A Robust Graph Theoretic Solution of Routing in Intelligent Networks

Algorithm 1.

ShortestPath(s, t, G)

1. Queue1.enQueue(s)

1. Queue2.enQueue(t)

2. InitializeDoubleSource(SPCost, Pi, Clr, s, t, G)

3.  Flag ← 0

4. While (Queue1 <> Null OR Queue2 <> Null) AND Flag = 0

5.  Do SPfromSource(SPCost, Pi, Clr, G)

6.   SPfromDestination(SPCost, Pi, Clr, G)

SPfromSource(G)

1. while u ← Queue1.deQueue() <> Green

2. for each v belongs to Adj[u]

3.  do if Clr_v = Green OR LightYellow OR LightRed

4.    then Clr_v ← LightYellow

5.      SPCost_v ← min(SPCost_u +W_(u,v), SPCost_v)

6.      If SPCost is updated

7.       Then Pi_v ← u

8.      If Clr_v = Green OR LightRed

9.       Then Queue.enQueue(v)

10.    If Clr_v = DarkRed

11.     Then Concatinate two paths and store

12.        CPCost ← min(CPCost_u + CPCost_v + w_(u,v), CPCost)

13.    If Clr_v = DarkYellow

14.     then skip

15. Clr_u ← LightRed

SPfromDestination(G)

1. while u ← Queue2.deQueue() <> Green

2. for each v belongs to Adj[u]

3. do if Clr_v = Green OR DarkYellow OR DarkRed

4.  then Clr_v ← DarkYellow

5.  SPCost_v ← min(SPCost_u +W_(u,v), SPCost_v)

6.  If SPCost_v is updated

7.   Then Pi_v ← u

8.  If Clr_v = Green OR DarkRed

9.   Then Queue.enQueue(v)

10. If Clr_v = LightRed

11.  Then Concatinate two paths and store

12.    SPCost_v ← min(SPCost_u + SPCost_v + w_(u,v), SPCost_v)

13. If Clr_v = LightYellow

14.    then skip

15. Clr_u ← DarkRed