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.
The strategy to migrate traffic from the cellular networks to the free and fast device-to-device networks, so as to mitigate the overloaded cellular network is referred to as mobile data offloading @cite_4 @cite_7 . @cite_4 formulated the problem of offloading data with various sizes and delay tolerances as a submodular function maximization problem. Our work is along the same lines in the sense that users try to rely on device-to-device transmission and avoid generating cellular network traffic as much as possible, thereby achieving data offloading.
@cite_4 @cite_7
2050848313 2006121063 2005108639 2013366448
To cope with the explosive traffic demands and limited capacity provided by the current cellular networks, Delay Tolerant Networking (DTN) is used to migrate traffic from the cellular networks to the free and high capacity device-to-device networks. The current DTN-based mobile data offloading models do not address the heterogeneity of mobile traffic and are based on simple network assumptions. In this paper, we establish a mathematical framework to study the problem of multiple mobile data offloading under realistic network assumptions, where 1) mobile data is heterogeneous in terms of size and lifetime, 2) mobile users have different data subscribing interests, and 3) the storage of offloading helpers is limited. We formulate the maximum mobile data offloading as a Submodular Function Maximization problem with multiple linear constraints of limited storage and propose greedy, approximated and optimal algorithms for different offloading scenarios. We show that our algorithms can effectively offload data to DTNs by extensive simulations which employ real traces of both humans and vehicles. Cellular networks, especially 3G networks, are currently overloaded with mobile data traffic. It is thus imperative to develop novel communication architectures and protocols for this problem. In this paper, we propose to exploit the delay-tolerant nature of non-realtime applications to offload mobile data traffic through opportunistic communications and WiFi networks. We argue that it is necessary for cellular network operators, WiFi service providers, and end-users to cooperate to make the offloading more effective. We also present several preliminary experiment results about the work we have been conducting, which show that it is promising to offload mobile data traffic in metropolitan area networks. Pushing data traffic from cellular to WiFi is an example of inter radio access technology (RAT) offloading. While this clearly alleviates congestion on the over-loaded cellular network, the ultimate potential of such offloading and its effect on overall system performance is not well understood. To address this, we develop a general and tractable model that consists of M different RATs, each deploying up to K different tiers of access points (APs), where each tier differs in transmit power, path loss exponent, deployment density and bandwidth. Each class of APs is modeled as an independent Poisson point process (PPP), with mobile user locations modeled as another independent PPP, all channels further consisting of i.i.d. Rayleigh fading. The distribution of rate over the entire network is then derived for a weighted association strategy, where such weights can be tuned to optimize a particular objective. We show that the optimum fraction of traffic offloaded to maximize SINR coverage is not in general the same as the one that maximizes rate coverage, defined as the fraction of users achieving a given rate. In heterogeneous cellular networks (HCNs), it is desirable to offload mobile users to small cells, which are typically significantly less congested than the macrocells. To achieve sufficient load balancing, the offloaded users often have much lower SINR than they would on the macrocell. This SINR degradation can be partially alleviated through interference avoidance, for example time or frequency resource partitioning, whereby the macrocell turns off in some fraction of such resources. Naturally, the optimal offloading strategy is tightly coupled with resource partitioning; the optimal amount of which in turn depends on how many users have been offloaded. In this paper, we propose a general and tractable framework for modeling and analyzing joint resource partitioning and offloading in a two-tier cellular network. With it, we are able to derive the downlink rate distribution over the entire network, and an optimal strategy for joint resource partitioning and offloading. We show that load balancing, by itself, is insufficient, and resource partitioning is required in conjunction with offloading to improve the rate of cell edge users in co-channel heterogeneous networks.