The aerospace integrated networks (AINs), leveraging satellites and unmanned aerial vehicles (UAVs), offers a promising solution for large-scale Internet of Remote Things (IoRT), effectively ensuring information freshness, i.e., low Age of Information (AoI). However, in resource-constrained and dynamic AIN environment, a key challenge is how to achieve fresh data by efficiently resolving mission conflicts across multiple IoRT devices, which requires advanced scheduling design for collaborative UAVs monitoring and UAVs-satellites data transmission. In this paper, we first construct a mission scheduling framework for collaborative monitoring and transmission utilizing the wide coverage of low earth orbit (LEO) satellites and the mobility of UAVs. Then, by considering constraints such as mission conflicts, energy consumption, and motion characteristics, we characterize the relationship between UAVs trajectories and IoRT demands. Based on this, we propose a multi-agent deep reinforcement learning (MADRL) algorithm that jointly optimizes UAV trajectories and transmission scheduling. The algorithm incorporates a filter layer to optimize UAV cooperation, preventing redundant device IoRT selection and resolving mission conflicts. Simulation results indicate that the proposed algorithm can reduce 22.9% AoI compared to the benchmark.

With the rise of low-altitude economy (LAE), the incorporation of unmanned aerial vehicles (UAVs) with ground networks can assist integrated localization and communication (ILAC) services for ground user equipment (UE). However, the UAV location and number significantly affect communication coverage and localization performance for ground UEs. Additionally, UAV energy consumption and operational lifespan constrain the number of UEs each UAV serves. In this paper, we investigate the UAV deployment and access control assisting with ground stations to provide both communication and localization service to potential UEs. We decompose the UAV cooperation problem into two subproblems, namely minimizing UAV problem and the UAV assignment problem. To minimize the UAV number, we design an iterative greedy search algorithm that utilizes criteria importance through intercriteria correlation (CRITIC) method to dynamically evaluate each candidate UAV selection. After that, we assign the UAV access for each UE and reduce the number of UE served by each UAV to extend the lifespan of the drones. The communication and localization assignment methods are proposed by analysis of the number of communication links per UAV and the redundancy of anchor nodes. Further, we adopt a probability-based search algorithm to solve the assignment problem. Numerical studies are conducted to verify our proposed approach compared to other benchmark methods.

In next-generation wireless networks, high-mobility unmanned aerial vehicles (UAVs) are promising to provide content coverage, where users can receive sufficient requested content within a given time. However, trajectory planning for multiple UAVs to provide content coverage is challenging since 1) UAVs cannot provide content coverage for all users due to the limited energy and caching storage, and 2) the trajectory planning of UAV is coupled with each other. Moreover, the complete information based trajectory planning methods are unusable since UAVs cannot obtain prior information on the rapidly changing environment. In this paper, we investigate the multi-UAV trajectory planning for energy-efficient content coverage. We first formulate an energy efficiency maximization problem considering recharging scheduling, which aims to reduce the total length of trajectories of UAVs under the quality of service (QoS) constraints. To settle environment uncertainty, the trajectory planning problem is modeled as two coupled multi-agent stochastic games, whose equilibrium constitute the optimal trajectory. To obtain the equilibrium, we propose a decentralized reinforcement learning algorithm, which can decouple the two games. We prove that the proposed algorithm can converge to the optimal solution of the Bellman equation with a higher rate compared to the centralized one. Moreover, simulation results show that the energy efficiency of the proposed algorithm is smaller than 5% compared the optimal, which is obtained with the prior information of environments.

The access point (AP) deployment is a fundamental task for constructing an accurate localization system. Existing literature mainly deals with the AP placement problem using optimal geometry analysis since the target-AP geometry will affect the localization performance. However, some non-ideal phenomena in practical scenario, e.g., the existence of obstacles, array orientation and path loss, will degrade the accuracy of angle-of-arrival (AoA) estimation as well as the localization accuracy. In this article, we reformulate the AP planning incorporating these factors. We decompose the problem into two subproblems, namely AP selection problem and error minimization problem. The AP selection problem selects the minimum number of APs to satisfy a desired localization accuracy, aided by a refined orientation updating procedure. We design a centralized and a distributed error minimization algorithm to further decrease the localization error. The centralized algorithm shows superiority in time efficiency. Nevertheless, the case with large number of APs may lead to excessive computational cost. Accordingly, we further devise the distributed algorithm which is adaptive to large-scale deployment. Numerical studies in indoor environments with barriers are conducted to verify our proposed approach.

While Wi-Fi is of great potential for indoor localization, the access points (APs) deployment in realistic indoor environments is particularly challenging due to the impact of various obstacles, e.g., walls, pillars or bookcases. The diverse obstacles create the troublesome non-line-of-sight and the multipath effect, which deteriorate the localization accuracy. In this paper, we study the effect of obstacles on the localization error and formulate the AP planning problem as a AP selection problem. This problem is decomposed into two subproblems, i.e., AP selection problem and error minimization problem. The AP selection problem aims to choose the minimum number of APs to satisfy the preset accuracy requirement. Furthermore, the error minimization problem improves the localization performance through optimizing the AP positions and array orientations. Extensive simulations show that our proposed method is adaptive to the obstacles and it achieves higher localization accuracy compared with the existing deployment method.

Signal processing on antenna arrays has recently received extensive attention in the area of angle-of-arrival (AoA)-based indoor localization. Although sufficient array elements can improve the resolution in the AoA estimation, the array orientation has not been well exploited in research into the localization performance. In this paper, we investigate the effect of array orientations on the performance of AoA-based indoor localization systems. Appropriate array orientation can efficiently reduce the uncertainty in AoA estimation, thereby improving the localization accuracy. Accordingly, we present OpArray, an accurate indoor localization system based on flexible array deployment. First, OpArray designs an array deployment scheme, which establishes the foundation for accurate AoA estimates. The deployment scheme can be easily implemented through array rotations so as to optimize array orientations at receivers. Second, OpArray incorporates two refined phase preprocessing algorithms to mitigate the impact of negative factors, which exist in the practical implementation. In addition, aided by an improved AoA estimation algorithm, OpArray can localize a target on commercial off-the-shelf Wi-Fi platforms. Our experiments in a multipath-rich indoor environment show that OpArray achieves a median localization error of 0.5 m and the 80th percentile error is 1.0 m, which outperforms the state-of-the-art localization systems.

This paper presents an accurate indoor localization system with irregular deployment of antennas. It can be feasibly deployed on commodity Wi-Fi infrastructures, without any hardware or firmware modifications. Aided by elaborate phase processing and an enhanced angle of arrival (AoA) estimation algorithm, our proposed system could provide higher localization accuracy under the coverage of two line-of-sight (LOS) access points (APs) compared to the state-of-the-art localization systems, where at least three APs are utilized to achieve the same accuracy. To be specific, a pertinent phase compensation and sanitization algorithm is designed to eliminate the additional factors that will distort the genuine channel state information (CSI). On this basis, we make the antennas irregularly deployed at each AP such that the linear array symmetry is removed and more AoAs can be obtained. In particular, with two APs equipped with 3 antennas irregularly deployed, up to 4 AoAs could be obtained (more than 2 AoAs with linear array), which provides the ability to localize a target in a 3-D space. Our experiments in a multipath rich indoor environment show that our system achieves a higher localization accuracy than the state-of-the-art localization systems, namely, a median error accuracy of 1.2 m in 2-D localization and 1.45 m in 3-D localization with two APs.