Dynamic Resource Allocation in an Adversarial Urban IoBT Environment

Abstract

The advances of the Internet of Battlefield Things (IoBT) would improve the flexibility and efficiency of military operations. Without an effective dynamic adversarial mechanism, soldier devices might malfunction, and machine intelligence technologies could hardly support military operations on a battlefield. In this paper, we propose a game theoretical model considering the adversarial and dynamic nature of the urban IoBT environment. Our algorithm is designed to optimize the whole network’s efficiency with the premise that both the attacking and defending parties can maximize their benefit. Meanwhile, we also attempt to consider the interactive effects of channel fading by using a Nakagami distribution based Markov process. The experimental results show that considering the impact of the adversarial and dynamic nature of urban IoBT, our proposed algorithm can improve network performance by 30%-50%.