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.

Camera-based facial-emotion recognition (FER) suffers from poor lighting, occlusion, and privacy exposure, whereas mmWave-only solutions lack the spatial detail required for fine-grained affect analysis. To close this gap, we present SwinLSTM-EmoRec (Shifted Window Transformer + Long Short-Term Memory Emotion Recognition). This non-contact dual-modal framework fuses micro-Doppler signatures captured by a TI IWR1443 mmWave radar with RGB imagery while treating radar as the primary, identity-obscured source and adaptively limiting reliance on RGB. Privacy is preserved because the cross-attention gate down-weights or bypasses RGB when illumination is poor or when potential identity exposure is detected, leaving decisions dominated by illumination-invariant radar dynamics. A shifted-window Swin Transformer extracts spatial facial cues, an LSTM models temporal radar dynamics, and a lightweight cross-attention layer aligns the two streams, boosting F1 by up to 4% over early, late, and self-attention baselines. On a 50-participant interactive-gaming dataset recorded under varied lighting and distances of 0.5–2 m, the system achieves 98.5% accuracy (F1 ≈ 0.98). It maintains 33.9 ms end-to-end latency on a 15 W Jetson Xavier NX edge device. Performance remains > 92% at 2 m, demonstrating robust, privacy-preserving FER robust, privacy-aware emotion sensing suitable for smart-home, tele-health, and e-sports IoT applications.

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 5G and Beyond-based Internet of Things (IoT) sensor networks, the end-to-end (E2E) route traverses via multiple heterogeneous network domains, necessitating interdomain interaction to guarantee and confirm Quality-of-Service (QoS) for low-power IoT devices applications. Moreover, in heterogeneous IoT sensor networks, the E2E path often encompasses domains with diverse QoS parameters or classes. The unique E2E requirements for delay, packet loss ratio (PLR), and other factors present further challenges. However, existing legacy network architectures and typical software-defined networking (SDN) models lack effective strategies for QoS provisioning tailored to the service requests of IoT low-power sensor devices. To address these issues, this study proposes a novel multiobjective SDN-based framework for IoT sensors, ensuring E2E QoS across multiple domains with heterogeneous traffic service classes (TSC). A two-layer SDN framework is presented to provision QoS for IoT sensors based on their specific service demands at the E2E network level. Central to the framework is the deployment of an optimal additive weighting module (OAWM), facilitating TSC ranking according to their weights and incorporating a priority mechanism for specific service parameters, such as delay, PLR, and jitter. Additionally, the global controller statistics enable the provisioning of E2E QoS by mapping the service requests from IoT sensors. Experimental evaluations are conducted to compare the proposed approach with existing schemes. The results validate the effectiveness of our proposed method, demonstrating improved E2E QoS provisioning and meeting the specific requirements of IoT sensors in precision agriculture with low-power IoT devices.

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.

Building an interpretable fault detection and diagnostic model based on few-shot circuit samples and prior information about circuit structures is of significant importance. To fill these gaps, we propose a graph-based multitask transfer learning (TL) method for fault detection and diagnosis of circuits under few-shot conditions. First, in order to model the interconnections of nodes in a circuit, the sample data is organized into a graph structure, and a semi-supervised graph-based structural feature fusion method is proposed. The proposed method can accept graph-structured data and process the data using feature fusion methods. Second, to improve the model performance under few-shot conditions, two TL mechanisms are proposed for the topological structure characteristics of analog circuits as well as circuit signal characteristics. Finally, through a parameter-shared strategy, we propose a task transfer-based fault diagnosis approach. Experimental results on three different circuits show that the proposed method has the best diagnostic accuracy compared to typical detection and diagnosis schemes.

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.

With the rapid development of semiconductor technologies, latches are becoming increasingly sensitive to multiple node upsets, such as triple node upsets and quadruple node upsets (QNUs). Therefore, they should be considered for safety-critical applications. To effectively tolerate QNUs, this paper proposes a QNU-tolerant latch design with moderate overhead. The latch mainly comprises two parallel storage cells, and three 2-input C-elements. When any four internal nodes are flipped at the same time, the output value of the latch will not be affected. Simulation results not only confirm the QNU tolerance of the proposed latch but also demonstrate that the latch can reduce by 40.93% delay, 40.73% area, 13.11% power, and 71.19% delay-area-power product (DAPP) on average compared to the existing QNU-tolerant latches.

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.

The Software Defined Networking (SDN) paradigm has shifted the network intelligence from the network devices to the centralized controller. The controllers are placed in a distributed manner in a network for reliability and load balancing. However, the placement of controllers considering the whole network is not an efficient approach because applying the objective function to the overall network is a challenging task. Therefore, the division of the network into clusters makes the assignment of the switches to the controller more efficient. The placement of the controller in a cluster not only reduces the latency between the switches and the controller but other objectives such as reliability, load balancing, robustness and energy saving can also be applied to the clusters. Therefore, in this poster, a multi-criteria-decision-making (MCDM) scheme known as the Analytical Network Process (ANP) is proposed for controller place selection using clustering.

Because disaster situations can cause damage to people and property, fast recognition and Countermeasure is important. IoT and MR, the technologies that have become popular nowadays, expected to play a key role in this domain. In this paper, we will introduce MR and IoT Convergence Platform with AI Support for Disaster Recognition This platform can recognize a disaster situation with sensors and AI system, and report to the user's MR devices. Also, we build a prototype using OneM2M-based Mobius Platform, Microsoft Hololens, and CNN-based AI Camera for proving feasibility.

The performance of IEEE 802.11e enhanced distributed channel access (EDCA) mechanism effects by the network loads. It is observed that when the number of users increases in each access category, the delay significantly increased in EDCA algorithm. The performance improvement in EDCA algorithm in fluctuating network load is a challenging task. To improve the performance of EDCA algorithm for delay sensitive services, we propose an adaptive contention window algorithm, which adjusts the contention window with the number of user in each access category. We introduce a simple but novel model for delay analysis based on EDCA mechanism. The proposed model analyzes the quality of service for the delay-sensitive applications such as voice and video. The simulation result verifies the significance of the proposed model compared to the EDCA model.

The performance of IEEE 802.11 distributed coordination function (DCF) protocol vitally depends on the number of contender stations and the contention window size (CW). The contribution of this paper is twofold. First, we analyze the performance of IEEE 802.11 DCF in term of CW optimization and the effect of CW on the throughput and collision probability for binary exponential backoff algorithm (BEB) used in the DCF. Secondly, we estimate the number of contender stations and find an optimal contention window CWoptfor the BEB based on the number contender stations, and compared the performance of the propose algorithm CWoptwith the BEB. The Propose algorithm dynamically adjusts the CW according to the network states. The simulation results show that the propose algorithm outperform the BEB in term of throughput improvement and collision probability.

In this poster we present an enhanced version of IEEE 802.21 Media Independent Handovers services (MIH) to optimized the network and handover performance. We added features and components in MIH architecture to enable the quick delivery of information to Mobile Nodes (MNs) to optimize the handover process. We added caches and database within Point of Access (PoA) and Media Independent Information Service (MIIS) respectfully to store Dynamic Network Information (DNI) and introduce timely updates mechanism for efficient distribution DNI among PoAs and MIIS to improve the network performance. We also manage to decrease the unnecessary Handovers (HOs) and enable the MNs to select the best candidate network during HO through efficient and effective decision making process which not only consider DNI but also application and user context information. We simulated our proposed architecture in NS-2 simulator and compared its performance with standard MIH. Our proposed architecture outperformed standard MIH in different performance aspects.

Recently, the focus of sensor network paradigm is changing for delivering multimedia contents such as audio and video streams and still images. However, most existing routing protocols are not very practical for transmitting multimedia contents in resource constrained sensor networks. In this paper, we propose an optimized nod-disjoint multi-path routing scheme resulting in throughput enhancement and load balancing.

Variable bit-rate (VBR) MPEG video traffic is highly bursty due to group of pictures structure, and shows time-variant statistical characteristics due to scene changes. These characteristics make it more difficult to manage network resources, and leads to the significant reduction in network utilization. We deal with the issues related to efficiently transmitting VBR MPEG video traffic over asynchronous transfer mode networks, while maintaining consistent visual quality and improving network utilization on the basis of real-time applications. First, we propose a joint encoder and channel-rate control scheme that comply with not only negotiated traffic parameters, but also constraints imposed by encoder and decoder buffers. Second, we propose a dynamic bandwidth renegotiation method by combining the above scheme with a traffic smoothing method that can make the peak rate close to the sustainable rate. The efficiencies of the proposed methods are compared with a few other competitive schemes such as transmission methods for unconstrained VBR and the constant bit-rate scheme with average bandwidth equal to that of the proposed methods.

The arrangement of the I-picture starting times of multiplexed variable bit rate (VBR) MPEG videos may significantly affect the cell loss ratio (CLR) characteristics of superposed traffic. In this paper, we deal with the problems due to the starting time arrangement of VBR MPEG videos. VBR MPEG video traffic is modeled by a sequence with time-varying and periodic picture-type dependent rate envelopes. From extensive investigations into the relationships between the starting time arrangement and the queueing performance, it is shown that the average power of superposed VBR MPEG video traffic can be a good measure for the burstiness of the traffic. Then, we can derive a starting time selection method for a newly requested VBR MPEG video that can minimize the CLR as well as the peak cell rate of the superposed traffic including the new request itself, and an efficient scheduling method called MC-scheduling is also proposed as an application of the starting time selection method. The exactness and efficiencies of the proposed methods are shown by comparing them with other scheduling methods in terms of the smoothness and the CLR performances.

The arrangement of I-picture starting times of multiplexed VBR MPEG videos may significantly affect the cell loss characteristics of superposed traffic. This paper presents a starting time selection method of a new request so that the minimum and maximum cell loss ratios (CLRs) of superposed traffic including the new request are expected. For this purpose, both single and superposed VBR MPEG video traffic are modeled as a time-varying periodic picture-type dependent rate envelope sequence. Through experiments and observations, we show that the average power of superposed VBR MPEG video traffic is a good measure for the burstiness of the traffic. The proposed starting time selection method is derived from these properties. The efficiencies of the proposed method are tested by comparison with other scheduling methods for the smoothness of the peak rate and the CLRs of the scheduled-superposed stream, and, we also provide connection admission control (CAC) methods applicable to ATM multiplexers which can manipulate the starting times of sources and video on demand (VOD) servers which can do that.

Some complicated features of VBR MPEG video traffic such as the different statistical properties according to different picture coding types and the periodic peaks due to I-pictures in the MPEG coding make it difficult to manage resources in ATM networks. In this paper, we discuss some problems of usage parameter control (UPC) and connection admission control (CAC) for VBR MPEG video sources in ATM networks, and propose an efficient traffic control framework for overcoming these problems. First, we model a VBR MPEG video traffic at slice level. Using this model, we analyze an ordinary leaky bucket (LB) system and present some shortcomings of the LB system for policing VBR MPEG video sources. To overcome these shortcomings, we propose a new UPC mechanism called the picture-type dependent LB (PDLB) system. We also discuss a key factor characterizing the cell loss characteristics of ATM multiplexer called 'the periodic-peak position effect'. To reflect this effect on CAC, we propose a new CAC method in cooperation with the PDLB mechanism.