Critical Location Spatial-Temporal Coverage Optimization in Visual Sensor Network
Abstract
:1. Introduction
2. Related Works
3. Network Model and Problem Formulation
3.1. Network Model
3.2. Problem Formulation
3.2.1. Definitions
3.2.2. Problem Formulation
4. Two-Phase Spatial-Temporal Coverage-Enhancing Method
Algorithm 1 TSCM |
|
4.1. Phase I: Maximize the Number of Covered Critical Locations Based on Particle Swarm Optimization Algorithm
4.2. Phase II: Sensor Node Scheduling for Maximizing the Spatial-Temporal Coverage
Algorithm 2 Matrix-based Decoding Algorithm |
1: Input: S, P, N, l, |
2: Output: Set of rounds for covered critical location |
3: Initialize , |
4: Calculate the values in according to Equations (2) and (3) |
5: for j=1,2,...,m do |
6: for k=1,2,...,N do |
7: for i=1,2,...,n do |
8: |
9: end for |
10: if |
11: |
12: end if |
13: end for |
14: end for |
5. Simulation Results
6. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Distributed ECT | STCOS | MinRedancy | RndScheduling | ||
---|---|---|---|---|---|
Number of sensor nodes | Maximum | 48.2% | 48.4% | 47.4% | 74.6% |
Average | 30.3% | 29.2% | 28.0% | 66.6% | |
Number of targets | Maximum | 20.7% | 14.2% | 15.0% | 59.9% |
Average | 11.8% | 12% | 12.3% | 58.4% | |
Sensing range | Maximum | 77.4% | 77.2% | 77.0% | 89% |
Average | 39.3% | 37.4% | 35.9% | 70.2% | |
The ratio of battery/network liftime | Maximum | 77.1% | 76.8% | 76.8% | 94.1% |
Average | 73.3% | 72.2% | 71.8% | 92.0% |
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Xiong, Y.; Li, J.; Lu, M. Critical Location Spatial-Temporal Coverage Optimization in Visual Sensor Network. Sensors 2019, 19, 4106. https://doi.org/10.3390/s19194106
Xiong Y, Li J, Lu M. Critical Location Spatial-Temporal Coverage Optimization in Visual Sensor Network. Sensors. 2019; 19(19):4106. https://doi.org/10.3390/s19194106
Chicago/Turabian StyleXiong, Yonghua, Jing Li, and Manjie Lu. 2019. "Critical Location Spatial-Temporal Coverage Optimization in Visual Sensor Network" Sensors 19, no. 19: 4106. https://doi.org/10.3390/s19194106