Uncertainty Modeling and Reduction in MANETs

Evaluating and quantifying trust stimulates collaboration in mobile ad hoc networks (MANETs). Many existing reputation systems sharply divide the trust value into right or wrong, thus ignoring another core dimension of trust: uncertainty. As uncertainty deeply impacts a node´s anticipation of others´ behavior and decisions during interaction, we include uncertainty in the reputation system. Specifically, we define a new uncertainty model to directly reflect a node´s confidence in the sufficiency of its past experience, and study how the collection of trust information affects uncertainty in nodes´ opinions. After defining a way to reveal and compute the uncertainty in trust opinions, we exploit mobility, one of the important characteristics of MANETs, to efficiently reduce uncertainty and to speed up trust convergence. Two different categories of mobility-assisted uncertainty reduction schemes are provided: the proactive schemes exploit mobile nodes to collect and broadcast trust information to achieve trust convergence; the reactive schemes provide the mobile nodes methods to get authenticated and bring their reputation in the original region to the destination region. Both of the schemes offer a controllable trade-off between delay, cost, and uncertainty. Extensive analytical and simulation results are presented to support our uncertainty model and mobility-assisted reduction schemes.