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.

Soil moisture is pivotal for precision irrigation, hydrologic prediction, and climate-resilient agriculture, yet a persistent gap remains between sparse in-situ probes and predominantly near-surface satellite retrievals. We present Polarimetric Wave Coherence Tomography (PWCT), a methodology that does not require soil-specific calibration and explores dual-polarized 77-81 GHz FMCW radar to recover root-zone moisture profiles. PWCT departs from amplitude-centric approaches by exploiting water-induced depolarization through: (i) a Polarimetric Coherence Index (PCI) that quantifies cross-polar phase decorrelation from subsurface reflections; (ii) frequency-hopped, orthogonal dual-pol chirps to better separate surface and volume interactions; and (iii) a Spatial Moisture Metric (SMM) derived fromkz-domain energy decay via wave-interference tomography. Implemented on a TI AWR1443 with a compact dual-pol MIMO setup, PWCT reconstructs depth profiles over 0-30 cm in 5 cm bins, requiring no soil-specific calibration but utilizing a one-time instrument-level calibration to ensure system consistency. We further introduce HydraNet, a physics-constrained network that ingests PCI-SMM features to deliver texture-agnostic VWC estimates. Across sand, loam, and clay, HydraNet achieves an RMSE of 0.82-2.15% VWC with TDR as the reference and demonstrates real-time edge inference. Results indicate that PWCT + HydraNet provides an accurate, low-maintenance framework for periodic field monitoring and irrigation decision support.

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.

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.

Soft-errors and aging are considered as two primary factors affecting the long-term reliability of aerospace integrated circuits (ICs). As one of the key components in aerospace ICs, latches play a pivotal role to ensure desirable circuit functionality. This paper presents a single-event-upset recovery latch, namely SRBML, with bias-temperature-instability (BTI)-mitigation. By optimizing its internal structure, the latch can recover from single-event-upsets (SEUs) and reduce the stress time of transistors in feedback loops to simultaneously mitigate the impact of BTI on the latch. Simulation results demonstrate that the soft error rate increase due to BTI is reduced by roughly 34% for SRBML after BTI-mitigation. In addition, the delay of SRBML is not affected, and the area and power increase are limited compared to BTI-unmitigated latches.

Abstract Although the Internet of Things (IoT) has been considered one of the most promising technologies to automate various daily life activities, that is, monitoring and prediction, it has become extremely useful for problem solving with the introduction and integration of artificial intelligence (AI)‐enabled smart learning methodologies. Therefore, due to their overwhelming characteristics, AI‐enabled IoTs have been used in different application environments, such as agriculture, where detection, prevention (if possible), and prediction of crop diseases, especially at the earliest possible stage, are desperately required. Bacterial stalk root is a common disease of tomatoes that severely affects its production and yield if necessary measures are not taken. In this article, AI and an IoT‐enabled decision support system (DSS) have been developed to predict the possible occurrence of bacterial stalk root diseases through a sophisticated technological infrastructure. For this purpose, Arduino agricultural boards, preferably with necessary embedded sensors, are deployed in the agricultural field of maize crops to capture valuable data at a certain time interval and send it to a centralized module where AI‐based DSS, which is trained on an equally similar data set, is implemented to thoroughly examine captured data values for the possible occurrence of the disease. Additionally, the proposed AI‐ and IoT‐enabled DSS has been tested on benchmark data sets, that is, freely available online, along with real‐time captured data sets. Both experimental and simulation results show that the proposed scheme has achieved the highest accuracy level in timely prediction of the underlined disease. Finally, maize crop plots with the proposed system have significantly increased the yield (production) ratio of crops.

The number of affirmed COVID-19 cases showed an enormous increase in the recent past throughout the globe. Keeping in view the catastrophic destruction of this devastating virus, there is a must-need situation to maximize the use of existing healthcare technologies such as the healthcare Internet of Things (H-IoT). In healthcare, patient wearable devices are widely recognized as a dormant technology with enormous capabilities to assess and combat various diseases, e.g., cough, seizure, temperature, heartbeat, and so on. As we know, in the H-IoT, patient-wearable devices are dispersed in an infrastructure-free environment that exposes them to several private and public coercion while accumulating and transmitting high sensitive data over the wireless communication channel. Therefore, security is the main concern of these applications, and thus, the primary focus of this article to outline the limitations and challenges in the present literature from 2019 to 2021, to identify the requirements of H-IoT applications used in the context of COVID-19. Following this, we will move one step ahead to explore the current security techniques adopted in these applications. Consequently, we will identify the network architectural, cryptographic, protocols, and operational security challenges during our study to recommend viable research directions and opportunities, which could be helpful and capable to minimize the network architecture, deployment, and maintenance cost with more productive outcomes.

One of the operational challenges in the Internet of Things (IoT) is load balancing, which is the focus of interest of this article. We propose a three-phase enhancedad hocon-demand distance vector (enhanced-AODV) routing protocol for multiwireless sensor networks (multi-WSNs). The three phases are categorized based on traffic priority, namely: 1) high priority; 2) low priority; and 3) ordinary network traffic. The network architecture is divided into chains, i.e., local and public chains, where the cluster heads (CHs) and base stations (BSs) are used, respectively, to manage the network traffic based on priority information with alternative route allocation. Moreover, our three-phase enhanced-AODV protocol provides traffic categorization with alternatives route allocation to minimize energy consumption and prolong the lifetime of participating devices in the network. The proposed model is implemented in the simulation environment to overview results statistics in terms of network lifetime, prioritize traffic, computation and communication costs, latency, and packet lost ratio (PLR). Findings from the simulation suggest that our scheme achieves 15% improvement in network lifetime, 17% latency, 22% PLR, and approximately 10% in the computation and communication costs of the network, in comparison to three other similar protocols.

In this article, we present a robust channel categorization scheme to fix data privacy and preservation problems in an Industrial Healthcare Internet of Things (IHC-IoT) network. The proposed model categorizes the transmission bandwidth into four independent channels for each device by defining triggering rules with respect to time for reception and transmission of data. Besides, our developed prototype, which is known as high optimal path channel triggering protocol (HOPCTP) ensures data privacy and preservation utilizing minimal network resources in an IHC-IoT network. Furthermore, the HOPCTP prototype enables client-side devices to transmit and receive data with four different independent communication channels following the triggering mechanism to avoid adversary device anticipation in the network. The categorized channels are triggered with a defined time period to change their transmission and reception functionality, which triggers the transmitted data between different channels. Same data transmission via four different channels ensures the confidentiality and integrity of data because if an attacker captures one channel of data, he will not be able to understand the full message. The convalescent communication infrastructure is developed among patient wearable devices followed by cluster heads, micro base station, and macro base station to ensure data privacy and preservation with better communication metrics. In addition, the objective of the HOPCTP prototype is to resolve the data privacy issues in delay-sensitive applications, i.e., IHC-IoT networks. To achieve this, the HOPCTP prototype promotes data preservation and communication in terms of authenticity, congestion, throughput, communication cost, and packet loss ratio. The result statistics of the proposed scheme demonstrate remarkable improvement over the existing schemes for aforementioned comparative metrics.

In this paper, we present a three-byte-based Media Access Control (MAC) protocol to resolve the mutual authentication problem in an Autonomous Internet of Vehicles (AIoV) network. Initially, the network architecture is divided into two chains, i.e. the local and public chain, wherein the local chain the authentication and communication process is controlled by Cluster head (CH), while in the public chain it is controlled by the base station (BS). The proposed paradigm uses the 48-bit MAC address of the vehicle’s embedded sensors for authentication, with the ability to alter the authentication parameters by triggering the last three bytes (24 bits) of the MAC address with a predetermined time interval. Persistent triggering of the last three bytes of an AIoV’s MAC address guarantees its integrity in the network because only legal vehicles are capable of initiating and validating the authentication request with the other vehicles in the network. Initially, the MAC addresses of all AIoVs are registered with the BS in the public chain through the concerned CH. Likewise, the MAC-address triggering of registered AIoVs is carried out in the BS with a defined time period and broadcasted in the public chain, which is further distributed through CHs in the local chain. Most of the computation is supervised by BS and CH in the public and local chains respectively, which minimize the client-side authentication complexity and enhances network efficiency in terms of authentication with 98.3% detection rate, communications, and computing costs, along with 11% improvement in the latency, 15% improvement in packet loss ratio (PLR), and throughput.

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.