Doctor of Philosophy
This thesis proposes the concept of the Policy-based Autonomic Smart City Management System, an innovative framework designed to comprehensively manage diverse aspects of urban environments, ranging from environmental conditions such as temperature and air quality to the infrastructure which comprises multiple layers of infrastructure, from sensors and devices to advanced IoT platforms and applications. Efficient management requires continuous monitoring of devices and infrastructure, data analysis, and real-time resource assessment to ensure seamless city operations and improve residents' quality of life. Automating data monitoring is essential due to the vast array of hardware and data exchanges, and round-the-clock monitoring is critical. Efficient resource use is key to cost reduction, making resource-sensitive infrastructure management crucial. This system is implemented based on the MAPE-K approach that collects the data, monitors it, analyzes it, and makes real-time decisions based on predefined policies without the need for human intervention.
The thesis introduces a novel model for an autonomic management system for smart cities, a general, end-to-end model of a smart city and delves into the algorithms and policies that underpin this system, illustrating how they interpret the data to optimize urban operations. Unique to the models is the assumption that smart cities will leverage existing platforms for IoT Management and monitoring. The autonomic management system assumes the presence of such components and leverages their capabilities. A prototype autonomic management system based on this is presented and used to demonstrate the approach. The primary objective of the Autonomic Smart City Management System is to enhance urban efficiency, sustainability, and overall quality of life for city residents, all while reducing the necessity for labor-intensive manual monitoring and management. By harnessing technology to streamline operations, this system aims to not only improve urban functionality but also result in long-term cost savings.
Summary for Lay Audience
An autonomic smart city management system is a computerized system that helps manage various aspects of a city from the environment, such as temperature and air quality, to the infrastructure such as the performance of computers and networks. Think of it like a "brain" for a city that uses sensors, cameras, and other technologies to collect data about what is happening in the city and manages those devices 24/7 without any human intervention. This system uses algorithms and policies to analyze data and make decisions to improve the city's operations. For example, if there is heavy traffic on a particular road, the system can automatically adjust traffic signals to reduce congestion. Similarly, if the response time of an application is higher than a threshold, the system can decrease it automatically by assigning more resources to a particular process. The purpose of an autonomic smart city management system is to enhance cities' efficiency, sustainability, and livability for their inhabitants while reducing the necessity for manual monitoring and management. By using technology to streamline operations, cities can be efficient and save money in the long run.
Okhovat, Elham, "Enhancing Urban Life: A Policy-Based Autonomic Smart City Management System for Efficient, Sustainable, and Self-Adaptive Urban Environments" (2023). Electronic Thesis and Dissertation Repository. 9895.
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