IEEE Communications Magazine - June 2017 - page 120

IEEE Communications Magazine • June 2017
obile crowdsensing is a promising technique in
which a large amount of mobile devices collective-
ly share data to measure, map, analyze, or estimate
any processes of common interest. Recent mobile devices
can collect vast quantities of data that are useful in a variety
of ways. For example, GPS and accelerometer data can be
used to locate potholes in cities, and microphones can be
used with GPS to map noise pollution. Taking advantage
of the ubiquitous presence of powerful mobile computing
devices, it has become an appealing method to businesses
that wish to collect data without making large-scale invest-
ments. Although plenty of sustainable and incentive mech-
anisms have been developed for mobile crowdsensing,
many challenges still need to be addressed. It is significant
to explore this timely research topic to support the develop-
ment of mobile crowdsensing.
Part 2 of this Feature Topic (FT) further gathers articles
about sustainable incentive mechanisms for mobile crowd-
sensing. The primary goal is to push the theoretical and
practical bounds forward for a deeper understanding in fun-
damental algorithms, modeling, and positioning over the next
decade, and analysis techniques from industry and academic
viewpoints on these challenges, thus fostering new research
streams to be addressed in the future. After a rigorous review
process, six papers have been selected to be published in
this issue of
IEEE Communications Magazine
The first article in this FT, “Near-Optimal Incentive Allo-
cation for Piggyback Crowdsensing” by Xiong
et al.
, investi-
gates the existing framework of piggyback crowdsensing and
formulate the optimization problems of incentive allocation,
with respect to the varying settings of incentive objectives
and constraints. One unified incentive allocation framework
is proposed with several optimization algorithms to passively
approximate the near-optimal solution. Theoretical analysis
along with an experiment using real-word datasets demon-
strate that the proposed algorithms could outperform com-
monly seen incentive allocation heuristics significantly.
The second article, “Crowdsourced Road Navigation:
Concept, Design, and Implementation” by Fan
et al.
, provides
a retrospective view of the past and present road navigation
technologies. This article then discusses very recent advances
with crowd intelligence, identifying the unique challenges
and opportunities therein. Furthermore, a case study is pre-
sented that utilizes the crowdsourced driving information to
combat the last mile puzzle for road navigation.
The next article, “The Accuracy-Privacy Trade-off of
Mobile Crowdsensing,” addresses the contradicting incen-
tives of privacy preservation by crowdsensing agents and
accuracy maximization and collection of true data by ser-
vice providers. In the trade-off, the crowdsensing agents are
incentivized to provide true data by being paid based on
their individual contribution to the overall service accuracy.
Moreover, a coalition strategy is proposed to allow agents to
cooperate in providing their data under one generalization
identity, increasing their anonymity privacy protection, and
sharing the resulting payoff.
Motivated by the lack of encouragement for data for-
warding in an opportunistic network, “Mobile Crowdsensing
in Software Defined Opportunistic Networks” by Dong
introduces a software defined opportunistic networking
: P
Linghe Kong
Kui Ren
Muhammad Khurram Khan
Qi Li
Ammar Rayes
Mérouane Debbah
Yuichi Nakamura
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