IOT Stream Analytics Platform

The Internet of Things (IoT) is changing people’s surrounding physical world into an information ecosystem that facilitate our everyday life. Billions of smart objects become data-generating “things” that can sense environmental changes and report their sensed data. Leveraging the huge amount of sensory information is a key issue to realize the IoT solutions in many areas. Adequate technologies are required for data collection, transmission, data processing, analysis, reporting, and advanced querying. In this thesis, an IoT Stream Analytics Platform that supports IoT application and service development is proposed: it provides user applications a way to capture flowing data from multitudes of data sources and provide analytical insights in real time based on user needs. Developers can conveniently build their IoT applications on this platform without having to consider the diversity and complexity of smart devices and their underlying networks

Window Queries Over Data Streams

Evaluating queries over data streams has become an appealing way to support various stream-processing applications. Window queries are commonly used in many stream applications. In a window query, certain query operators, especially blocking operators and stateful operators, appear in their windowed versions. Previous research work in evaluating window queries typically requires ordered streams and this order requirement limits the implementations of window operators and also carries performance penalties. This thesis presents efficient and flexible algorithms for evaluating window queries. We first present a new data model for streams, progressing streams, that separates stream progress from physical-arrival order. Then, we present our window semantic definitions for the most commonly used window operators—window aggregation and window join. Unlike previous research that often requires ordered streams when describing window semantics, our window semantic definitions do not rely on physical-stream arrival properties. Based on the window semantic definitions, we present new implementations of window aggregation and window join, WID and OA-Join. Compared to the existing implementations of stream query operators, our implementations do not require special stream-arrival properties, particularly stream order. In addition, for window aggregation, we present two other implementations extended from WID, Paned-WID and AdaptWID, to improve excution time by sharing sub-aggregates and to improve memory usage for input with data distribution skew, respectively. Leveraging our order-insenstive implementations of window operators, we present a new architecture for stream systems, OOP (Out-of- Order Processing). Instead of relying on ordered streams to indicate stream progress, OOP explicitly communicates stream progress to query operators, and thus is more flexible than the previous in-order processing (IOP) approach, which requires maintaining stream order. We implemented our order-insensitive window query operators and the OOP architecture in NiagaraST and Gigascope. Our performance study in both systems confirms the benefits of our window operator implementations and the OOP architecture compared to the commonly used approaches in terms of memory usage, execution time and latency

Middleware Architecture for Sensing as a Service

The Internet of Things is a concept that envisions the world as a smart space in which physical objects embedded with sensors, actuators, and network connectivity can communicate and react to their surroundings. Recent advancement in information and communication technologies make it possible to make the IoT vision a reality. However, IoT devices and consumers of data from these IoT devices can be owned by different entities which makes IoT data sharing a real challenge. Sensing as a Service is a concept that is influenced by the cloud computing term “Every Thing as a Service”. Sensing as a Service enables sensor data sharing. Sensing as a Service middleware enables IoT applications to access data generated by sensing devices owned by other entities. IoT applications are charged by the Sensing as a Service middleware for the amount of sensor data they use. This thesis addresses the architectural design of a cloud-based Sensing as Service middleware. The middleware enables sensor owners to sell their sensor data through the Internet. IoT applications can collect, and analyze sensors through the middleware API. We propose multitenancy algorithms for the middleware resource management. In addition, we propose a SQL-Like language that can be used by IoT applications for sensing service discovery, and sensor stream analytics. The evaluation of the middleware implementation shows the effectiveness of the algorithm