Storage Management Strategy in Mobile Phones for Photo Crowdsensing
Abstract
:1. Introduction
- We propose a storage management framework, which includes the parameter collection module, utility calculation module, and decision module to address the photo crowdsensing problem in mobile phones.
- We propose two utility-based Storage Management strategies in mobile phones for Photo Crowdsensing (SMPC), one is for maximizing delivery ratio (SMPC-R), and the other one is for minimizing average delay (SMPC-D).
2. Related Work
2.1. Buffer Management in DTNs
2.2. Photo Crowdsourcing
3. Network Model and Problem Formulation
3.1. Network Model
3.2. Problem Formulation
4. Proposed Storage Management Framework
4.1. Parameter Collection Module
4.2. Utility Calculation Module
4.3. Sending and Deleting Decisions
Algorithm 1 SMPC |
Input: |
Capacity of photos in a user’s storage: n |
for a new arriving photo: m |
Output: |
Sending photo: , Deleting photo: |
|
5. Utility-Based Storage Management Strategy
5.1. Mobility Pattern in Mobile Crowdsensing
5.2. Maximization of Delivery Ratio
5.3. Minimization of Average Delay
5.4. Parameters Collection
5.4.1. Collection of
5.4.2. Collection of
5.4.3. Collection of
6. Performance Evaluation
6.1. The Traces Used and Settings
6.2. Strategies and Performances in Comparison
- 1.
- Delivery ratio: successfully delivered photo number divided by total number of photos.
- 2.
- Average delay: required average time for the photos that have been delivered.
6.3. Simulation Results
6.3.1. Performances of SMPC-R and SMPC-D
6.3.2. Distribution of Utility
7. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Symbol | Meaning |
---|---|
N | the number of users minus one |
uploaded deadline for the sensing photo i | |
remaining live time for photo i | |
elapsed time for photo i from its generation time | |
to the current time () | |
copy number of photo i in the storages when the time is | |
copy number of deleted photo i when the time is | |
number of devices ever carry photo i when the time is | |
number of users that have ever been in any PoI | |
in photo i’s elapsed time | |
variable for intermeeting time distribution among users | |
expected intermeeting time between users () | |
variable for intermeeting time distribution of user and PoI | |
expected intermeeting time between user and PoI () | |
utility of photo i | |
probability to finish delivering photo i to PoIs when | |
probability to finish delivering the undelivered photo i | |
when the remaining time is | |
probability to finish delivering photo i | |
expected delivery delay for photo i after elapsed time | |
expected delivery delay for photo i within remaining time | |
delivery delay for photo i |
Parameter | Random-Waypoint | Traces | ||
---|---|---|---|---|
Roma | Epfl | Geolife | ||
Simulation Time | 800,850,900,950,1000 | |||
TTL | 600∼800 | 700∼900 | 400∼600 | |
Time Unit (s) | 1 | 15 | 30 | 5 |
Number of PoIs | 10 | 11 | 12 | 12 |
PoI Radius (m) | 300 | 200 | 80 | 300 |
User Number | 100 | 158 | 368 | 727 |
Connection Range | 30 | 10 | 10 | 150 |
Storage Space | 3∼7 | 5∼9 | 3∼7 | 8∼12 |
Photo Interval | 1,2,3,4,5 |
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Wang, E.; Qu, Z.; Liang, X.; Meng, X.; Yang, Y.; Li, D.; Meng, W. Storage Management Strategy in Mobile Phones for Photo Crowdsensing. Sensors 2020, 20, 2199. https://doi.org/10.3390/s20082199
Wang E, Qu Z, Liang X, Meng X, Yang Y, Li D, Meng W. Storage Management Strategy in Mobile Phones for Photo Crowdsensing. Sensors. 2020; 20(8):2199. https://doi.org/10.3390/s20082199
Chicago/Turabian StyleWang, En, Zhengdao Qu, Xinyao Liang, Xiangyu Meng, Yongjian Yang, Dawei Li, and Weibin Meng. 2020. "Storage Management Strategy in Mobile Phones for Photo Crowdsensing" Sensors 20, no. 8: 2199. https://doi.org/10.3390/s20082199
APA StyleWang, E., Qu, Z., Liang, X., Meng, X., Yang, Y., Li, D., & Meng, W. (2020). Storage Management Strategy in Mobile Phones for Photo Crowdsensing. Sensors, 20(8), 2199. https://doi.org/10.3390/s20082199