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Trustworthiness of crowdsourcing
contributors. Future research should
focus on studying the context-dependent features of contributors’ trustworthiness. The credibility of a crowd
member (that is, a human user or another service) determines how much
other service consumers may trust its
reported ratings regarding the services it has invoked. This allows us
to differentiate between service trust
and feedback trust. For instance, a
service that is not trustworthy as a
service provider may be trustworthy
as a judge of the behavior of other service providers, and vice versa. Trade-off strategies for selecting users with
different cost and trustworthy levels
for crowdsourcing should also be explored. Selecting the most appropriate crowdsourcing contributors or
crowd workers will require services
that interact and combine information from contributors, service providers, and third-party sources, such
as job listings and social media.
Internet of Things. In traditional
service computing, a service composition focuses on finding an effective
combination of component services.
Recent work has suggested that a service composition in the Io T needs to
find an effective combination of both
component services and devices.
emerging Io T architectures based on
service composition, there is a need
to find an effective combination of
component services, data services
supported by cloud platform services,
and devices. Cloud component services are an integral part of the IoT,
because they are needed to manage
device Web representations, contexts,
and related data processing services
anywhere and without the need to rely
on a computing center.
Complementary to the current
work on service discovery and integra-
tion, an important and novel direc-
tion in Io T research lies in the area of
device discovery and integration. Ex-
isting results based on a combination
of Semantic Sensor Networks (SSN)
and OpenIo T18 provide a device layer
architecture and related functionality
for IoT device discovery and integra-
tion. SSN defines an ontology that
is used to describe Io T things and
to find devices from the attributes of
the data they produce. Nevertheless,
Integration of IoT things can be
greatly facilitated by discovering the
correlations among things. However,
correlations among IoT things are
usually difficult to detect. Unlike hu-
man social networks, where people
are well connected, things often have
limited explicit interconnections. An
interesting direction is multi-hop
connections, which leverage human-
to-things interactions to correlate
Io T things.
Graph-based approaches and machine-learning techniques can help
discover hidden interesting relationships among IoT things, and thus
suggest interesting and novel integration patterns.
Service computing has a bright future
supporting the tremendous advances
in emerging areas of computing such
as mobile computing, cloud computing, big data, social computing, and
beyond. We make the case in this
manifesto that the potential of service computing is far greater than
what has been achieved so far. We lay
down a path forward to take service
computing to new heights of innovation. To forge ahead, we make the
important statement that, for the service computing paradigm to succeed,
it needs to be decoupled from the
technology of the day. The challenges
may be difficult but the payoffs are
great and there is no reason why an
ambitious research agenda would
not produce enormous benefits for
computer science and society.
This research was made possible by
LP120200305, DP150100149, and
DP160103595 grants from Australian
Research Council and NPRP 7-481-1-
088 and NPRP 9-224-1-049 grants from
the Qatar National Research Fund (a
member of The Qatar Foundation).
The statements made herein are solely
the responsibility of the authors.