Edge Computing: challenges, solutions and architectures arising from the integration of Cloud Computing with Internet of Things

Abstract

The rapid spread of the Internet of Things (IoT) is causing the exponential growth of objects connected to the network, in fact, according to estimates, in 2020 there will be about 3/4 devices per person totaling of over 20 billion connected devices. Therefore, the use of content that requires intensive bandwidth consumption is growing. In order to meet these growing needs, the computing power and storage space are transferred to the network edge to reduce the network latency and increase the bandwidth availability. Edge computing allows to approach high-bandwidth content and sensitive apps to the user or data source and is preferred to use it for many IoT applications respect to cloud computing. Its distributed approach addresses the needs of IoT and industrial IoT, as well as the immense amount of data generated by smart sensors and IoT devices, which would be costly and time-consuming to send to the cloud for processing and analysis. Edge computing reduces both the bandwidth needed and the communication among sensors and cloud, which can negatively affect the IoT performance. The goal of edge computing is to improve efficiency and reduce the amount of data transported to the cloud for processing, analysis and storage. The research activity carried out during the three years of the Ph.D. program focused on the study, design and development of architectures and prototypes based on the Edge Computing in various contexts such as smart cities and agriculture. Therefore, the well-known paradigms of Fog Computing and Mobile Edge Computing have been faced. In this thesis, will be discussed the work carried out through the exploitation of the Fog Computing and Mobile Edge Computing paradigms, considered suitable solutions to address the challenges of the fourth industrial revolution.