14-08-2013, 04:10 PM
Mobile Data Offloading through Opportunistic Communications and Social Participation
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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.
INTRODUCTION
to the proliferation of smartphones (e.g., Apple’s
iPhone and Nokia N95), mobile operating systems
(e.g., Google’s Android and Symbian OS), and online
social networking services, Mobile Social Networks (MoS-
oNets) have begun to attract increasing attention in recent
years. The development of MoSoNets has already evolved
from the simple extensions of online social networking
sites to powerful mobile social software and applications
[17], [20], [32], [34], [36]. Currently, a large percentage of
mobile data traffic is generated by these mobile social
applications and mobile broadband-based PCs [2]. A side
effect of the explosion of these applications, along with
other mobile applications, is that 3G cellular networks are
currently overloaded. According to AT&T’s media news-
room.
RELATED WORK
In this section, we review the existing related work in three
categories: cellular traffic offloading, information diffusion/
dissemination, and mobile social networks.
Cellular Traffic Offloading
There are two types of existing solutions to alleviate the
traffic load on cellular networks: offloading to femtocells
and WiFi networks.
Femtocell for Indoor Environments
Originally, the femtocell technology (i.e., access point base
stations) was proposed to offer better indoor voice and data
services of cellular networks. Femtocells work on the same
licensed spectrum as the macrocells of cellular networks
and thus do not require special hardware support on
mobile phones. But customers may need to install short-
range base stations in residential or small-business envir-
onment, for which they will provide Internet connections.
Due to their small cell size, femtocells can lower transmis-
sion power and achieve higher signal-to-interference-plus-
noise ratio (SINR), thus reducing the energy consumption
of mobile phones. Cellular operators can reduce the traffic
on their core networks when indoor users switch from
macrocells to femtocells. A literature review about the
technical details and challenges of femtocells can be found
in Chandrasekhar et al. [13].
Other Wireless Networks
Diffusion has also been widely studied in wireless sensor
networks and cellular networks. Directed diffusion [27] is a
data-centric dissemination paradigm for sensor networks,
in the sense that the communication is for named data
(attribute-value pairs). It achieves energy efficiency by
choosing empirically good paths, and by caching data and
processing it in-network. The parametric probabilistic
sensor network routing protocol [6] is a family of multi-
path and light-weight routing protocols for sensor net-
works. It determines the forwarding probability of inter-
mediate sensors based on various parameters, including the
distance between these sensors, and the number of traveled
hops of a message. Zhu et al. [49] propose solutions to
prevent the spread of worms in cellular networks by
patching only a small number of phones. They construct a
social relationship graph of mobile users where the weights
of edges are determined by the amount of traffic between
two mobile phones and use this graph to represent the most
likely spreading path of worms. After partitioning the
graph, they can select the optimal set of phones to separate
these partitions and block the spreading of worms.
Problem Statement
As we mentioned in Section 1, we aim to study how to
choose the initial target set with only k users, such that we
can maximize the expected number of infected users. This
number will translate into the decrease of mobile data traffic.
If there are totally n subscribed users and m users finally
receive the information before the deadline, the amount of
reduced mobile data traffic will be n À ðk þ ðn À mÞÞ 1⁄4
m À k. For a given mobile user, delivery delay is defined to
be the time between when a service provider delivers the
information to the k users until a copy of it is received by that
user. Service providers will send the information to a user
directly through cellular networks, if he or she fails to
receive the information before the delivery deadline.
TARGET-SET SELECTION
We first prove the submodularity of the information
dissemination function for the contact graph of mobile users,
which leads to the greedy algorithm. The information
dissemination function is the function that maps the target
set to the expected number of infected users of the
information dissemination process. Then we present the
details of the greedy algorithm and the proposed heuristic
algorithm.
Opp-Off Implementation
We implement a simplified version of Opp-Off using
Bluetooth for two reasons. First, as we have demonstrated
that the energy consumption of WiFi scanning is much
higher than that of Bluetooth inquiry. Second, Bluetooth is
available on almost all the modern mobile phones. Whereas,
only relatively few smartphones have WiFi interface. When
mobile phones run Opp-Off, the program first starts a
content server as a thread and then the main part is a loop
that performs device discovery using Bluetooth inquiry
(hci_inquiry function call in BlueZ). After two phones
discover each other, they will start another client thread to
connect to the content servers on the remote phones. If they
can establish a connection, they will start transmitting data
packets until the content transfer is finished or the connec-
tion is broken (e.g., because they are not within the Bluetooth
communication range of each other due to movement).
Pull Probability
We first evaluate the performance of Random algorithm for
different pull probabilities using the Portland trace. We
show the mobile data traffic load for different sizes of target
set, from 5 to 3,000, and pull probabilities, 0.01, 0.05, and
0.1, in Fig. 7. The x-axis is the size of target set and the y-axis
show the mobile traffic load, in terms of the number of
cellular messages. Every user who fails to receive the
information before the delivery deadline will consume a
cellular message. Moreover, each user in the target set will
also consume a cellular message. The delivery deadline is
1 hour. For each combination of the size of target set and
pull probability, we run the simulation 10,000 times and
report the average value. The horizontal dotted line shows
the amount of cellular messages without offloading, which
is the same as the total number of subscribed users. As we
can see from this figure, even for the very simple random
algorithm, it can reduce the amount of mobile data traffic by
up to 81.42 percent when the pull probability is 0.1. When
we reduce the pull probability to 0.01, it can still offload
mobile data traffic by up to 69.73 percent.
Energy Consumption
Energy consumption may be the most important issue for the
deployment of mobile applications. As we mentioned in
Section 5, the three main phases of opportunistic commu-
nications are device discovery, content discovery, and data
transfer. Although we have chosen Bluetooth for device
discovery, we use fixed parameters (e.g., inquiry duration
and interval, and inquiry scan window and interval) in our
current implementation. We believe dynamically changing
these parameters according to user mobility patterns may
make the device discovery procedure more energy efficient.
For example, when users are not moving, larger inquiry
interval may be a better choice. Since device discovery is a
common component for several mobile applications like
Social Serendipity [17] and Media Sharing [32], its energy
consumption can also be amortized by them. Moreover, for
data transfer, we suggested to replace Bluetooth using WiFi to
save battery life.
CONCLUSION
We propose to offload mobile data traffic through oppor-
tunistic communications and investigate the target-set
selection problem for information delivery in MoSoNets.
We present three algorithms for this problem, Random,
Heuristic, and Greedy, and evaluate their performance
through trace-driven simulation, using both large-scale
synthetic and real-world mobility traces. The simulation
results show that Greedy performs the best, followed by
Heuristic. Although the Greedy algorithm may not be
practical, it is the basis of the Heuristic algorithm which
exploits the regularity of human mobility. We also imple-
ment a prototype of the information delivery framework
using Nokia N900 smartphones and study its feasibility for
moving phones. Our preliminary experimental results
show that during their short contacts mobile phones can
exchange up to 1.48 MB data.