Qutaiba Ibrahim Ali

Industrial Internet of Things (IIoT) applications operating in safety-critical environments—such as chemical plants, oil refineries, and disaster response systems—demand ultra-low latency, deterministic communication, and absolute message reliability, particularly during emergency scenarios. Traditional Software-Defined Networking (SDN) controllers often fall short under such dynamic conditions due to static routing mechanisms, fixed flow timeout policies, and lack of real-time congestion awareness. This paper proposes an enhanced SDN architecture integrating a lightweight XGBoost-based machine learning model into the POX I2_learning controller to proactively predict and mitigate congestion in IIoT environments. The proposed system dynamically adjusts flow routing, optimizes timeouts, and enables congestion-aware path selection while maintaining compatibility with industry protocols like OPC UA and GOOSE. Designed for edge deployment, the controller achieves sub-5 ms latency, 0% packet loss, and up to 35% fewer control messages across simulated emergency scenarios involving bursty and multi-zone traffic. Extensive evaluations using a real-time Mininet-based emulation framework reveal notable gains in latency stability, throughput, scalability, and processing efficiency. This work offers a practical and deployable solution for resilient, intelligent, and standards-compliant IIoT networking in mission-critical environments.

The increasing integration of Wireless Sensor Networks (WSNs) and Internet of Things (IoT) technologies across domains such as smart cities, healthcare, and industrial automation demands sensor platforms that are not only efficient and scalable but also adaptable to intelligent and secure applications. While many earlier reviews focused on general specifications and often included outdated hardware, this paper presents an up-to-date and comprehensive review of Commercial Off-The-Shelf (COTS) platforms that are actively used in current research and industry. By systematically excluding nodes that are no longer produced and incorporating newly available sensor nodes, this study addresses critical gaps in previous surveys and ensures practical relevance. COTS platforms are categorized into Single Board Microcontrollers (SBMs) and Single Board Computers (SBCs), providing a clear and structured framework that considers performance, energy consumption, cost, and AI readiness. The review further highlights emerging trends such as the adoption of secure edge computing, real-time behavioral analytics, and on-device learning, which are increasingly vital for modern deployments. Moreover, the growing interest in Reduced Instruction Set Computer Five (RISC-V) architectures is discussed, offering open-source flexibility and enhanced performance as a promising alternative for future WSN implementations, although its supporting software ecosystem is still maturing. Finally, the paper introduces a data-informed decision- support framework to help practitioners select appropriate platforms based on applicationspecific constraints and performance trade-offs. By bridging the gap between traditional hardware classifications and evolving computational demands, this work provides a valuable and practical guide for researchers and developers navigating the rapidly changing WSN and IoT landscape.

In this paper, we propose a systematized analytical and AI-assisted framework to compute the end-to-end latency of an unmanned aerial vehicle (UAV)-attached wireless sensor network (WSN) node communicating over a 5G linked communication system and to identify and minimize the latency within...

Live Virtual Reality (VR), streaming has stringent motion-to-photon latency demands and it is highly susceptible to transport-layer and network-level cyber attacks. Despite the fact that most protocols such as protocols such as TLS 1.3 or DTLS can provide high level of cryptographic assurances, their fixed implementation has a performance cost that can make immersive interaction in dynamic adversarial environments unstable.The current paper provides a protocol that is a latency-constrained LLM-enhanced adaptive transport-layer security protocol named LARS-VR, which is designed to support real-time VR streaming. The threat intensity, cryptographic overhead and end-to-end latency are modeled as dynamically coupled state variables on a constrained discrete time control architecture. Security adaptation is formulated as a finite-time constrained search problem over a finite admissible action set, as immersive latency feasibility is a system-wide property that is strictly enforced. The proposed architecture will guarantee limited threat evolution and stable immersive performance without having encrypted data and modifying cryptographic primitives. Conditional simulation and analytical formulation under Wi-Fi 6 conditions display as much as 18 per cent reduction in mean latency, 15-20 per cent increase in effective throughput and a substantial reduction in latency variance compared to fixed secure setups. These results constitute a mathematically constructed paradigm of cognitive and latencyconscious transport-layer security in immersive VR and other systems that are sensitive to delay.

Making artificial intelligence move to the edge is a promise of more privacy, lower latency, and offline, but it has to confront the realities of consumer hardware resources. The given paper represents a thorough taxonomy of the on-device LLM optimization technique, which summarizes the discoveries of 2023-2025. A granular review of optimization methods allows us to note a severe bifurcation in the research field: an excessive use of the Llama model family to benchmark against an increasing fragmentation of hardware targets, which is ranging between Raspberry pi to specialized Jetson modules. Our research underscores the fact that optimization that fits all should be left in the past and that to be successful, deployment should be constraint-based. The literature is proposed by us to compare the methods in terms of compression, accuracy, speedup, and sparsity. The results show that the most uniform energy advantages are obtained with quantization, although hybrid hardware- aware methods are the only way to break existing latency bottlenecks on the heterogeneous systems.

Active Noise Cancellation (ANC) began as a theoretical idea in the 1930s and has now spread to include broad uses in consumer electronics, industrial, and automotive industries. This conceptual study provides a thorough explanation of ANC technology spanning more than 90 years of development, from Paul Lueg's early 1936 patent to the most recent deep learning methods. Adaptive filtering algorithms such as LMS, NLMS, FxLMS, FuLMS, and RLS variants are closely examined, along with new neural network architectures like Convolutional Recurrent Networks (CRN) and Attentive Recurrent Networks (ARN), which achieve sub-4 ms latency with noise cancellation improvements of 5-6 dB over traditional methods. The noise reduction parameters, computational complexity, power consumption, and convergence characteristics are all included in the thorough performance comparisons presented in this study. Using benchmark datasets like NOISEX-92 and DEMAND for algorithm evaluation, we examine feedforward, feedback, and hybrid system designs used in consumer headphones, automobile cabins, aviation, and HVAC systems. Given their greater resistance to broadband noise and nonlinear distortions, our conceptual study shows that deep learning approaches are the ANC technology of the future. However, conventional FxLMS maintains the industry standard because of its computational efficiency (O(N) complexity).

Conventional Intelligent Speed Assistance (ISA) systems primarily rely on static map data or camerabased sign recognition, which limits their adaptability to dynamic driving conditions and real-time speed regulation. To address this gap, this paper proposes an AIbased Intelligent Speed Limiting System (ISLS) that integrates GPS, digital map APIs, and real-time vehicular data to predict and adjust vehicle speed proactively. The system employs a Long Short-Term Memory (LSTM) model for predictive speed adaptation based on upcoming road geometry, traffic context, and environmental inputs. A reinforcement learning-based control module ensures smooth and safe throttle or braking actions according to predicted limits. The design further incorporates hardware-level safety through isolation circuits and protective elements verified by simulation. A Python-based experimental testbed validates the proposed method in terms of response time and speed deviation; the results show advantages of the proposed method over the classical ISA systems. Hence, the proposed ISLS advanced a step closer to an adaptive, context-aware, and safety-sensitive speed control of the vehicle.