Future Edge Cloud and Edge Computing for Internet of Things Applications
Abstract
The Internet is evolving rapidly toward the future Internet of Things (IoT) which will potentially connect billions or even trillions of edge devices which could generate huge amount of data at a very high speed and some of the applications may require very low latency. The traditional cloud infrastructure will run into a series of difficulties due to centralized computation, storage, and networking in a small number of datacenters, and due to the relative long distance between the edge devices and the remote datacenters. To tackle this challenge, edge cloud and edge computing seem to be a promising possibility which provides resources closer to the resource-poor edge IoT devices and potentially can nurture a new IoT innovation ecosystem. Such prospect is enabled by a series of emerging technologies, including network function virtualization and software defined networking. In this survey paper, we investigate the key rationale, the state-of-the-art efforts, the key enabling technologies and research topics, and typical IoT applications benefiting from edge cloud. We aim to draw an overall picture of both ongoing research efforts and future possible research directions through comprehensive discussions.
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In smart city surveillance systems, edge nodes are typically positioned between the monitoring devices and the cloud server.
“In smart city surveillance systems, edge nodes are typically positioned between the monitoring devices and the cloud server [24].”
From A Survey of Video Surveillance Systems in Smart City (2023) · cites this paper as [24]
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Reliability-Aware Network Service Provisioning in Mobile Edge-Cloud Networks
Li, J., Liang, W., Huang, M., & Jia, X. (2020) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2020.2970048 · Cited by 81 · see citing works
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LiKe: Lightweight Certificateless Key Agreement for Secure IoT Communications
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Multiuser Computation Offloading and Resource Allocation for Cloud–Edge Heterogeneous Network
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A Novel Collision-Aware Adaptive Data Rate Algorithm for LoRaWAN Networks
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Dew Computing Architecture for Cyber-Physical Systems and IoT
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Moving Deep Learning to the Edge
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- Authors
- Pan, J., & McElhannon, J.
- Affiliations
- University of Missouri–St. Louis (US)
- ORCID
- Pan, Jianli
- Venue
- IEEE Internet of Things Journal
- Published
- 2018-02
- Volume
- 5
- Issue
- 1
- Pages
- 439-449
- ISSN
- 2327-4662 (ISSN-L)
- OpenAlex
- W2766401293
- Cited by
- 868
- References
- 17
- Citations by year
- 2026: 47 · 2025: 89 · 2024: 114 · 2023: 104 · 2022: 105 · 2021: 145 · 2020: 143 · 2019: 86 · 2018: 34 · 2017: 1
- FWCI
- 48.10
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Internet of Things Journal (publishedVersion)
- Funders
- National Security Agency (H98230-17-1-0393, H98230-17-1-0352)
- University of Missouri System Research Board Award
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Computer Networks and Communications › Software-Defined Networks and 5G
- Physical Sciences › Computer Science › Hardware and Architecture › Energy Efficiency in Computing
- Keywords
- edge-cloud computing, edge computing, Internet of Things, low latency, network function virtualization, software-defined networking, IoT applications, resource-constrained edge devices
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