Taming the Underlying Challenges of Reliable Multihop Routing in Sensor Networks

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

2. Key Observations • Many wireless links are lossy • Loss rate may change dynamically • Environmental factors • Highly correlated behavior of an application • Routing should consider these underlying factors • 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

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

4. Empirical Observation of Link Characteristics • 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 • One transmitter sends packets 200 packets at the rate of 8 packets/sec • Remaining nodes counts the number of successfully received packets

5. Empirical Results

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

7. Spherical radio range assumption in current research • Localization, Sensing Coverage, Topology Control • Radio Irregularity • 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 • 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 • DOI Concept • 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 • Usethe estimated link quality as the cost metric for routing • Good estimator should: • React quickly to potentially large changes in link quality • Stable • Small memory footprint • Simple, lightweight computation

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

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

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

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

13. #Good neighbors maintainable (table size 40)

14. Cost-based routing • Minimize #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 • Rate of parent change • Parent snooping • Cycles • Duplicate packet elimination • Queue management • Relation to link estimation

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

18. Performance Evaluation: Tested Routing Algorithms • Shortest Path • 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 • t = 70%: only consider the links in the effective region • t = 40%: also consider good links in the transitional region

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

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

21. Empirical study of a sensor field • Evaluate SP(40%), SP(70%), MT • 50 Berkeley motes • 5 * 10 grid w/ 8 foot spacing • 90% link quality in 8 feet • 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 1000ft2 Link Estimation E2E Success Rate

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

26. Questions?