Mobile edge computing (MEC)-enabled air-ground networks are a key component of 6G, employing aerial base stations (ABSs) such as unmanned aerial vehicles (UAVs) and high-altitude platform stations (HAPS) to provide dynamic services to ground IoT devices (IoTDs). These IoTDs support real-time applications (e.g., multimedia and Metaverse services) that demand high computational resources and strict quality of service (QoS) guarantees in terms of latency and task queue management. Given their limited energy and processing capabilities, IoTDs rely on UAVs and HAPS to offload tasks for distributed processing, forming a multi-tier MEC system. This paper tackles the overall energy minimization problem in MEC-enabled air-ground integrated networks (MAGIN) by jointly optimizing UAV trajectories, computing resource allocation, and queue-aware task offloading decisions. The optimization is challenging due to the nonconvex, nonlinear nature of this hierarchical system, which renders traditional methods ineffective. We reformulate the problem as a multi-agent Markov decision process (MDP) with continuous action spaces and heterogeneous agents, and propose a novel variant of multi-agent proximal policy optimization with a Beta distribution (MAPPO-BD) to solve it. Extensive simulations show that MAPPO-BD outperforms baseline schemes, achieving superior energy savings and efficient resource management in MAGIN while meeting queue delay and edge computing constraints.

--Unmanned Aerial V ehicles (UA Vs) in Wireless Power Transfer (WPT)-assisted Internet of Things (IoT) systems face the following challenges: limited resources and suboptimal trajectory planning. Reinforcement learning-based trajectory planning schemes face issues of low search efficiency and learning instability when optimizing large-scale systems. T o address these issues, we present an Attention-based UA V Trajectory Optimization (AUTO) framework based on the graph transformer, which consists of an Attention Trajectory Optimization Model (A TOM) and a Trajectory lEarNing Method based on Actor-critic (TENMA). In A TOM, a graph encoder is used to calculate the self-attention characteristics of all IoTDs, and a trajectory decoder is developed to optimize the number and trajectories of UA Vs. TENMA then trains the A TOM using an improved Actor-Critic method, in which the real reward of the system is applied as the baseline to reduce variances in the critic network. This method is suitable for high-quality and large-scale multi-UA V trajectory planning. Finally, we develop numerous experiments, including a hardware experiment in the field case, to verify the feasibility and efficiency of the AUTO framework. I NTRODUCTION With the advancement of 5G, the Internet of Things (IoT) has become widely used in a variety of fields, including environmental monitoring, healthcare, and industry 4.0, among others. However, due to limited transmitting power and battery capacity, Internet of Things Devices (IoTDs) perform poorly in long-distance communication.

This paper presents an approach for energy-neutral Internet of Things (IoT) scenarios where the IoT devices (IoTDs) rely entirely on their energy harvesting capabilities to sustain operation. We use a Markov chain to represent the operation and transmission states of the IoTDs, a modulated Poisson process to model their energy harvesting process, and a discrete-time Markov chain to model their battery state. The aim is to efficiently manage the duty cycling of the IoTDs, so as to prolong their battery life and reduce instances of low-energy availability. We propose a duty-cycling management based on K- nearest neighbors, aiming to strike a trade-off between energy efficiency and detection accuracy. This is done by incorporating spatial and temporal correlations among IoTDs' activity, as well as their energy harvesting capabilities. We also allow the base station to wake up specific IoTDs if more information about an event is needed upon initial detection. Our proposed scheme shows significant improvements in energy savings and performance, with up to 11 times lower misdetection probability and 50\% lower energy consumption for high-density scenarios compared to a random duty cycling benchmark.

We consider the optimization of distributed resource scheduling to minimize the sum of task latency and energy consumption for all the Internet of things devices (IoTDs) in a large-scale mobile edge computing (MEC) system. To address this problem, we propose a distributed intelligent resource scheduling (DIRS) framework, which includes centralized training relying on the global information and distributed decision making by each agent deployed in each MEC server. More specifically, we first introduce a novel multi-agent ensemble-assisted distributed deep reinforcement learning (DRL) architecture, which can simplify the overall neural network structure of each agent by partitioning the state space and also improve the performance of a single agent by combining decisions of all the agents. Secondly, we apply action refinement to enhance the exploration ability of the proposed DIRS framework, where the near-optimal state-action pairs are obtained by a novel L\'evy flight search. Finally, an imitation acceleration scheme is presented to pre-train all the agents, which can significantly accelerate the learning process of the proposed framework through learning the professional experience from a small amount of demonstration data. Extensive simulations are conducted to demonstrate that the proposed DIRS framework is efficient and outperforms the existing benchmark schemes.

To reap the benefits of the Internet of Things (IoT), it is imperative to secure the system against cyber attacks in order to enable mission critical and real-time applications. To this end, intrusion detection systems (IDSs) have been widely used to detect anomalies caused by a cyber attacker in IoT systems. However, due to the large-scale nature of the IoT, an IDS must operate in a distributed manner with minimum dependence on a central controller. Moreover, in many scenarios such as health and financial applications, the datasets are private and IoTDs may not intend to share such data. To this end, in this paper, a distributed generative adversarial network (GAN) is proposed to provide a fully distributed IDS for the IoT so as to detect anomalous behavior without reliance on any centralized controller. In this architecture, every IoTD can monitor its own data as well as neighbor IoTDs to detect internal and external attacks. In addition, the proposed distributed IDS does not require sharing the datasets between the IoTDs, thus, it can be implemented in IoTs that preserve the privacy of user data such as health monitoring systems or financial applications. It is shown analytically that the proposed distributed GAN has higher accuracy of detecting intrusion compared to a standalone IDS that has access to only a single IoTD dataset. Simulation results show that, the proposed distributed GAN-based IDS has up to 20% higher accuracy, 25% higher precision, and 60% lower false positive rate compared to a standalone GAN-based IDS.