Systems solutions for emerging mobility challenges in smart cities
Date
2023
Authors
Journal Title
Journal ISSN
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Publisher
University of Delaware
Abstract
The rise of smart cities has presented numerous mobility challenges, initiating the need for innovative system solutions that can adapt to evolving user mobility requirements. As the demand for efficient and sustainable mobility solutions continues to increase, it is crucial to highlight the significance of implementing mobility-aware solutions in edge computing and smart buildings, as the integral components of smart cities. This dissertation aims to tackle emerging mobility challenges in smart cities: mobility-aware solutions in edge computing focusing on exploring intelligent computation offloading and content delivery techniques for smart devices and mobility management in the context of smart buildings, with a specific focus on elevator systems. ☐ In the context of edge computing, the proliferation of mobile devices, such as smartphones and laptops, has resulted in the development of new mobile applications. These applications, especially those that offering high-volume multimedia services, face challenges in improving user experience due to the limited battery capacity and computation power of mobile user equipment (UEs). The resource-intensive nature of these applications and the growing demand for them have imposed a considerable burden on UEs and mobile networks. To address these challenges, a novel approach called mobile edge computing (MEC) has emerged, which involves deploying computing and storage nodes at the network edges close to mobile devices. MEC offers various benefits, including reduced latency, increased bandwidth, and improved battery life. ☐ Two key solutions that have gained importance in edge computing are computation offloading and efficient content delivery. Computation offloading involve transferring resource-intensive tasks from UEs to more powerful servers on edge, thus alleviating the burden on UEs. This offloading mechanism can enhance application performance, reduce energy consumption, and ultimately enhance the user experience. On the other hand, efficient content delivery plays a crucial role in managing the surging demand for multimedia services. By implementing intelligent strategies for content caching and delivery at the network edge, latency can be minimized, bandwidth usage can be optimized, and the overall quality of service can be significantly improved. However, uncertainties arising from user mobility patterns and fluctuations in application specifications pose challenges in determining the optimal location for computation offloading or content storage to minimize latency. ☐ In smart buildings, as cities get more crowded and buildings grow taller, there are special challenges in making sure people can move around easily. Elevators, which help people go up and down in tall buildings, have problems like too many people using them at once, making people wait a long time, and not using the elevators efficiently. To make sure people can move around smoothly and quickly, we need to find ways to manage mobility in smart buildings and make the elevator schedules work better. It's important to come up with smart strategies to make sure everyone can move around easily and avoid waiting too long for the elevators to arrive. ☐ This dissertation systems solutions to address these mobility challenges. The first work proposes a novel offloading approach called OAMC, which considers mobility-aware computation offloading in MEC. OAMC takes into account the dynamics of mobile applications and minimizes turnaround time by considering offloading latency, migration delay, and execution time. It also utilizes prediction models based on future application specifications. ☐ Building upon the first work, the second work presents two additional offloading approaches, S-OAMC and G-OAMC. These approaches assign applications to cloudlets based on expected future locations and specifications predicted using Matrix Completion, a machine learning method. S-OAMC employs a sampling-based approximation dynamic programming approach for scalability and near-optimal solutions, while G-OAMC utilizes a fast greedy-based approach for low-turnaround time offloading decisions. ☐ The third work focuses on component selection for QoS-aware content delivery in 5G-enabled MEC. It introduces an efficient online learning approach called QCS-MAB, which achieves bounded performance. QCS-MAB autonomously learns the optimal component selection for UEs, aiming to minimize latency during content delivery. Additionally, it proposes a deep learning approach called QCS-DNN to provide near-optimal solutions in real time based on historical data for massive-scale problems. ☐ The fourth work investigates mobility management within smart buildings, specifically focusing on elevator systems. Elevator scheduling plays a crucial role in optimizing travel time and energy consumption within smart buildings. By leveraging techniques such as game theory and observing user interactions, more efficient scheduling of elevator systems can be achieved. This study introduces the request coalition formation mechanism (RCFM), a dynamic and energy-efficient elevator scheduling approach that utilizes cooperative game theory. The RCFM aims to reduce overall elevator movement, resulting in reduced waiting times and energy consumption for users in smart buildings.
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Keywords
Smart cities, Elevator systems, Mobility challenges, Mobile user equipment, Mobile edge computing, Smart buildings