Abstract
Network-enabled sensing and actuation devices are key enablers to connect real-world objects to the cyber world. The Internet of Things (IoT) consists of network-enabled devices and communication technologies that allow connectivity and integration of physical objects (Things) into the digital world (Internet). Dealing with the data deluge from heterogeneous IoT resources and services imposes new challenges on indexing, discovery and ranking mechanisms. Novel indexing and discovery methods will enable developing applications that use on-line access and retrieval of ad-hoc IoT data. Investigation of the related work leads to the conclusion that there has been significant work on processing and analysing sensor data streams. However, there is still a need for integrating solutions that contemplate the work-flow from connecting IoT resources to make their published data indexable, searchable and discoverable. This research proposes a set of novel solutions for indexing, processing and discovery in IoT networks. The work proposes novel distributed in-network and spatial indexing solutions. The proposed solutions scale well and provide up to 92% better response time and higher success rates in response to data search queries compared to a baseline approach. A co-operative, adaptive, change detection algorithm has also been developed. It is based on a convex combination of two decoupled Least Mean Square (LMS) windowed filters. The approach provides better performance and less complexity compared to the state-of-the-art solutions. The change detection algorithm can also be applied to distributed networks in an on-line fashion. This co-operative approach allows publish/subscribe based and change based discovery solutions in IoT. Continuous transmission of large volumes of data collected by sensor nodes induces a high communication cost for each individual node in IoT networks. An Adaptive Method for Data Reduction (AM-DR) has been proposed for reducing the number of data transmissions in IoT networks. In AM-DR, identical predictive models are constructed at both the sensor and the sink nodes to describe data evolution such that sensor nodes require transmitting only their readings that deviate significantly from actual values. This has a significant impact on reducing the data load in IoT data discovery scenarios. Finally, a solution for quality and energy-aware resource discovery and accessing IoT resources has been proposed. The solution effectively achieves a communication reduction while retaining a high prediction accuracy (i.e. only a deviation of ±1.0 degree between actual and predicted sensor readings). Furthermore, an energy cost model has been discussed to demonstrate how the proposed approach reduces energy consumption significantly and effectively prolongs the network lifetime.