What the Fog? Edge Computing Revisited: Promises, Applications and Future Challenges

Abstract

Edge computing brings computing and storage resources closer to (mobile) end users and data sources, thus bypassing expensive and slow links to distant cloud computing infrastructures. Often leveraged opportunistically, these heterogeneous resources can be used to offload data and computations, enabling upcoming demanding applications such as augmented reality and autonomous driving. Research in this direction has addressed various challenges, from architectural concerns to runtime optimizations. As of today, however, we lack a widespread availability of edge computing-partly because it remains unclear which of the promised benefits of edge computing are relevant for what types of applications. This article provides a comprehensive snapshot of the current edge computing landscape, with a focus on the application perspective. We outline the characteristics of edge computing and its postulated benefits and drawbacks. To understand the functional composition of applications, we first define common application components that are relevant w.r.t. edge computing. We then present a classification of proposed use cases and analyze them according to their expected benefits from edge computing and which components they use. Furthermore, we illustrate existing products and industry solutions that have recently surfaced and outline future research challenges. INDEX TERMS Edge computing, heterogeneous networks, next generation networking mobile applications, Internet of Things, ubiquitous computing.

A Multi-Layer LoRaWAN Infrastructure for Smart Waste Management

“…The potentialities of adopting edge computing paradigms in a broad sense were pointed out [ 33 ], while the benefits in the ambit of smart cities frameworks were widely discussed in the survey [ 34 ]. Applying edge computing in LPWAN (e.g., LoRa based ones) whose nodes have limited computational capabilities is challenging but there exist studies which proved the feasibility and indicated the benefits [ 35 ].…”

Deep Learning Based Smart Survilance Robot

“…The main motivation of this work is to identify the pitfalls in cloud video synthesis to process large-volume of data to avoid latency in critical applications [11] [12]. Cloud computing aims enormous resources that can be flexibly used to fit dynamic demands which leads to avoid substantial latencies when using services placed at distant locations [9], [10] 3.WORKING PRINCIPLE There are Three Different Sections to be discussed…”

A Discussion on Context-Awareness to Better Support the IoT Cloud/Edge Continuum

“…Fog Computing [10], also known as Edge computing [43], extends the Cloud Computing paradigm to the "Edges" of the network, bringing in new opportunities to explore applications and services. By assisting the placement of storage and data processing closer to the data sources, Edge Computing brings in benefits in terms of latency and energy consumption [44]- [48], for instance.…”