الفهرس 
Comparison between fog computing with IoT and COTOnly 14 pages are availabe for public view
 |  | from | 98 |  |  |
| | | from | 98 | | |
Abstract
Developing technologies such as Internet of Things (IoT) is part of the elegant health care world that needs cognizant of latency computation for requests processing in authentic time. IoT system data created are typically handled via a cloud infrastructure due to on-call applications and the ability to scale the computing in the cloud model. However, processing IoT applications on a cloud-only basis is not an efficient suggestion for certain IoT applications, specifically health care appliances. Fog computing exists amid cloud and IoT appliances to fix this trouble. These fog systems are close to the users and are capable of data computing, flexible communication, and local storage rather than cloud storage. Fog provides faster response and higher quality services. Fog handling can therefore be deemed the ablest option to enable elegant health care IoT to enhance efficient and safe services to many IoT smart health care users. Mobility one of the crucial arguments in running IoT elegant health care applications in the fog computing world. Several studies have recently emerged to address device mobility in diversity from cutting edge research areas, including fog computing and cloudlets. However, dealing with end-user mobility becomes even more challenging with fog-assisted IoT healthcare frameworks because low latency in mobility support and fog node locating is a vital prerequisite. In this thesis we propose some terms and concepts relating to the context of this study will be discussed .In addition a comprehensive survey on fog computing including characteristics and advantages. Furthermore the implementation of IoT with fog computing is also addressed by illustrating the implementation advantages, evolving IoT applications and challenges that have arisen. The second proposal is to present A Location-Aware Fog Positioning Algorithm (LAFPA) that maintains the connection between the mobile end-user and the ideal fog node. It offers application response times that are 40% to 50% quicker than those of other distribution methods’ proposed fog nodes, plus real-time system performance improvements. In addition, the second proposal is provide a fog computing architecture with hand-over strategy that is tailored to user mobility and that reduces latency for location-sensitive operations and service stability with great data broadcast quality. In our design, it is now essential to connect with other FNs in order to proceed with a request for a new job rather than finishing a task or uploading it to the cloud. This improves end-to-end communication by 35%–45%.
While decreasing the typical network delay by 30%–40%. Finally we propose a fog node (FN) allocation and mobility -adapted fog computing architecture for healthcare applications. The architecture is based on an algorithm for mobility Management over Fog Geographical Allocation (MMFGA) that offer slow latency for location sensitive operations and service stability with high data broadcast quality for user mobility in fog computing. The proposed architecture minimizes average network lateness by 40%–50%, bandwidth by 30%–40%, and end-to-end communication by 35%–55%.