Mobile data offloading

Abstract: 3G networks are currently overloaded, due to the increasing popularity of various applications for smartphones. Offloading mobile data traffic through opportunistic communications is a promising solution to partially solve this problem, because there is almost no monetary cost for it. We propose to exploit opportunistic communications to facilitate information dissemination in the emerging Mobile Social Networks (MoSoNets) and thus reduce the amount of mobile data traffic. As a case study, we investigate the target-set selection problem for information delivery. In particular, we study how to select the target set with only k users, such that we can minimize the mobile data traffic over cellular networks. We propose three algorithms, called Greedy, Heuristic, and Random, for this problem and evaluate their performance through an extensive trace-driven simulation study. Our simulation results verify the efficiency of these algorithms for both synthetic and real-world mobility traces. For example, the Heuristic algorithm can offload mobile data traffic by up to 73.66 percent for a real-world mobility trace. Moreover, to investigate the feasibility of opportunistic communications for mobile phones, we implement a proof-of-concept prototype, called Opp-off, on Nokia N900 smartphones, which utilizes their Bluetooth interface for device/service discovery and content transfer.

Abstract: One of the most engaging challenges for mobile operators today is how to manage the exponential data traffic increase. Mobile data offloading stands out as a promising and low-cost solution to reduce the burden on the cellular network. To make this possible, we need a new hybrid network paradigm that leverages the existence of multiple alternative communication channels. This entails significant modifications in the way data are handled, affecting also the behavior of network protocols. In this paper, we present a comprehensive survey of data offloading techniques in cellular networks and extract the main requirements needed to integrate data offloading capabilities into today's mobile networks. We classify existing strategies into two main categories, according to their requirements in terms of content delivery guarantees: delayed and nondelayed offloading. We overview the technical aspects and discuss the state of the art in each category. Finally, we describe in detail the novel functionalities needed to implement mobile data offloading in the access network, as well as current and future research challenges in the field, with an eye toward the design of hybrid architectures.

Abstract: This is a quantitative study on the performance of 3G mobile data offloading through WiFi networks. We recruited about 100 iPhone users from a metropolitan area and collected statistics on their WiFi connectivity during about a two and half week period in February 2010. We find that a user is in WiFi coverage for 70% of the time on average and the distributions of WiFi connection and disconnection times have a strong heavy-tail tendency with means around 2 hours and 40 minutes, respectively. Using the acquired traces, we run trace-driven simulation to measure offloading efficiency under diverse conditions e.g. traffic types, deadlines and WiFi deployment scenarios. The results indicate that if users can tolerate a two hour delay in data transfer (e.g, video and image up-loads), the network can offload 70% of the total 3G data traffic on average. We also develop a theoretical framework that permits an analytical study of the average performance of offloading. This tool is useful for network providers to obtain a rough estimate on the average performance of offloading for a given inputWiFi deployment condition.

Abstract: The unprecedented growth of mobile data traffic challenges the performance and economic viability of today's cellular networks and calls for novel network architectures and communication solutions. Mobile data offloading through third-party Wi-Fi or femtocell access points (APs) can significantly alleviate the cellular congestion and enhance user quality of service (QoS), without requiring costly and time-consuming infrastructure investments. This solution has substantial benefits both for the mobile network operators (MNOs) and the mobile users, but comes with unique technical and economic challenges that must be jointly addressed. In this paper, we consider a market where MNOs lease APs that are already deployed by residential users for the offloading purpose. We assume that each MNO can employ multiple APs, and each AP can concurrently serve traffic from multiple MNOs. We design an iterative double-auction mechanism that ensures the efficient operation of the market by maximizing the differences between the MNOs' offloading benefits and APs' offloading costs. The proposed scheme takes into account the particular characteristics of the wireless network, such as the coupling of MNOs' offloading decisions and APs' capacity constraints. Additionally, it does not require full information about the MNOs and APs and creates non-negative revenue for the market broker.