# A Low Complexity Persistent Reconnaissance Algorithm for FANET
###### Abstract
To cope with the deterioration of the network connection caused by node vulnerability, ways to implement distributed connection maintenance and node importance assessment are extensively investigated in [9]. With the popularity of FANET technology, it has received much research interest in numerous practical applications [10; 11; 12; 13]. To realize remote command and situational awareness, the authors in [10] constructed a cooperative monitoring network consisting of multiple UAVs and ground stations. A multi-relay UAV selection scheme based on fuzzy optimization is developed to realize the tradeoff between surveillance performance and connectivity maintenance. The articles [11; 12] focus on the application of FANETs in disaster relief. In [11], [PERSON] et al. deal with the continuous sensing and monitoring of the geographical location of a specific disaster event. Their paper introduces and demonstrates various protocol stacks. A network simulator (NS-3) and a robot simulator (Gazebo) are used in synergy to simulate the disaster event boundary monitoring process. In addition, [PERSON] et al. [12] propose a distributed algorithm, dPSO, to provide network support for victims and ambulance personnel in disaster areas. In the process of urban's digital and intelligent development, FANET technology is envisaged to play an important part. [PERSON] et al. [13] designed an enhanced Ant Colony Optimization (ACO) technique for traffic detection in remote urban areas, which improves the network life and the number of received packets compared to comparison algorithms. In the case when continuous reconnaissance is required, the detection range expansion, information transmission, and even network segmentation of a FANET remain hard nuts to crack. Moreover, the safety of UAVs cannot be guaranteed due to natural factors and various enemy air defense operations [14; 15; 16; 17]. For example, a common natural threat stems from local magnetic field variations. Sudden magnetic field change can interfere with the magnetic compass used for UAV positioning. The UAV incorrectly assumes that the change in magnetic compass data is due to its position movement and makes corrective actions to deal with it. These actions continue with the magnetic disturbance, and the UAV is out of control as seen from the ground. Moreover, in a hostile environment, the enemy can decrypt the communication protocol of UAVs to gain control. That is a common threat that UAVs face in the process of reconnaissance. Furthermore, acoustic waves are also used to strike UAVs. When the location of a UAV is detected, the acoustic transmitter sends acoustic waves of a specific frequency to its direction, triggering a resonance effect of the UAV's gyroscope. Once the gyroscope becomes abnormal, the UAV's inability to identify angles could cause it to crash. Some studies did take the above-mentioned issues into account. [PERSON] et al. considered the possible threats at the data transfer protocol level, such as hostile devices altering or masking the signals received by UAVs' GPS receivers [15]. They proposed a new method to evaluate UAV security threats based on Two-Criteria Likelihood-Impact scales. In [16], electromagnetic interference is considered, and the interference suppression is realized by optimizing the airborne antenna. Taking targets' movement into account, [PERSON] et al. [17] designed a cooperative UAV tracking method based on a sparse A* search and Standoff tracking algorithm. It realizes continuous tracking of moving targets in the task area. However, as far as reconnaissance missions are concerned, none of the above literature considers the dynamics of the threat to drones. Nowadays, various jamming and hijacking capabilities have been integrated into diversiform mobile anti-reconnaissance devices, making the UAVs' threat change frequently. Only when the security of UAVs is guaranteed can various network architecture schemes and collaborative tasks be executed normally.