Due to the recent developments in mobile network technology and the supply of mobile devices, services that require high computing power and fast access speed, such as machine learning and multimedia streaming, are attracting attention. Mobile Edge Computing (MEC) has emerged. MEC allows servers to be located close to users to efficiently handle these services and provides users with ultra-low latency content delivery and powerful computing services. However, there has been a lack of research into the architecture required to efficiently use the computing power and resources of MEC. So, this paper proposes hierarchical MEC architecture in which MEC servers (MECS) are arranged in a hierarchical scheme to provide users with rapid content delivery, high computing performance, and efficient use of server resources.

Recently, new mobile applications and services have appeared thanks to the rapid development of mobile devices and mobile network technology. Cloud computing has played an important role over the past decades, providing powerful computing capabilities and high-capacity storage space to efficiently deliver these mobile services to mobile users. Nevertheless, existing cloud computing delegates computing to a cloud server located at a relatively long distance, resulting in significant delays due to additional time to return processing results from a cloud server. These unnecessary delays are inconvenient for mobile users because they are not suitable for applications that require a real-time service environment. To cope with these problems, a new computing concept called Multi-Access Edge Computing (MEC) has emerged. Instead of sending all requests to the central cloud to handle mobile users’ requests, the MEC brings computing power and storage resources to the edge of the mobile network. It enables the mobile user device to run the real-time applications that are sensitive to latency to meet the strict requirements. However, there is a lack of research on the efficient utilization of computing resources and mobility support when mobile users move in the MEC environment. In this paper, we propose the MEC-based mobility management scheme that arranges MEC server (MECS) as the concept of Zone so that mobile users can continue to receive content and use server resources efficiently even when they move. The results show that the proposed scheme reduce the average service delay compared to the existing MEC scheme. In addition, the proposed scheme outperforms the existing MEC scheme because mobile users can continuously receive services, even when they move frequently.

The Multi-Access Edge Computing (MEC) paradigm provides a promising solution to solve the resource-insufficiency problem in user mobile devices by offloading computation-intensive and delay-sensitive computing services to nearby edge nodes. However, there is a lack of research on the efficient task offloading and mobility support when mobile users frequently move in the MEC environment. In this paper, we propose the mobile personal MEC architecture that utilizes a user’s mobile device as an MEC server (MECS) so that mobile users can receive fast response and continuous service delivery. The results show that the proposed scheme reduces the average service delay and provides efficient task offloading compared to the existing MEC scheme. In addition, the proposed scheme outperforms the existing MEC scheme because the existing mobile user devices are used as MECS, enabling low-latency service and continuous service delivery, even as the mobile user requests and task sizes increase.

The combination of machine learning with healthcare services is emerging. However, when machine learning functions are executed on resource-constrained mobile consumer devices, the computing overhead will increase, and the user experience will deteriorate. In essence, the existing approaches delegate such tasks to a central cloud data center. However, data processing on remote cloud servers results in a long response latency. Therefore, this article proposes a machine learning-based smart recipe recommendation system that uses a local mobile edge computing server. The proposed system delegates the machine learning and recipe search tasks to the mobile edge computing server, thereby reducing the response latency for data processing and the computational burden placed on mobile user devices.

As the number of mobile devices and IoT (internet of things) devices has explosively increased, various contents are created anytime and anywhere. To meet such trend, current internet architecture has exposed many limitations such as high control overhead due to multistaged address resolution, frequent location updates, and network congestion. Recently, information centric networking (ICN) is considered as new networking architecture to redesign current internet’s content exchange paradigm. In current ICN architecture, whenever a mobile node moves to a new domain, it needs long latency to configure and confirm the temporary name prefix. So, this paper presents an efficient name prefix configuration mechanism in mobile CCN to reduce the latency needed for the name prefix configuration during MCS’s handover. From the performance analysis, the proposed mechanism is shown to provide lower control overhead and lower resource consumption.