#339:
C. Qu,
P. Calyam,
J. Yu,
A. Vandanapu,
O. Opeoluwa,
K. Gao,
S. Wang,
R. Chastain, and
K. Palaniappan
Future Generation Computer Systems,
Volume 125,
pgs. 247-262,
2021
Abstract,
Bibtex,
PlainText,
PDF,
URL,
DOI,
Google Scholar
Multi-Unmanned Aerial Vehicle (UAV) systems with high-resolution cameras have been found useful for operations such as smart city and disaster management. These systems feature Flying Ad-Hoc Networks (FANETs) that connect the computation edge with UAVs and a Ground Control Station (GCS) through air-to-ground wireless network links. Leveraging the edge/fog computation resources effectively with energy-latency-awareness, and handling intermittent failures of FANETs are the major challenges in supporting video processing applications. In this paper, we propose a novel “DroneCOCoNet” framework for drone video analytics that coordinates intelligent processing of large video datasets using edge computation offloading and performs network protocol selection based on resource-awareness. We present two edge computation offloading approaches, i.e., heuristic-based and reinforcement learning-based approaches. These approaches provide intelligent task sharing and co-ordination for dynamic offloading decision-making among UAVs. Our scheme handles the problem of computation offloading tasks in two separate ways: (i) heuristic decision-making process, and (ii) Markov decision process; wherein we aim to minimize the total computation costs as well as latency in the edge/fog resources while minimizing video processing times to meet application requirements. Our experimental results show that our heuristic-based offloading decision-making scheme enables lower scheduling time and energy consumption for low drone-to-ground server ratios. In comparison, our dynamic reinforcement learning-based decision-making approach increases the accuracy and saves overall time periodically. Notably, these results also hold in various other multi-UAV scenarios involving largely different numbers of detected objects in e.g., smart farming, transportation traffic flow monitoring and disaster response.
@article{Qu2021:FGCS_DroneCOCoNet,
author = "C. Qu and P. Calyam and J. Yu and A. Vandanapu and O. Opeoluwa and K. Gao and S. Wang and R. Chastain and K. Palaniappan",
title = "DroneCOCoNet: Learning-based edge computation offloading and control networking for drone video analytics",
year = 2021,
journal = "Future Generation Computer Systems",
volume = 125,
pages = "247-262",
keywords = "edge/fog computation offloading, drone video analytics, mobile edge computing, learning-based scheme, data processing in fog computing",
doi = "https://doi.org/10.1016/j.future.2021.06.040",
url = "https://www.sciencedirect.com/science/article/pii/S0167739X21002351"
}
C. Qu, P. Calyam, J. Yu, A. Vandanapu, O. Opeoluwa, K. Gao, S. Wang, R. Chastain, and K. Palaniappan. DroneCOCoNet: Learning-based edge computation offloading and control networking for drone video analytics. Future Generation Computer Systems, volume 125, pages 247-262, 2021.
