Routing and Clustering

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. • LEACH • W. R. Heinzelman, A. Chandrakasan, H. Balakrishnan," Energy-efficient communication protocol for wireless microsensor networks," HICSS 2000. • HEED • O. Younis, S. Fahmy, "Distributed Clustering in Ad-hoc Sensor Networks: A Hybrid, Energy-Efficient Approach ," IEEE Infocom 2004.

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 (Er) • Secondary parameter: communication cost (used to break ties), i.e., maximize energy and minimize cost

27. Algorithm at node v • Initialization • Discover neighbors within cluster range • Compute the initial cluster head probability CHprob = f(Er/Emax) • 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 CHprob . • CHprob = min(CHprob * 2, 1) • Repeat until CHprob reaches 1 • Main processing • If cluster head is found, join its cluster • Otherwise, elect to be cluster head • Finalization

28. Example Discover neighbors (0.4,3) a10 (0.6,2) a13 (0.1,4) c2 a11 (0.2,2) (0.2,5) a7 a8 Compute CHprob and cost a12 (0.5,3) (0.2,3) c3 (0.2,3) a9 (0.8,4) (0.1,4) (0.1,2) Elect to become cluster head c1 a6 a5 (0.9,4) a14 (0.5,4) a2 c4 Resolve ties a4 (0.6,4) a3 (0.3,2) (0.7,5) (0.2,3) Select your cluster head (0.3,2) a1

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