## 1 Introduction  
The Internet of Drones (IoD) is a new paradigm in which a set of flying nodes, known as drones or Unmanned Aerial Vehicles (UAVs), communicate with one another or with ground infrastructure via the Internet to perform various tasks, such as environmental monitoring, video streaming, surveillance, smart agriculture, urban planning, object tracking, and disaster management [1].  
IoD standards facilitate the regulation of various solutions for flying objects developed by different manufacturers, enabling them to communicate with one another across various hardware architectures, platforms, and communication protocols. Several IoD standardization initiatives have been promoted by various industry and government standardization organizations, as well as specific interest groups, including the Third Generation Partnership Project (3 GPP) [2], the International Telecommunication Union Telecommunication Standardization Sector (ITU-T) [3], and the European Telecommunications Standards Institute (ETSI) [4].  
The IoD offers many advantages by integrating drones with the Internet of Things (IoT), such as extending connectivity and coverage; increasing reliability, storage, and processing capacity; improving resistance to weather conditions; and enhancing energy supply for IoT nodes [5].  
IoD technologies can be classified into four distinct categories: IoD cloud computing, IoD edge computing, IoD fog computing, and IoD cellular networks [6]. IoD cloud computing provides significant computational and storage capacity for drone-collected data by transferring the data to cloud servers. In contrast, IoD edge and fog computing technologies aim to reduce data transmission latency by enabling local processing and storage of UAV-collected data at edge and fog nodes within the network [7; 8].  
The IoD cellular network is regarded as a highly promising technology for real-time applications due to its exceptional reliability, high data rates, and low latency. Moreover, IoD cellular networks can significantly improve IoD performance in various aspects, including connectivity, accessibility, monitoring, management, navigation, and cost efficiency [9].  
The task-offloading mechanism aims to distribute tasks among resource nodes while jointly allocating communication, computation, and storage resources. According to [10], task offloading typically relies on two parameters. The first parameter efficiently solves a multi-objective optimization problem to determine an optimal operating point, while the second parameter focuses on the dynamic nature of task offloading. Theoretical analysis methods and stability controllers have been developed to ensure robustness against rapid changes in parameters, resource availability, and network conditions [11].  
In resource management systems, offloading and scheduling are complementary but distinct processes that play crucial roles in optimizing system performance. Task offloading refers to the transfer of computational tasks from resource-constrained devices (such as drones in the IoD) to more capable computing nodes, such as edge servers, fog base stations, or the cloud. This process aims to reduce the computational load on the local device and minimize processing delays, energy consumption, or both. In contrast, task scheduling involves determining the order, priority, and resource allocation for tasks either at the source device or the receiving node (e.g., fog or edge server) to optimize the use of available resources, such as CPU, memory, and network bandwidth. Some works consider both task offloading and scheduling in the IoD; for instance, the authors of [12] proposed a model in which tasks are not only offloaded to edge servers but also scheduled based on their dependencies and deadlines. This combined approach ensures the timely execution of tasks while balancing the computational load across the network.