Abstract
Recent advances in the Internet of Things (IoT) provides rich opportunities to future mobility services for the development of more flexible solutions. Instead of using a fixed set of data sources or services, applications can benefit from those flexible mechanisms by adapting to change in the sensing environment such as sensor disappearance/degradation or service unavailability. In this paper, we contribute with an approach that enables dynamic selection of the services for mobility to meet requirements of autonomous driving use-cases. Our approach is focused on different mobility services using available data sources and data processing methods with their related quality parameters. Those services are inspired by the standards and the dynamics of real-test road. We present a prototypical implementation of the mechanism for optimal service selection in an autonomous driving test environment and evaluated our testing results with respect to correctness and performance.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
DigiNet-PS project website: http://diginet-ps.de.
- 2.
JIAC V, http://www.jiac.de/agent-frameworks/jiac-v/ [Accessed: May 30, 2019].
- 3.
CPLEX Optimizer: https://www.ibm.com/analytics/cplex-optimizer [Accessed: May 30, 2019].
- 4.
Apache Kafka: https://kafka.apache.org/ [Accessed: May 30, 2019].
References
3GPP specification for 5G. https://www.3gpp.org/release-15. Accessed 30 May 2019
Kurz, M., Hoelzl, G., Ferscha, A., et al.: The OPPORTUNITY framework and data processing ecosystem for opportunistic activity and context recognition. Int. J. Sens. Wirel. Commun. Control 1(2), 102–125 (2011)
Perera, C., Zaslavsky, A., Christen, P., Georgakopoulos, D.: Sensing as a service model for smart cities supported by Internet of Things. In: Proceedings of the Transactions ETT, p. 113 (2013)
Zaslavsky, A., Perera, C., Georgakopoulos, D.: Sensing as a service and big data. In: International Conference on Advances in Cloud Computing (ACC 2012), Bangalore, India, pp. 21–29 (2012)
Wagner, M., Reichle, R., Geihs, K.: Context as a service - requirements, design and middleware support. In: 2011 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops), pp. 220–225 (2011)
Banerjee, P., et al.: Everything as a service: powering the new information economy. Computer 44(3), 36–43 (2011)
Moore, P., Xhafa, F., Barolli, L.: Context-as-a-service: a service model for cloud-based systems. In: 2014 Eighth International Conference on Complex, Intelligent and Software Intensive Systems, pp. 379–385. IEEE (2014)
Lützenberger, M., et al.: A multi-agent approach to professional software engineering. In: Cossentino, M., El Fallah Seghrouchni, A., Winikoff, M. (eds.) EMAS 2013. LNCS (LNAI), vol. 8245, pp. 156–175. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-45343-4_9
Chen, N., Cardozo, N., Clarke, S.: Goal-driven service composition in mobile and pervasive computing. IEEE Trans. Serv. Comput. 11(1), 49–62 (2018)
Eryilmaz, E., Trollmann, F., Albayrak, S.: An architecture for dynamic context recognition in an autonomous driving testing environment. In: 2018 IEEE 11th Conference on Service-Oriented Computing and Applications (SOCA), pp. 9–16, November 2018
Strunk, A.: QoS-aware service composition: a survey. In: Proceeding of 8th European Conference on Web Services (ECOWS), pp. 67–74 (2010)
Villalonga, C., Roggen, D., Lombriser, C., Zappi, P., Tröster, G.: Bringing quality of context into wearable human activity recognition systems. In: Rothermel, K., Fritsch, D., Blochinger, W., Dürr, F. (eds.) QuaCon 2009. LNCS, vol. 5786, pp. 164–173. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-04559-2_15
Manzoor, A., Truong, H.-L., Dustdar, S.: Quality of context: models and applications for contextaware systems in pervasive environments. Knowl. Eng. Rev. 29(2), 154–170 (2014)
Hibner, A., Zielinski, K.: Semantic-based dynamic service composition and adaptation. In: Proceedings of the IEEE Services Congress, pp. 213–220 (2007)
Eryilmaz, E., Trollmann, F., Albayrak, S.: Quality-aware service selection approach for adaptive context recognition in IoT. In: Proceedings of the 9th International Conference on the Internet of Things (IOT 2019). ACM, Bilboa (2019, to appear)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Eryilmaz, E., Khan, M.A., Trollmann, F., Albayrak, S. (2019). Adaptive Service Selection for Enabling the Mobility of Autonomous Vehicles. In: Chatzigiannakis, I., De Ruyter, B., Mavrommati, I. (eds) Ambient Intelligence. AmI 2019. Lecture Notes in Computer Science(), vol 11912. Springer, Cham. https://doi.org/10.1007/978-3-030-34255-5_14
Download citation
DOI: https://doi.org/10.1007/978-3-030-34255-5_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-34254-8
Online ISBN: 978-3-030-34255-5
eBook Packages: Computer ScienceComputer Science (R0)
