A Game Theory-based Reverse Vickrey Auction for Dynamic Pricing in Edge Computing

Low-latency communication is critical for effectively functioning Internet of Things (IoT) systems, including applications in healthcare monitoring, industrial automation, and smart cities. However, the vast number of IoT Devices often overwhelms cloud providers, necessitating the adoption of edge computing to reduce latency and distribute computational load. This paper introduces a novel dynamic pricing mechanism for edge computing based on a reverse Vickrey auction, leveraging game theory to balance competitive pricing with profitability for Edge Servers. We develop utility functions for both IoT Devices and Edge Servers, optimizing the markup to maximize edge server utility while adhering to the budget constraints of IoT Devices. Simulations were conducted with 10 Edge Servers and 50 IoT Devices over 2,000 time units. Results show that the proposed mechanism significantly reduces wait times, with most tasks completed within 10-time units. Moreover, dynamic pricing increases Edge Server profits by up to 10 times in some instances compared to fixed markup approaches. Notably, one server's total profit rose from 7,702 to 76,130 and another from 57,965 to 94,298, underscoring the enhanced profitability and utilization. These findings confirm that our reverse Vickrey auction strategy effectively balances low-latency demand with sustainable operational revenue for Edge Servers, offering a robust and scalable solution for edge-centric IoT environments.