The emergence of 6G-enabled Vehicle-to-Everything (V2X) networks has created unprecedented demand for ultra-reliable, low-latency spectrum allocation across heterogeneous entities including vehicles, IoT devices, and industrial systems. Current spectrum allocation methods suffer from exponential computational complexity, extensive information sharing requirements, and poor scalability in dense networks. This paper proposes AMADRL (Attention-based Multi-Agent Deep Reinforcement Learning), a novel framework employing dual critic networks with multi-head self-attention mechanisms for intelligent spectrum allocation. The dual critic architecture resolves individual-collective optimization conflicts through local critics for independent entity optimization and a global critic with attention-based coordination. Our approach significantly reduces information sharing requirements while handling heterogeneous QoS demands across diverse entity types. Comprehensive experimental evaluation comparing AMADRL against state-of-the-art baselines including MADDPG, MAAC, QMIX, attention-based methods (A-DDPG, MHA-DQN), and game-theoretic approaches reveals that AMADRL achieves superior performance across multiple metrics including spectrum utilization efficiency, interference mitigation, and network scalability, while preserving user privacy and satisfying strict latency constraints required by safety-critical and industrial use cases.

The integration of satellite, aerial, and terrestrial networks in Satellite–Air–Ground Integrated Networks (SAGIN) enhances connectivity but also introduces new vulnerabilities to multi-layer jamming attacks. These attacks—originating from space-based, air-based, and ground-based sources—exhibit diverse signal characteristics, resource constraints, and durations, posing significant threats to communication performance and system security. A single mitigation technique is often insufficient to address these varied challenges effectively. In this paper, we propose a multi-layer adaptive jamming mitigation framework that dynamically adapts to the type of jamming encountered, with a particular focus on threats targeting Low Earth orbit (LEO) satellites within SAGIN. We evaluate a range of mitigation techniques and analyze their performance across different jamming scenarios. Our results show that tailored mitigation strategies are essential in SAGIN to achieve higher Signal-to-Noise Ratio (SNR) and lower Bit Error Rate (BER), highlighting the importance of jamming-aware defenses for enhancing the resilience and security of SAGIN systems.

6G networks are expected to significantly benefit from advanced wireless local area technologies such as Wireless Gigabit (WiGig), which operates in the 60 GHz frequency band. This band supports extremely high data rates and low latency, making it ideal for next-generation wireless applications such as the metaverse and holograms. However, WiGig signals are highly susceptible to attenuation from physical obstructions, resulting in frequent handovers and connectivity disruptions. Traditional reactive handover mechanisms are often slow due to latency in decision-making and processing overhead. However, proactive handover strategies that leverage channel prediction can enhance network reliability and improve the quality of service. This paper investigates the feasibility of using statistical methods, specifically the auto-regressive integrated moving average (ARIMA) model, to predict the received signal strength indicator (RSSI) in real-world indoor WiGig environments. Our results indicate that ARIMA exhibits poor predictive accuracy, with a root mean square error (RMSE) of 15 dBm, which may trigger inaccurate handover decisions by initiating handovers under strong signal conditions or failing to respond under weak ones. To overcome this shortcoming, we propose an ensemble learning-based channel prediction approach utilizing the random forest (RF) algorithm. Our results show that the RF model significantly outperforms ARIMA by effectively capturing the nonlinear dynamics of real-world indoor WiGig channels. Specifically, the RF model achieves a 90% reduction in both mean absolute error and RMSE, and a 99% reduction in mean squared error, offering a promising solution for robust proactive handover management in 6G networks.

Emerging wireless local area networks, such as WiGig that operate in the extremely high-frequency band (60 GHz) hold significant potential for the development of next-generation 6G networks by offering high throughput and low latency. However, the 60 GHz band is prone to severe signal degradation due to channel blockages, leading to frequent handovers and challenges in maintaining seamless connectivity. Reactive handover strategies can result in service delays due to overhead and decision-making latency. To tackle these issues, proactive approaches that utilize machine learning (ML) and deep learning (DL) are becoming increasingly popular for network optimization in WiGig networks. However, existing ML/DL models are often tailored to specific network environments, making them susceptible to concept drift — a phenomenon where even minor environmental changes can significantly degrade network performance due to incorrect decision-making. This paper investigates scenarios and environmental changes that can trigger concept drift in WiGig networks. We conduct real-world experiments to analyze the statistical behavior of received signal strength, highlighting the potential for concept drift. Based on our findings, we propose a direction for identifying concept drift in WiGig networks.

The convergence of Intelligent Reflecting Surfaces (IRS) and Terahertz (THz) communications represents a transformative advancement for sixth-generation (6G) wireless networks, yet presents unprecedented challenges in system optimization. This paper addresses the critical challenge of joint optimization between IRS phase shifts and THz resource allocation in dynamic Internet of Things (IoT) environments, focusing on real-time adaptation to rapidly changing channel conditions. We propose a novel Adaptive Online Multi-Agent Deep Deterministic Policy Gradient (MADDPG) framework that leverages dynamic experience weighting to automatically adjust learning based on detected environmental changes. Our approach incorporates a multi-resolution buffer structure that balances recent observations with historical patterns, enabling both rapid adaptation and long-term optimization while considering the unique characteristics of THz-band propagation and IRS reflection patterns. The framework employs explicit coordination protocols between IRS controllers and resource managers, significantly improving convergence in non-stationary environments. Comprehensive simulations using realistic THz channel models and practical IRS configurations demonstrate that our proposed framework achieves a 45% improvement in system throughput, a 38% reduction in end-to-end latency, and a 30% enhancement in energy efficiency compared to conventional optimization approaches. More significantly, our solution demonstrates unprecedented adaptation capabilities, recovering 90% of optimal performance within 5 ms after abrupt environmental changes a critical requirement for future 6G networks. The framework maintains robust performance under diverse conditions, including high user mobility scenarios and adverse atmospheric conditions, while exhibiting linear computational scaling with increasing IRS elements (tested up to 512 elements). These results establish the viability of Adaptive Online MADDPG-based joint IRS-THz optimization for practical 6G deployments, particularly in dynamic IoT environments where traditional communication approaches face significant limitations.

Satellite communication is increasingly essential and widely used, especially with the rapid development of the Internet of Things (IoT) and networks beyond fifth-generation (B5G), providing ubiquitous coverage. However, the current reactive approaches to optimize resources have become inadequate due to the massive rise in IoT traffic with varying patterns and limited resources of satellite networks. These approaches fail to predict dynamic traffic and its requirements. To efficiently allocate satellite communication resources as necessary, there is a need to proactively predict traffic demand. We propose utilizing mobile edge computing-enabled high altitude platform stations (HAPS) to predict traffic from ground users to satellite networks at HAPS. However, resource control based on traffic prediction in satellite networks faces challenges such as the wastage of resources or insufficient resource availability due to misaligned traffic variations and resource control timing. To overcome these challenges, we propose a dynamic scheduling strategy for resource control based on traffic demand prediction. This strategy aims to reduce resource wastage in satellite communication systems. Our proposed approach can predict traffic with high accuracy and allocate resources with minimal difference between achievable throughput and required throughput, demonstrating high resource utilization. We evaluated the effectiveness of our scheduling strategy through simulation analysis by comparing it with periodic resource control.

The Internet of Things (IoT) has created acute network security concerns owing to their weak protocols and limited system resources. Vulnerable IoT devices increase the risk of compromising other devices connected to the network. Hence, vulnerability and risk assessments are necessary for IoT devices. Correspondingly, the Internet-wide port scan (IWPS) technique has garnered significant attention for its ability to discover and probe Internet-wide connected IoT devices. However, IWPS performance depends on the end wireless local area network (WLAN) state (e.g., congestion and signal-to-interference-plus-noise ratio). Scans oblivious to such dynamic WLAN factors can cause probe packet loss while increasing port-scan delays, which reduces the discovery rate, which misses the point of network security. Therefore, in this study, we propose a novel holistic approach to identifying WLAN environmental states based on round-trip time and probe-packet responses. To demonstrate the effectiveness of the proposed approach, we perform extensive experiments on real WLAN environments with various devices. The accuracy of the estimated states was validated at greater than 90% by analyzing the captured probe data at each WLAN.

Despite unprecedented advancements, wireless local area network (WLAN) technologies for the Internet of Things (IoT), such as IEEE 802.11ah (i.e., WiFi-HaLow), are prone to serious security threats, owing to their constrained computational and memory resources, which limit the use of heavyweight intrusion protection and security protocols. To address this problem, security administrators (sec-admins) must perform regular and comprehensive vulnerability assessments of IoT devices. An Internet-wide port scan (IWPS) is the initial step. However, the medium access control mechanism of IEEE 802.11ah, designed specifically for heterogeneous IoT traffic and low-power operations, can degrade network performance in the case of traditional port-scan traffic. Moreover, Internet-security (IPSec) protocol support is mandatory for IPv6-enabled IoT devices to ensure data confidentiality, integrity, and availability. Although the objective of a port scan is to improve IoT security, the resultant network performance can adversely affect IPSec services. Therefore, in this study, we optimize the IWPS to maximize the IoT security over IEEE 802.11ah WLAN. To this end, we propose novel mathematical models to evaluate IoT security based on port-scan network performance and IPsec services, which derives an optimal scan rate for sec-admins. The effectiveness of the proposed framework is verified by comprehensive numerical analysis, which shows that our approach minimizes the risk to IoT devices while probing them at an optimal scan rate.

With the emergence of Internet of Things (IoT), network security has become an area of acute concern owing to susceptibilities of IoT security that can be exploited to attack other devices and network infrastructures. Internet-Wide Port Scan (IWPS), a well-established network sifting mechanism that identifies threats and defensive mechanisms, is gaining attention to probe IoT networks and identify vulnerable IoT devices. A key enabler for IoT networks is the Wireless Local Area Network (WLAN) that comprises of numerous heterogeneous devices such as smartphones, computers, IoT devices, and so on. Hence, efficient probing of such networks can be challenging; since networks can easily experience congestion, poor signal-to-noise-ratio (SINR), etc. that can result in loss of probe packets and subsequently low discovery rate. In this paper these issues have been addressed and a holistic classification algorithm has been proposed to identify the states of heterogeneous WLAN environment based on real-time and historical measurements. Such a classification can assist in choosing scan strategies for improved IWPS performance. The experimental results presented in this paper reveal that the proposed algorithm is very effective to classify such a complex and heterogeneous environment.

In recent years, vulnerable Intenet of Things (IoT) devices have engendered several distributed denial of service (DDoS) attacks owing to the generation of massive IoT botnets by various IoT malware such as Mirai, Persirai and among others. IoT devices are vulnerable owing to constrained memory and computation resources that restrict the implementation of complex security protocols and anti-malware programs. In recent times, there have been attempts to implement periodic Internet-Wide port scan (IWPS) to identify vulnerable IoT devices at each wireless local area network (WLAN). IoT WLAN standard such as IEEE 802.11ah has been designed with the objective of low power consumption and massive connectivity, but is constrained by its low network performance particularly on traditional scan traffic; thus it can result in security degradation. Hence, in this paper, we propose novel models for security analyses of IoT based on the network performance of IWPS over IEEE 802.11ah. The results verify that the network-Oblivious IWPS can degrade IoT security and increase risk.

The latest evolution of cellular technologies, i.e., 5G including long term evolution-advanced (LTE-A) Pro and 5G new radio promises enhancement to mobile technologies for the Internet of Things (IoT). Despite 5G's vision to cater to IoT, yet some of the aspects are still optimized for human-to-human (H2H) communication. More specifically, the existing group paging mechanism in LTE-A Pro has not yet clearly defined approaches to group, mobile IoT devices (MIDs) having diverse characteristics, such as discontinuous reception (DRX) and data transmission frequency (DTF) with various mobility patterns. Inappropriate grouping of MIDs may lead to increased energy consumption and degraded quality of service, especially in terms of packet arrival delay (PAD) and packet loss rate (PLR). Therefore, in this paper, we devise novel models to estimate PAD, PLR, and energy consumption for MIDs, specifically for the group paging mechanism. Based on the proposed models, we formulate an optimization problem with the objective to minimize energy consumption of MIDs, while providing required PAD and PLR. The nonlinear convex optimization problem addressed herein is solved using the Lagrangian approach, and the Karush-Kuhn-Tucker conditions have been applied to derive optimal characteristics for MIDs to join the group, namely, DRX and DTF. The extensive numerical results presented verify the effectiveness of the proposed method, and the mathematical models demonstrate the superiority of our proposed approach over random grouping approach concerning significant energy consumption of MIDs.

The evolution of cellular networks under Long Term Evolution (LTE) has paved the path for LTE-Advanced (LTE-A) Pro that proposes forward LTE enhancements for Machine Type Communications (MTC) and meets the stringent requirements for realization of the Internet-of-Things (IoT). This paper identifies possible improvements in LTE-A Pro's existing group paging scheme, which is more expedient for human-to-human communications and inadequate for IoT applications. We propose a novel energy efficient group paging scheme by considering diverse IoT characteristics including Quality of Service considerations. Simulation results reveal that our proposed approach can significantly reduce energy consumption of IoT devices over existing group paging schemes.

A fuzzy match index for scale‐invariant feature transform (SIFT) features is proposed in this study that cumulatively involves all the test SIFT keypoints in the decision‐making process. The new fuzzy SIFT classifier is adapted successfully for robust face recognition from complex backgrounds without using any face cropping tools and using only a single training template. The further incorporation of entropy weights ensures that the facial features have a greater role in the soft decision‐making as compared with the background features. The highlights of the authors’ work are: (i) The development of a novel highly efficient fuzzy SIFT descriptor matching tool; (ii) incorporation of feature entropy weights to highlight the contribution of facial features; (iii) application to robust face recognition from uncropped images having diverse backgrounds with a single template for each subject. The authors thus allow for weak supervision of the face recognition experiment and obtain high accuracy for 20 subjects of the CALTECH‐256 face database, 133 subjects of the labelled faces for the wild dataset and 994 subjects of the FERET database, with state‐of‐the‐art comparisons indicating the supremacy of the authors’ approach.