Mobile Data Offloading through Opportunistic Communications and Social Participation

Abstract

Abstract3G 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% 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.

Impact of Selfishness in Device-to-Device Communication Underlying Cellular Networks

“…However, not all the associated constraints are linear constraints, indicating that this constrained optimization problem does not belong to the category of linear programming problems. Fortunately, we can use the reformulation linearization technique (RLT) [3] to transform those nonlinear constraints into linear expressions and, consequently, we can use the existing optimization tool kits, such as CPLEX [8] and YALMIP [12], to solve this constrained maximization problem. By solving this constrained maximization problem, we obtain the total amount of data that can be transmitted successfully, namely, the maximized objective value.…”

“…A mobile phone user with dying battery may for example refuse to contribute the scarce battery resource to D2D communications just to improve the downloading speed of others, and many users may not be pleased to devote part of the limited buffer of their mobile devices to safeguarding the interests of strangers. Unfortunately, current researches have assumed that UEs are always willing to act in a cooperative way and fail to consider the inherent selfishness of UEs in the D2D communication systems [1], [3], [5], [10], [11], which may lead to overestimation of the system performance and misinterpretation of the relevant properties. Obviously, if most users are unwilling to participate in D2D transmission, the resources cannot be utilized sufficiently, and a D2D underlaying cellular system will not able to operate successfully.…”

On the feasibility of a user-operated mobile content distribution network

“…As mentioned in [9] the integration of an effective incentive mechanism in a user-operated offloading mechanism is a challenging problem. The application content provider reduces its expenses to the CDN provider that manages its content and the corresponding cellular providers significantly decrease the stress on their links.…”

Studying Mobile Internet Technologies with Agent Based Mean-Field Models

“…Thus, a new trend, usually referred to as mobile data offloading, has emerged. That is, while the cellular infrastructure will continue to provide essential wide-area coverage and support for high-mobility users, it will be complemented with WiFi hotspots, toward which data traffic should be offloaded whenever possible [9,13].…”