Increasing storage sizes and WiFi Bluetooth capabilities of mobile devices have made them a good platform for opportunistic content sharing. In this work we propose a network model to study this in a setting with two characteristics: 1. delay tolerant; 2. lack of infrastructure. Mobile users generate requests and opportunistically download from other users they meet, via Bluetooth or WiFi. The difference in popularity of different web content induces a non-uniform request distribution, which is usually a Zipf's law distribution. We evaluate the performance of different caching schemes and derive the optimal scheme using convex optimization techniques. The optimal solution is found efficiently using a binary search method. It is shown that as the network mobility increases, the performance of the optimal scheme far exceeds the traditional caching scheme. To the best of our knowledge, our work is the first to consider popularity ranking in performance evaluation.
It is helpful to find a unified mathematical model to capture the fundamental natures of node mobility. @cite_1 summarized several common methods of mobility modelling. The most widely used and studied model is the random waypoint model. A host randomly chooses a destination (waypoint) and moves toward the destination in a straight line, with a constant speed randomly selected in @math . After reaching the destination, a host pauses for a random period before moving to the next destination. A recent investigation on the pattern of individual mobility @cite_0 reveals that individual mobility patterns are largly indistinguishable after correcting for differences in travel distances and the inherent anisotropy of each trajectory.
@cite_0 @cite_1
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The random waypoint model is a commonly used mobility model in the simulation of ad hoc networks. It is known that the spatial distribution of network nodes moving according to this model is, in general, nonuniform. However, a closed-form expression of this distribution and an in-depth investigation is still missing. This fact impairs the accuracy of the current simulation methodology of ad hoc networks and makes it impossible to relate simulation-based performance results to corresponding analytical results. To overcome these problems, we present a detailed analytical study of the spatial node distribution generated by random waypoint mobility. More specifically, we consider a generalization of the model in which the pause time of the mobile nodes is chosen arbitrarily in each waypoint and a fraction of nodes may remain static for the entire simulation time. We show that the structure of the resulting distribution is the weighted sum of three independent components: the static, pause, and mobility component. This division enables us to understand how the model's parameters influence the distribution. We derive an exact equation of the asymptotically stationary distribution for movement on a line segment and an accurate approximation for a square area. The good quality of this approximation is validated through simulations using various settings of the mobility parameters. In summary, this article gives a fundamental understanding of the behavior of the random waypoint model. In this paper we study the so-called random waypoint (RWP) mobility model in the context of cellular networks. In the RWP model the nodes, i.e., mobile users, move along a zigzag path consisting of straight legs from one waypoint to the next. Each waypoint is assumed to be drawn from the uniform distribution over the given convex domain. In this paper we characterise the key performance measures, mean handover rate and mean sojourn time from the point of view of an arbitrary cell, as well as the mean handover rate in the network. To this end, we present an exact analytical formula for the mean arrival rate across an arbitrary curve. This result together with the pdf of the node location, allows us to compute all other interesting measures. The results are illustrated by several numerical examples. For instance, as a straightforward application of these results one can easily adjust the model parameters in a simulation so that the scenario matches well with, e.g., the measured sojourn times in a cell.