1. Routing and Clustering Xing Zheng 01/24/05

2. References Routing A. Woo, T. Tong, D. Culler, "Taming the Underlying Challenges of Reliable Multihop Routing in Sensor Networks," ACM SenSys 2003.Taming the Underlying Challenges of Reliable Multihop Routing in Sensor Networks LEACH W. R. Heinzelman, A. Chandrakasan, H. Balakrishnan," Energy- efficient communication protocol for wireless microsensor networks," HICSS 2000. Energy- efficient communication protocol for wireless microsensor networks, HEED O. Younis, S. Fahmy, "Distributed Clustering in Ad-hoc Sensor Networks: A Hybrid, Energy-Efficient Approach," IEEE Infocom 2004.Distributed Clustering in Ad-hoc Sensor Networks: A Hybrid, Energy-Efficient Approach

3. #1 Taming the Underlying Challenges of Reliable Multi-hop Routing in Sensor Networks

4. Routing Issues in WSN Substantially different from traditional ad-hoc wireless networks Traditional setting Assume 802.11 links (Abstract away the underlying physical layer and MAC protocol) Independent pair-wise connections Abstract the applications Sensor Networks Resource-constrained nodes Low-power radios Multi-hop aggregation Application-specific communication pattern

5. Underlying Factors Connectivity graph Discovered by nodes observing communication events and sharing the information Connectivity A statement of the likelihood of successful communication Nodes Nearby nodes may be in communication most of the time, but not always. Less reliable communication with distant nodes, but a few may have strong connectivity Lossy links and dynamic loss rates

6. About this study Routing algorithms should take into account these underlying factors and be evaluated in concert with the low level estimation mechanisms under realistic loads. Stages Empirical link characteristics Link estimation Neighborhood table management Routing protocol Target application A large collection of nodes route periodically sampled data over multiple hops to an individual sink.

7. Link Characteristics Set up a platform to measure loss rates between many different pairs of nodes at different distances Observations suggest a simple means of capturing probabilistic link behavior in simulations Create a link quality model For each directed node pair at a given distance A link probability is associated based on the mean and variance extracted from the empirical data. Each simulated packet transmission is filtered out with this probability.

8. Empirical Results

9. Link Estimation Individual nodes estimate link quality by observing packet success and loss events. Link quality is used in routing protocols’ cost metrics. Requirements: React quickly to potentially large changes in link quality Stable A small memory footprint Simple to compute

10. WMEWMA Based on snoopy techniques Passive probing Loss can be inferred by tracking the sequence numbers. Window mean with EWMA Based on historical observations Compute an average success rate over a time period Can track the empirical trace fairly well

11. Neighborhood Management Neighborhood table Record information about nodes from which it receives packets Limited size Question: How does a node determine which nodes it should keep in the table? To seek a neighborhood management algorithm that will keep a sufficient number of good neighbors in the table Similar to cache management

12. Management Policies Insertion Upon hearing from a non-resident source Adaptive down-sampling technique The probability of insertion: the neighbor table size / the number of distinct neighbors Eviction RR, FIFO, Least-Recently Heard, CLOCK, etc. Reinforcement FREQUENCY algorithm A frequency count for each entry in the table Reinforce good neighbors during insertion

13. Routing Framework

14. Routing protocol Distance-vector based algorithms Parent selection Access the neighborhood table to select a set of potential parents MT (Minimum Transmission) cost metric: the expected number of transmissions along the path For each link, MT cost is estimated by 1/(Forward link quality) * 1/(Backward link quality).

15. Evaluation Remarks Link quality estimation and neighborhood management are essential to reliable routing. Minimum expected transmissions is an effective metric for cost-based routing. The combinations of these techniques can yield high end-to-end success rates.

16. #2 Energy-Efficient Communication Protocol for Wireless Micro- sensor Networks

17. LEACH Low-Energy Adaptive Clustering Hierarchy Designed for minimizing energy dissipation in sensor networks Model of sensor networks Base station: fixed and far located from sensors Nodes: homogeneous and energy-constrained

18. Conventional Approaches Directional vs. multi-hop Short system lifetime

19. Clustering LEACH Self-organized adaptive clustering protocol Key features Localized coordination and control for cluster set-up and operation Randomized rotation of the cluster heads and the corresponding clusters Local compression to reduce global communication

20. Algorithm Run by rounds Advertisement Phase A node becomes a cluster head if Random(0,1) < T(n), which is a threshold in the system. Cluster heads broadcasts an advertisement message using a CSMA MAC protocol. Non-cluster-head nodes decide to join the cluster with the largest signal length heard from its head.

21. Algorithm (cont.) Each node reports to its cluster head using a CSMA protocol. Based on all the messages received within the cluster, the head node creates a TDMA schedule for intra-node transmission. During data transmission, non-cluster-nodes can be turned off until the node’s allocated transmission time.

22. Strengths Dynamic cluster distribution Extend system lifetime

23. Weaknesses Assumes uniform energy consumption for cluster heads in cluster rotation. Does not guarantee a good cluster head distribution Randomly selection of heads can result in faster death of some nodes.

24. #3 Distributed Clustering in Ad-hoc Sensor Networks: A Hybrid, Energy-Efficient Approach

25. HEED Hybrid Energy-Efficient Distributed Clustering Design goals: Prolonging network lifetime by distributing energy consumption Terminating in O(1) iterations Minimizing low control overhead Producing well-distributed cluster heads and compact clusters

26. Clustering Parameters For electing cluster heads Primary parameter: residual energy (E r ) Secondary parameter: communication cost (used to break ties), i.e., maximize energy and minimize cost

27. Algorithm at node v Initialization Main processing Finalization Discover neighbors within cluster range Compute the initial cluster head probability CH prob = f(E r /E max ) If v received some cluster head messages, choose one head with min cost If v does not have a cluster head, elect to become a cluster head with CH prob. CH prob = min(CH prob * 2, 1) Repeat until CH prob reaches 1 If cluster head is found, join its cluster Otherwise, elect to be cluster head

28. Example Compute CH prob and cost Elect to become cluster head Resolve ties Select your cluster head (0.2,2) (0.4,3) (0.2,3) (0.1,2) (0.1,4) (0.6,2) (0.2,5) (0.5,3) (0.8,4) (0.2,3) (0.6,4) (0.5,4) (0.1,4) (0.9,4) (0.3,2) (0.7,5) (0.3,2) (0.2,3) a1 c4 a3 a2 a5a6 c3 a12 a11 a13 a9 a7 a8 a4 a10 c2 c1 a14 Discover neighbors

29. HEED vs. LEACH Longer lifetime Less energy consumption

30. Conclusions Hybrid approach Heads are selected based on residual energies Nodes join cluster to minimize communication cost Terminates in a constant number of iterations Independent of network diameter Location-unaware Prolongs system lifetime