Role of human behaviour attributes in mobile crowd sensing: a systematic literature review
| Published date | 13 March 2017 |
| DOI | https://doi.org/10.1108/DPRG-05-2016-0023 |
| Date | 13 March 2017 |
| Pages | 168-185 |
| Author | Neetima Agarwal,Sumedha Chauhan,Arpan Kumar Kar,Sandeep Goyal |
| Subject Matter | Information & knowledge management,Information management & governance,Information policy |
Role of human behaviour attributes in
mobile crowd sensing: a systematic
literature review
Neetima Agarwal, Sumedha Chauhan, Arpan Kumar Kar and Sandeep Goyal
Neetima Agarwal is
Assistant Professor and
Sumedha Chauhan is
Assistant Professor, both
at the School of
Management, GD Goenka
University, Gurgaon,
India. Arpan Kumar Kar
is Assistant Professor at
DMS, Indian Institute of
Technology Delhi, New
Delhi, India.
Sandeep Goyal is Head
at Shared Value Initiative
India, Institute for
Competitiveness,
Gurgaon, India.
Abstract
Purpose –Mobile crowd sensing (MCS) is a new paradigm enabled by Internet of Things (IoT) in which
sensor-rich ubiquitous devices collect and share the data over a large geography. Human behaviour
attributes (perception, comprehension and projection) play a key role in the decision-making process
for sharing and processing the data. This study aims to understand how situation awareness plays an
important role in MCS in an IoT ecosystem.
Design/methodology/approach –A systematic literature review was conducted by following a
rigorous search protocol that identified a total of 470 peer-reviewed research papers. These papers
were further filtered and finally 31 relevant papers were selected.
Findings –The major issues and concerns arising due to human participation in the MCS system were
identified. Further, probable strategies were explored to deal with the challenges resulting due to certain
human behaviour attributes.
Practical implications –This study provides the recommendations to address the major challenges
related to the MCS system, which in turn may enhance the adoption of emerging smart
technology-driven services.
Originality/value –The study is original and is based on the existing literature and its interpretation.
Keywords Internet of Things, Systematic literature review, Mobile devices, Human behaviour,
Mobile crowd sensing
Paper type Research paper
1. Introduction
Today, the number of devices equipped with ubiquitous computing and communication
capability is increasing tremendously. This growth is attributed to the low-cost
air-interfaces, reduction in cost and size of chipset and the extension of internet (Jara et al.,
2013). The ubiquitous devices such as mobile phones are being augmented with sensing,
computing and communication capabilities by connecting them together to facilitate the
effect of networked smart things. Trillions of networked smart things capture incredible
amount of data and provide unparalleled opportunities to understand the environment
around humans (Guo et al., 2011), reaching the pervasive paradigm of the Internet of
Things (IoT). IoT allows people and things to be connected any time, any place, with
anything and anyone, ideally using any path/network and any service (Gusmeroli et al.,
2010).
Crowd sensing is the capability through which a service requestor can use the networked
devices to collect sensor-based data for obtaining a specific service (Crowley et al., 2013).
Networked devices collect and process the sensor-based data before sending the
information to the requestor. Mobile crowd sensing (MCS) enables the mobile users to
share local knowledge attained by the sensor-equipped mobile devices (Guo et al., 2015).
Received 31 May 2016
Revised 14 October 2016
Accepted 4 November 2016
PAGE 168 DIGITAL POLICY, REGULATION AND GOVERNANCE VOL. 19 NO. 2, 2017, pp. 168-185, © Emerald Publishing Limited, ISSN 2398-5038 DOI 10.1108/DPRG-05-2016-0023
In this context, MCS is an emerging form of networked wireless sensing in which devices
owned by humans require their intervention for sensing actions. There are a number of such
applications, e.g. Taxi CrowdShipping application discovers the community movement
patterns from the taxi data and assigns tasks to the selected taxis, CrowdSense@Place
opportunistically captures the images and audio clips crowdsourced from smartphones to
link place visits with place categories and Movi application enables the co-located mobile
phones to collaboratively sense their ambience and recognize the socially interesting
events (Guo et al., 2015). In a cloud-centric IoT ecosystem, sensed data collected by the
cloud undergo data mining and data analysis for information and knowledge discovery
(Kantarci and Mouftah, 2014).
Involvement of users in the sensing activity can be categorized into participatory and
opportunistic sensing. Participatory sensing involves active while opportunistic sensing
involves the passive participation of users to contribute the data (Antoni´
cet al., 2016;Lane
et al., 2010). Users extract various reports and receipts through mobile devices and tag
them with the time and geo-reference coordinates for facilitating participatory sensing.
Situation awareness is “knowing what’s going on” (Endsley, 1995). It is based on the
integration of knowledge resulting from recurrent situation assessments (Haddawy et al.,
2015). Human behaviour attributes, viz., perception, comprehension and projection,
facilitate the awareness of the path to be followed to achieve the desired outcome of a
situation (Salfinger et al., 2015). Perception refers to the inferential thinking based on the
information retrieved from various sensors. It covers the situation detecting, merging of
internal information and augmenting them using various internal and external sources.
Comprehension further exploits this information to assess the situations. It covers
automated situation assessment, situation learning and situation profiling. Finally,
projection estimates the encountered situations (Salfinger et al., 2015).
MCS collects the data from two sources: mobile devices and social networks. The data are
collected from users as well as participants who share the local knowledge (Guo et al.,
2011). As human behaviour attributes critically impact the process of MCS, it is important
to analyse them. Based on the gap in the existing literature, the following research question
has been identified:
RQ1. What roles are played by the human behaviour attributes (perception,
comprehension and projection) in MCS in an IoT ecosystem?
While quite a few studies have explored the different dimensions of human behaviour in the
context of MCS-based technologies and services, there is a lack of studies which connect
the existing studies conducted in silos and ground them back into a framework which
explains the dynamics of human behaviour.
This study is an attempt to explain how human behaviour attributes work in different
situation contexts in the MCS system by conducting an in-depth systematic literature
review. Further, this paper also provides the recommendations for dealing with each
problem as discussed by various researchers. Most importantly, given the contemporary
relevance of the topic, this paper also provides key insights to the future researchers and
practitioners. The remaining part of this study has been organized as follows: Section 2
gives an overview of the situation awareness model used in this study. Section 3 describes
the systematic literature review process. Section 4 reports the findings and results of the
review. Section 5 discusses and provides the recommendations for the effective MCS
system, while Section 6 presents the concluding remarks and potential areas for further
research on this topic.
2. Situation awareness model
Regarding the process of decision-making and situation awareness, literature provides a
number of models, such as the Plan-Do-Check-Act model (Shewhart and Deming, 1939),
VOL. 19 NO. 2 2017 DIGITAL POLICY, REGULATION AND GOVERNANCE PAGE 169
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