ENERGY-EFFICIENT FORWARDING STRATEGIES FOR GEOGRAPHIC ROUTING in LOSSY WIRELESS SENSOR NETWORKS

1. ENERGY-EFFICIENT FORWARDING STRATEGIES FOR GEOGRAPHIC ROUTING in LOSSY WIRELESS SENSOR NETWORKS Presented by Prasad D. Karnik

2. Introduction • Geographic Routing protocols are efficient in wireless networks: • Nodes need to know location of direct neighbors (min state stored). • State propagation not required beyond a singe hop (conserve energy and bandwidth). • Greedy mechanism is main component of Geographic Routing. • Greedy Algorithm can be efficient under following conditions : • Sufficient network density • Accurate localization. • High link reliability. (Link reliability is unlikely in realistic deployments).

3. Weak-Link problem • At each step in greedy mechanism, packet is forwarded to the neighbor closest to the destination. • This node may have poor linkage with the current node . • Such linkage is called weak-linkand would result in high rate of packet drop.

4. Purpose of the Paper • To identify and illustrate the most energy-efficient black-listing strategy. • To study the energy and reliability trade-offs in depth, both analytically and through simulations, under a realistic packet loss model.

5. Prior Work Considerations/Observations • All geographic routing protocols designed assuming ideal channels. • No prior study of schemes for lossy networks. • Research based on idealized assumptions, • Circular radio range • Perfect coverage within that range. • Observations • Packet reception over distance is non-uniform • The coverage area of radio is neither circular nor convex.

6. Definitions • Delivery Rate (r) : % of packets sent by source and reached the sink • Total no. of Transmissions (t) : total no. pf packets sent by the network to attain the delivery rate. • Energy Efficiency (Eeff) : no. of packets delivered to the sink for each unit energy.

7. Assumptions • Node know the location and the link’s Packet Reception Rate (PRR) of their neighbors. • Nodes are randomly distributed.

8. Limitation • Models do not consider • means of energy savings such as sleep/awake cycles, transmission power control. • Sources of energy consumption like processing and sensing. • Network disconnections (problem in low-density scenarios).

9. Design of forwarding strategies • Distance based forwarding :- nodes need to know the distance to their neighbors. • Reception based forwarding :- need to know the PRR of the neighbor along with the distance.

10. Distance based forwarding • Original Greedy : • each node forwards to the neighbor closest to the destination • Distance-based blacklisting : • each node blacklists nodes above a certain distance. • Packet is forwarded to the node closest to destination from the remaining neighbors.

11. Reception-based Forwarding • Absolute Reception based Blacklisting : • Each node blacklists nodes that have reception rate below certain level. • Relative Reception-based Blacklisting : • Each node blacklists node that are closer to the destination and have a reception rate below certain threshold. • Then the node forwards the packet to the node closest to the destination from the remaining set. • Best Reception Neighbor : • Node forwards to the node having highest reception rate amongst the nodes closer to the destination

12. Reception-based Forwarding • Best PRR*distance : • For each neighbor closer to destination , the product of reception rate and distance improvement is computed and the neighbor with highest value is chosen. Distance improvement reflects how much the packet gets closer to the destination and is calculated as : 1 – d(nbr,dst) / d(node,dst) D(nbr,dst) = distance between neighbor and destination D(node,dst) = distance between node and destination.

13. Simulation • Random static networks size ranging from 100-1000 nodes having same radio range. • Radio range is 40 ft. • Network density • Small density ( 8 neighbors / range ) • Moderate density ( 26 neighbors/range) • Very high density ( 100-200 neighbors/range) • Node considered as neighbor if at least 1% reception rate. • 100 packets are transmitted from random source to random destination. • Results computed over an average of 100 results. • If packet is dropped response depends upon whether ARQ (Automatic Retransmission Request) is used or not. • If ARQ used, packet is retransmitted until packet is delivered or a max count is reached. • If ARQ not used, packet is lost.

14. Simulation • Performance criterion • Delivery rate • Total no. of transmissions • Energy efficiency

15. Simulation Results • Blacklisting Strategies: • 1000 nodes , ARQ = 10. • Distance-based: • Delivery rate is low at low thresholds (low reception rate links cant guarantee packet delivery). • High threshold, delivery rate decreases (greedy disconnections when all nodes closer to destination are blacklisted). • Lower density, high possibility of greedy disconnections, optimum threshold moves left. • Eeff decreases at high threshold, coz of multiple hops before being dropped by greedy disconnection.

17. Results (contd.) • Reception Based blacklisting: • Absolute Reception-based : • very high densities, higher thresholds increase the delivery rate (possibility of disconnections is low) • Lower densities, greedy disconnections cause delivery rate dropping low at low threshold

19. Results (contd.) • Relative Reception-based blacklisting • All densities, higher thresholds improve delivery rate ( no risk of greedy disconnections). • Eeff improves (reduced retransmission overhead). • Threshold values are not comparable(different no. of neighbors, different neighbor distances).

21. Comparison (contd.) • Delivery rate low at lower densities ( greedy failures ). • Best Reception and PRR*distance have highest delivery rate (avoid greedy disconnections). • PRR * distance and Absolute Reception-based are most energy efficient.

22. Comparison (contd.) • Results for different distance ranges • Delivery rate depends on traffic pattern and distance between expected source and destinations • Study conducted by categorizing source-destination pairs. • Eeff and delivery rate studied at different distance ranges. • Fixed density of 26 neighbors/range.

23. Comparison of Forwarding Strategies 1000 nodes , 10 retransmissions Use optimum energy efficient threshold at each density (obtained from previous results)

25. Comparison (contd.) • Delivery rate depends on traffic pattern and distance between expected source and destinations • Study conducted by categorizing source-destination pairs. • Eeff and delivery rate studied at different distance ranges. • Fixed density of 26 neighbors/range.

27. Results • PRR * distance, a very effective strategy • Close to highest for delivery rate and highest for Eeff. • Slightly lower than absolute reception for small number of hops if the destination has good reception but not highest PRR * distance value. • No density dependent • Best Reception • High delivery rate but Eeff lower due to distance-hop tradeoff. • Absolute reception • High Eeff (no overheads on links with low reception). • Delivery rate lower ( greedy disconnections).

28. Effects of ARQ and Network size • Comparing ARQ with 10 retransmissions, infinite transmissions and no ARQ • ARQ important for larger networks ( without ARQ, the probability of delivering a packet over more hops decreases faster than using ARQ). • Density = 26 neighbors/range • Greedy method: • Delivery rate increases by using more transmissions (since neighbor has 1% reception rate). Infinite transmissions achieve perfect delivery. • Energy efficiency degrades with more retransmissions ( extra overhead from retransmitting on bad links) • PRR * distance • Energy efficiency of 10 retransmissions is highest (limited overhead). • High delivery rate ( not perfect as infinite retransmissions).

31. Conclusion • Greedy method result in poor packet delivery rate. • Reception based forwarding strategies are more efficient than distance-based strategies • PRR * distance proved to be a very effective metric for making geographic forwarding decisions, particularly when ARQ is employed.

32. Future Work • Extend the model for the study of face routing in lossy networks. • Extend the scope of study considering • the problem of inaccurate locations • Scenarios where link losses vary with time. • Explore the usage of power control in geographic routing.

33. Mathematical Model