A Context-Aware Architecture for Smart Applications with Enabled Adaptation and Reasoning Capabilities

The term ''smart city'' refers to an instrumented, interconnected, and intelligent city built by leveraging Information and Communication Technologies (ICT). In such a city, a combination of embedded hardware and software involving sensors, actuators, and a host of mobile devices and wearables that are connected to the Internet of Things (IoT) networks will sense data in different contexts and automatically drive desired adaptations through actuators. Through adaptations, city planners, professionals, and researchers aim to optimize resource consumption and cost of providing services while improving the quality of life for the ever increasing urban population. To fully realize this goal, a context-aware and data-centric inference is a necessity. A system is said to be context-aware if it is able to adapt its operations to the current context without explicit user intervention. This thesis proposes a generic context-aware system architecture for development of smart city applications. The proposed architecture puts special emphasis on privacy and security, incorporating mechanisms to protect the system and sensitive information at each layer of the architecture. Furthermore, this architecture integrates with a reasoning component, whose inference engine can be driven by logic or other formalisms. A prototype implementation and a case study done in this thesis indicate the practical merits of the proposed architecture and provide a proof of concept

Characterization and Efficient Management of Big Data in IoT-Driven Smart City Development

Smart city is an emerging initiative for integrating Information and Communication Technologies (ICT) in effective ways to support development of smart cities with enhanced quality of life for its citizens through safe and secure context-aware services. Major technical challenges to realize smart cities include resource use optimization, service delivery without interruption at all times in all aspects, minimization of costs, and reduction of resource consumption. To address these challenges, new techniques and technologies are required for modeling and processing the big data generated and used through the underlying Internet of Things (IoT). To this end, we propose a data-centric approach to IoT in conceptualizing the “things” from a service-oriented perspective and investigate efficient ways to identify, integrate, and manage big data. The data-centric approach is expected to better support efficient management of data with complexities inherent in IoT-generated big data. Furthermore, it supports efficient and scalable query processing and reasoning techniques required in development of smart city applications. This article redresses the literature and contributes to the foundations of smart cities applications