# Smartphone-Based Task Scheduling in UAV Networks for Disaster Relief
## 1 Introduction  
The number of natural disasters has significantly increased in recent decades [1]. In addition to enhancing disaster prevention measures, improving post-disaster rescue operations is also extremely necessary. Since adequate and comprehensive data from the environment can help rescuers carry out accurate rescue operations, data collection and processing after the disaster are extremely important. The unmanned aerial vehicles (UAVs) can be equipped with different types of modules, such as the information acquisition module, the communication module, and the computing module. UAVs are widely used in post-disaster rescue because of their high data collection capability, high mobility, and strong adaptability to complex environments [2].  
To ensure their flexibility and maneuverability during flight, UAVs are often limited in weight and size to achieve a lightweight and compact design, which, at the same time, limits the capabilities of the computing module, the communication module and the battery power module. Hence, the processing capability of a UAV is quite limited in the case of a massive amount of real-time data [3]. Nowadays, smartphones (SPs) are the most common devices that humans carry around on a daily basis. Due to the rapid development of today's technology, the computing power of SPs has been greatly enhanced. Additionally, SPs support multiple communication modes, such as Bluetooth, Wireless Fidelity, Global System for Mobile Communications, and Code Division Multiple Access. Even in disasterscenarios, there are still a large number of available SPs that have a large number of idle computing resources.  
Based on the characteristics of SPs, we chose to develop SPs as mobile assistance devices to help UAVs perform complex computational tasks in disaster scenarios. We first modeled the disaster based on the disaster level. Then, by considering the diversity of tasks in real-world scenarios, Quality of Service (QoS) coefficients were introduced to measure the performance requirements of different types of tasks. Finally, by considering both the cost of UAVs and the execution utility of SPs during the task execution process, we established a multi-objective optimization problem and designed a Multi-Objective Mutation-Immune Bat (MOMIB) algorithm to optimize our proposed problem.  
The contributions of this study are as follows:  
* Based on the wide distribution range and the enhanced computing capacity of SPs, we innovatively developed SPs as mobile assistance computing devices for UAVs task offloading in post-disaster rescue. The use of SPs not only enlarges the computing power of UAVs but also fully utilizes idle resources. As far as we know, this is the first study proposing a method in which smartphones are employed in post-disaster rescue.
* According to the characteristics of different types of tasks in the actual environment, we introduced QoS coefficients with the aim of measuring various tasks. Then, we differentiated among different task types based on these coefficients and modeled the earthquake disaster by dividing it into two scenarios based on different characteristics.
* To optimize the proposed problem, we designed a MOMIB algorithm which makes use of the characteristics of the immune algorithm to improve the traditional bat algorithm. Compared with the bat algorithm, the improved algorithm has better global solution and convergence.
* Extensive simulation experiments show that, compared with other relevant strategies, MOMIB achieves the best optimization results, i.e., the lowest UAV cost and the highest SP utility.  
The organizational structure of this paper is introduced. The system model is introduced in Section 3. In Section 4, we report the calculations of the task computation cost of UAVs and the utility of SPs. In Section 5, we state our multi-objective optimization problem and provide details on the proposed MOMIB algorithm. The simulation experiment are conducted in Section 6. At last, we summarize this study in Section 7.