# An Efficient Network Coding-Based Fault-Tolerant Mechanism in WBAN for Smart Healthcare Monitoring Systems

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## Abstract

**:**

## 1. Introduction

## 2. Related Work

## 3. Network Model

## 4. Design of Network Coding Tree Algorithm

## 5. Design of Greedy Grouping Algorithm

## 6. Numerical Results and Analysis

**A.**

**Performance Indicators**

**B.**

**Simulation Results and Analysis**

## 7. Conclusions

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## References

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**Figure 4.**Coding tree algorithm implementation process. (

**a**) Tree-construction; (

**b**) Result of two iterations; (

**c**) Result of tree-modification; (

**d**) Tree establishment.

**Figure 8.**Comparison of the total number of generated leaves nodes between breadth-first search (BFS) and depth-first search (DFS) algorithms.

**Figure 11.**Comparison of ED with the number of packets sent forNetwork Coding-based Fault-tolerant Mechanism (NCFM), 1 + 1 and 1:N.

Algorithm for Constructing Coding Tree |
---|

1: Use the DFS (depth-first search) or BFS (breadth-first search) algorithm to search the entire network topology to generate a node-rooted tree rooted at ${L}_{s}$. |

2: If there exist sensors which are leaf nodes in tree, denote it as u. |

3: End if |

4: Keep on search until finding one u’ neighbor node who is not u’ parent node, denote it as x. |

5: connect the node u and x to construct a ring, denote it as C. |

6: Traverse all the nodes in ring C until finding one sensor v whose ${L}_{s}$ neighbor node w in not in ring C. |

7: IF node u and v are already connected, then Cut the loop directly in front or behind v. |

8: Else |

9: Cut the ring on both sides of v, Repeat steps 4–6 until tree trimming ends. |

10: End if |

11: keep on Pruning trees until There is no leaf node in the tree. |

12: The algorithm ends. |

Names of Data Structures | Functions |
---|---|

FindTree | Look for DFS_tree |

ModTree | Prune DFS_tree and allocate encoding coefficient |

Greedy_Group | Logically group the topology that does not meet the coding algorithm condition |

GaloisField | Galois field, where encoding is done |

nc_node | Define network coding node class |

nc_generate_xy | Generate the node coordinates, by which connectivity degree of nodes can be determine |

test_connectivity | Measure the connectivity of network topology |

nc_independence | Judge whether the received encoding combinations is linearly independent |

nc_peform_transmission | Select the node to send packets, and do the encoding operation |

nc_update | Judge whether the received encoding combinations is linearly independent, and done the decoding operation |

Algorithm for Constructing Greedy Grouping |
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1: Define the variable |

2: Stores the scheduled source node, $SchdSet=\phi $; |

3: Initialize the encoding group number, $coding\_lable=0$; |

4: Initialize the cluster number, $cluster\_id=0$; |

5: Start grouping |

6: Initialize source node index in group, $index=0$; |

7: Calculate the minimum cut h between ${L}_{s}$ and destination node |

8: Define a boolean variable Found, If the source node joins the current cluster, it is true, $Found=TRUE$; |

9: $While(|SchdSet|<|{U}_{s}|)do$ |

10: $x=\phi $; |

11: $if(index>h-2||Found==FALSE)then$ |

12: $cluster\_id++$; |

13: $index++$; |

14: End if |

15: $coding\_lable++$; |

16: x={choose the sensor u with maximum connectivity degree}, $u\notin SchdSet$ |

17: $if(x==\phi )then$ |

18: $Found=FALSE$; |

19: Else |

20: $SchdSet=SchdSet\cup x$; |

21: $Schedule[cluster\_id][index]=x$; |

22: Complete a node grouping, jump to the next one |

23: End if |

24: End While |

Parameters Value |
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Attenuation model two ray |

Channel capacity 2 Mbit/s |

Signal transmission range 250 m |

Signal interference range 550 m |

Packet size 512 Byte |

Output queue type FIFO (First-In First-Out) |

Cache capacity 50 packets |

energy model generic radio energy model |

Simulation area 1000 m × 1000 m |

© 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

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**MDPI and ACS Style**

Peng, Y.; Wang, X.; Guo, L.; Wang, Y.; Deng, Q.
An Efficient Network Coding-Based Fault-Tolerant Mechanism in WBAN for Smart Healthcare Monitoring Systems. *Appl. Sci.* **2017**, *7*, 817.
https://doi.org/10.3390/app7080817

**AMA Style**

Peng Y, Wang X, Guo L, Wang Y, Deng Q.
An Efficient Network Coding-Based Fault-Tolerant Mechanism in WBAN for Smart Healthcare Monitoring Systems. *Applied Sciences*. 2017; 7(8):817.
https://doi.org/10.3390/app7080817

**Chicago/Turabian Style**

Peng, Yuhuai, Xiaojie Wang, Lei Guo, Yichun Wang, and Qingxu Deng.
2017. "An Efficient Network Coding-Based Fault-Tolerant Mechanism in WBAN for Smart Healthcare Monitoring Systems" *Applied Sciences* 7, no. 8: 817.
https://doi.org/10.3390/app7080817