Edge analytics in the internet of things
Open accessAbstract
High-data-rate sensors, such as video cameras, are becoming ubiquitous in the Internet of Things. This article describes GigaSight, an Internet-scale repository of crowd-sourced video content, with strong enforcement of privacy preferences and access controls. The GigaSight architecture is a federated system of VM-based cloudlets that perform video analytics at the edge of the Internet, thus reducing the demand for ingress bandwidth into the cloud. Denaturing, which is an owner-specific reduction in fidelity of video content to preserve privacy, is one form of analytics on cloudlets. Content-based indexing for search is another form of cloudlet-based analytics. This article is part of a special issue on smart spaces.
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The hierarchical architecture has gradually linked cloudlets to other forms of Edge Computing, particularly as local solutions to high latency and low bandwidth problems for IoT applications.
“Logically, this hierarchical architecture has gradually linked cloudlets to other forms of Edge Computing, in particular as local solutions to high latency and low bandwidth problems for IoT applications [97].”
From Edge AI: A survey (2023) · cites this paper as [97]
The Internet section (page not provided) describes cloudlets as the middle tier of a device–cloudlet–cloud hierarchy. It says cloudlets were originally motivated by latency, while their use in the IoT-focused GigaSight system is based solely on bandwidth. It does not describe a gradual linkage to other forms of edge computing. (LLM, conf=0.85)
Experimental — accuracy not yet measuredThe edge computing paradigm offers a way to process data at the edge of the network to address concerns such as bandwidth saving, as well as data safety and privacy.
“As noted by Satyanarayanan et al. [32,33] and Shi et al. [34,35], the edge computing paradigm offers a way to process data at the edge of the network to address concerns such as bandwidth saving, as well as data safety and privacy.”
From Edge-Computing Video Analytics for Real-Time Traffic Monitoring in a Smart City (2019) · cites this paper as [32]
Deep learning shows high accuracy while computing a large number of network parameters.
“Moreover, it shows high accuracy while computing a large number of network parameters[36].”
From 6G Communication Technology: A Vision on Intelligent Healthcare (2021) · cites this paper as [36]
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- Authors
- Satyanarayanan, M., Simoens, P., Xiao, Y., Pillai, P., Chen, Z., Ha, K., Hu, W., & Amos, B.
- Affiliations
- Carnegie Mellon University (US)
- Ghent University (BE)
- Aalto University (FI)
- Intel (United States) (US)
- Venue
- IEEE Pervasive Computing
- Published
- 2015-04
- Volume
- 14
- Issue
- 2
- Pages
- 24-31
- ISSN
- 1536-1268 (ISSN-L)
- OpenAlex
- W2094547360
- Cited by
- 430
- References
- 9
- Citations by year
- 2026: 7 · 2025: 16 · 2024: 13 · 2023: 20 · 2022: 30 · 2021: 41 · 2020: 70 · 2019: 77 · 2018: 74 · 2017: 53 · 2016: 24 · 2015: 5
- FWCI
- 59.60
- Type
- journal article
- Language
- en
- Open access
- green
- biblio.ugent.be
- Locations
- IEEE Pervasive Computing (publishedVersion)
- Ghent University Academic Bibliography (Ghent University) (submittedVersion, other-oa)
- Funders
- National Science Foundation
- Carnegie Mellon University
- Defense Advanced Research Projects Agency
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Keywords
- edge analytics, Internet of Things, cloudlets, video analysis, privacy preservation, video denoising, content-based indexing, federated systems, access control, smart environments, bandwidth reduction
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