A Bayesian Game Model for Dynamic Channel Sensing Intervals in Internet of Things

Abstract

A Bayesian game theoretic model is developed to dynamically select channel sensing intervals in a massively dense network of Internet of Things. In such networks, the core objective is to minimize every node's energy consumption while having incomplete information about other nodes actively communicating in the network. Selecting channel sensing intervals in a medium access control (MAC) protocol is absolutely crucial, especially in massively dense networks, and selecting intelligently these intervals can optimize the overall network energy consumption while also minimizing latency during the information transfer. In the proposed model, a sensing interval chosen by a node is dynamically derived using current and previous incoming traffic patterns at other nodes in the vicinity. This paper shows that formulating the problem of channel sensing intervals as a Bayesian game model can extensively improve the performance of a MAC protocol when incorporating information from other nodes within the network.