Game Theory-Based UAV-Cloud for Service Selection Architecture in Flying Ad Hoc Networks

Abstract

The rapid progression of Cloud Computing (CC) technology has ushered in innovative ecosystem concepts such as Mobile Cloud Computing (MCC). In this context, the incorporation of Unmanned Aerial Vehicles (UAVs) into these cloud ecosystems has unlocked new avenues for use cases such as delivery services, disaster response, and surveillance. However, this integration presents challenges in resource management and service selection due to the unique constraints of drones and variations in service quality. This paper proposes a Game Theory-based UAV-cloud of Service Selection Architecture (GT-SSA) to address resource management and service selection challenges. By leveraging game theory in our proposal, GT-SSA optimizes decision-making for Client Drones and Provider Drones, enhancing service selection efficiency. GT-SSA proved its resilience to scalability concerns, as evidenced in Discovery Delay, Consumption Delay, End-to-End Delay, and Energy consumption. Moreover, when GT-SSA is compared with the Game Theory approach for Cloud Services in MEC- and UAV-enabled networks (GTCS), GT-SSA outperforms GTCS in terms of Successful Execution Rate, Average Execution Time, and Energy consumption. Our research also reveals that game theory surpasses fuzzy logic in terms of service selection efficiency.