Multi-access edge computing (MEC)-based network slicing (MEC-NS) enables MEC network service providers (MEC-NSPs) to deploy autonomous virtual networks (slices) that deliver customized MEC services to edge Internet of Things devices (EIoTDs) with diverse quality-of-service (QoS) requirements, bringing flexibility to MEC resource management. However, developing an efficient slice-based computation task offloading and slice resource allocation (SCTOSRA) policy remains challenging due to constrained slice resources during slicing periods, evolving dynamics of the slice operating environment, and the difficulty of acquiring global information on connected EIoTDs. This paper proposes a novel adaptive and intelligent SCTOSRA scheme powered by personalized federated dueling double deep Q-learning (PerFedD3QL), which addresses these issues through three key innovations: (i) a dynamic regularization framework that enables robust adaptation across heterogeneous slice operating environments; (ii) an integrated knowledge distillation (KD) mechanism that mitigates non-IID data effects and curbs model drift; and (iii) a two-stage aggregation architecture combining parameter averaging and ensemble distillation to enhance model convergence and cross-slice generalization. PerFedD3QL constructs personalized local D3QL models at each slice and coordinates their training via federated learning to derive globally optimal SCTOSRA policies, which aim to reduce inference latency and energy consumption for connected EIoTDs while protecting data privacy and adapting to changing operational environment states of network slices over time. Simulation results demonstrate the effectiveness of the proposed PerFedD3QL-based SCTOSRA algorithm, which improves performance in reducing time delay and energy consumption compared to baseline methods while maintaining strong personalization and privacy preservation across varying slice scenarios.

The proliferation of Internet of Things (IoT) technologies and ubiquitous connectivity has led to uncrewed aerial vehicles (UAVs) playing key role as edge servers, revolutionizing the wireless communications landscape by facilitating computing and caching resources closer to ground users (GUs). This advancement significantly alleviates core network loads, reduces latency, and guarantees content availability even in congested or remote areas. However, jointly optimizing UAV caching strategies and trajectories gives rise to a multi-task optimization (MTO) problem. This paper introduces a novel multi-task geo-temporal caching (MT-GTC) framework that addresses the interplay between UAV caching mechanisms and trajectory optimization in a cohesive manner. Leveraging a proposed multi-task learning (MTL) model for joint optimization of UAV caching and trajectory design, we develop a federated learning cloud-assisted knowledge distillation (FL-CAKD) scheme to preserve data privacy and adapt to data heterogeneity. FL-CAKD transfers knowledge from a cloud model orchestrator (CMO), which houses a large and sophisticated teacher model, to a lightweight on-device MTL student models using soft target distributions instead of large model parameters, significantly reducing communication costs. MT-GTC optimizes caching and trajectories to maximize cache hits and minimize latency. Evaluations on real-world mobility datasets demonstrate up to 95% cache hit rates and 21% lower delays compared to baselines.

We investigate the computing resource allocation in multi-access edge network slicing (NS) in the context of revenue and multi-access edge computing (MEC) resource management. The significant variety of slice resource utilization levels across slice tenants (i.e., Mobile Virtual Network Operators (MVNOs)) challenges MEC resource management in NS with MEC, leading to virtual machine resource (VMR) (i.e., computing resource) wastage or scarcity. As a result, for efficient MEC resource management, the infrastructure provider (InP) encourages dynamic resource sharing and trading (DRST) of unutilized slice VMR quotas. Nevertheless, cellular network security and privacy issues deter MVNOs from collaborating on effective DRST. The security characteristics inherent in blockchain have recently gained much interest for secure resource trading. Thus, this paper proposes a unique hierarchical blockchain-based inter-slice computing resource trading (ISCRT) scheme for peer-to-peer (P2P) MVNOs in an autonomous multi-sliced MEC-based 5G network. For secure ISCRT transactions, a consortium blockchain network with hyperledger smart contracts (SC) is designed. We model the demand and pricing problems of buyer and seller MVNOs for the unutilized VMRs using a two-stage Stackelberg game. Then, to obtain the Stackelberg equilibrium (SE), an enhanced dueling double deep Q-network (D3QN) algorithm is proposed, which intelligently determines the optimal demand and pricing policies of MVNOs for the unutilized VMRs during ISCRT transactions at negotiation intervals. Simulation analysis shows that the proposed enhanced D3QN algorithm outperforms benchmark schemes in terms of the MVNO slice-level satisfaction and VMR utilization while reducing double-spending attacks in ISCRT settings by 16% and increasing both players’ utility.

In the fifth-generation (5G) wireless networks, Edge-Internet-of-Things (EIoT) devices are envisioned to generate huge amounts of data. Due to the limitation of computation capacity and battery life of devices, all tasks cannot be processed by these devices. However, mobile-edge computing (MEC) is a very promising solution enabling offloading of tasks to nearby MEC servers to improve quality of service. Also, during emergency situations in areas where network failure exists, unmanned aerial vehicles (UAVs) can be deployed to restore the network by acting as Aerial Base Stations and computational nodes for the edge network. In this article, we consider a central network controller who trains observations and broadcasts the trained data to a multi-UAV cluster network. Each UAV cluster head acts as an agent and autonomously allocates resources to EIoT devices in a decentralized fashion. We propose model-free deep reinforcement learning (DRL)-based collaborative computation offloading and resource allocation (CCORA-DRL) scheme in an aerial to ground (A2G) network for emergency situations, which can control the continuous action space. Each agent learns efficient computation offloading policies independently in the network and checks the statuses of the UAVs through Jain’s Fairness index. The objective is minimizing task execution delay and energy consumption and acquiring an efficient solution by adaptive learning from the dynamic A2G network. Simulation results reveal that our scheme through deep deterministic policy gradient, effectively learns the optimal policy, outperforming A3C, deep$Q$-network and greedy-based offloading for local computation in stochastic dynamic environments.