.png){kind=icon}
Return
Optimal Resource Allocation for Crowdsourced Image Processing
.png){kind=icon}
PRE
AI
DOI：10.1109/TMC.2022.3182317.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Crowdsourced image processing has the potential to vastly impact response timeliness in various emergency situations. Because images can provide extremely important information regarding an event of interest (hits), sending the right images to an analyzer as soon as possible is of crucial importance. In this paper, we consider the problem of optimally assigning resources, both local (CPUs in phones) and remote (network-based GPUs) to mobile devices for processing images, ultimately sending those of interest to a centralized entity while also accounting for the energy consumption at the distributed nodes. To that end, we use the dual-path Network Utility Maximization (NUM) framework, coupled with a hit-ratio estimator and energy costs, to enable a distributed implementation of the system. We include analysis of different hit-ratio estimators using realistic trace data, first considering immediate and then delayed feedback. We address accuracy concerns when estimating the likelihood of future image hits and provide a window-based heuristic for scenarios when hit-ratio feedback is severely delayed. Our TCP-inspired window-method predicts both image hit likelihood and current wireless network congestion with great effectiveness. Results are validated using both synthetic simulations and real-life traces.
Keywords:
Crowdsourcing
optimization
resource allocation
multi-path NUM
Journal
IF:
9.2
Papers:
5.9K
Citations:
1.8W
Organization
Cited Papers
ShareSave
ShareSaveShareSaveShareSaveShareSaveOutput Power Levels of 4G User Equipment and Implications on Realistic RF EMF Exposure Assessments.png)
IEEE ACCESS
OAAI
IEEE ACCESSIF3.6
OAAIShareSaveShareSave
View more