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FAST DATA COLLECTION IN TREE-BASED WIRELESS SENSOR NETWORKS

Platform : java

IEEE Projects Years : 2012 - 13

FAST DATA COLLECTION IN TREE-BASED WIRELESS

SENSOR NETWORKS

ABSTRACT:

 

We investigate the following fundamental question - how fast can information be collected from a wireless sensor network organized as tree? To address this, we explore and evaluate a number of different techniques using realistic simulation models under the many-to-one communication paradigm known as converge cast. We first consider time scheduling on a single frequency channel with the aim of minimizing the number of time slots required (schedule length) to complete a converge cast.

 

We combine scheduling with transmission power control to mitigate the effects of interference, and show that while power control helps in reducing the schedule length under a single frequency, scheduling transmissions using multiple frequencies is more efficient. We give lower bounds on the schedule length when interference is completely eliminated, and propose algorithms that achieve these bounds. We also evaluate the performance of various channel assignment methods and find empirically that for moderate size networks of about 100 nodes, the use of multi-frequency scheduling can suffice to eliminate most of the interference.

 

Then, the data collection rate no longer remains limited by interference but by the topology of the routing tree. To this end, we construct degree-constrained spanning trees and capacitated minimal spanning trees, and show significant improvement in scheduling performance over different deployment densities. Lastly, we evaluate the impact of different interference and channel models on the schedule length.

 


 

ALGORITHM USED:

 

1. BFSTIMESLOTASSIGNMENT.

2. LOCAL-TIMESLOTASSIGNMENT

                                                        

Algorithm 1 BFS-TIMESLOTASSIGNMENT

1. Input: T = (V, ET )

2. While ET _= φ do

3. e ← next edge from ET in BFS order

4. Assign minimum time slot t to edge e respecting adjacency and interfering constraints

5. ET ← ET \ {e}

6. end while

 

Algorithm 2 LOCAL-TIMESLOTASSIGNMENT

1. node.buffer = full

2. if {node is sink} then

3. Among the eligible top-subtrees, choose the one with the largest

number of total (remaining) packets, say top-subtree i

4. Schedule link (root(i), s) respecting interfering constraint

5. else

6. if {node.buffer == empty} then

7. Choose a random child c of node whose buffer is full

8. Schedule link (c, node) respecting interfering constraint

9. c.buffer = empty

10. node.buffer = full

11. end if

12. end if

 


 



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