Paper Info
Title
Litedge: Towards Light-weight Edge Computing for Efficient Wireless Surveillance System
Abstract
Wireless surveillance systems are rapidly gaining popularity due to their easier deployability and improved performance. However, cameras inside are generating a large amount of data, which brings challenges to the transmission through resource-constrained wireless networks. Observing that most collected consecutive frames are redundant with few objects of interest (OoIs), the filtering of these frames can dramatically relieve the transmission pressure. Additionally, real-world environment may bring shielding or blind areas in videos, which notoriously affects the accuracy of frame analysis. The collaboration between cameras facing at different angles can compensate for such accuracy loss. In this work, we present Litedge, a light-weight edge computing strategy to improve the QoS (i. e., the latency and accuracy) of wireless surveillance systems. Two main modules are designed on edge cameras: (i) the light-weight video compression module for frame filtering, mainly realized by model compression and convolutional acceleration; and (ii) the collaborative validation module for error compensation between the master-slave camera pair. We also implement an enhanced surveillance system prototype from real-time monitoring and pre-processing on edge cameras to the backend data analysis on a server. Experiments based on real-world collected videos show the efficiency of Litedge. It achieves 82% transmission latency reduction with a maximal 0.119s additional processing delay, compared with the full video transmission. Remarkably, 91.28% of redundant frames are successfully filtered out, greatly reducing the transmission burden. Litedge outperforms state-of-the-art light-weight AI models and video compression methods by balancing the QoS balance ratio between accuracy and latency.
Year
DOI
Venue
2019
10.1145/3326285.3329066
2019 IEEE/ACM 27th International Symposium on Quality of Service (IWQoS)
Keywords
Field
DocType
QoS,wireless surveillance system,light-weight AI,edge computing,collaborative validation
Edge computing,Wireless network,Wireless,Latency (engineering),Computer science,Quality of service,Filter (signal processing),Real-time computing,Data compression,Processing delay
Conference
ISSN
ISBN
Citations 
1548-615X
978-1-7281-6661-2
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Yutong Liu1205.75
Linghe Kong277072.44
Muhammad Hassan300.34
Long Cheng424618.23
Guangtao Xue545652.52
guihai chen63537317.28