The rapid development of the Unmanned Aerial Vehicle(UAV) brings much convenience to our life. However, security and privacy problems caused by UAVs are gradually exposed. This paper analyzes UAV safety from three aspects, including sensors, communications and multi-UAVs. A UAV relies on different sensors to locate and calculate its flight attitude, which means spoof and attacks on sensors are fatal. On the one hand, wrong information from sensors will lead UAVs to make wrong judgments. On the other hand, damage to sensors can cause UAVs to fail to obtain information and severely cause UAVs to crash. Information exchange between UAV and Ground Control Station(GCS) relies on communication links and an unsafe link is susceptible to attacks. Multi-UAVs applications rely on stable network among UAVs. Ad-hoc network the mainstream of current networking method but it still exists many potential dangers. Besides, another possibility of privacy disclosure caused by aerial photos is also mentioned. These photos often contain private information such as location and shooting time which is likely to be leaked when the photographer shares photos on social applications. Finally, we summarize the paper and discuss the future research direction.

Unmanned aerial vehicle technology has made great progress in the past and is widely used in many fields. However, they are unable to meet large-scale and complex missions with a limited energy reserve. Only multiple unmanned aerial vehicles (multi-UAV) work together to better cope with this problem and have been extensively studied. In this paper, a new systematic framework is proposed to solve the problem of multi-UAV collaborative task allocation. It is formulated as a combinatorial optimization problem and solved by the improved clustering algorithm. The purpose is to enable multi-UAV to complete tasks with lower energy consumption. As the number of UAVs rises, it also appears the flight safety issues such as collisions among the UAVs, an improved multi-UAV collision-resistant method based on the improved artificial potential field is proposed. Besides, the UAVs connected with the internet are vulnerable to the various type of network attacks, a method based on the intrusion detection system is proposed to resist the network attack during multi-UAV mission execution. We have also proposed an improved method to improve the accuracy of task allocation further. In addition, an online real-time path planning is proposed to enhance the robustness of multi-UAV to cope with sudden problems. Finally, the numerical simulations and real physical flying experiments showed that the proposed method could provide a viable solution for multi-UAV task allocation; moreover, compared with other task allocation methods, our method has great performance.

With the rapid development of the network and the informatization of society, how to improve the accuracy of information is an urgent problem to be solved. The existing method is to use an intelligent robot to carry sensors to collect data and transmit the data to the server in real time. Many intelligent robots have emerged in life; the UAV (unmanned aerial vehicle) is one of them. With the popularization of UAV applications, the security of UAV has also been exposed. In addition to some human factors, there is a major factor in the UAV’s endurance. UAVs will face a problem of short battery life when performing flying missions. In order to solve this problem, the existing method is to plan the path of UAV flight. In order to find the optimal path for a UAV flight, we propose three cost functions: path security cost, length cost, and smoothness cost. The path security cost is used to determine whether the path is feasible; the length cost and smoothness cost of the path directly affect the cost of the energy consumption of the UAV flight. We proposed a heuristic evolutionary algorithm that designed several evolutionary operations: substitution operations, crossover operations, mutation operations, length operations, and smoothness operations. Through these operations to enhance our build path effect. Under the analysis of experimental results, we proved that our solution is feasible.

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