The space-air-ground integrated network has emerged as a critical enabler to achieve high-capacity 6 G communications. However, frequent handovers between satellites and gateways, along with unbalanced gateway traffic, significantly degrade the overall transmission capacity. To address these issues, this paper proposes a balanced satellite-ground scheduling architecture based on the analytic hierarchy process, called AHP-BSA. First, three spatiotemporal parameters (interconnection time$R(t)$, transmission capacity$C(t)$, and propagation delay$D_{p}$) are defined. The rationality and consistency of AHP-BSA is proved using the spatiotemporal parameters. In the parameter calculation phase of AHP-BSA, this paper further proves the relationship between$R(t)$and outage probability, as well as between$D_{p}$and transmission efficiency. Then, a parameter optimization algorithm is designed in AHP-BSA. These spatiotemporal parameters are normalized, weighted, and incorporated into the parameter optimization algorithm. Through stability-aware adjustment, it adjusts scheduling decisions in response to link dynamics and real-time fluctuations. Simulation results confirm that AHP-BSA outperforms existing methods in transmission capacity, time complexity, and long-term traffic balance.

In agricultural production, fixed-wing UAVs are widely used for image acquisition of pest detection because of their long endurance. However, due to its characteristic of being constrained by the turning radius, it causes the long turning path and much energy consumption. Thus, this article designs an algorithm based on turning span selection (TSS) for fixed-wing UAV, which plans a coverage path with the shortest turning path as possible. First, the target region model of the farmland is established. Then, by analyzing the relationship between turning radius and turning span of fixed-wing UAV, three different turning strategies are proposed. Finally, according to the pointer network model and the actor-critic algorithm in reinforcement learning, the TSS algorithm is designed. Simulation results show that the proposed TSS algorithm can effectively plan a flight path with a shorter turning path, and has obvious performance improvement compared with the existing algorithms

The paper addresses the challenge of scheduling multiple output priority queues of a switch in a real-world setting characterized by bursty traffic and diverse traffic priorities. Existing queue scheduling methods primarily employ two types of strategies: priority-based scheduling and weight-based scheduling. However, there is a lack of scheduling methods that can simultaneously handle bursty traffic in a timely manner and maintain priority-based scheduling. This paper addresses this issue by formulating the problem as a restless multi-armed bandit problem, and a queue scheduling method is proposed to balance priority service provisioning and bursty traffic processing. The proposed queue scheduling method operates efficiently in a timely manner based on instant queue length and utilizes the Whittle index method to achieve an asymptotically optimal solution. This design facilitates packet forwarding by considering the instant states of queues, offering improvements over existing methods. Experimental results demonstrate that the proposed method achieves a more efficient balance between priority service provisioning and bursty traffic processing compared to other state-of-the-art methods. Additionally, the proposed method results in better balanced network performance metrics, such as queue length, packet delay, and packet loss, thus efficiently supporting various applications that generate bursty traffic randomly.

Preserving user location and trajectory privacy while using location-based service (LBS) is an important issue. To address this problem, we first construct three kinds of attack models that can expose a user's trajectory or path while the user is sending continuous queries to a LBS server. Then we propose the k- anonymity trajectory (KAT) algorithm, which is suitable for both single query and continuous queries. Different from existing works, the KAT algorithm selects k-1 dummy locations using the sliding widow based k- anonymity mechanism when the user is making single queries and selects k-1 dummy trajectories using the trajectory selection mechanism for continuous queries. We evaluate and validate the effectiveness of our proposed algorithm by conducting simulations for the single and continuous query scenarios.