Real-Time Air quality index application for the City of Cape Town: An event-Driven system with Kafka, CQRS and Clojure

The World Health Organization estimates around 3.7 million premature deaths world-wide were due to ambient air pollution in 2012, 88% of which occurred in low to middle income countries such as South Africa. This project focuses on the development of an event-driven real-time air quality index application for the City of Cape Town. Event streams are being more commonly adopted in data centric applications that aim to produce trend analyses and prediction models. Event-driven systems store immutable raw event data, providing both a history of what has happened in the database, and the current state, thereby assisting with debugging and providing audit trail support. In addition to increasing public accessibility to the city's air quality information, the application has been designed for scalability, extensibility and data analysis through the incorporation of the Command Query Responsibility Segregation, Event Sourcing and Model-View-Controller architectural patterns. The design of the application itself serves as a basic reusable template for any new applications that may require the scalability, extensibility and is inherently data-centric nature as is found in this implementation. By taking advantage of the city's existing air quality monitoring sensor network, the real-time application has the capability to highlight problematic areas within the City of Cape Town with regard to high pollution levels, create greater public awareness, and lays the foundation for the future development of predictive air quality models and pollution forecasts

A File System Abstraction for Sense and Respond Systems

The heterogeneity and resource constraints of sense-and-respond systems pose significant challenges to system and application development. In this paper, we present a flexible, intuitive file system abstraction for organizing and managing sense-and-respond systems based on the Plan 9 design principles. A key feature of this abstraction is the ability to support multiple views of the system via filesystem namespaces. Constructed logical views present an application-specific representation of the network, thus enabling high-level programming of the network. Concurrently, structural views of the network enable resource-efficient planning and execution of tasks. We present and motivate the design using several examples, outline research challenges and our research plan to address them, and describe the current state of implementation.Comment: 6 pages, 3 figures Workshop on End-to-End, Sense-and-Respond Systems, Applications, and Services In conjunction with MobiSys '0

Programming patterns and development guidelines for Semantic Sensor Grids (SemSorGrid4Env)

The web of Linked Data holds great potential for the creation of semantic applications that can combine self-describing structured data from many sources including sensor networks. Such applications build upon the success of an earlier generation of 'rapidly developed' applications that utilised RESTful APIs. This deliverable details experience, best practice, and design patterns for developing high-level web-based APIs in support of semantic web applications and mashups for sensor grids. Its main contributions are a proposal for combining Linked Data with RESTful application development summarised through a set of design principles; and the application of these design principles to Semantic Sensor Grids through the development of a High-Level API for Observations. These are supported by implementations of the High-Level API for Observations in software, and example semantic mashups that utilise the API

Architectures for Wireless Sensor Networks

Various architectures have been developed for wireless sensor networks. Many of them leave to the programmer important concepts as the way in which the inter-task communication and dynamic reconfigurations are addressed. In this paper we describe the characteristics of a new architecture we proposed - the data-centric architecture. This architecture offers an easy way of structuring the applications designed for wireless sensor nodes that confers them superior performances

Enabling stream processing for people-centric IoT based on the fog computing paradigm

The world of machine-to-machine (M2M) communication is gradually moving from vertical single purpose solutions to multi-purpose and collaborative applications interacting across industry verticals, organizations and people - A world of Internet of Things (IoT). The dominant approach for delivering IoT applications relies on the development of cloud-based IoT platforms that collect all the data generated by the sensing elements and centrally process the information to create real business value. In this paper, we present a system that follows the Fog Computing paradigm where the sensor resources, as well as the intermediate layers between embedded devices and cloud computing datacenters, participate by providing computational, storage, and control. We discuss the design aspects of our system and present a pilot deployment for the evaluating the performance in a real-world environment. Our findings indicate that Fog Computing can address the ever-increasing amount of data that is inherent in an IoT world by effective communication among all elements of the architecture