In a multi-UAV system, some collected data should be transmitted to relevant UAVs or ground station for processing when energy resources are insufficient or computation consumption is large. In this paper, we researched the energy-saving strategies for data aggregation on the combination of store-carry-forward (SCF) routing and hop-by-hop routing, which are suitable for large-scale multi-UAV networks, and proposed Single Coalition Strategy (SCS) and Coalition Formation Strategy (CFS) based on the coalition game theory. In SCS, all source UAVs transmit data individually or can form a coalition. The coalition aggregates all data to a ferry UAV for transmission, the ferry choose between SCF routing mode and hop-by-hop routing mode with the help of predicted energy-cost calculation. In CFS, all source UAVs form a coalition structure by coalition formation game (CFG) algorithm according to data amount, location, and network topology. Each coalition can aggregate data to its own ferry, and then the ferry transmits data in the optimized mode. We analyze the performance of SCS and CFS by simulations, the average transmission energy consumption of a UAV in SCS and CFS are about 30% of hop-by-hop routing mode. Based on the results, we recommend that SCS should be used when the application scenario is small and the number of UAVs is limited. In the case where the application scenario is great and the number of UAVs is large, CFS with the Utilitarian order and limited message propagation hops is recommended.

Unmanned Aerial Vehicles (UAVs) are widely used as mobile information collectors for sensors to prolong the network time in Wireless Sensor Networks (WSNs) due to their flexible deployment, high mobility, and low cost. This paper focuses on the scenario where rotary-wing UAVs complete information collection mission cooperatively. For the first time, we study the problem of minimizing the mission completion time for a multi-UAV system in a monitoring scenario when considering the information collection quality. The mission completion time includes flying time and hovering time. By optimizing the trajectories of all UAVs, we minimize the mission completion time while ensuring that the information of each sensor is collected. This problem can be formulated as a mixed-integer non-convex one which has been proved to be NP-hard. To solve the formulated problem, we first propose a hovering point selection algorithm to select appropriate hovering points where the UAVs can sequentially collect the information from multiple sensors. We model this problem as a BS coverage problem with the information collection quality in consideration. Then, we use a min-max cycle cover algorithm to assign these hovering points and get the trajectory of each UAV. Finally, with the obtained UAVs trajectories, we further consider the UAVs can also collect information when flying and optimize the time allocations. The performance of our algorithm is verified by simulations, which show that the mission completion time is minimum compared with state-of-the-art algorithms.

Nowadays, Unmanned Aerial Vehicles (UAVs) have received growing popularity in the Internet-of-Things (IoT) which often deploys many sensors in a relatively wide region. Since the battery capacity is limited, sensors cannot transmit over a long distance. It is necessary for designing efficient sensor data collection mechanisms to prolong the lifetime of the IoT and enhance data collection efficiency. In this paper, we consider a UAV-enabled data collection scenario, where multiple heterogeneous UAVs with different energy constraints are employed to collect data from sensors. The value of data depends on the importance of the monitoring area of the sensor and the freshness of collected data. Our objective is to maximize the data collection utility by jointly optimizing the communication scheduling and trajectory of each UAV. The data collection utility is determined by the amount and value of the collected data. This problem is a variant of multiple knapsack problem, which is a classical NP-hard problem. First, we transform the initial problem into a submodular function maximization problem under energy constraints, and then we design a novel trajectory planning algorithm to maximize the data collection utility, while accounting for different values of data and different energy constraints of heterogeneous UAVs. Finally, under different network settings, the performance of the proposed trajectory planning algorithm is evaluated via extensive simulations. The results show that the proposed algorithm can obtain maximum data collection utility.

Millimeter wave (mmWave) unmanned aerial vehicle (UAV)-aided networks have enormous application potential due to their large bandwidth and ultra-high speed, being regarded as an effective technology for improving the reliability of military and civilian fields. However, due to their complex electromagnetic spectrum environment and the sensitivity of mmWaves to blocking effects, its performance analysis faces certain difficulties. This article investigates the coverage and network capacity of mmWave UAV-aided networks under significant blocking effects and complex electromagnetic environments; for this purpose, we equipped each UAV with mmWave antennas featuring adjustable beamwidth and direction. A Matérn hard-core point process (MHCPP) with repulsion constraints was also employed to reflect the minimum distance constraints to isolate the mutual interference between UAVs. Then, using a stochastic geometric analysis, we derived the coverage and capacity characteristics and further obtained a closed-form expression for the network coverage probability. Finally, the simulation results showed that the network throughput could reach 86% when the density of UAVs was half of that of ground base stations (GBSs) in the city center, validating the efficiency and accuracy of our theoretical derivations.

This paper considers a reconnaissance scenario where multiple unmanned aerial vehicles (UAVs) provide services cooperatively for multiple users with different priorities. Although users all expect to acquire their needed information in a short time, the degree of urgency to meet their demands is different when taking their diverse priorities into account. A priority-driven multiuser satisfaction model is designed, where users’ satisfaction is quantified based on their different priorities, information acquisition demands, and the time they obtain their desired information. To ensure high priority users’ fast information acquisition while avoiding excessive delay in low priority users’ information acquisition, a batchwise information backhaul strategy is adopted. In each batch, a UAV is selected as the data ferry through a consultative mechanism to carry information back to users. This reconnaissance process is formulated as a cooperative path planning problem, where the optimization objective is maximizing users’ total satisfaction, while an intelligent algorithm is proposed to solve this problem effectively. The simulation results show that compared with traditional path planning algorithms without considering user satisfaction, our proposed algorithm can guarantee faster information acquisition for users with higher priorities, which leads to higher satisfaction. In addition, the applicability of our proposed reconnaissance strategy and path planning algorithm in different situations is also analyzed.