The increasing heterogeneity of network services in next-generation environments require intelligent and adaptive routing frameworks that can align with diverse and dynamic application level service requirements. Software-Defined Networking (SDN) decouples the control plane from data plane to shift the complexity from core network devices. Moreover, Intent-Based Networking (IBN), when integrated with SDN, provides a centralized and programmable platform to translate high level user intents into actionable network configurations. This paper proposes a hybrid quality of service (QoS)-aware routing framework that combines the Analytic Hierarchy Process (AHP) with adaptive Artificial Intelligence (AI) to support intentdriven decision-making in SDN. The AHP module interprets user-defined service intents into weighted QoS priorities, while the AI component dynamically refines these weights based on the network telemetry. This integration enables context-aware path selection that balances structure, adaptability, and explainability. Experimental results on real-world network topologies demonstrate that the proposed framework consistently outperforms benchmark and state-of-the-art approaches in terms of end-to-end (E2E) delay, packet loss, jitter, throughput, and intent satisfaction. Moreover, the system achieves fast re-routing convergence and maintains a low controller overhead, making it suitable for scalable, transparent, and high-performance deployment in IBN-compliant SDN infrastructures.

As the Internet of Things (IoT) and artificial intelligence (AI) technologies are rapidly evolving, smart healthcare has emerged as a transformative solution to enhance healthcare quality and optimize resource allocation. This study introduces FEI-Hi, a federated edge intelligence paradigm that integrates edge computing with federated learning (FL) to enable secure and efficient medical data processing. FEI-Hi comprises three principal layers: FL layer, which facilitates cross-device collaborative training through encrypted model updates; aggregation layer, which refines the global model by consolidating updates; and edge layer, which performs local data processing and model inference. FEI-Hi leverages distributed intelligent computation, model parameter compression, and efficient node clustering to enhance the accuracy and efficiency of medical data processing significantly. By employing Wasserstein distance for clustering and parameter selection, FEI-Hi ensures model convergence and stability. Experimental results on multiple medical datasets demonstrate a 30% improvement in the model training speed and an F1-score exceeding 90%, surpassing the state-of-the-art (SOTA) benchmarks in model parameter transfer efficiency, training speed, and accuracy.

In dense Internet of Things (IoT) networks, where numerous devices transmit simultaneously, the conventional periodic advertising scheme, with its fixed advertising period, results in persistent collisions, leading to significant delays and higher energy consumption. These issues become more pronounced as the network grows. To overcome these limitations, this work proposes a novel adaptive periodic advertising scheme that dynamically adjusts the advertising period for each device for each transmission, effectively reducing collisions, improving delay performance, and enhancing energy efficiency. Based on the proposed scheme, an analytical framework is developed to analyze delay and energy consumption. In addition, an analytical expression determining the schedule of each advertiser, perfectly syncing the scanner for efficient reception, is derived. As a result, it significantly improves the reliability of the proposed scheme. Furthermore, based on analytical framework, simplified closed-form expressions are derived to choose the optimal advertising period. Moreover, based on these closed-form analytical expressions, algorithms are proposed that autonomously optimize the delay in such applications. The performance of the proposed scheme has also been compared with conventional periodic advertising scheme, and it is found that the proposed scheme outperforms conventional scheme in terms of delay and energy. Lastly, analytical results were validated using a custom-built simulation framework based on the widely recognized Riverbed Modeler (formerly OPNET).

Recent advancements in Bluetooth Low Energy (BLE) have made it a promising solution for delay-sensitive and energy-constrained IoT applications, such as robotic automation in industrial settings. However, existing BLE service architectures, namely the BLE beacon-to-user and beacon-gateway-server-user models, either suffer from high delays, unreliable performance, or a reliance on internet connectivity, which is often limited in environments such as underground parking areas, airports, and supermarkets. Additionally, these architectures use BLE legacy advertising, which offers limited throughput and thus contributes to further delays. To address these limitations, this paper proposes a novel BLE-centric enhanced service architecture that minimizes the delay experienced by users and enhances the energy efficiency of BLE beacons. Based on the proposed service architecture, we first develop an analytical model to evaluate delay and then derive simplified closed-form expressions for selecting optimal transmission parameters. These parameters minimize the delay experienced by users and improve the energy efficiency of BLE beacons. The proposed architecture also leverages BLE extended and periodic advertising modes, which offer higher throughput, thereby further reducing delay and improving overall performance. Additionally, lightweight algorithms are introduced to adapt these parameters dynamically based on network conditions. The proposed model is validated through simulations, showing strong agreement with the analysis and confirming its practical effectiveness.

The rapid development of intelligent transportation and autonomous driving technologies, driven by the Internet of Vehicles (IoV), faces significant challenges owing to data and system heterogeneity. These challenges stem from the multidomain nature of the IoV and threats such as data leaks and model-finding attacks, which complicate data processing and model training. To address these issues, in this study, we proposed federated multi-domain learning for IoV (FMD-IoV). FMD-IoV addresses data heterogeneity by employing clustered techniques to group similar viewpoints and multidomain machine learning to map diverse data types into a unified feature space. To address the system heterogeneity caused by diverse vehicle types, the framework introduces a similarity-based aggregation method and model weight de-regularization to enhance robustness and generalizability. Experimental results demonstrated that FMD-IoV reduced the mean square error (MSE) by 0.05 on the Synthia dataset and 0.13 on the CityScape dataset compared with the state-of-the-art methods. Moreover, it maintained or improved the MSE as the number of nodes increased, demonstrating its adaptability to complex scenarios and large-scale data. These results highlight the flexibility, resilience, and efficacy of FMD-IoV in multi-view data fusion within large-scale IoV environments.

Future sixth-generation (6G) mobile communication networks are expected to include new features such as the network data analysis function (NWDAF), which will allow network operators to integrate machine learning (ML)-based data analysis techniques into their networks. This will allow NWDAF to identify, safeguard against, and handle various types of anomalous behaviors on user devices. To this end, this study applies fair federated learning (FL) to the 3GPP standard NWDAF architecture and embeds the designed multi-task ML model to detect traffic anomalies in different types of user devices. However, there is a problem of different task demands when the same ML model is used for optimization between different tasks. Therefore, a global alternating gradient projection (AGP) technique is presented in this study. It can be applied to many tasks and utilized to solve minimization problems. The two gradient projection phases comprise each iteration of the AGP. These steps update various tasks at regular intervals, thereby providing a regularized version of the gradient to the original multi-task objective function, which results in optimal task performance. The simulation results demonstrate that the proposed multi-task ML model can simultaneously detect traffic anomalies of different types of user devices in NWDAF and outperforms state-of-the-art models in detecting multi-task anomalies in NWDAF. The experimental evaluation also implied that the designed FL applies superior anomaly detection performance in NWDAF scenarios and has lower communication overhead than that of the traditional NWDAF without affecting the ML performance.

This paper proposes a novel radiation-hardened high-reliability SRAM cell, namely SHRCO, with 12 transistors for robust value storage as well as 6 transistors for parallel access operations. Using separated and error-interceptive feedback paths, the proposed cell has a complete self-recoverability from single-node upset (SNUs) at all single nodes and an excellent self-recoverability from double-node upsets (DNUs) at a part of node pairs. In addition, the proposed cell has superior access operation speed due to the inclusion of extra parallel access transistors. Simulation results show that the proposed cell has the largest number of node pairs that can self-recover from DNUs. Moreover, compared to the existing radiation-hardened SRAM cells, the proposed cell saves 28% of read time and 3% of write time on average.

As the semiconductor technology continues to advance, integrated circuits (ICs) are becoming increasingly sensitive to soft errors, e.g., double-node upsets (DNUs) and triplenode upsets (TNUs), induced by harsh radiation. In this paper, a low-cost latch design, namely ICLTR, using input-split inverters (ISIs) and C-elements to provide complete TNU recovery, is proposed. ICLTR consists of seven ISIs, seven 2-input C-elements and a clock-gated inverter, and all these elements are interlocked. Simulation results show the complete TNU recovery for ICLTR. The simulation results also show that ICLTR can save 59.5% of the transmission delay, 36.1% of the power consumption and 81.6% of the delay-area-power product (DAPP) on average when compared with the same type of TNU recovery latch designs.

In recent years, with the increase in users' demand for location services, the research of Indoor Positioning Systems (IPS) has attracted much attention. Many researchers proposed schemes to estimate the user's location based on the Received Signal Strength Indicator (RSSI) values of wireless technologies. However, the RSSI value is affected by signal interference seriously. This causes the accuracy of the positioning to be greatly reduced. To solve this problem, we use Computer Vision (CV) to replace traditional solutions in this paper. CV is known for its high performance and low complexity. The proposed scheme is capable of inferring the current location of users in the possible candidates from the interior structural features of buildings captured by cameras and Computer-Aided Design (CAD) drawing.

Bluetooth low-energy (BLE) technology is one of the most promising communication technologies applicable to a variety of Internet of Things (IoT) services. The neighbor discovery process (NDP) plays a key role in BLE-enabled IoT services. The basic NDP (B-NDP) specified in BLE specification 4.0 has a limitation in supporting large numbers of BLE devices, owing to its use of three channels. To overcome the limitation of B-NDP, advanced NDP (A-NDP) has been recently introduced in BLE specification 5.0. However, most existing studies have focused on B-NDP, with very few studies having been conducted on A-NDP. In this article, we propose a model for analyzing the effective transmission rate for a BLE advertiser with an A-NDP operation. Using the proposed model, we also propose a performance model to maximize the transmission rate by optimally setting the BLE parameters. The proposed models are validated by comparing with extensive simulation results. It also demonstrates that the maximum transmission rate by the proposed models can be achieved with low energy consumption.