HeteroEdge: Addressing Asymmetry in Heterogeneous Collaborative Autonomous Systems

Simple item page
dc.contributor.authorAnwar, Mohammad Saeid
dc.contributor.authorDey, Emon
dc.contributor.authorDevnath, Maloy Kumar
dc.contributor.authorGhosh, Indrajeet
dc.contributor.authorKhan, Naima
dc.contributor.authorFreeman, Jade
dc.contributor.authorGregory, Timothy
dc.contributor.authorSuri, Niranjan
dc.contributor.authorJayarajah, Kasthuri
dc.contributor.authorRamamurthy, Sreenivasan Ramasamy
dc.contributor.authorRoy, Nirmalya
dc.date.accessioned2023-05-25T18:41:16Z
dc.date.available2023-05-25T18:41:16Z
dc.date.issued2023-05-05
dc.description.abstractGathering knowledge about surroundings and generating situational awareness for IoT devices is of utmost importance for systems developed for smart urban and uncontested environments. For example, a large-area surveillance system is typically equipped with multi-modal sensors such as cameras and LIDARs and is required to execute deep learning algorithms for action, face, behavior, and object recognition. However, these systems face power and memory constraints due to their ubiquitous nature, making it crucial to optimize data processing, deep learning algorithm input, and model inference communication. In this paper, we propose a self-adaptive optimization framework for a testbed comprising two Unmanned Ground Vehicles (UGVs) and two NVIDIA Jetson devices. This framework efficiently manages multiple tasks (storage, processing, computation, transmission, inference) on heterogeneous nodes concurrently. It involves compressing and masking input image frames, identifying similar frames, and profiling devices to obtain boundary conditions for optimization.. Finally, we propose and optimize a novel parameter split-ratio, which indicates the proportion of the data required to be offloaded to another device while considering the networking bandwidth, busy factor, memory (CPU, GPU, RAM), and power constraints of the devices in the testbed. Our evaluations captured while executing multiple tasks (e.g., PoseNet, SegNet, ImageNet, DetectNet, DepthNet) simultaneously, reveal that executing 70% (split-ratio=70%) of the data on the auxiliary node minimizes the offloading latency by approx. 33% (18.7 ms/image to 12.5 ms/image) and the total operation time by approx. 47% (69.32s to 36.43s) compared to the baseline configuration (executing on the primary node).en_US
dc.description.sponsorshipThis work has been partially supported by NSF CAREER Award #1750936 and U.S.Army Grant #W911NF2120076.en_US
dc.description.urihttps://arxiv.org/abs/2305.03252en_US
dc.format.extent9 pagesen_US
dc.genrejournal articlesen_US
dc.genrepreprintsen_US
dc.identifierdoi:10.13016/m2pxfk-w8w1
dc.identifier.urihttps://doi.org/10.48550/arXiv.2305.03252
dc.identifier.urihttp://hdl.handle.net/11603/28075
dc.language.isoen_USen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Information Systems Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.relation.ispartofUMBC Center for Real-time Distributed Sensing and Autonomy
dc.relation.ispartofUMBC Student Collection
dc.rightsThis work was written as part of one of the author's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law.en_US
dc.rightsPublic Domain Mark 1.0*
dc.rights.urihttp://creativecommons.org/publicdomain/mark/1.0/*
dc.titleHeteroEdge: Addressing Asymmetry in Heterogeneous Collaborative Autonomous Systemsen_US
dc.typeTexten_US
dcterms.creatorhttps://orcid.org/0000-0002-1290-0378en_US
dcterms.creatorhttps://orcid.org/0000-0003-2868-3766en_US
dcterms.creatorhttps://orcid.org/0000-0003-3445-9779en_US

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
2305.03252.pdf
Size:
4.73 MB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
2.56 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

UMBC Information Systems Department
UMBC Center for Real-time Distributed Sensing and Autonomy
UMBC Faculty Collection
UMBC Student Collection