Development of Sensor Registry System-Based Predictive Information Service Using a Grid
"> Figure 1
<p>Expressed road segments on a map.</p> "> Figure 2
<p>Concept of the network coverage information-based sensor registry system (NC-SRS) using a segment.</p> "> Figure 3
<p>Concept of the SRS-based predictive information service (SRS-PIS) using a grid.</p> "> Figure 4
<p>Grid-based path identification by user points.</p> "> Figure 5
<p>Weights of grid in eight directions.</p> "> Figure 6
<p>Examples of (<b>a</b>) determining service-disabled grid and (<b>b</b>) grouping service-disabled grid.</p> "> Figure 7
<p>Results of measuring signal strength: (<b>a</b>) SKTelecom and (<b>b</b>) KT.</p> "> Figure 8
<p>Teaming situation for SKTelecom: (<b>a</b>) 5 × 5 grids and (<b>b</b>) 15 × 15 grids.</p> "> Figure 9
<p>Teaming situation for KT: (<b>a</b>) 5 ×5 grids and (<b>b</b>) 15 × 15 grids.</p> "> Figure 10
<p>Box plots of signal strength per grid: (<b>a</b>) SKTelecom and (<b>b</b>) KT.</p> "> Figure 11
<p>Result of path identification for trajectory passing through open square in (<b>a</b>) the NC-SRS and (<b>b</b>) the SRS-PIS.</p> ">
Abstract
:1. Introduction
2. Related Works
2.1. Grid-Based Path Prediction
2.2. Mobile Crowdsensing Approach
3. The Proposed Method
3.1. Problem Statement
3.2. The Sensor-Registry-System-Based Predictive Information Service
- (1)
- Fetch user location: The user location is read from a smartphone or a wearable device and stored to a server.
- (2)
- Predict user paths: Path prediction is performed using collected user locations. As a prediction result, a grid that has a high probability is selected from grids that are adjacent to the currently located grid for a user.
- (3)
- Check enabled network coverage: That the predicted grid enables a network service is checked. If the predicted grid is in a service-disabled area, the predicted grid is expanded to include a service-disabled grid group.
- (4)
- Filter sensors: The list of sensors that match the predicted grid registered in the SRS is extracted.
- (5)
- Provide matched sensor information: The user is provided with the information of sensors in the list from the SRS.
3.3. The Path Prediction Algorithm
3.4. Grouping Service-Disabled Grids
- (1)
- Set grid information: To determine the grid, the starting point (latitude and longitude), end point (latitude and longitude), and number of horizontal and longitudinal axes are set.
- (2)
- Load strength information: The premeasured signal strength corresponding to the divided grid area is read.
- (3)
- Determine service-disabled grid: The signal strength is stored as the representative signal strength, which is the weakest among the critical signal strengths in the grid, and a service-disabled grid is set. The signal strength threshold determines the service-disabled grid.
- (4)
- Group service-disabled grids: To make adjacent grids, a group that has only service-disabled grids.
- (5)
- Store the group number: The determined group number is stored in the grid information database.
Algorithm 1: disabled_coverage_measuring (DS[], G[]) | |
Input: set DS[]: disabled signal strength group, a grid group G[] | |
Output: set G[]: checked grid group | |
1: | for each G do |
2: | for each DS[]: ds do |
3: | if ds.GPS in G.range then |
4: | g.Eabled = false |
5: | g.Rep_strength = ds.strength |
6: | endif |
7: | endfor |
8: | endfor |
9: | return G[] |
Algorithm 2: disabled_coverage_grouping (DG[]) | |
Input: set DG[] = {g1, g2, …, gm} of disabled grids | |
Output: set DGG[][] of disabled grid groups | |
1: | Let Q be a queue |
2: | n ← 0 //group number |
3: | while DG is not empty |
4: | g = DG.dequeue(0) |
5: | DGG[n].add(g) |
6: | Q.enqueue(g) |
7: | while Q is not empty |
8: | v = Q.dequeue() |
9: | Temp[] = DG[] |
10: | for each grid:Temp do |
11: | if isAdjacentGrid(v) then grid |
12: | DGG[n].add(grid) |
13: | Q.enqueue(grid) |
14: | DG.remove(grid) |
15: | endif |
16: | endfor |
17: | endwhile |
18: | n ← n + 1 |
19: | endwhile |
20: | return DGG[][] |
4. Experimental Implementation and Evaluation
4.1. Experiment of Data Transfer Rate
4.2. Implementation and Evaluation of the Grouping Algorithm
4.3. Comparison among SRS-Based Systems
4.4. Comparison between a Segment-Based Method and a Grid-Based Method
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
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Signal Strength | Download (Mbps) | Upload (Mbps) | PING (ms) |
---|---|---|---|
−120 dB (RSRP, 4G) | disabled | disabled | disabled |
−115 dB (RSRP, 4G) | 0.91 | disabled | 289 |
−110 dB (RSRP, 4G) | 7.50 | disabled | 53.3 |
−105 dB (RSRP, 4G) | 37.4 | 2.68 | 34.8 |
−100 dB (RSRP, 4G) | 53.8 | 4.42 | 33.3 |
−86 dB (RSRP, 4G) | 77.1 | 4.97 | 31.4 |
−81 dB (RSSI, 3G) | 3.14 | 0.12 | 64.2 |
Feature | PP-SRS | NC-SRS | Jung et al. | SRS-PIS |
---|---|---|---|---|
path prediction | support | support | support | support |
path prediction algorithm | directed weight | directed weight | undirected weight | directed weight |
network coverage information | not support | support | not support | support |
network stability | unstable | stable | unstable | stable |
map structure type | segment | segment | grid | grid |
map structure pre-building | need | need | not need | not need |
area extension cost | high | high | low | low |
preprocessing performance | medium | medium | fast | fast |
Process | NC-SRS | SRS-PIS |
---|---|---|
preprocessing | loading all of roads that are manually defined. | creating grids automatically by geographical positions of target area. |
path identification | identifying a currently located segment for a user by projecting a user position into the road expressed as a line. | identifying a currently located grid consisting of GPS position for a user. |
path learning | learning the identified paths based on segments. | learning the identified paths based on grids. |
path prediction algorithm | measuring frequency for segment weights with directions. | measuring frequency for grid weights with directions. |
result of path prediction | a maximum weighted segment that is connected to a currently located segment. | a maximum weighted grid that is contiguous to a currently located grid. |
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Jung, H.; Jeong, D.; Lee, S. Development of Sensor Registry System-Based Predictive Information Service Using a Grid. Sensors 2018, 18, 3620. https://doi.org/10.3390/s18113620
Jung H, Jeong D, Lee S. Development of Sensor Registry System-Based Predictive Information Service Using a Grid. Sensors. 2018; 18(11):3620. https://doi.org/10.3390/s18113620
Chicago/Turabian StyleJung, Hyunjun, Dongwon Jeong, and Sukhoon Lee. 2018. "Development of Sensor Registry System-Based Predictive Information Service Using a Grid" Sensors 18, no. 11: 3620. https://doi.org/10.3390/s18113620
APA StyleJung, H., Jeong, D., & Lee, S. (2018). Development of Sensor Registry System-Based Predictive Information Service Using a Grid. Sensors, 18(11), 3620. https://doi.org/10.3390/s18113620