T. Butt, I. Phillips, L. Guan, G. Oikonomou, "Adaptive and Context-aware Service Discovery for the Internet of Things", in Proc. 6th conference on Internet of Things and Smart Spaces (ruSMART 2013), St.Petersburg, Russia, pp. 36-47, 2013
The Internet of Things (IoT) vision foresees a future Internet encompassing the realm of smart physical objects, which offer hosted functionality as services. The role of service discovery is crucial when providing application-level, end-to-end integration. In this paper, we propose trendy: a RESTful web services based Service Discovery protocol to tackle the challenges posed by constrained domains while offering the required interoperability. It provides a service selection technique to offer the appropriate service to the user application depending on the available context information of user and services. Furthermore, it employs a demand-based adaptive timer and caching mechanism to reduce the communication overhead and to decrease the service invocation delay. trendy’s grouping technique creates location-based teams of nodes to offer service composition. Our simulation results show that the employed techniques reduce the control packet overhead, service invocation delay and energy consumption. In addition, the grouping technique provides the foundation for group-based service mash-ups and localises control traffic to improve scalability.
T. Butt, I. Phillips, L. Guan, G. Oikonomou, "TRENDY: An Adaptive and Context-Aware Service Discovery Protocol for 6LoWPANs", in Proc. Third International Workshop on the Web of Things (WoT 2012), Newcastle, UK, pp. 2:1-2:6, 2012
We propose, TRENDY, a new registry-based Service Discovery protocol with context awareness. It uses CoAP-based RESTful web services to provide a standard interoperable interface which can be easily translated from HTTP. In addition, TRENDY introduces an adaptive timer and grouping mechanism to minimise control overhead and energy consumption. TRENDY's grouping is based on location tags to localise status maintenance traffic and to compose and offer new group based services. Our simulation results show that TRENDY techniques reduce the control traffic considerably and also reduce the energy consumption, while offering the optimal service selection.
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