Pilot overhead poses a significant challenge in mmWave massive multiple-input multiple-output (MIMO) systems, as it fundamentally limits the accurate acquisition of channel state information (CSI). In this paper, we propose an enhanced adaptive channel estimation technique that leverages compressive sensing (CS) principles to effectively mitigate pilot overhead while maintaining high estimation accuracy. The proposed approach combines compressive sampling matching pursuit (CoSaMP) and sparsity adaptive matching pursuit (SAMP) algorithms, augmented by a novel iterative reweighting strategy and adaptive thresholding mechanism. The simulation results demonstrate that the proposed method achieves superior normalized mean square error (NMSE) performance compared to traditional CS-based techniques. Furthermore, the proposed technique achieves a substantial reduction in computational complexity and pilot overhead compared to traditional channel estimation methods, offering significant improvements in the performance of mmWave MIMO systems.

The fusion of Artificial Intelligence (AI) and the Internet of Things (IoT) in surveillance systems, known as the Artificial Intelligence of Things (AIoT), represents a major leap in security technology, enabling advanced monitoring and real-time data processing. However, this integration also presents a new frontier of complex security challenges. This comprehensive survey examines the evolving landscape of AIoT security in surveillance, focusing on critical issues such as the integration of heterogeneous devices, vulnerabilities inherent in edge computing and distributed processing, and the increasing threat of adversarial attacks on AI algorithms. We also address regulatory and compliance challenges that arise in this domain. The survey highlights the urgent need for robust security frameworks and proposes cutting-edge solutions, including advanced encryption mechanisms, lightweight communication protocols, and specialized Intrusion Detection and Prevention Systems (IDPS). Furthermore, we emphasize the significance of ethical considerations, particularly the implementation of zero-trust architectures and the necessity for ethical oversight to maintain user trust and ensure regulatory compliance. By synthesizing the latest research and identifying future directions, this paper provides essential insights for advancing AIoT security in surveillance and offers valuable guidance for researchers and industry practitioners navigating this rapidly evolving field.

Non-Orthogonal Multiple Access (NOMA) has emerged as a key enabler for massive connectivity in next-generation networks, while Intelligent Reflecting Surfaces (IRS) provide a transformative approach to enhancing spectral efficiency by reconfiguring the wireless propagation environment. However, integrating IRS with downlink NOMA presents significant challenges in power allocation, user grouping, and interference management, with existing methods often struggling to balance power efficiency, quality-of-service (QoS) guarantees, and computational scalability. This paper investigates a two-stage power allocation framework for IRS-assisted downlink NOMA. The first stage performs intra-group optimization, jointly adjusting user-level power allocation and IRS phase shifts to minimize power consumption while meeting QoS requirements based on an iterative optimization procedure. The second stage optimizes inter-group, allocating power across user groups to maximize the number of served users based on a sequential fixing programming procedure. A detailed complexity analysis demonstrates the computational efficiency of the framework, enabling real-time deployment in dynamic networks. The simulation results confirm that the proposed approach achieves superior sum rate performance while minimizing total power consumption and maintaining QoS guarantees.

Visual impairment affects millions worldwide, creating significant barriers to environmental interaction and independence. Existing assistive technologies often rely on cloud-based processing, raising privacy concerns and limiting accessibility in resource-constrained environments. This paper explores the integration and potential of open-source AI models in developing a fully offline assistive system that can be locally set up and operated to support visually impaired individuals. Built on a Raspberry Pi 5, the system combines real-time object detection (YOLOv8), optical character recognition (Tesseract), face recognition with voice-guided registration, and offline voice command control (VOSK), delivering hands-free multimodal interaction without dependence on cloud infrastructure. Audio feedback is generated using Piper for real-time environmental awareness. Designed to prioritize user privacy, low latency, and affordability, the platform demonstrates that effective assistive functionality can be achieved using only open-source tools on low-power edge hardware. Evaluation results in controlled conditions show 75–90% detection and recognition accuracies, with sub-second response times, confirming the feasibility of deploying such systems in privacy-sensitive or resource-constrained environments.