The vast amount of content generated in the Meta verse and unpredictable user demands make real-time optimization of communication, computing, and caching increasingly challenging. These issues highlight the need for intelligent mechanisms that reduce redundant content transmission and improve resource efficiency. To address this, joint semantic aware caching and rendering schemes that leverage content similarity are proposed to enable reusability across Metaverse environments. The goal is to optimize user-server associations, caching, and rendering decisions to efficiently utilize network resources, thereby maximizing resource savings and service quality. Reusing content across heterogeneous Metaverse environments, however, requires a learning algorithm capable of adapting to diverse task settings. To this end, a lifelong learning–based algorithm, Deep-Centralized ELLA (DC-ELLA), incorporating dictionary learning is developed to accommodate diverse user requests by dynamically extracting knowledge from different semantic environments. Simulation results show that the proposed caching and rendering schemes significantly outperform traditional approaches, while DC-ELLA enhances convergence speed and stability, demonstrating superior performance in dynamic scenarios. By exploiting knowledge and content from prior requests, the approach achieves scalable adaptation to new Metaverse environments.

As the Metaverse evolves with developments in AI, semantic communication, edge computing, and blockchain, it encounters challenges in adapting to dynamic environments and meeting rising communication and computation needs. In this article, we propose a semantic-aware UAV-based architecture tailored to the dynamic Metaverse that leverages UAV swarms consisting of a collection UAV and edge UAV servers. By mapping different semantic features of Metaverse environments, such as amount of tasks, arrival rate, throughput, and latency requirements, we jointly optimize the mobility, task allocation, and resource allocation in a dynamic Metaverse system. First, a particle swarm optimization-based collection-edge mobility algorithm (PSO-CEMA) is designed to optimize the mobility of UAV servers. Second, to facilitate timely and stable task allocation with reduced complexity, we propose a dual-queue system and a Lyapunov drift function-based dynamic programming task allocation algorithm (LDF-DPTAA). Then, we adopt lifelong learning and design a collection-edge joint training and processing algorithm (LL-CJTPA) to optimize the dynamic allocation of computational resources in the swarm. Finally, we integrate our algorithms into a PSO-LDF-LL algorithm to serve the dynamic Metaverse system. Simulation results show that our approach effectively optimizes UAV servers’ positions and task allocation, significantly reduces the training time when facing new tasks, and enhances the stability and efficiency of the network in dynamic settings while reducing congestion.

Heterogeneous Internet of Things (IoT) networks, which operate using various protocols and spectrum bands like WiFi, Bluetooth, Zigbee, and LoRa, bring many opportunities to collaborate and achieve timely data collection. However, several challenges must be addressed due to heterogeneous data patterns, coverage, spectrum bands, and mobility. This paper introduces a cross-technology IoT network architecture design that facilitates collaboration between service providers (SPs) to share their spectrum bands and offload computing tasks from heterogeneous IoT devices using multi-protocol mobile gateways (M-MGs). The objective is to minimize the age of information (AoI) and energy consumption by jointly optimizing collaboration between M-MGs and SPs for bandwidth allocation, relaying, and cross-technology data scheduling. A pricing mechanism is presented to incentivize different levels of collaboration and matching between M-MGs and SPs. Given the uncertainty due to mobility and task requests, we design a cross-technology federated matching algorithm (CT-Fed-Match) based on a multi-agent actor-critic approach in which M-MGs and SPs learn their strategies in a distributed manner. Furthermore, we incorporate federated learning to enhance the convergence of the learning process. The numerical results demonstrate that our CT-Fed-Match-RC algorithm with cross-technology and relaying collaboration reduces the AoI by 30 times and collects 8 times more packets than existing approaches.

Satellite-airborne-terrestrial edge computing networks (SATECNs) emerge as a global solution for Internet of Things (IoT) applications in 6G. However, their highly dynamic nature with uncertain varying topology and network traffic makes their management and control more challenging. In this paper, we consider a scenario in which IoT devices, UAVs, and satellites with different edge computing capabilities make decisions to balance the freshness of information and energy consumption. Since SATECNs are highly dynamic and data freshness optimization requires timely decisions, we present a new lifelong learning computing resource allocation algorithm (LL-SATEC) that adapts to the environment by exploiting knowledge transfer between devices in different layers in SATECNs and previous experience. Lifelong learning is a promising machine learning algorithm that learns continuously without requiring a new training phase and avoids catastrophic forgetting. Our goal is to find computing resource allocation policies online for IoT devices, UAVs, and satellites that optimize the overall average age-of-information and energy trade-off. Numerical results show that our approach significantly accelerates learning compared to traditional reinforcement learning algorithms, achieves three times lower AoI and energy consumption in just a few iterations, and avoids catastrophic forgetting.