Link Scheduling Algorithm with Interference Prediction for Multiple Mobile WBANs
<p>Interference prediction and avoidance at each WBAN coordinator.</p> "> Figure 2
<p>An example mobility scenario of five mobile WBANs.</p> "> Figure 3
<p>Average SINR in a WBAN for different numbers of WBANs in the deployment area.</p> "> Figure 4
<p>Average number of neighbors of a WBAN for different number of WBANs in the deployment area.</p> "> Figure 5
<p>Superframe for multiple WBANs.</p> "> Figure 6
<p>An example of intra-WBAN communication of interfered WBANs.</p> "> Figure 7
<p>An example of inter-WBAN communication in two interfering WBANs: (<b>a</b>) negotiation between two WBANs; and (<b>b</b>) data transmission.</p> "> Figure 8
<p>Packet delivery ratio: (<b>a</b>) varying the number of WBANs; (<b>b</b>) varying traffic load at each sensor node; and (<b>c</b>) varying the number of sensors per WBAN.</p> "> Figure 9
<p>End-to-end delay: (<b>a</b>) varying the number of WBANs; (<b>b</b>) varying traffic load at each sensor node; and (<b>c</b>) varying the number of sensors per WBAN.</p> "> Figure 9 Cont.
<p>End-to-end delay: (<b>a</b>) varying the number of WBANs; (<b>b</b>) varying traffic load at each sensor node; and (<b>c</b>) varying the number of sensors per WBAN.</p> "> Figure 10
<p>Network throughput: (<b>a</b>) varying the number of WBANs; (<b>b</b>) varying traffic load at each sensor node; and (<b>c</b>) varying the number of sensors per WBAN.</p> "> Figure 11
<p>Energy consumption at the coordinator: (<b>a</b>) varying the number of WBANs; (<b>b</b>) varying traffic load at each sensor node; and (<b>c</b>) varying the number of sensors per WBAN.</p> ">
Abstract
:1. Introduction
2. Related Works
3. Interference Prediction for Mobile WBANS
3.1. Network Model
3.2. Bayesian Inference Classifier for Interference Prediction
4. Link Scheduling Algorithm Avoiding Interference in Multiple Mobile WBANs
4.1. MAC Superframe for Multiple WBANs
4.2. Common Scheduling
4.3. Negotiation and Scheduling Algorithm
Algorithm 1: LSIP algorithm |
Input: NBi(t), SFm, Ii, NIi, ts |
Output: scheduled superframe |
Initialize: t = 0 |
// Phase 1: Calculate length of superframe |
1. Bi broadcasts {Ii, NIi} to all members in NBi(t) |
2. For each Cj ∈ NBi(t) |
3. Receive {Ij, NIj} |
4. Create a common list of neighbors: CI(t) = {sj(t) ∪ si(t), Ii ∪ Ij | j ∈ NBi(t), i ∈ NBj(t)}, CNI(t) = {NIi ∪NIj | j ∈ NBi(t), i ∈ NBj(t) } |
5. End For |
6. Calculate LCAP and LSP as in (6) and (7), respectively |
7. TISF = LSP + LCAP |
8. If TISF > SFm |
9. Calculate TSPm as in (12) |
10. Calculate Ti as in (11) |
11. Update LSP = TSPm |
12. End If |
// Phase 2: TDMA scheduling by using greedy algorithm |
13. For each sensor s ∈ CI(t) |
14. Return the sensor sx with the highest contention value in CI(t) |
15. Assign time slot to sx with length ts (ts is transmission time of sx) |
16. Update t = t + ts |
17. Remove s of CI(t) |
18. If t > LSP |
19. break |
20. End If |
21. End For |
5. Performance Evaluation
5.1. Simulation Environment
5.2. Simulation Results and Discussion
5.2.1. Packet Delivery Ratio
5.2.2. End-to-End Delay
5.2.3. Network Throughput
5.2.4. Energy Consumption
6. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Environment | Description |
---|---|
Static (S) | A single WBAN in a residential environment or a hospital with a single patient node and a fixed bedside hub. |
Semi-dynamic (SD) | Slowly moving ambulatory patients in an elder care facility requiring infrequent and/or event-based low-rate data transfers. |
Dynamic (D) | Fast moving ambulatory patients in a hospital with several WBANs collecting continuous data traffic from many sensor nodes. |
Category | No Interference | Short Time | Long Time |
---|---|---|---|
Duration of interference | 0 | <10 s | >10 s |
Number of neighbors | <2 neighbors | 2–6 neighbors | >6 neighbors |
SINR | >6 dB | 1–6 dB | <1 dB |
SINR | Number of Neighbors | Previous State | Current State |
---|---|---|---|
1–6 dB | <2 neighbors | Short IF | No IF |
1–6 dB | <2 neighbors | Long IF | Short IF |
1–6 dB | 2–6 neighbors | Short IF | Short IF |
1–6 dB | 2–6 neighbors | Long IF | Long IF |
1–6 dB | >6 neighbors | Short IF | Long IF |
1–6 dB | >6 neighbors | Long IF | Long IF |
>6 dB | 2–6 neighbors | No IF | No IF |
>6 dB | 2–6 neighbors | Short IF | No IF |
>6 dB | 2–6 neighbors | Long IF | Short IF |
>6 dB | >6 neighbors | No IF | Short IF |
>6 dB | >6 neighbors | Shor IF | Long IF |
>6 dB | >6 neighbors | Long IF | Long IF |
Scenario | Current State | Next State |
---|---|---|
SNIR = 2.0735; Deg = 5; Previous_state = noIF | Short IF | Short IF |
SNIR = 0.65; Deg = 6; Previous_state = ShortIF | Long IF | Long IF |
SNIR = −2.6; Deg = 5; Previous_state = LongIF | Long IF | Long IF |
SNIR = −4; Deg = 9; Previous_state = ShortIF | Long IF | Long IF |
SNIR = 11; Deg = 7; Previous_state = LongIF | Short IF | Short IF |
Parameters | Value |
---|---|
Number of WBANs | 5–25 (default: 10) |
Number of sensors per WBAN | 5–25 (default: 10) |
Simulation area | 10 m × 10 m |
Transmission range | 2 m |
Velocity of each WBAN | 0–1.5 m/s |
Direction of motion vector | Random |
Simulation time | 500 s |
Slot allocation length | 10 s |
Negotiation time | 10 ms to N × 10 ms |
Packet size | 100 bytes |
Packet transmission rate | 1–5 packets/s (default: 2) |
Tx power consumption | 31.2 mW |
Rx power consumption | 27.3 mW |
Data rate | 250 kbps |
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Le, T.T.T.; Moh, S. Link Scheduling Algorithm with Interference Prediction for Multiple Mobile WBANs. Sensors 2017, 17, 2231. https://doi.org/10.3390/s17102231
Le TTT, Moh S. Link Scheduling Algorithm with Interference Prediction for Multiple Mobile WBANs. Sensors. 2017; 17(10):2231. https://doi.org/10.3390/s17102231
Chicago/Turabian StyleLe, Thien T. T., and Sangman Moh. 2017. "Link Scheduling Algorithm with Interference Prediction for Multiple Mobile WBANs" Sensors 17, no. 10: 2231. https://doi.org/10.3390/s17102231
APA StyleLe, T. T. T., & Moh, S. (2017). Link Scheduling Algorithm with Interference Prediction for Multiple Mobile WBANs. Sensors, 17(10), 2231. https://doi.org/10.3390/s17102231