A Review on Edge-Computing: Challenges in Security and Privacy

Abstract

Vehicular Ad-hoc Networks (VANETs) enable vehicle-to-everything communication, where vehicles disseminate messages to a Road Side Unit (RSU) periodically. All RSUs send data to a cloud or a central server for detection and analysis of traffic congestion situations on the roadways globally. The existing cloud computing approach is inefficient for analyzing massive amounts of data in a short amount of time while still meeting the needs of consumers. Due to its limited scalability, flexibility, and connectivity, conventional vehicular networks have several issues in resource placement and administration, affecting the quality of service (QoS) and severely affecting VANET services and entire network effectiveness. To address these issues, a novel architecture is known as edge computing - which allows the decentralization of data preprocessing from the clouds to the edge of the network - had been positioned to solve the issues that have arisen while employing cloud computing method. Edge computing is defined by its ability to implement with VANETs to calculate, store, and deliver delay-sensitive communications to vehicles on deadline. Less latency, network off-loading, and context-awareness are just a few of the benefits that it might bring to the global vehicular network (location, environment factors, etc.). Mobile edge computing (MEC), fog computing (FC), and cloudlet are the primary methods to edge computing that have been developed. This paper presents a survey on cloud and edge computing, a detailed comparison of the existing research, characteristics, requirements for enabling edge computing, and challenges.