Ubiquitous IoT in the Automotive Domain

Abstract

Ubiquitous IoT systems open new ground in the automotive domain. With the advent of autonomous vehicles, there will be several actors that adapt to changes in traffic, and decentralized adaptation will be a new type of issue that needs to be studied. This chapter investigates the effects of adaptive route planning when real-time online traffic information is exploited. Simulation results show that if the agents selfishly optimize their actions, then in some situations the ubiquitous IoT system may fluctuate and the agents may be worse off with real-time data than without real-time data. The proposed solution to this problem is to use anticipatory techniques, where the future state of the environment is predicted from the intentions of the agents. This chapter concludes with this conjecture: if simultaneous decision making is prevented, then intention-propagation-based prediction can limit the fluctuation and help the ubiquitous IoT system converge to the Nash equilibrium.