to meet the achievable performance in the real radio environment.

In this paper, we describe the implementation and validation of the next generation wireless local area network (WLAN) physical (PHY) layer transceiver using a dual-band and multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) scheme. The maximum data rate of the designed system is 270Mbps in 40MHz bandwidth and 2 streams mode. It uses 2 transmit and 3 receive antennas due to reasonable complexity and performance. For implementation and verification of this system, we set up the design flow. To reduce the design time, we propose intermediate design steps which use conventional C language by devising a few rules for hardware description. The system performance is measured from the generated C simulator. We implement all circuits in a single chip

A network-based robot is a robot that explores service servers in the network environment for analyzing sensor data and making decision. Since network-based humanoid architecture was proposed, it's possible to reduce costs of robots. We hope robots will be all around at each home. Therefore, normal users who are not experts need to be able to control various robots with easy commands. We developed a scripting language, named CCSLR (Common Command-Scripting Language for network-based Robots) to help users and developers who control various robots in ubiquitous environments. If a user composes a script in CCSLR commands, CCSLR translator system translates the script into suitable low-level command. Therefore, with a set of high-level commands, CCSLR is able to represent low-level commands in common way. Before making the robotic system understand commands that a user input, first, we need to determine what command is. High-level command should lit into both a user intention and a low-level control for heterogeneous robots. We developed the CCSLR and its translator system within the network-based humanoid architecture [1].

The performance of a receiver front-end limits the quality and range of the given communication link. An appropriate design based on well-defined system parameters and architecture can make a huge difference in the performance, cost and marketability of the entire system. In particular, there is a need for improved digital automatic gain control (AGC) for use in multi input multi output orthogonal frequency division multiplexing (MIMO-OFDM) systems with application to wireless local area networks (WLANs), targeted for the upcoming 802.11n standard based in H. Yu et al. (2004) and H. Yu et al. (2005). In this paper, we propose an efficient algorithm and implementation of the digital AGC for next generation WLANs. The proposed AGC algorithm has two feedback loops for gain control to improve convergence speed, and at the same time maintains the stability of the AGC circuit. Also, a complete set of parameters for practical implementation is obtained by various experiments with fixed point constraints and accuracy requirements.

In this paper, the robust time and frequency offset tracking algorithms and architecture for high throughput wireless local area network (WLAN) systems are presented. The designed architecture has feedback and feed forward compensation loop with well defined loop filter coefficients obtained by various experiments for practical implementation. Considered functional blocks include residual CFO estimator, carrier phase estimator, and timing error estimator. In addition to the algorithm descriptions of these blocks, the implementation method is presented, as they are modeled in the floating/cycle and bit true fixed point C model. These C models have been used as a reference for RTL coding and its verification. The design has been implemented successfully on an FPGA device. The proposed tracking method produces results very close to that of the offset-free system.

In this paper, we describe the design and verification of the next generation wireless local area network (WLAN) physical (PHY) layer transceiver using a dual-band and multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) scheme. The maximum data rate of the designed system is 243 Mbps in 40 MHz bandwidth and 2 streams mode. It uses 2 transmit and 3 receive antennas due to reasonable complexity and performance. For implementation and verification of this system, we set up the design flow. To reduce the design time, we propose intermediate design steps which use conventional C language by devising a few rules for hardware description. The system performance is measured from the generated C simulator. We implement all circuits in Xilinx FPGAs.

This paper presents a high accuracy and low complexity timing offset estimation algorithm for multi input multi output orthogonal frequency division multiplexing (MIMO-OFDM) receivers in the tracking mode. First of all we propose an effective architecture of phase tracking for MIMO-OFDM receivers. Then we describe two timing offset estimation algorithms; pilot-aided and CFO-aided timing offset estimation. The first algorithm is a timing phase estimation that can minimize the effect of distorted scattered pilots due to severe fading. We show that the performance of the proposed pilot-aided timing offset estimation very close to that of an offset-free system. The second algorithm attempts to use the computation result of carrier frequency offset estimation in order to reduce complexity. The proposed CFO-aided timing offset estimation has very low complexity comparable to that of the pilot subcarrier aided schemes without performance degradation

In the recent draft of the 802.11e MAC, the hybrid coordinator (HC) gives transmission opportunity (TXOP) for a station to transmit burst frames in TXOP for improving throughput efficiency. In this paper, we express the expected throughput for the block ack policy in polled TXOP as a closed form function of the number of burst MSDUs, the number of fragmented MPDUs, the data payload length, the frame retry count, the wireless channel condition, and the selected PHY mode. Based on our simulation study and analysis, we show that the TXOP and proper ack policy can enhance system performance, and that there is a block ack efficient block size and frame length to transmit the burst frames for each PHY mode. Moreover, we also show that when the block ack mechanism is combined with link adaptation, it has better throughput performance. Based on the condition of the efficient block ack mechanism, we propose a block size based polling scheme.