Economical Behavior Modeling and Analyses for Data Collection in Edge Internet of Things Networks

Abstract

Internet of Things (IoT) is progressively becoming an essential aspect of daily life that can be sensed anywhere and anytime, transforming the traditional lifestyle into a high-tech one. Numerous applications in the edge are brought to life based on IoT infrastructures. Especially, edge computing has witnessed the proliferation and impact of IoT-enabled devices benefiting from the data collection and computation capabilities of IoT. However, establishing an IoT from scratch can be monetarily expensive, and leasing the existing sub-networks confronts the potentially dishonest behavior of service providers. To address these issues, we propose a novel framework of leasing edge IoT networks and analyze the influence of sub-network owners’ dishonest behavior on the network. We model the interaction between the edge user and the owners of sub-networks by a Stackelberg game with a unique equilibrium, jointly analyzing the pricing and data collection mechanisms. The Primal-dual Decomposition algorithm and its theoretical analyses are provided for the corresponding strategies of the edge user and sub-network owners. Evaluations demonstrate that the proposed algorithm in the leasing model can save data collection cost up to 53% compared with existing data collection strategies, and illustrate the difference in network performance compared with the game without dishonest owners.