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Correspondence regarding this article should be addressed to Athman Bouguettaya

( [e-mail])

© 2017 ACM-0782/17/04 $15.00

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. 8

Internet of Things. In traditional service computing, a service composition focuses on finding an effective combination of component services. 19 Recent work has suggested that a service composition in the Io T needs to find an effective combination of both component services and devices. 11 In 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.

Conclusion

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.

Acknowledgments

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.