As drone-based applications proliferate, paramount contactless sensing of airborne drones from the ground becomes indispensable. This work demonstrates concentrating on propeller rotational speed will substantially improve drone sensing performance and proposes an event-camera-based solution, EventPro. EventPro features two components: Count Every Rotation achieves accurate, real-time propeller speed estimation by mitigating ultra-high sensitivity of event cameras to environmental noise. Every Rotation Counts leverages these speeds to infer both internal and external drone dynamics. Extensive evaluations in real-world drone delivery scenarios show that EventPro achieves a sensing latency of 3 ms and a rotational speed estimation error of merely 0.23%. Additionally, EventPro infers drone flight commands with 96.5% precision and improves drone tracking accuracy by over 22% when combined with other sensing modalities. Demo: https://eventpro25.github.io/EventPro/.

For precise, efficient, and safe drone landings, ground platforms should real-time, accurately locate descending drones and guide them to designated spots. While mmWave sensing combined with cameras improves localization accuracy, lower sampling frequency of traditional frame cameras compared to mmWave radar creates bottlenecks in system throughput. In this work, we upgrade traditional frame camera with event camera, a novel sensor that harmonizes in sampling frequency with mmWave radar within ground platform setup, and introduce mmE-Loc, a high-precision, low-latency ground localization system designed for precise drone landings. To fully exploit thetemporal consistencyandspatial complementaritybetween these two modalities, we propose two innovative modules:(i)the Consistency-instructed Collaborative Tracking module, which further leverages the drone's physical knowledge of periodic micro-motions and structure for accurate measurements extraction, and(ii)the Graph-informed Adaptive Joint Optimization module, which integrates drone motion information for efficient sensor fusion and drone localization. Extensive experiments (30+ hours) demonstrate that mmE-Loc attains 0.083$m$localization accuracy and 5.12$ms$end-to-end latency, outperforming four state-of-the-art methods by over 48% and 62%, respectively.

For precise, efficient, and safe drone landings, ground platforms should real-time, accurately locate descending drones and guide them to designated spots. While mmWave sensing combined with cameras improves localization accuracy, lower sampling frequency of traditional frame cameras compared to mmWave radar creates bottlenecks in system throughput. In this work, we replace traditional frame camera with event camera, a novel sensor that harmonizes in sampling frequency with mmWave radar within ground platform setup, and introduce mmE-Loc, a high-precision, low-latency ground localization system designed for drone landings. To fully leverage the temporal consistency and spatial complementarity between these modalities, we propose two innovative modules, consistency-instructed collaborative tracking and graph-informed adaptive joint optimization, for accurate drone measurement extraction and efficient sensor fusion. Real-world experiments in landing scenarios from a drone delivery company demonstrate that mmE-Loc outperforms SOTA methods in both accuracy and latency.

In this paper, we propose a hierarchical information acquisition system composed of a marine stationary sensor layer and an autonomous underwater vehicle (AUV) motion layer. Specifically, in the sensor layer, we design an energy-efficient clustering protocol based on the improved K-Means algorithm (ECBIK), which can implement uniform classification and select the cluster head dynamically according to energy awareness. Compared with the traditional K-Means and LEACH algorithm, our method achieves lower energy consumption and higher node survival rate, which can balance the energy load effectively to extend the life of the network. Additionally, in the AUV motion layer, we define the rotation-angle of AUV and analyze its influence quantitatively for the AUV information collection. Meanwhile, a novel Ant Colony (ACO) algorithm based on Markov Reward Process (MRP) is proposed for AUV path planning. As the simulation experiments indicate, our algorithm can achieve shorter distance, smaller angle, and faster convergence speed in path optimization.

This paper considers value based information collection for underwater acoustic sensor networks (UWASNs). In the considered system, the sensor nodes collect, store and update monitoring information with an initial value related to associated events. The value of information (VoI), however, decays with time. An autonomous underwater vehicle (AUV) is dispatched to retrieve data from the sensor nodes through acoustic communication. Our objective is to find the optimal traversal path for the AUV to maximize the VoI of the whole network. To achieve this goal, we first establish a realistic model for characterizing the behaviors of AUV and sensor nodes as well as the challenging environment, based on which the expression of the total VoI is derived. Then, we formulate the problem as a combinatorial optimization problem. We provide an optimal solution for this problem based on the branch and bound (BB) method, in which the lower bound (LB) and upper bound (UB) calculation strategies are specifically designed. A near-optimal heuristic algorithm based on the ant colony method is also adopted for further reducing computation complexity. Finally, simulations validate the effectiveness of the proposed algorithms.

The Internet of Underwater Things (IoUT) shows great potential in realizing the smart ocean. Underwater acoustic sensor networks (UWASNs) are the main existing form of IoUT but face with reliable data transmission problems. To tackle this issue, this article considers using the autonomous underwater vehicle (AUV) as a mobile collector to construct a reliable hierarchical information collection system while the Value of Information (VoI) is used as a main metric to measure the Quality of Information (QoI). We first establish a realistic model for characterizing the behaviors of AUV and sensor nodes as well as the challenging environments. Then, to construct a hierarchical architecture, we design a sink node (SN) selection scheme by jointly considering VoI conservation and energy load balancing. After that, we focus on AUV path planning with the objective of maximizing the VoI of the total network. We formulate the problem as a combinatorial optimization problem and provide an integer linear programming (ILP) model for this problem. An optimal algorithm based on the branch-and-bound (BB) method is proposed for seeking for the optimal solution, in which the lower bound and upper bound calculation strategies are specifically designed. Two near-optimal heuristic algorithms based on the concepts of the ant colony algorithm (ACA) and the genetic algorithm (GA) are also provided for further reducing the computation complexity. Finally, simulations validate the effectiveness of the proposed algorithms.

Unmanned aerial vehicles (UAVs) have been widely used in a range of compelling applications. However, some of them are incompetent in tackling with computation-intensive tasks due to limited processing capability and battery life. In this paper, we combine the mobile edge computing and traditional cloud computing techniques for offloading the tasks from multi-UAV systems. Specifically, we jointly optimize the task scheduling and resource allocation in the heterogeneous cloud architecture, where we strike a power-delay trade-off of the system relying on the queue theory and Lyapunov optimization, followed by its optimal strategy analysis in each time slot. Moreover, we conceive an iterative algorithm with a closed-form solution at each iteration round in order to reduce the computational complexity. Finally, numerical results demonstrate both the feasibility and effectiveness of our proposed scheme. This paper validates that the heterogeneous cloud structure can be the beneficial for improving quality-of-service performance of multi-UAV systems.

Unmanned aerial vehicle (UAV) communication is a promising technology for Internet of Things (IoT) systems. In this paper, we combine UAV communication and nonorthogonal multiple access (NOMA) for constructing high capacity IoT uplink transmission systems, where UAVs are used as aerial base stations for collecting data from IoT nodes while NOMA is invoked for uplink transmission. We aim to maximize the system capacity by jointly optimize the subchannel assignment, the uplink transmit power of IoT nodes, and the flying heights of UAVs. We commence by proposing an efficient subchannel assignment algorithm relying on the classic K-means clustering method and matching theory. Then, we determine both the distributed uplink transmit power of IoT nodes and flying heights of UAVs based on successive optimization approach. An alternative optimization algorithm is also proposed for finding the near-optimal solutions. Finally, the numerical results demonstrate the superiority of our proposed scheme.

Unmanned aerial vehicles (UAVs) have been widely used in a range of compelling applications. In this paper, we integrate both the networking techniques and the cloud computing tasks of multi-UAV systems. We commence by proposing an energy efficient scheme for selecting the gateway of UAVs invoked for relaying data to the heterogenous cloud. Then, relying on queuing theory and Lyapunov optimization, we strike a power-delay trade-off by jointly optimizing the computational task scheduling and resource allocation in the heterogeneous cloud architecture, which is comprised of an edge cloud and a powerful remote cloud. We analyze the optimal resource-allocation strategy for each time slot and an iterative algorithm is conceived for reducing the computational complexity. Finally, our numerical results demonstrate the superiority of the proposed scheme.

With growing human maritime activities, supporting low-cost and high-speed information services for users at sea has become an imperative focus. In this paper, we consider a maritime relay multicast system including a shore-based base station and several offshore relay nodes, and propose an energy efficient relay design scheme. Specifically, we formulate the relay design problem as a power minimization problem under users' quality-of-service (QoS) constraints, and the problem is approximated and solved using the feasible point pursuit successive convex approach. Furthermore, an iterative algorithm is proposed with exponential complexity. In order to reduce the computational complexity, a low-complexity distributed algorithm is conceived and its closed-form solution is derived. Finally, simulation results show that our proposed scheme is beneficial in terms of achieving a higher communication rate as well as of yielding a better energy efficiency.