An empirical study of latency in an emerging class of edge computing applications for wearable cognitive assistance
Open accessAbstract
An emerging class of interactive wearable cognitive assistance applications is poised to become one of the key demonstrators of edge computing infrastructure. In this paper, we design seven such applications and evaluate their performance in terms of latency across a range of edge computing configurations, mobile hardware, and wireless networks, including 4G LTE. We also devise a novel multi-algorithm approach that leverages temporal locality to reduce end-to-end latency by 60% to 70%, without sacrificing accuracy. Finally, we derive target latencies for our applications, and show that edge computing is crucial to meeting these targets.
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Colorspace modifications can reduce the video frame size by as much as 62%.
“Our choice of color space modifications can reduce the video frame size by as much as 62%.”
From Mez: An Adaptive Messaging System for Latency-Sensitive Multi-Camera Machine Vision at the IoT Edge (2021) · cites this paper as [23]
Section 2.2 describes streaming image frames in MJPEG format, and Section 3.3 discusses compressing frames and transmitting them. Neither passage, nor the additional passages searched for “62%” and color-space modifications, reports a 62% reduction in frame size from changing color space. (LLM, conf=0.72)
Experimental — accuracy not yet measuredRemoving artifact information can reduce the video frame size by as much as 98%.
“Removing artifact information can reduce the video frame size by as much as 98%.”
From Mez: An Adaptive Messaging System for Latency-Sensitive Multi-Camera Machine Vision at the IoT Edge (2021) · cites this paper as [23]
The Gabriel Platform section (page unavailable) describes streaming a subset of image frames in MJPEG format, and §3.3 (page unavailable) describes compressing frames before transmission. Neither reports removing artifact information or a 98% reduction in frame size; searches for those terms and “98%” found no supporting passage in our copy. (LLM, conf=0.72)
Experimental — accuracy not yet measuredModifying resolution can reduce the video frame size by as much as 84%.
“Modifying resolution can reduce the video frame size by as much as 84%.”
From Mez: An Adaptive Messaging System for Latency-Sensitive Multi-Camera Machine Vision at the IoT Edge (2021) · cites this paper as [23]
Section 2.2 describes capturing 640×360 frames, and section 3.5 discusses compression and transmission differences across devices. Neither reports changing resolution or an 84% reduction in frame size (searched: resolution, frame size, 84%). (LLM, conf=0.70)
Experimental — accuracy not yet measuredBlurring the video frames can reduce the video frame size by as much as 46%.
“Blurring the video frames can reduce the video frame size by as much as 46%.”
From Mez: An Adaptive Messaging System for Latency-Sensitive Multi-Camera Machine Vision at the IoT Edge (2021) · cites this paper as [23]
Section 2.2 describes capturing and streaming video frames, and Section 3.3 discusses frame compression and transmission latency. Neither passage reports blurring frames or a 46% reduction in frame size; searches for “blurring” and “46%” found no supporting passage in our copy. (LLM, conf=0.75)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
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- Authors
- Chen, Z., Hu, W., Wang, J., Zhao, S., Amos, B., Wu, G., Ha, K., Elgazzar, K., Pillai, P., Klatzky, R., Siewiorek, D., & Satyanarayanan, M.
- Affiliations
- Carnegie Mellon University (US)
- Intel (United States) (US)
- ORCID
- Venue
- Proceedings of the Second ACM/IEEE Symposium on Edge Computing
- Publisher
- ACM
- Published
- 2017-10-12
- Pages
- 1-14
- OpenAlex
- W2766304518
- Cited by
- 213
- References
- 37
- Citations by year
- 2026: 6 · 2025: 13 · 2024: 18 · 2023: 21 · 2022: 21 · 2021: 31 · 2020: 47 · 2019: 35 · 2018: 21
- FWCI
- 22.38
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- ACM/IEEE Symposium on Edge Computing (SEC) (publishedVersion)
- Funders
- National Science Foundation (CNS-1518865)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Computer Science › Hardware and Architecture › Energy Efficiency in Computing
- Keywords
- edge computing, latency, wearable devices, cognitive support, interactive applications, temporal locality, 4G LTE, wireless networks, mobile hardware
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