a few complex service systems have
Challenges in service composition.
Partly fueled by the recent widespread
successes of big data, the composition
of large numbers of services into a coherent system is an emerging research
area. We assess the scale involved as
follows. In 2008, a survey discovered
5,077 WSDL-described Web services
available on the Internet.
1 In 2016,
there are almost 15,000 Web services
registered in a public website.e In addition, we believe that a large proportion of modern Web services are non-WSDL-described, such as those that
are cloud-based. The current popularity of cloud computing has inspired
the fast growth of cloud-based services and a 2013 survey discovered 6,686
13 In the era of smartphones, millions of apps are down-loadable from cloud-based app stores.
It is estimated that there are 1. 6 million Android apps and 1. 5 million
Apple apps as of July 2015.f Precisely
and efficiently searching for services
from these large-scale repositories is
becoming a critical challenge.
In the setting of big data that might
be accessed simultaneously by numerous services, existing service selection,
composition, and recommendation
approaches that mostly assume a static data environment are inadequate.
Composition technologies that address consistency should be explored.
In the setting of the IoT, Smart City
can be viewed as a typical example of
large-scale service composition, where
millions of diverse and heterogeneous
digital devices and services are integrated dynamically for provisioning
multiple real-time functions or user-customized functions. Selecting and
composing services from such numerous and ever-changing devices and
services to fulfill user requirements in
a real-time and context-aware fashion
is a difficult mission, which requires
In large social networks, such as
Facebook and Twitter, in which bil-
lions of users register and most users
have hundreds of friends or followers
on average (on average, 338 friends per
Trustworthiness of crowdsourcing
contributors. Some service users’ opin-
ions might be unfair and even mali-
cious towards particular service prod-
ucts. Unfairness or maliciousness is
context-dependent, affected by the vari-
ation in services, time, and locations.
A key issue is the selection of crowd-
sourcing contributors based on their
trustworthiness, taking into account
the context in which they operate.
Testing platform. Hitherto, there is
no standardized testing platform to
compare trust and reputation models.
There is a strong demand for designing appropriate evaluation metrics to
compare trust and reputation models
Challenges in the Io T. The Internet
of Things (IoT) is an emerging and
promising area that proposes to turn
every tangible entity into a node on the
Internet. More specifically, the tangible entities (“things”) are any Internet-connected sensor, camera, display,
smartphone, or other smart communicating devices. The Io T poses two fundamental challenges:
communication with things, and management of
things. Service technologies can help
address these challenges, through
provisioning communication and interoperability means to the Io T, such
as REST and service composition models. One challenge is that things are
resource-constrained and traditional
standards, such as SOAP and BPEL, are
too heavyweight to be applicable in the
IoT. Furthermore, existing models of
service composition cannot be directly
used for Io T interoperation, because of
architectural differences. In contrast
to the single-type Web service component model (such as, services), the Io T
component model is heterogeneous
and multilayered (for example, devices, data, services, and organizations).
Innovative models are required for
IoT composition. Specifications of the
functionalities of the Io T are a key challenge. Through the diverse, mobile,
and context-aware devices, data and
services can simultaneously generate
diverse and context-aware functions.
Hence, in the Io T, desired functionality of components is more dynamic
and context-aware than in traditional
settings, which brings significant complexity in the composition process.
In a nutshell, the IoT involves
user in Facebookg in 2010 and 208 fol-
lowers per user in Twitterh in 2013), the
resultant big data is complex as well as
large. Service composition based on
social relations poses fundamental se-
Challenges in crowdsourcing-based
reputation. Trust plays an important
role in the functioning of a service
ecosystem. It is, however, difficult to
establish trust when services that of-
fer similar functionalities compete.
Reputation is an effective approach in
social networks for predicting cred-
ibility based on past behavior gleaned
from consumers. It is often difficult to
ascertain reputation in an open and of-
ten anonymous environment. Hence,
reputation and crowdsourcing are im-
portant approaches for deriving trust.
Computing the reputation of a ser-
vice entity in a community is realized
by collecting all individual users’ opin-
ions toward this entity in this commu-
nity. Crowdsourcing provides an effec-
tive means for data collection through
collaborations within the community.
Nevertheless, several research chal-
lenges remain in the computation of
Quality of crowdsourcing. The major
difference between crowdsourcing and
traditional user feedback collection is
that crowdsourcing is more likely to
use financial rewards and other incentives to motivate participation. However, it is unclear how these factors influence the quality of crowdsourcing.
In addition, service users’ opinions are
typically ambiguous, context-dependent, and based on individual preference. Ambiguity refers to what users
express instead what they really think.
Users’ opinions may also be affected
by time, location, and social factors
(for example, social relations between
service users or between service users
and service providers.
12 Some users
may have particular preferences on
certain service products.
10 There is a
strong need to predict the outcome of
crowdsourcing reputation given that
the reputation is influenced by several