DACC-Comm: DNN-Powered Adaptive Compression and Flow Control for Robust Communication in Network-Constrained Environment

dc.contributor.authorDey, Emon
dc.contributor.authorRavi, Anuradha
dc.contributor.authorLewis, Jared
dc.contributor.authorKumar, Vinay Krishna
dc.contributor.authorFreeman, Jade
dc.contributor.authorGregory, Timothy
dc.contributor.authorSuri, Niranjan
dc.contributor.authorBusart, Carl
dc.contributor.authorRoy, Nirmalya
dc.date.accessioned2025-04-01T14:55:04Z
dc.date.available2025-04-01T14:55:04Z
dc.date.issued2025-01
dc.description2025 17th International Conference on COMmunication Systems & NETworkS (COMSNETS), 06-10 January 2025, Bengaluru, India
dc.description.abstractRobust communication is vital for multi-agent robotic systems involving heterogeneous agents like Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs) operating in dynamic and contested environments. These agents often communicate to collaboratively execute critical tasks for perception awareness and are faced with different communication challenges: (a) The disparity in velocity between these agents results in rapidly changing distances, in turn affecting the physical channel parameters such as Received Signal Strength Indicator (RSSI), data rate (applicable for certain networks) and most importantly "reliable data transfer", (b) As these devices work in outdoor and network-deprived environments, they tend to use proprietary network technologies with low frequencies to communicate long range, which tremendously reduces the available bandwidth. This poses a challenge when sending large amounts of data for time-critical applications. To mitigate the above challenges, we propose DACC-Comm, an adaptive flow control and compression sensing framework to dynamically adjust the receiver window size and selectively sample the image pixels based on various network parameters such as latency, data rate, RSSI, and physiological factors such as the variation in movement speed between devices. DACC-Comm employs state-of-the-art DNN (TABNET) to optimize the payload and reduce the retransmissions in the network, in turn maintaining low latency. The multi-head transformer-based prediction model takes the network parameters and physiological factors as input and outputs (a) an optimal receiver window size for TCP, determining how many bytes can be sent without the sender waiting for an acknowledgment (ACK) from the receiver, (b) a compression ratio to sample a subset of pixels from an image. We propose a novel sampling strategy to select the image pixels, which are then encoded using a feature extractor. To optimize the amount of data sent across the network, the extracted feature is further quantized to INT8 with the help of post-training quantization. We evaluate DACC-Comm on an experimental testbed comprising Jackal and ROSMaster2 UGV devices that communicate image features using a proprietary radio (Doodle) in 915-MHz frequency. We demonstrate that DACC-Comm improves the retransmission rate by ≈17% and reduces the overall latency by ≈12%. The novel compression sensing strategy reduces the overall payload by ≈56%.
dc.description.sponsorshipThis work has been supported by ONR grant #N00014-23-1-2119, NSF grant #2233879, and U.S. Army Grant #W911NF2120076.
dc.description.urihttps://ieeexplore.ieee.org/abstract/document/10885774/
dc.format.extent9 pages
dc.genreconference papers and proceedings
dc.identifierdoi:10.13016/m2nykx-hnzy
dc.identifier.citationDey, Emon, Anuradha Ravi, Jared Lewis, Vinay Krishna Kumar, Jade Freeman, Timothy Gregory, Niranjan Suri, Carl Busart, and Nirmalya Roy. "DACC-Comm: DNN-Powered Adaptive Compression and Flow Control for Robust Communication in Network-Constrained Environment." In 2025 17th International Conference on COMmunication Systems and NETworks (COMSNETS), 575-83, 2025. https://doi.org/10.1109/COMSNETS63942.2025.10885774.
dc.identifier.urihttps://doi.org/10.1109/COMSNETS63942.2025.10885774
dc.identifier.urihttp://hdl.handle.net/11603/37858
dc.language.isoen_US
dc.publisherIEEE
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Student Collection
dc.relation.ispartofUMBC Information Systems Department
dc.relation.ispartofUMBC Center for Real-time Distributed Sensing and Autonomy
dc.relation.ispartofUMBC Faculty 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.
dc.rightsPublic Domain
dc.rights.urihttps://creativecommons.org/publicdomain/mark/1.0/
dc.subjectImage coding
dc.subjectAdaptive Congestion Control
dc.subjectReceivers
dc.subjectSensors
dc.subjectFeature extraction
dc.subjectRobot sensing systems
dc.subjectReceived signal strength indicator
dc.subjectAdaptive Compressive Sensing
dc.subjectPhysiology
dc.subjectQuantization (signal)
dc.subjectPayloads
dc.subjectimproved QoS
dc.subjectAdaptive systems
dc.titleDACC-Comm: DNN-Powered Adaptive Compression and Flow Control for Robust Communication in Network-Constrained Environment
dc.typeText
dcterms.creatorhttps://orcid.org/0000-0002-1290-0378
dcterms.creatorhttps://orcid.org/0009-0005-5840-132X

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