Mobile Crowd Sensing for Internet of Things: A Credible Crowdsourcing Model in
Mobile-sense Service
Jian An
1, 2
, Xiaolin Gui
1, 2
, Jianwei Yang
1, 2
, Yu Sun
1, 2
, Xin He
3
1
School of Electronics and Information, Xi’an Jiaotong University , Xi’an 710049, China
2
Shaanxi Province Key Laboratory of Computer Network, Xi’an Jiaotong University, Xi’an 710049, China
3
School of Software, Henan University, Kaifeng 475001, China
anjian@mail.xjtu.edu.cn, xlgui@mail.xjtu.edu.cn, Yang_jw@stu.xjtu.edu.cn, sunyu@stu.xjtu.edu.cn, hxsyjkf@foxmail.com
Abstract —
Various types of micro-sensors in smart
communication devices can measure a significant amount of
potentially useful information. Mobile Crowd Sensing (MCS)
between users with smart mobile devices is a new trend of
development in Internet of Things. With the powerful sensing
capability of smart device and user mobility, various services
could be provided by building a trusted chain between service
requesters and suppliers. In this paper, we first analyze and
summarize the current status of the MCS technology, and a
novel credible crowdsourcing service model is proposed based
on MCS according to mobility, sociality and complexity of
mobile users. Then, some key technologies of model are given
in details. Finally, we give the focus of future research work.
Keywords — Mobile crowd sensing; Internet of Things;
Credible interaction; Crowdsourcing service model.
I. INTRODUCTION
As the development of technologies such as the Internet
of Things (IoT) and the Big Data continues, mankind has
entered an unprecedented information age. Intelligent
terminals, social networks, 3G/4G networks and other new
media and network access technologies have become an
indispensable part of people's daily life, entertainment,
office, and so on, which enhanced the breadth and depth of
people’s collection, analysis and utilization of data. The
Gartner released the Top 10 Predictions of 2014, in which it
predicates that IoT will be the fastest-growing, largest
market potential and the most attractive emerging economy,
therefore becoming the focus of attention in the field of
networking [1].
Mobile Crowd Sensing (MCS) refers to the technology
that uses mobile terminals like smart phones to collect and
analyze the information of people and surrounding
environments, analyzes statistical characteristics and
activity patterns of social groups based on the mass of
information, then mines the data to reveal hidden
information of group behaviors, dynamic evolution of the
network structure and service related attributes, and
ultimately provides useful information and services to end
users [2]. MCS provides a new way of perceiving the
world, by involving anyone in the process of sensing, to
greatly extend the service of IoT and build a new generation
of intelligent networks that interconnect things-things,
things–people and people-people.
The decision-making role of people in crowd sensing
will inevitably lead to a new revolution of networking
technology [3]. As a service provider and consumer, people
are not only the traditional consumer of information, but
also the participants and decision-makers. The network
composed of mobile devices carried by people will be the
main platform for future networking applications. In this
network, the mobility, randomness and space-time
complexity of the people bring technical challenges in data
sensing and data transferring, as is described in detail in the
following aspects.
Firstly, at present, 190 million of Twitter’s 250 million
global users use mobile devices, while Facebook has 900
million mobile users among its grand tally of 1 billion. The
use of mobile phones, tablets and other mobile intelligent
terminals will have a huge impact on the growth of Internet
users. With the powerful capabilities of sensing and
computing, intelligent terminals can quickly collect multi-
source, heterogeneous information of mobile users and their
service interactions, but this also results in the "information
overload" problem. How to study the relationship among
sensing data from multiple sources to unveil hidden
information like groups behavior patterns, network
architecture and services attributes is critical to ultimately
provide the services that meet user preferences [4].
Secondly, the behavior of mobile nodes in terms of
activities carried out in certain social relations is a reflection
of the relationship among mobile subjects, revealing the
inherent nature of the relationship between the mobile
users. In addition, a mobile node has a social nature that
refers to the characteristics of a person appeared as one of
the society engaging in social interaction activities, such
that the movement trajectories and activity patterns are not
aimless or disorganized [5].
Finally, in MCS, data collection, delivery and service
interaction are all based on mobile nodes. Recent researches
[6-8] usually assumed that sensor nodes have mutual trust
and can receive, forward, and pass any data between each
other. But in real life, there is no such pre-existing trust
relationship among mobile nodes, thus a node only
forwards data or perception requests to familiar nodes and
ignores the requests or data from unfamiliar nodes. One of
the main problems of the crowd sensing service in the
future will be to study the credible crowd sourcing and
incentive mechanism of mobile nodes to further expand the
breadth and depth of data perception [9].
In summary, the leading role of mobile nodes (user) in
the crowd sensing will inevitably cause evolutions of
related technologies. How to provide high-quality, diverse,
customizable mobile sensing service to meet specific
preferences of users in a mobile, dynamic and distributed
2015 IEEE International Conference on Multimedia Big Data
978-1-4799-8688-0/15 $31.00 © 2015 IEEE
DOI 10.1109/BigMM.2015.62
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