Maximum Lifetime Routing in Wireless Sensor Networks

1. Maximum Lifetime Routing in Wireless Sensor Networks by Collins Adetu Nicole Powell Course: EEL 5784 Instructor: Dr. Ming Yu

2. Overview • What are Wireless Sensor Network (WSN) • Applications of WSN • The Energy Efficiency Problem • Solution: Flow Augmentation Algorithm • Simulation Results • Conclusions • Questions

3. Gateway Sensor What is a Wireless Sensor Network? • A wireless sensor network is an ad hoc network of sensors and gateways communicating wireless amongst each other. Fig. 1. Diagram Illustrating a Wireless Sensor Network

4. Applications of Wireless Sensor Networks • Used in military applications for battlefield surveillance • Used for detecting seismic activity in earthquake and volcanic prone regions • Used in ecosystem monitoring e.g. firetower sensors for monitoring forest fires • Used in weather forecasting and hurricane prediction

5. The Energy Efficiency Problem • Because of the compact nature of wireless sensors they are normally equipped with small-sized batteries e.g. AA batteries • Smaller batteries mean less power available for communication • Therefore, optimizing sensor battery lifetime is a matter of utmost concern • Collectively optimizing battery lifetime for all nodes in the network increases the network’s lifetime

6. Flow Augmentation Algorithm • The paper we researched provides a solution to the energy efficiency problem by using the flow augmentation algorithm • The algorithm uses a relative energy metric, which it dynamically updates, to compute the most energy efficient communication path • Data flows through the most energy efficient path at all times, hence maximizing network lifetime

7. Compute Residual Energy Flow Augmentation Algorithm Computes residual energy only on nodes traveled on shortest path. Uses energy-cost metric formula Calculated using Dijkstra, Bellman-Ford etc. Compute Energy Cost Metric Calculate Most Energy-Efficient Path Continue until first node dies

8. Flow Augmentation Algorithm • The cost metric is computed using the formula: whereEnergy expended transmitting data (J/bits) Energy used in processing received data (J/bits) Ei and Ej = Initial energy of the transmitting and receiving node (J) and = Residual energy of the transmitting and receiving node (J) x1, x2, x3 = positive weights • Residual Energy is computed as follows: Note: This applies for both transmitting and receiving nodes, lambda = packet size

9. Simulation Results • The objective of our simulation was to obtain similar results as proposed in the paper • In our simulation, • 20 nodes were distributed randomly over a 50m by 50m area • Sensors were initialized with 10J of energy • Source and destination nodes were randomly selected • 50 instances were simulated with different source and destination nodes in order to compute an average network lifetime • The input parameters to the Flow Augmentation algorithm were the weights, x1, x2, and x3 i.e. FA(1,1,1) means x1=x2=x3=1 are passed as input parameters to the algorithm • All simulations were done using MATLAB

10. Simulation Results Performance of FA(1,x,x) compared with FA(1,x,0)

11. Simulation Results Performance of FA(1,x,x) for various λ = Data Packet Size

12. Conclusion • The network lifetime increases as the sensor energy weights are increased when using the Flow Augmentation algorithm • An increase in data packet size (lambda), produces an adverse effect on the network lifetime • Our simulation results match closely to those obtained in the paper • The paper went further to compare the flow augmentation algorithm with other energy-efficient routing algorithm. The flow augmentation algorithm out performed the other algorithms

13. References • Chang, J.-H., and L. Tassiulas. "Maximum Lifetime Routing in Wireless Sensor Networks." IEEE ACM TRANSACTIONS ON NETWORKING. 12 (2004): 609-619. • Tanenbaum, Andrew S. Computer Networks. 4th ed. New Jersey: Prentice Hall PTR, 2003.

14. Questions