A Hybrid SWIM Data Naming Scheme Based on TLC Structure
<p>The overall design of the naming scheme.</p> "> Figure 2
<p>Generation of aggregate object identifiers (OID).</p> "> Figure 3
<p>The format of digest type-length-content (TLC).</p> "> Figure 4
<p>Mixed naming structure combination.</p> "> Figure 5
<p>“Hierarchical component-based” hybrid naming structure.</p> "> Figure 6
<p>Example of the naming of “hierarchical-flat-attribute” based on TLC structure.</p> "> Figure 7
<p>The name-based data forwarding process in SWIM.</p> "> Figure 8
<p>Experimental test environment.</p> "> Figure 9
<p>Comparison of error resolution rate between Bloom filter and prefix aggregation.</p> "> Figure 10
<p>Comparison of query time of different layers in the mixed naming method.</p> "> Figure 11
<p>Comparison of query time for large-scale content names.</p> "> Figure 12
<p>Comparison of the average query time under different numbers of layers.</p> ">
Abstract
:1. Introduction
2. Related Works
3. Naming Scheme of SWIM Data
3.1. Overall Structure of the Naming Scheme
3.2. TLC Structure in Naming Scheme
3.3. TLC Structure Polymerization
3.4. Mixed Structure Naming Based on TLC
3.5. Algorithm Implementation of Hybrid Naming Scheme
Algorithm 1. Flow of hybrid naming scheme. |
Input: Service resource call information |
Output: content name |
1: Procedure SHash(Prefix) 2: 3: Hash [0…k] 4: Word 5: Hash DJB(Prefix) 6: For i 7: 8: 9: 10: 11: If 12: 13: Or else, if 14: 15: 16: Or else x > div 17: 18: 19: End |
3.6. Routing Based on Swim Content Name
4. Experiment and Result Analysis
4.1. Experimental Environment
4.2. Analysis of Fault Tolerance of Route Resolution Based on TLC Aggregation
4.3. Analysis of Search Efficiency and Scalability Based on Hybrid Naming
- Compared with a single TLC naming method, the naming mechanism based on “layer-plane” can effectively control the length of resource naming.
- After route aggregation, you can use Bloom Filter to quickly find the content of plane components.
4.4. Comparative Analysis
4.4.1. Query Time Analysis of Different Naming Schemes
4.4.2. Scalability Analysis of Different Naming Schemes
4.4.3. Analysis of Detection Rate and False Alarm Rate of Different Schemes under Attack
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Label Field | Attribute Value | Attribute Value | |||
---|---|---|---|---|---|
Entity domain label | Provider | Pro 1 | Pro 2 | Pro 3 | Pro 4 |
Resource Category | Aviation | Flight | Meteorological | Other | |
Area | Area A | Area B | Area C | Area D | |
Behavior domain label | Popularity | Level 1 | Level 2 | Level 3 | Level 4 |
Credibility | A | B | C | D |
Computer Setup | Remarks |
---|---|
processor | Intel(R) Core (TM) i5-3470 |
main frequency | 3 GHz |
RAM | 4 GB |
operating system | Linux |
database systems | MySQL 5.6 |
software | MyEclipse 10 |
application server | Tomcat 6.0 |
browser | IE 9.0 |
Layer | Name Length | Percentage | Number of Names |
---|---|---|---|
1 | 4.11 | 0.01 | 224 |
2 | 14.82 | 27.30 | 2,129,835 |
3 | 20.63 | 15.38 | 391,377 |
4 | 25.76 | 0.85 | 21,664 |
5–9 | 31.86 | 0.06 | 1694 |
Layer | Name Length | Percentage | Number of Names |
---|---|---|---|
1 | 6.15 | 0.01 | 1248 |
2 | 16.34 | 75.34 | 7,150,441 |
3 | 20.56 | 22.06 | 2,106,879 |
4 | 27.65 | 2.56 | 264,978 |
5–9 | 35.67 | 0.05 | 1578 |
Data Set | Test 1 | Test 2 | Test 3 | Test 4 | Test 5 |
---|---|---|---|---|---|
Data size | 1 m | 2 m | 3 m | 4 m | 5 m |
Data set | Test 6 | Test 7 | Test 8 | Test 9 | Test 10 |
Data size | 6 m | 7 m | 8 m | 9 m | 10 m |
Scheme | Query Time of Different Methods Under Different Data Volume | |||||||||
---|---|---|---|---|---|---|---|---|---|---|
2 | 4 | 6 | 8 | 10 | 12 | 14 | 16 | 18 | 20 | |
naming based on TLC structure | 0.06 | 0.06 | 0.061 | 0.06 | 0.061 | 0.061 | 0.062 | 0.062 | 0.061 | 0.061 |
hierarchical naming | 0.058 | 0.052 | 0.055 | 0.060 | 0.060 | 0.059 | 0.06 | 0.2 | 0.6 | 1 |
flat naming | 0.063 | 0.061 | 0.064 | 0.061 | 0.060 | 0.061 | 0.061 | 0.22 | 0.65 | 0.99 |
Scheme | Detection Rate (DR) and False Positive Rate (FPR) of Different Methods | |||||
---|---|---|---|---|---|---|
Attack 1 | Attack 2 | Comprehensive Situation | ||||
DR | FPR | DR | FPR | DR | FPR | |
Naming based on TLC structure | 99.96 | 0.003 | 96 | 0.855 | 97.5 | 0.42 |
Hierarchical naming | 99 | 5.76 | 91.89 | 9.91 | 95.9 | 7.84 |
Flat naming | 98 | 3.46 | 90.56 | 9.45 | 95.2 | 6.25 |
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Wu, Z.; Cui, B. A Hybrid SWIM Data Naming Scheme Based on TLC Structure. Future Internet 2020, 12, 142. https://doi.org/10.3390/fi12090142
Wu Z, Cui B. A Hybrid SWIM Data Naming Scheme Based on TLC Structure. Future Internet. 2020; 12(9):142. https://doi.org/10.3390/fi12090142
Chicago/Turabian StyleWu, Zhijun, and Bohua Cui. 2020. "A Hybrid SWIM Data Naming Scheme Based on TLC Structure" Future Internet 12, no. 9: 142. https://doi.org/10.3390/fi12090142
APA StyleWu, Z., & Cui, B. (2020). A Hybrid SWIM Data Naming Scheme Based on TLC Structure. Future Internet, 12(9), 142. https://doi.org/10.3390/fi12090142