Randomized fault-tolerant virtual backbone tree to improve the lifetime of Wireless Sensor Networks

1. Randomized fault-tolerant virtual backbone
tree to improve the
lifetime of Wireless Sensor Networks
Sajjad Rostami
Azad University of Qazvin
Thursday, May 28, 2015
Reviews processed and recommended for publication to the Editor-in-Chief by Associate Editor Dr. Srinivasan Rajavelu.
Autors: K. Suganthi(a), B. Vinayagasundaram(b) , J. Aarthi(a)
a:Department of Computer Technology, Anna University, Chennai 600044, India
b:Department of Information Technology, Anna University, Chennai 600044, India

2. iSchool
Today’s Topics
 Introduction to wireless sensor networks (WSN)
 Network lifetime
 Virtual backbones
 Issues and challenges
 Proposed FTVBT model
 Randomized FTVBT
 Conclusion

3. iSchool
What’s the WSN?
 deployed in a region that needs to be monitored
 Large numbers of sensors
 low power
 low cost
 memory/computationally-constrained
 Scope of energy consumption by WSNs
 Communication
 Sensing
 computation

4. iSchool
Network lifetime & Virtual backbone
 network lifetime has been defined in various
ways:
 mean expiration time
 specific fraction of nodes remains
 first loss of reporting
 Virtual backbone algorithms:
 Energy-aware Virtual Backbone Tree (EVBT)
 Multihop cluster-based stable backbone tree
 Virtual backbone Tree Algorithm for Minimal energy
consumption (ViTAMin).

5. iSchool
Energy-efficient routing algorithms
 cluster
 Use a hop limit-based model where the sink is within
the hop limit of a cluster member that transfers the data
directly to the sink node
 virtual backbone-based routing
 provide an infrastructure for efficient communication
with the sink node
 All of the communication between a particular sensor
node and the sink node occurs via the tree nodes

6. iSchool
Backbone Construction algorithms
 EVBT
 the sink node transmits a broadcast request (BCR)
packet to all of the nodes in its sensing range
 m-EVBT
 ViTAMin finds the shortest path to the sink for each tree
node by adding a distance parameter to track the
existing BCR packet

7. iSchool
Strategies used to select the tree nodes

8. iSchool
Strategies used to select the tree nodes
 Modified EVBT (m-EVBT)
 Reduces the total energy consumption by utilizing the
distance between nodes and energy
 Like EVBT, the energy consumption information is
passed via an EVBT construction request (ECR) packet
 Only nodes with energy greater than the threshold are
considered for selection as tree nodes
 Distributed Breadth First Search are run

9. iSchool
Issues and challenges

10. iSchool
FTVBT model

11. iSchool
FTVBT model(Backbone construction)

12. iSchool
FTVBT model(Finding a suitable parent for
child tree nodes)

13. iSchool
FTVBT model(Finding a parent of
associated non-tree nodes)

14. iSchool
FTVBT model(Backbone reconstruction)

15. iSchool
FTVBT model(Backbone Reconstruction
(continued))

16. iSchool
Randomized FTVBT
 FTVBT selects tree nodes deterministically based
on a fitness factor.
 Instead of always selecting the tree with the
highest fitness factor, it is possible to select all of
the tree nodes as parents based on their fitness
factor.

17. iSchool
FTVBT maintains the N-N lifetime
 Let path(n) be the path from the sink to a node n
For all nodes ‘n’ the path is identified through the
tree nodes
 When the nodes are far apart, it might not be
possible to cover it using a single virtual
backbone
 when the number of dependents is greater than
the average number of reachable local tree
nodes, the number of dependents is reduced
 Ideally, this value should be twice the average
number of dependents in nearby tree nodes

18. iSchool
FTVBT maintains the N-N lifetime
 some of the non-tree nodes are converted into
tree nodes when it is necessary to cover all the
nodes
 According to the N-of-N lifetime concept, if there
are few nodes left in the deployed area,
backbones must be formed that comprise part of
the network
 Until the number of alive nodes becomes zero,
the sink collects data and maintains the NN
lifetime

19. iSchool
Simulation

20. iSchool
Simulation

21. iSchool
Conclusion
 FTVBT method identifies hotspots and distributes
the dependents across tree nodes, thereby:
 Increasing the durability of the virtual backbone
 Increasing the overall network lifetime of WSNs

22. iSchool
Thanks for Attention
