Topology Management in Ad Hoc Wireless Network EE206A: Class Presentation

1. Topology Management in Ad Hoc Wireless NetworkEE206A: Class Presentation Rupesh K. Goel Chaitanya Sharma{rupgoel, csharma}@ee.ucla.edu May 29, 2002

2. Outline • Motivation • Four Algorithms • ASCENT • STEM • Span • Cone-Based • Conclusions

3. Topology Management Deciding on - which nodes turn on- when they turn on, and- at what Tx power …. So that network connectivity is maintained.

4. Motivation for Topology Control • High power • High interference • Low Throughput

5. Motivation for Topology Control • Low power • Low interference • High Throughput • Global Connectivity

6. Tradeoffs () • In sensor nodes, energy conservation desirable Energy conservation == longer network life • Energy  node density  • Energy   latency  • Energy   Tx power 

7. Energy Usage – Important Metric • Energy consumption of the radio dominates that of the sensors and CPU • perform event detection continuously • Observation: radios consume about the same energy in idle state than Tx and Rx state. • The only energy efficient mode of the radio is the sleep mode • put radio to sleep as often as possible

8. ASCENTAdaptive Self-Configuring sEnsor Networks Topologies. Alberto Cerpa & Deborah EstrinUCLA Energy  Node Density

9. Problem Description • Given an area A, a total number of nodes N distributed with a certain probability distribution  in A, we would like to find a subset of nodes that covers area A.

10. Problem Space • Goal: exploit the redundancy provided by high density to extend the system lifetime while providing a basic communication backbone that adapts to application needs • Problem: • few active nodes: • distance between neighboring nodes high  increase message loss • energy required to transmit the data over the longer distances will be prohibitive • too many active nodes: • at best, expending unnecessary energy; • at worst the nodes may interfere with one another by congesting the channel.

11. Neighbor Announcements Messages Help Messages Data Message Data Message Source Source Sink Sink Sink Source Passive Neighbor Active Neighbor (a) Communication Hole (b) Self-configuration transition (c) Final State ASCENT Basics • The nodes can be in activeorpassivestate. • Active nodes are part of the topology (or stay awake) and forward data packets (using an orthogonal routing mechanism). • Nodes in passive state can be sleeping or collecting network measurements. They do notforward any packets. • Each node measures the number of neighbors and packet loss locally. • Each node then makes an informed decision to join the network topology or to sleep by turning its radio off.

12. after Tt Test Active • neighbors < NT • and • loss > LT • loss < LT & help neighbors > NT (high ID for ties); or loss > loss T0 after Tp Sleep Passive after Ts State Transitions NT: neighbor threshold LT: loss threshold T?: state timer values (p: passive, s: sleep, t: test)

13. Energy Savings Analysis NT: # nodes that have radio on. Tp: passive state timer Ts: sleep state timer = Tp/Ts =PowerSleep/PowerIdle = 0.004

14. Energy Savings Energy Savings (normalized to the Active case, all nodes turn on) as a function of density. ASCENT provides significant amount of energy savings, with a factor of 5 for high density scenarios.

15. STEMSparse Topology and Energy Management Curt Schurgers, Vlasios Tsiatsis, Saurabh Ganeriwal, Mani SrivastavaUCLA STEMEnergy Latency STEM + GAF Energy  Density  Latency

16. Energy Conservation Strategy • Observe: Most of the time, the network is only monitoring, waiting for an event to happen • New strategy: put nodes to sleep and only wake them up when they need to participate in data forwarding Nodes have their radio in sleep mode to conserve energy Nodes turn on their radio, when they need to communicate

17. Listen TRx T Sleep time How can a Sleeping Node be reached? zzzzzzz • Solutions • Each node wakes up occasionally to listen for wake up messages • Low duty cycle paging – save energy.

18. A B Active mode Interference Polling mode C Sleep mode D f1 Sleep Active time Listen f2 Sleep time Implementation Issues • Problem: Wake up messages can collide with ongoing data Tx • Solution: Use separate channel for wake up messages.

19. Wakeup plane: f1 Data plane: f2 Principle of STEM • Low duty cycle paging channel to wake up a neighboring node • Use separate radio for the paging channel to avoid interference with regular data forwarding • Trades off energy savings for setup latency

20. Power f1 Tx Time Power f2 Tx /Rx Sleep Initiator node Target node Rx Power f1 Sleep Time Power f2 Tx /Rx Sleep High Level Operation of STEM

21. Detailed Operation of STEM Initiator node f1 B1 B2 1. beacon received Train of beacon packets TRx 2. beacon acknowledge f1 Target node

22. STEM: Wake up Messages • STEM-B: • Send wake packets (Beacons) to target node • Smaller Latency time (TS) than STEM-T • ACK sent back to initiator Node also turns on its data radio if it hears a collision while listening • STEM-T: • Send a Tone • All nodes hearing the tone wake up • No ACK

23. STEM-B T (s) Energy versus period for STEM-B STEM-B (collisions) STEM-T STEM-T STEM-B (no collisions) T (s) T (s) Energy versus period for STEM-T Average setup latency per hop versus Wake up period STEM: Energy Analysis • fw: wakeup frequency •  = fw . tburst •  = T / TRx •  = Pwakeup / Pdata

24. GAF: Geographic Adaptive Fidelity Energy  Density • Conserve traffic forwarding capacity • Divide network in virtual grids • Each node in a grid is equivalent from a traffic forwarding perspective • Keep 1 node awake in each grid at each time • GAF reduces the energy by a factor M’ • This factor is a function of the average number of nodes in a grid: M for uniformly random node deployment

25. Combining STEM and GAF • As in GAF, 1 node is active in each grid • the grid can be considered a virtual node • Observe: In GAF, the leader has to keep its radio on all the time • Absence of traffic in the monitoring state not exploited • This virtual node runs the STEM protocol • Requires changes in leader election scheme

26. Relative energy saving versus density for TGAF = 5 hours Leader election overhead for STEM-T hours GAF STEM-B/T + GAF = 4 = 6 = 8 STEM-T + GAF = 92   STEM-T + GAF = 92 = 4, 6, 8   STEM + GAF: Energy Analysis • GAF leader election overhead increases with  •  affects choice of STEM-B or STEM-T STEM alone Energy Savings (compared to no topology management) as a function of density. STEM + GAF providesenergy savings, with a factor of 100 for practical settings

27. SpanAn Energy-Efficient Coordination Algorithm for Topology Maintenance in Ad Hoc NetworksBenjie Chen, Kyle Jamieson, Hari Balakrishnan, & Robert MorrisMIT Energy  Node Density

28. Span • A power saving technique for multi-hop ad hoc networks that reduces energy consumption without significantly diminishing the capacity or connectivity of the network • Adaptively elects coordinators from all nodes in the network • Non-coordinators remain in power saving mode and periodically check to see if they should wake up and become coordinators

29. Goals • Ensure that enough coordinators are elected so that every node can reach one coordinator • Rotate the roles of coordinator amongst all nodes • Minimize the number of coordinators (without suffering from significant capacity loss or increased latency) • Decisions based on local information only

30. Coordinator Announcement • Eligibility Rules: if two neighbors of a non-coordinator cannot reach other within two coordinators, the node should be a coordinator

31. Randomized Back-off Delay • Announcement contention is resolved by delaying announcements with a randomized back-off delay • Er = energy remaining at node • Em = maximum amount of energy • Ci = number of new connections through node i • Ni = number of neighbors for node i • R = random number in [0, 1] • T = round-trip delay for packet over wireless link

32. Coordinator Withdrawal • Each node periodically checks whether it should withdraw as a coordinator • A node withdraws if all of its neighbors can reach each other directly or with other coordinators • For fairness, after some period of time, a coordinator withdraws if all neighbors can reach each other via other neighbors, even if these are not coordinators (allows neighbors to act as coordinators)

33. Results

34. Cone-BasedDistributed Topology Control Algorithm for Wireless Multi-hop NetworksLi Li, Joseph Y. Halpern, Paramvir Bahl, Yi-Man Wang, & Robert WattenhoferCornell University & Microsoft Research Energy  Tx Power

35. Goals GR = (V, ER) subset is G = (V, E) • G consists of all the nodes in GR, but with fewer edges • If u and v are connected in GR, they are still connected in G

36. Basic Algorithm CBTC(α) { Nu = 0; Du = 0; pu = po; while (pu < P and gap-α(Du)) do pu = Increase(pu); bcast(u, pu, (“Hello”, pu)); Nu = Nu U {v: v discovered}; Du = Du U {diru(v): v discovered}; }

37.  No! There’s an -gap! Cone-Based Algorithm with Angle  Need a neighbor in every α-cone. Can I stop?

38. Two Symmetric Sets • If   150 • E+ = { (u,v): (u,v)  Eor (v,u)  E } • Symmetric closure of E • G+ = (V, E+ ) • If   120 • E- = { (u,v): (u,v)  Eand (v,u)  E } • Asymmetric edge removal • G- = (V, E- )

39. Properties of the Basic Algorithm • Counterexample for  = 150 + 

40. Counterexample for  = 150 + 

41. Counterexample for  = 150 + 

42. Optimization • Shrink-Back Operation • Allows “boundary nodes” to broadcast with less power • Asymmetric Edge Removal • If   120, remove all asymmetric edges • Pairwise Edge Removal • If  < 60, remove the longer edge e2 B e1  A e2 C

43. Results

44. Conclusion • Presented four algorithms • ASCENT and Span exploit node density • STEM provides an analytical model to tradeoff energy, density, latency • Cone Based algorithm is a mathematical model that reduces Transmission power only

45. References • L. Li, J.Y. Halpern, P. Bahl, Y.M. Wang, and R.Wattenhofer, “Analysis of a Cone-Based Distributed Topology Control Algorithm for Wireless Multi-hop Networks”, in Proc. ACM PODC, August 2001. • C. Schurgers, V. Tsiatsis, S. Ganeriwal, and M. Srivastava, “Optimizing Sensor Networks in the Energy-Latency-Density Space”, to appear in IEEE Transactions on Mobile Computing. • A. Cerpa and D. Estrin, “ASCENT: Adaptive Self-Configuring sEnsor Networks Topologies”, in Proc. INFOCOM, June 2002. • B. Chen, K. Jamieson, H. Balakrishnan, and R. Morris, “Span: An Energy-Efficient Coordination Algorithm for Topology Maintanence in Ad Hoc Wireless Networks”, in Proc. MobiCom 2001.

46. Acknowledgements • Many parts of the presentation are based on slides obtained from: • Alberto Cerpa (ASCENT) • Saurabh Ganeriwal (STEM) • Benjie Chen (Span) • L.Li, J.Y. Halpern (Cone Based Algorithm)