Provision of adaptive and context-aware service discovery for the Internet of Things

The IoT concept has revolutionised the vision of the future Internet with the advent of standards such as 6LoWPAN making it feasible to extend the Internet into previously isolated environments, e.g., WSNs. The abstraction of resources as services, has opened these environments to a new plethora of potential applications. Moreover, the web service paradigm can be used to provide interoperability by offering a standard interface to interact with these services to enable WoT paradigm. However, these networks pose many challenges, in terms of limited resources, that make the adaptability of existing IP-based solutions infeasible. As traditional service discovery and selection solutions demand heavy communication and use bulky formats, which are unsuitable for these resource-constrained devices incorporating sleep cycles to save energy. Even a registry based approach exhibits burdensome traffic in maintaining the availability status of the devices. The feasible solution for service discovery and selection is instrumental to enable the wide application coverage of these networks in the future. This research project proposes, TRENDY, a new compact and adaptive registry-based SDP with context awareness for the IoT, with more emphasis given to constrained networks, e.g., 6LoWPAN It uses CoAP-based light-weight and RESTful web services to provide standard interoperable interfaces, which can be easily translated from HTTP. TRENDY's service selection mechanism collects and intelligently uses the context information to select appropriate services for user applications based on the available context information of users and services. In addition, TRENDY introduces an adaptive timer algorithm to minimise control overhead for status maintenance, which also reduces energy consumption. Its context-aware grouping technique divides the network at the application layer, by creating location-based groups. This grouping of nodes localises the control overhead and provides the base for service composition, localised aggregation and processing of data. Different grouping roles enable the resource-awareness by offering profiles with varied responsibilities, where high capability devices can implement powerful profiles to share the load of other low capability devices. Thus, it allows the productive usage of network resources. Furthermore, this research project proposes APPUB, an adaptive caching technique, that has the following benefits: it allows service hosts to share their load with the resource directory and also decreases the service invocation delay. The performance of TRENDY and its mechanisms is evaluated using an extensive number of experiments performed using emulated Tmote sky nodes in the COOJA environment. The analysis of the results validates the benefit of performance gain for all techniques. The service selection and APPUB mechanisms improve the service invocation delay considerably that, consequently, reduces the traffic in the network. The timer technique consistently achieved the lowest control overhead, which eventually decreased the energy consumption of the nodes to prolong the network lifetime. Moreover, the low traffic in dense networks decreases the service invocations delay, and makes the solution more scalable. The grouping mechanism localises the traffic, which increases the energy efficiency while improving the scalability. In summary, the experiments demonstrate the benefit of using TRENDY and its techniques in terms of increased energy efficiency and network lifetime, reduced control overhead, better scalability and optimised service invocation time

Adaptive and context-aware service discovery for the Internet of Things

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

TRENDY: an adaptive and context-aware service discovery protocol for 6LoWPANs

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