You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
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.

Cited Abstract(s):
[1]: 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.

[2]: 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.
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 [1] [2] . [1] 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.