Unmanned aerial vehicles (UAVs) have emerged as crucial components in advancing mobile edge computing (MEC), leveraging their proximity to edge nodes and scalable nature. This synergy holds significant promise within the Internet of Things (IoT) and Beyond 5G (B5G) domains. In this article, we concentrate on optimizing the shrinking ratio, a Quality of Experience (QoE) metric, within large-scale IoT networks empowered by UAV-enhanced MEC via joint optimizing task offloading, resource allocation, and UAV trajectories. This joint optimization problem presents significant challenges due to the intertwined nature of multiuser computing mode selection and strong coupling between user equipments (UEs) waiting time and UAV trajectory. To tackle these challenges, we formulate the problem as a mixed integer nonlinear programming (MINLP) problem and propose an iterative algorithm named BTOU by decomposing the original problem into two subproblems using the block coordinate descent (BCD) framework. For the task offloading and resource allocation subproblem, we present two algorithms: one employs a low-complexity greedy game-theoretic approach suitable for a large number of UEs, while the other leverages the penalty successive convex approximation (PSCA) technique along with first-order Taylor expansion approximation to achieve high-solution quality. For the UAV trajectory planning subproblem, we transform it into a Miller-Tucker–Zemlin (MTZ) model and devise a solution strategy. Extensive simulation results validate the effectiveness of our proposed algorithm, showcasing rapid convergence and a notable improvement in QoE of over 10% compared to benchmark methods.

Device-to-device (D2D) is a pivotal technology in the next generation of communication, allowing for direct task offloading between mobile devices (MDs) to improve the efficient utilization of idle resources. This paper proposes a novel algorithm for dynamic task offloading between the active MDs and the idle MDs in a D2D–MEC (mobile edge computing) system by deploying multi-agent deep reinforcement learning (DRL) to minimize the long-term average delay of delay-sensitive tasks under deadline constraints. Our core innovation is a dynamic partitioning scheme for idle and active devices in the D2D–MEC system, accounting for stochastic task arrivals and multi-time-slot task execution, which has been insufficiently explored in the existing literature. We adopt a queue-based system to formulate a dynamic task offloading optimization problem. To address the challenges of large action space and the coupling of actions across time slots, we model the problem as a Markov decision process (MDP) and perform multi-agent DRL through multi-agent proximal policy optimization (MAPPO). We employ a centralized training with decentralized execution (CTDE) framework to enable each MD to make offloading decisions solely based on its local system state. Extensive simulations demonstrate the efficiency and fast convergence of our algorithm. In comparison to the existing sub-optimal results deploying single-agent DRL, our algorithm reduces the average task completion delay by 11.0% and the ratio of dropped tasks by 17.0%. Our proposed algorithm is particularly pertinent to sensor networks, where mobile devices equipped with sensors generate a substantial volume of data that requires timely processing to ensure quality of experience (QoE) and meet the service-level agreements (SLAs) of delay-sensitive applications.

Capturing long-term dependency from historical behaviors is the key to the success of sequential recommendation; however, existing methods focus on extracting global sequential information while neglecting to obtain deep representations from subsequences. Previous research has revealed that the restricted inter-item transfer is fundamental to sequential modeling, and some potential substructures of sequences can help models learn more effective long-term dependency compared to the whole sequence. To automatically find better subsequences and perform efficient learning, we propose a sequential recommendation model with a gated recurrent unit and Transformers, abbreviated as GAT4Rec, which employs Transformers with shared parameters across layers to model users’ historical interaction sequences. The representation learned by the gated recurrent unit is used as the gating signal to identify the optimal substructure in user sequences. The fused representation of the subsequence and edge information is extracted by the encoding layer to make the corresponding recommendations. Experimental results on four well-known publicly available datasets demonstrate that our GAT4Rec model outperforms other recommendation models, achieving performance improvements of 5.77%, 1.35%, 11.58%, and 1.79% in the normalized discounted cumulative gain metric (NDCG@10), respectively.

Unmanned aerial vehicles (UAVs) have increasingly become integral to multi-access edge computing (MEC) due to their flexibility and cost-effectiveness, especially in the B5G and 6G eras. This paper aims to enhance the quality of experience (QoE) in large-scale UAV-MEC networks by minimizing the shrinkage ratio through optimal decision-making in computation mode selection for each user device (UD), UAV flight trajectory, bandwidth allocation, and computing resource allocation at edge servers. However, the interdependencies among UAV trajectory, binary task offloading mode, and computing/network resource allocation across numerous IoT nodes pose significant challenges. To address these challenges, we formulate the shrinkage ratio minimization problem as a mixed-integer nonlinear programming (MINLP) problem and propose a two-tier optimization strategy. To reduce the scale of the optimization problem, we first design a low-complexity UAV partition coverage algorithm based on the Welzl method and determine the UAV flight trajectory by solving a traveling salesman problem (TSP). Subsequently, we develop a coordinate descent (CD)-based method and an alternating direction method of multipliers (ADMM)-based method for network bandwidth and computing resource allocation in the MEC system. Extensive simulations demonstrate that the CD-based method is simple to implement and highly efficient in large-scale UAV-MEC networks, reducing the time complexity by three orders of magnitude compared to convex optimization methods. Meanwhile, the ADMM-based joint optimization method achieves approximately an 8% reduction in shrinkage ratio optimization compared to baseline methods.

Unmanned aerial vehicles (UAVs) have increasingly become integral to multi-access edge computing (MEC) due to their flexibility and cost-effectiveness, especially in the B5G and 6G eras. This paper aims to enhance the Quality of Experience (QoE) in large-scale UAV-MEC networks by minimizing the shrinkage ratio through optimal decision-making in computation mode selection for each user device (UD), UAV flight trajectory, bandwidth allocation, and computing resource allocation at edge servers. However, the interdependencies among UAV trajectory, binary task offloading mode, and computing/network resource allocation across numerous IoT nodes pose significant challenges. To address these challenges, we formulate the shrinkage ratio minimization problem as a mixed-integer nonlinear programming (MINLP) problem and propose a two-tier optimization strategy. To reduce the scale of the optimization problem, we first design a low-complexity UAV partition coverage algorithm based on the Welzl method and determine the UAV flight trajectory by solving a Traveling Salesman Problem (TSP). Subsequently, we develop a coordinate descent (CD) based method and an alternating direction method of multipliers (ADMM) based method for network bandwidth and computing resource allocation in the MEC system. The CD-based method is simple to implement and has a low computational complexity, while the ADMM-based method can further enhance the optimization result through joint optimization. Extensive simulations demonstrate that our proposed algorithms perform well in large-scale edge networks and outperform other representative benchmark methods.

Device to Device (D2D) is a pivotal technology in the next generation of communication, allowing for direct task offloading between mobile devices (MDs) to improve the efficient utilization of idle resources. This paper proposes a novel algorithm for dynamic task offloading between active MDs and idle MDs in the D2D-MEC (Mobile Edge Computing) system by deploying multi-agent deep reinforcement learning (DRL) to minimize the long-term average delay of delay-sensitive tasks under deadline constraints. Our core innovation is a dynamic partitioning scheme for idle and active devices in the D2D-MEC system, accounting for stochastic task arrivals and multi-time-slot task execution, which has been insufficiently addressed in existing literature. We adopt a queue-based system to formulate a dynamic task offloading optimization problem. To address the challenges of large action space and coupling of actions across time slots, we model the problem as a Markov Decision Process (MDP) and perform the multi-agent DRL through Multi-Agent Proximal Policy Optimization (MAPPO). We employ a centralized training with decentralized execution (CTDE) framework to enable each MD to make offloading decisions solely based on its local system state. Extensive simulations demonstrate the efficiency and fast convergence of our algorithm. In comparison with the existing sub-optimal results deploying single-agent DRL, our algorithm reduces the average task completion delay by 11.0% and the ratio of dropped tasks by 17.0%.