In this paper, a non-orthogonal multiple access (NOMA) visible light communication (VLC) system is investigated. The system uses angle diversity receivers (ADRs) to provide high data rates. The ADR has 4 branches, each directed to a different direction. An 8m x 4m sized room is modelled to study the resource allocation to users according to their channel conditions to maximize the data rate. The results show that using ADRs improves the data rate by an average of 35% compared to a system using wide FOV receivers.

The continuous evolvement of IoT networks has introduced significant optimization challenges, particularly in resource management, energy efficiency, and performance enhancement. Most state-of-the-art solutions lack adequate adaptability and runtime cost-efficiency in dynamic 6G-enabled IoT environments. Accordingly, this paper proposes the Trust-centric Economically Optimized 6G-IoT (TEO-IoT) framework, which incorporates an adaptive trust management system based on historical behavior, data integrity, and compliance with security protocols. Additionally, dynamic pricing models, incentive mechanisms, and adaptive routing protocols are integrated into the framework to optimize resource usage in diverse IoT scenarios. TEO-IoT presents an end-to-end solution for security management and network traffic optimization, utilizing advanced algorithms for trust score estimation and anomaly detection. The proposed solution is emulated using the NS-3 network simulator across three datasets: Edge-IIoTset, N-BaIoT, and IoT-23. Results demonstrate that TEO-IoT achieves an optimal resource usage of 92.5% in Edge-IIoTset and reduces power consumption by 15.2% in IoT-23, outperforming state-of-the-art models like IDSOFT and RAT6G.

The expansion of 6G-enabled IoT networks demands integrated solutions for optimizing computational, communication, and energy resources. Existing methods often treat power efficiency and resource allocation independently, limiting overall performance gains. This study introduces FLARE, a federated learning (FL)-based framework for adaptive resource management and optimization in 6G-IoT. FLARE integrates an adaptive learning rate scheduler $\eta _{t}=\eta _{0}/(1+\alpha t)$ , gradient sparsification (Top- $k$ ) and uniform quantization to minimize communication overhead, and model pruning with weight quantization for energy reduction. A novel dynamic route optimization algorithm jointly minimizes delay and power consumption via continuous flow-based updates, supported by a virtualization-aware server activation model. An economic optimization module applies dynamic pricing $P_{\text {dynamic}}(t)$ and incentive mechanisms to balance demand and cost. NS-3 simulations on Edge-IIoTset, N-BaIoT, and IoT-23 show that FLARE improves resource utilization by up to 5%, power efficiency by 4%, and network performance by 3%–5% compared to leading baselines under varied demand scenarios.

The intelligent transportation systems (ITS), including autonomous driving technologies, have increased the need for a capable communication system. The optical domain offers a promising spectrum for supporting multi-connection and high-data-rate applications. This paper proposes a dynamic resource allocation algorithm for vehicle-to-vehicle (V2V) 6G visible light communication (VLC) systems. A wavelength division multiple access (WDMA) method is utilized as a technique for supporting multiple connections. Two optimization objectives of the resource allocation are evaluated, which are referred to as Max SNR and Max spectral efficiency (SE) objectives. The best resource assignment for each vehicle is obtained by using the optimized resource allocation model. Five scenarios are examined where vehicles are moved in this work. The Max SE objective shows a fair allocation of resources based on the SNR compared to the Max SNR objective. In addition, a dynamic algorithm is developed for real-time solutions. The proposed dynamic algorithm can provide suboptimal resource allocation at 0.001 s, whereas the Max SE MILP model provides the optimal resource allocation in around 1 min. Thus, the proposed dynamic scheme can be used in real-time applications.

Optical wireless communication (OWC) systems have been the subject of a significant amount of interest as they can be used in sixth generation (6G) wireless communication to provide high data rates and support multiple users simultaneously. This paper investigates the impact of receiver orientation on resource allocation in optical wireless systems, using a wavelength division multiple access (WDMA) scheme. Three different systems that have different receiver orientations are examined in this work. Each of these systems considers 8 simultaneous users in two scenarios. WDMA is utilised to support multiple users and is based on four wavelengths offered by Red, Yellow, Green and Blue (RYGB) LDs for each AP. An angle diversity receiver (ADR) is used in each system with different orientations. The optimised resource allocations in terms of wavelengths and access point (AP) is obtained by using a mixed-integer linear programming (MILP) model. The channel bandwidth and SINR are determined in the two scenarios in all systems. The results show that a change in the orientation of the receiver can affect the level of channel bandwidth and SINR. However, SINRs in both scenarios for all users are above the threshold (15.6 dB). The SINR obtained can support data rate of 5.7 Gbps in both scenarios in all systems.

Light Fidelity (LiFi) has emerged as a promising wireless communication technology for enabling indoor networks in future sixth-generation (6G) systems. It can offer very high data rates, low electromagnetic interference, elevated spatial reuse and inherent physical layer security advantages. In LiFi-based indoor deployments, however, the characteristically small coverage footprint of each AP can produce frequent handovers when users move across the indoor environment. Conventional key management approaches adopt static key lifetime policies that expire session keys after a fixed duration regardless of user movement conditions. This creates a fundamental inefficiency where static policies can generate key refresh operations that are not required. Further, an additional signaling overhead can happen when users are stationary or leave session keys active for comparatively long periods under high mobility conditions. This paper proposes a lightweight mobility-aware adaptive session key lifetime control mechanism that dynamically adjusts session key validity duration. The mechanism introduces a three-component mobility metric that aggregates user speed, handover frequency and AP residence time which is used to compute a normalized mobility risk score. The mobility score drives an adaptive lifetime formula that contracts or extends key validity according to mobility-associated exposure conditions. The proposed mechanism operates entirely within the post-authentication session phase and does not introduce new cryptographic primitives, authentication protocols or key generation algorithms. Compared with fixed session key lifetime policies of 120 s, 300 s and 600 s, the Adaptive Key Lifetime Control Engine (AKLCE) dynamically adapts the session key lifetime according to user mobility, reducing refresh operations and signaling overhead under low mobility while progressively shortening the scheduled key lifetime under higher mobility to limit key exposure. This adaptive behavior enables AKLCE to provide mobility-dependent adaptation of session key lifetime that enables a flexible trade-off between key exposure duration and signaling overhead.