1. Taming the Underlying Challenges of Reliable Multihop Routing in Sensor Networks

2. Key Observations Many wireless links are lossy Many wireless links are lossy Loss rate may change dynamically Loss rate may change dynamically Environmental factors Environmental factors Highly correlated behavior of an application Highly correlated behavior of an application Routing should consider these underlying factors Routing should consider these underlying factors A lot of existing work on routing are based on abstract MAC & physical layer model A lot of existing work on routing are based on abstract MAC & physical layer model Simply assume 802.11 takes care of MAC layer issues Simply assume 802.11 takes care of MAC layer issues

3. Contributions Empirical link quality observation Empirical link quality observation Connectivity analysis Connectivity analysis Likelihood of the success of a communication Likelihood of the success of a communication Distance, residual energy, congestion, channel contention,… Distance, residual energy, congestion, channel contention,… Link quality estimation Link quality estimation Neighborhood management Neighborhood management Routing for periodic data collection applications Routing for periodic data collection applications

4. Empirical Observation of Link Characteristics Measure loss rates between many different pairs of nodes at different distances Measure loss rates between many different pairs of nodes at different distances A sequence of linearly arranged sensor nodes with a spacing of 2 feet A sequence of linearly arranged sensor nodes with a spacing of 2 feet One transmitter sends packets 200 packets at the rate of 8 packets/sec One transmitter sends packets 200 packets at the rate of 8 packets/sec Remaining nodes counts the number of successfully received packets Remaining nodes counts the number of successfully received packets

5. Empirical Results

6. A simple probabilistic means can be used to capture the link behavior in simulations A simple probabilistic means can be used to capture the link behavior in simulations Connected region Connected region Transitional region: link probability with mean & variance from the empirical data Transitional region: link probability with mean & variance from the empirical data Disconnected region Disconnected region

7. Spherical radio range assumption in current research Spherical radio range assumption in current research Localization, Sensing Coverage, Topology Control Localization, Sensing Coverage, Topology Control Radio Irregularity Radio Irregularity Deepak Ganesan, etc., “Complex Behavior at Scale: An Experimental Study of Low- Power Wireless Sensor Networks”, UCLA/CSD-TR 02-0013, 2002 Deepak Ganesan, etc., “Complex Behavior at Scale: An Experimental Study of Low- Power Wireless Sensor Networks”, UCLA/CSD-TR 02-0013, 2002 Alberto Cerpa, etc., “SCALE: A Tool for Simple Connectivity Assessment in Lossy Environments”, CENS-TR 03-0021, 2003 Alberto Cerpa, etc., “SCALE: A Tool for Simple Connectivity Assessment in Lossy Environments”, CENS-TR 03-0021, 2003 Jerry Y. Zhao, etc., “Understanding Packet Delivery Performance in Dense Wireless Sensor Network”, ACM SenSys, 2003 Jerry Y. Zhao, etc., “Understanding Packet Delivery Performance in Dense Wireless Sensor Network”, ACM SenSys, 2003 Alec Woo, etc., “Taming the Underlying Challenges of Reliable Multihop Routing in Sensor Networks”, ACM SenSys, 2003 Alec Woo, etc., “Taming the Underlying Challenges of Reliable Multihop Routing in Sensor Networks”, ACM SenSys, 2003 DOI Concept DOI Concept Tian He, etc., “Range-Free Localization Schemes in Large Scale Sensor Networks”, MobiCom, 2003 Tian He, etc., “Range-Free Localization Schemes in Large Scale Sensor Networks”, MobiCom, 2003

8. Link Estimation Individual nodes estimate link quality by observing packet success and loss events Individual nodes estimate link quality by observing packet success and loss events Use the estimated link quality as the cost metric for routing Use the estimated link quality as the cost metric for routing Good estimator should: Good estimator should: React quickly to potentially large changes in link quality React quickly to potentially large changes in link quality Stable Stable Small memory footprint Small memory footprint Simple, lightweight computation Simple, lightweight computation

9. WMEWMA Snooping Snooping Track the sequence numbers of the packets from each source to infer losses Track the sequence numbers of the packets from each source to infer losses Window mean with EWMA Window mean with EWMA WMEWMA(t, a) = (#packets received in t) / max(#packets expected in t, packets received in t) WMEWMA(t, a) = (#packets received in t) / max(#packets expected in t, packets received in t) t, a: tuning parameters t, a: tuning parameters t: #message opportunities t: #message opportunities Take average in a window Take average in a window Take EWMA of the average Take EWMA of the average

10. WMEWA (t =30, a =0.6)

11. Neighborhood Management Neighborhood table Neighborhood table Record information about nodes from which it receives packets Record information about nodes from which it receives packets How does a node determine which nodes it should keep in the table? How does a node determine which nodes it should keep in the table? Keep a sufficient number of good neighbors in the table Keep a sufficient number of good neighbors in the table Similar to cache management Similar to cache management

12. Management Policies Insertion Insertion Heard from a non-resident source Heard from a non-resident source Adaptive down-sampling technique Adaptive down-sampling technique Probability of insertion = N/T = neighbor table size / #distinct neighbors Probability of insertion = N/T = neighbor table size / #distinct neighbors At most N messages can be inserted for every T messages At most N messages can be inserted for every T messages Eviction Eviction FIFO, Least-Recently Heard, CLOCK, Frequency FIFO, Least-Recently Heard, CLOCK, Frequency

13. #Good neighbors maintainable (table size 40)

14. Cost-based routing Minimize #retransmissions Minimize #retransmissions A longer path w/ fewer #retransmission could be better than a shorter path w/ more #retransmissions! A longer path w/ fewer #retransmission could be better than a shorter path w/ more #retransmissions!

15. Routing Framework

16. Other Routing Issues Parent selection Parent selection Rate of parent change Rate of parent change Parent snooping Parent snooping Cycles Cycles Duplicate packet elimination Duplicate packet elimination Queue management Queue management Relation to link estimation Relation to link estimation

17. Cost metric MT (Minimum Transmission) metric: MT (Minimum Transmission) metric: Expected number of transmissions along the path Expected number of transmissions along the path For each link, MT cost is estimated by 1/(Forward link quality) * 1/(Backward link quality). For each link, MT cost is estimated by 1/(Forward link quality) * 1/(Backward link quality).

18. Performance Evaluation: Tested Routing Algorithms Shortest Path Shortest Path SP: A node is a neighbor if a packet is received from it SP: A node is a neighbor if a packet is received from it SP(t): A node is a neighbor if its link quality exceeds the threshold t SP(t): A node is a neighbor if its link quality exceeds the threshold t t = 70%: only consider the links in the effective region t = 70%: only consider the links in the effective region t = 40%: also consider good links in the transitional region t = 40%: also consider good links in the transitional region

19. Minimum Transmission (MT) Minimum Transmission (MT) Use the expected #transmissions as the cost metric Use the expected #transmissions as the cost metric Broadcast Broadcast Periodic flooding Periodic flooding Choose a parent based on the source address of the 1 st flooding message in each epoch Choose a parent based on the source address of the 1 st flooding message in each epoch Destination Sequence Distance Vector (DSDV) Destination Sequence Distance Vector (DSDV) Choose a parent based on the freshest sequence number from the root Choose a parent based on the freshest sequence number from the root Maintain a minimum hop count when possible Maintain a minimum hop count when possible Ignore link quality – consider a node a neighbor once heard from it Ignore link quality – consider a node a neighbor once heard from it Periodically reevaluate Periodically reevaluate

20. Packet level simulations Built a discrete time, event-driven simulator in Matlab Built a discrete time, event-driven simulator in Matlab

21. Empirical study of a sensor field Evaluate SP(40%), SP(70%), MT Evaluate SP(40%), SP(70%), MT 50 Berkeley motes 50 Berkeley motes 5 * 10 grid w/ 8 foot spacing 5 * 10 grid w/ 8 foot spacing 90% link quality in 8 feet 90% link quality in 8 feet 3 inches above the ground 3 inches above the ground

22. Link Quality of MT Hop Distribution

23. E2E success rate Stability

24. Irregular Indoor Network 30 nodes scattered around an indoor office of 1000ft 2 30 nodes scattered around an indoor office of 1000ft 2 E2E Success Rate Link Estimation

25. Conclusions Link quality estimation and neighborhood management are essential to reliable routing Link quality estimation and neighborhood management are essential to reliable routing WMEWMA is a simple, memory efficient estimator that reacts quickly yet relatively stable WMEWMA is a simple, memory efficient estimator that reacts quickly yet relatively stable MT (Minimum Transmissions) is an effective metric for cost-based routing MT (Minimum Transmissions) is an effective metric for cost-based routing The combinations of these techniques can yield high E2E success rates The combinations of these techniques can yield high E2E success rates

26. Questions?