Managing data dynamics, streams and sharing in the internet of things.

Abstract

Recently, the Internet of Things (IoT) has gained momentum in connecting everyday objects to the Internet and facilitating machine-to-human and machine-to-machine communication with the physical world. IoT offers the capability to connect and integrate both digital and physical entities, enabling a whole new class of applications and services. This thesis firstly reviews the state-of-the-art research efforts in IoT from datacentric perspectives, including data stream processing, data storage models, complex event processing, and searching in IoT by identifying an IoT data taxonomy, which includes ten key data elements of IoT data under three categorizations. In this thesis, we focus ourselves on three aspects of data management in IoT: data dynamics, data velocity, and data incompleteness. More specifically, we study data dynamics in dynamic graphs, handle data velocity in streams, and tackle data incompleteness via sharing. In IoT, connections and relations between things are universal and highly dynamic. It is natural to model these connections and relations using dynamic graphs. Meanwhile, shortest path computation is one of the most fundamental operations for managing and analyzing graphs. In this thesis, we focus on the problem of computing the shortest path distance in graphs subject to edge failures. We propose SIEF, a Supplemental Index for Edge Failures in a dynamic graph, which is based on distance labeling, to support distance queries in dynamic graphs with edge failures efficiently. In IoT, one challenging issue is how to disseminate streaming data to relevant consumers efficiently. Semantic technologies aim to facilitate machine-to-machine (M2M) communication and are attracting more and more interest from both academia and industry, especially in the emerging IoT. This thesis leverages semantic technologies, such as Linked Data, which can facilitate M2M communications to build an efficient information dissemination system for semantic IoT. The system integrates Linked Data streams generated from various data collectors and disseminates matched data to relevant data consumers based on triple pattern queries registered in the system by the consumers. We also design new data structures, TP-automata and CTP-automata, to meet the high performance needs of Linked Data dissemination. To tackle data incompleteness, we consider large-scale information sharing scenarios among mobile objects in IoT. By leveraging semantic techniques, we propose broadcasting Linked Data on-air to allow simultaneous access to the information and to achieve better scalability. We introduce a novel air indexing method to reduce the information access latency and energy consumption. We also study the data placement problem of periodic XML data broadcast in IoT environments to facilitate data sharing in IoT. Taking advantage of the structured characteristics of XML data, we present a theoretical analysis on the XML data placement on a wireless channel, which forms the basis of our novel data placement algorithm. This thesis also discusses on-going and emerging IoT applications, and open research issues for processing and managing IoT data. Several representative domains where IoT can make profound changes are explored, and some key directions for future research and development from a data-centric perspective are identified.