E-STORE: An Energy-constrained Smartphone
Storage for Near Real-time Disaster Image Sharing
Pengfei Zuo, Yu Hua, Dan Feng, Zhenhua Nie, Min Fu, Yuanyuan Sun
Wuhan National Laboratory for Optoelectronics, School of Computer
Huazhong University of Science and Technology, Wuhan, China
Background and Challenges
■ Disaster environments
— Images sharing for disaster relief
■ Challenges
— Image redundancy
— Energy constraint
— Limited bandwidth
■ Existing schemes
— Eliminate the redundant images in the forwarding
path of network transmission
— Overlook the energy constraint in smartphones
The Proposed E-STORE System
■ Energy-aware redundancy elimination in the source
— Challenges: 1) High time and energy overheads for calculating image
features;
2) The size of image feature is quite large, even larger than the image
size
— Solutions: 1) Energy-aware Dynamic Compression Scheme (Step 2);
2) A Conversion Algorithm (Step 3)
■ Fast query index for real-time response (Step 7)
— Locality sensitive hashing: map the similar contents to the same bucket
— Cuckoo hashing: deal with space inefficiency caused by LSH
■ Low battery
—Energy-aware Threshold Setting Scheme (Step 8)
■ Large-size image compression before uploading (Step 11-1)
— The high-quality images are not necessary for such disaster
environments
—Further reduce the bandwidth overhead
Yes
Smartphone
Server
1.Select images and obtain the
remaining energy of battery
5.Receive the data
2. Compress images based on
the remaining energy
6.Identify the similar images
among the uploaded images
3.Extract the SIFT points and
convert to image fingerprints
7.Query the image
fingerprints in server index
4.Upload the image
fingerprints and the
parameter of energy
8.Generate the threshold T
depending on the parameter
of the remaining energy
10.Do the similar
images exist？
9.Respond with the query
results
No
11-1.Compress and upload
the images
12.Receive the images
11-2.Do not upload
The workflow of E-STORE
Preliminary Results
4500
70
50
Direct uploading
E-STORE
40
30
20
10
0
3500
3000
Direct uploading
E-STORE
2500
2000
1500
1000
500
0%
10%
20%
30%
40%
50%
60%
70%
Redundancy rate
Future Work
■ Evaluate the performance of E-STORE using real-world datasets
■ Different network bandwidth and loads with a large number of smartphones
■ Measure and analyze the energy overhead of smartphones
Image upload delay (s)
4000
60
Network traffic (MB)
■ Evaluation configuration
— Dataset: 50 images(60MB)
— Emulate the network bandwidth in the disaster
environments: 128Kbps
— Redundancy ratio: from 0% to 100%
■ Preliminary results
— 40% to 99.9% bandwidth saving
— 33.9% to 93.8% time saving
80%
90% 100%
0
0%
10%
20%
30%
40%
50%
60%
70%
Redundancy rate
80%
90% 100%