Towards Content Dissemination in Subway Mobility: Re-Evaluating the Performance of Power and Interest Aware PeopleRank

Abstract

In this paper, we explore the performance of an opportunistic network forwarding algorithm, namely Power and Interest Aware PeopleRank (PIPeR) in a subway mobility environment. PIPeR is known to perform well according to known metrics in delivering data taking into consideration both the interest in the disseminated data, along with power conservation. The algorithm was only tested however using pedestrian mobility models. Subway mobility is a candidate for many other useful cases for opportunistic content dissemination such as that of armed conflicts where civilian populations live in subway environments for safety without fixed network infrastructure. In this work, we implement and evaluate the PIPeR algorithm in a subway model using the AnyLogic simulator. Our results show a significant increase in the f-measure by 61% and decrease in the delay by 41% in comparison to the pedestrian mobility environment. In addition to that, we pinpoint some areas of interest where content dissemination happens vigorously yet at the expense of some increase in cost and power consumption, which we subsequently alleviate by using a particular hibernation model that decreases the power consumption by 43 % at the expense of only 6% reduction in delivery ratio.