## 1 Introduction  
The use of unmanned aerial vehicles (UAVs), also termed drones, is anticipated to incline at exceptional rates because of growing curiosity from investors, hobbyists, and researchers; the quantity of drones is quickly rising, and the Internet of Drones (IoD) environment and number of related applications are expanding rapidly, in which an infinite number of multi-sized drones flawlessly communicate with one another via area service providers whose objective it is to understand and align the accessibility of drones to a controlled airspace and operate a navigation service [1]. The economic development of drone industries in the United States, involving military scouting, traffic, wildlife surveillance, urban safety scrutiny of infrastructure, on-demand package supply, aerial photography,etc., is reported to be substantial for industries or business operations [2; 3]. Figure 1 depicts the procedure of IoD. Even though the IoD network offers numerous benefits, it also carries numerous susceptibilities which should be handled, the most important criteria of which are privacy and security problems [4]. However, the IoD network system is commonly placed for real-time application zones where end users need to obtain real-time information from UAVs which are connected to a particular area. So, there are high chances of security assaults occurring, leading to the colossal ruin of the data exchange functions inside the network [5; 6; 7]. An attacker can gain access to the keys and block transmissions. For the accessibility of keys, the intruder can abuse a susceptibility in the IoD network and its application zones. The intruder might alter this information, resulting in the misguidance of the receivers. However, IoD access management is a significant variable, and security problems regarding authorization and accessibilities must be emphasized [8; 9]. This applies to the data in transfer, which should be protected for confidentiality, authenticity, and integrity. Most of the privacy and security methods advanced by authors were intended to ensure security measures on the IoD network [10; 11]. The approaches focused on mitigating problems which influence the secure localization of drones or privacy and security necessities linked with the IoD network system. Localization fault assaults impede the reliability position of UAVs, leading to distressing costs for the overall functionality of the IoD network [12]. Additionally, privacy and security necessities are the objectives that decide the functions and capacities of the IoD network attained in justifying certain privacy and security measures [13]. The privacy and security needs of the IoD network involve authenticity, integrity, availability, privacy preservation, and confidentiality [14]. [PERSON] et al. [15] suggested a powerful privacy-preserving validation architecture. With the help of a lightweight online/offline signature model, this architecture guarantees verification effectiveness while placing resource constraints on small-scale UAVs. Considering the high mobility of UAVs, a prediction validation model is explored by using mobile edge computing (MEC) in this architecture for reducing the verification expenses for possible verification activity. [PERSON] et al. [16] developed an Unmanned Traffic Management (UTM)-Chain, a light-weight blockchain-related security model with hyper ledger fabric for UTM of lower-altitude UAV that fits the storage and computation resource constraints of UAV. Furthermore, UTM-Chain offers secured and unchangeable traffic information among their ground control stations and the UAVs. In [8], a blockchain-related security model for cyber-physical systems is introduced for ensuring secured transmission of datasets amongst drones. In this model, the miner node is chosen by deep learning (DL)-based  
Figure 1: Application areas of IoD. technique, that is, a deep Boltzmann machine, with features such as flight time of the drone, computation resource, and the available battery power.