Edge Computing: Applications, State-of-the-Art and Challenges

Abstract

The Internet of Things (IoT) is now infiltrating into our daily lives, providing important measurement and collection tools to inform us of every decision. Millions of sensors and devices continue to generate data and exchange important information through complex networks that support machine-to-machine communication and monitor and control critical smart world infrastructure. As a strategy to alleviate resource congestion escalation, edge computing has become a new paradigm for addressing the needs of the Internet of Things and localization computing. Compared to well-known cloud computing, edge computing migrates data calculations or storage to the edge of the network near the end-user. Thus, multiple compute nodes distributed across the network can offload computational pressure from a centralized data center and can significantly reduce latency in message exchanges. Besides, the distributed architecture balances network traffic and avoids spikes in traffic in the IoT network, reduces latency between edge/cloud servers and end-users, and reduces response time for real-time IoT applications compared to traditional cloud services. In this article, we conducted a comprehensive survey to analyze how edge computing can improve the performance of IoT networks. We classify edge calculations into different groups based on the architecture and study their performance by comparing network latency, bandwidth usage, power consumption, and overhead. Through the systematic introduction of the concept of edge computing, typical application scenarios, research status, and key technologies, it is considered that the development of edge computing is still in the initial stage. There are still many problems in practical applications that need to be solved, including optimizing edge computing performance, security, interoperability, and intelligent edge operations management services.

Security Implications of Edge Computing in Cloud Networks

“…Edge computing pays great attention to processing data closer to where it was generated to obtain fast responses. However, it becomes more work to maintain strong security and quick reactions simultaneously [24]. Undoubtedly, adding security measures may slow down the internal operation, affecting the real-time nature of edge computing.…”

A Review of Privacy and Security of Edge Computing in Smart Healthcare Systems: Issues, Challenges, and Research Directions

“…A smartphone, for example, as an edge is the interface between people and the cloud, a gateway in a smart home is the interface between house things and the cloud, and a Micro Data Center (MDC) or a Cloudlet [56] is the interface between a mobile device and the cloud. The crucial aspect of edge computing is that computation should occur close to data sources [57] . The edge computing paradigm has several advantages over the traditional cloud computing model, including real-time data processing and analysis, high levels of security and privacy protection, scalability, robustness, low traffic, and location awareness [58,59] .…”

Application of Big Data Privacy Protection Based on Edge Computing in the Prediction of Martial Arts Training Movement Trajectory

“…Edge computing is a new computing mode, which transfers data processing tasks from the central server to the edge devices of the network to reduce network latency and improve data processing efficiency (Wang, 2019). The concept of edge computing originates from the development of the Internet of Things (IoT) because the amount of data generated by IoT devices is huge.…”