# A UAV-Assisted Stackelberg Game Model for Securing IoMT Healthcare Networks
## 1 Introduction
### Internet of Medical Things Assisted UAV Network Architecture and Challenges  
Figure 2 depicts a three-layer architecture for an IoMT-assisted UAV network consisting of the monitoring or doctor (DR) layer, the network layer, and the patients' layer. The DR layer monitors, collects, and stores data from various sources, including medical devices, sensors, and UAVs. This layer includes storage servers, data management  
Figure 1: Taxonomy of IoMT.  
software, and security protocols to ensure data integrity and confidentiality. The network layer connects the devices and components of the system, encompassing communication protocols, routing algorithms, and network topology design to ensure reliable and efficient data transmission between devices in the patients' layer and the DR layer. The patient layer includes medical devices and sensors that directly interact with patients, such as wearable devices, monitoring equipment, and medical implants. The network layer sends patient data to the DR layer for medical analysis.  
Using unmanned aerial vehicles (UAVs) in healthcare settings can impact patient privacy and data security in several ways. One of the main concerns is the potential for unauthorized access to sensitive patient information during data transmission between network-layer devices and patient-layer devices. Additionally, UAVs can be vulnerable to cyberattacks, data breaches, and unauthorized access by third parties, which can lead to the loss or theft of patient data. While using UAVs in healthcare settings can offer many benefits, mitigating the potential risks to patient privacy and data security is essential. By implementing strong security measures and policies, it is possible to ensure that UAVs can be used safely and effectively in healthcare environments while protecting patient privacy and data security. To address these concerns, one potential solution is using the Stackelberg game theory model. This model can provide a secure and efficient communication network for IoMT devices in healthcare settings. The model uses UAVs as an assistant for the IoMT devices, which can help to ensure secure data transmission. The model also incorporates a security mechanism using a lightweight Nash equilibrium to protect the data transmitted between the network and patient layer devices. This helps prevent unauthorized access to sensitive patient information and protects patients' privacy.  
Despite the potential benefits of IoMT-assisted UAV network architecture, many challenges must be overcome to ensure the effective implementation of such a system. Protecting the security and confidentiality of sensitive medical data during transmission across networked devices is one of the most significant obstacles. This requires sophisticated security measures and encryption technologies to prevent unwanted access and data breaches. Integration and interoperability of many medical devices and sensors at the patient layer, which may employ multiple communication protocols or data formats, is  
Figure 2: IoMT-assisted UAVs network architecture.  
an additional issue. Ensuring compatibility and seamless integration of these devices is critical for accurate data collection and analysis.  
Additionally, using UAVs in the network layer introduces challenges such as limited battery life, signal interference, and navigation in remote or difficult-to-access areas. Addressing these challenges will be critical for successfully deploying and operating IoMT-assisted UAV network architecture. To further enhance the security of this architecture, the proposed work can be used to implement secure routing and protect against potential rogue devices and malicious nodes. This approach can facilitate the secure transmission of sensitive medical data, ultimately improving the quality of care, quality of service (QoS), and patient health outcomes. For a better understanding of the terms used in this paper, we have described each abbreviation with its definition in Table 1.