Reconciling the Theory and Practice of (Un)Reliable Wireless Broadcast

1. Reconciling the Theory and Practice of (Un)Reliable Wireless Broadcast Gregory Chockler Murat Demirbas Seth Gilbert Nancy Lynch Calvin Newport Tina Nolte MIT, CSAIL

2. A better model for wireless broadcast • For sensor-actuator networks dependability becomes crucial • Developing reliable and provably correct algorithms for wireless ad hoc networks require a formal model of wireless broadcast • What makes a good model ? • Usability: Simplicity & Power • Model should allow algorithm designers to ignore the low-level details • Model should allow complicated and interesting algorithms • Realism: Fidelity to the real world phenomena • Algorithms designed and verified using the model should work as expected in the real world

3. Myths of wireless broadcast models • Wireless broadcast is reliable Origin: “Coordinated Attack” is unsolvable in the presence of unreliable channels [even a single message loss] FLP result Reality check: Collisions are common place* • Even under low traffic loads, MAC layer (collision avoidance by back off) cannot avoid all concurrent transmissions • Hidden Node Effect • 802.11, BMAC, SMAC, TMAC • Electromagnetic interference *Understanding packet delivery performance in dense WSN, Zhao Govindan Taming the underlying challenges of multihop routing in SN, Woo et al. Experimental evaluation of wireless simulation assumptions, Kotz et al. Complex behavior at scale: An experimental study of …Ganesan et al.

4. Myths of wireless broadcast models • Transmitter can detect collision • Collisions are uniform Origin: Ethernet model Reality check: Transmission power swamps the receiver Collisions are partial • Transmission is partially affected by transmissions in neighboring regions • Physical characteristics of radio broadcast

5. Myths of wireless broadcast models • Collision detection is reliable Origin: Ethernet model / Convenience Reality check: • Capture effects prevent reliable collision detection* • A strong transmission captures the receiver and a weaker transmission goes unnoticed • Noisy channels prevent reliable collision detection * Exploiting the Capture Effect for Collision Detection and Recovery, Whitehouse, Woo, Jiang, Polastre, Culler. EmNetsII, 2005.

6. Contributions • Partial Collision Model (PCM) • Vote-veto algorithm for consensus in PCM** • Two phase algorithm that tolerates partial collisions • Constant round consensus • Solvability of consensus under varying quality of collision detection** ** Consensus and Collision Detectors in Wireless Ad Hoc Networks, Chockler, Demirbas, Gilbert, Newport, Nolte. To appear inPODC 05

7. Outline of the talk • Preliminaries • Collision Detection (CD) • Partial Collision Model (PCM) • Vote-Veto Algorithm • Ongoing Work

8. Preliminaries • Ad hoc network, number and id of nodes unknown • Nodes can fail by crashing anytime • broadcast is an atomic operation (unlike in one-to) • Synchronized rounds • Broadcast at most one message • Receive messages • Perform state transition

9. Radio broadcast • Basic properties • Integrity: each message received is previously broadcast • No duplication: each message is received at most once • Eventual Collision Freedom (ECF) • Exists a round r_ecf s.t. ForAll r>r_ecf, if at most b nodes broadcast in r, then all correct nodes receive all messages in r • MAC protocols can satisfy collision free period for unknown r_ecf, b • To achieve ECF, we employ an active-passive service that obliviously provides eventual good advice to nodes

10. Collision detection Necessary for coping with undetectable message loss • Receiver side monitoring and notification of collisions • No info wrt # of lost messages or identities of senders • Completeness: Ability to detect collisions • Majority-complete: a collision is detected if a majority of messages in a round is lost • 0-complete: collision is detected if all messages in a round is lost • Accuracy: No false positives • Always and eventually accurate CD • Receiver side collision detection is easily implementable in mote and 802.11 platforms

11. Partial Collision Model • Integrity, No duplication, ECF, and CD • No restriction on the pattern of collisions Usability: • Simple • Powerful enough to solve consensus in constant rounds for majority-complete CD and log rounds for 0-complete CD Realism: • Both ECF and majority/0-complete CD are easily achievable in current wireless networks (Mote and 802.11)** • We have implementations for Mica2/TinyOS platform *Exploiting the Capture Effect for Collision Detection and Recovery, A new backoff alg for the IEEE802.11 distributed coordination fn., …

12. Consensus problem • Agreement: No two nodes in P decide differently • Validity: Decision v is proposed by a node in P • Termination: All correct nodes eventually decide Single-hop consensus • All nodes in P are within single-hop • Building block for multi-hop consensus Applications: • Beam control scenario • Virtual Traffic Lights scenario • Reliable (multi-hop) reprogramming

13. Vote-Veto algorithm • Two phases: vote and veto • The algorithm is adaptive, employs active-passive service • Vote phase: • Every active node sends out its vote • If a node hears no collision, the node updates its vote to minimum of received votes • If a node hears collision or different votes, it decides to veto • Veto phase: • If no veto messages are received or collisions detected, then a node can decide, else nodes continue to next round • Intuition: By having a dedicated veto phase, effects of collision is detectable

14. Proof (for majority-complete CD) Let r be the first round any node decides Since no node vetoed in r, every node received only a single vote and no collision detection during vote phase in r-1 Since maj-◊AC detects when ≥ half the messages are lost, each node receives a majority of messages broadcasted in vote phase in r-1 Since every majority set intersects, all nodes received the same unique vote. ECF, active-passive service, eventual accuracy leads to a vote phase where every participant converge to a vote. After the second vote phase no node vetoes, hence every node decides in at most 5 rounds after Earliest-Stabilization-Time.

15. Consensus results overview • Vote-Veto algorithm for AC, Maj-AC, ◊AC, ◊Maj-AC • Three phase algorithm that iterates over each bit of the vote for 0-AC, 0-◊AC

16. Implementation • Mica2/TinyOS platforms • Collision detection may be maj-complete • Detects a preamble of a message during reception of a message • Courtesy of Whitehouse et al. • case RX_STATE: {...if (((data_in == (0xaa)) || (data_in == (0x55)))) {signal CollisionDet.collision();}… • Vote-Veto implementation is around 250 lines of NesC • Experiments on MistLab MIT (motelab)

17. Ongoing work • We have implemented Virtual Traffic Lights on Mica2/TinyOS • Experiments with up to 15 nodes in single hop on the MistLab testbed(MIT/CSAIL) show success • Ongoing work on evaluation of CD • Practical implementation of the Virtual Node architecture • Implementation of a reliable virtual node by means of maintaining a replicated state on unreliable, mobile nodes • Efficient multi-hop consensus algorithms • Extending to Byzantine faults

18. Bonus material  • Consensus algorithm using 0-complete CD • Consensus for no-collision-freedom model

19. 3 phase algorithm for 0-◊AC consensus • Phase 1 : • Each active node proposes an estimate • Every node adopts the minimum estimate it hears • Phase 2 : one round for each bit in the estimate • If a node has 1 in the corresponding bit, it broadcasts a message • If a node has 0, it listens & decides to veto if it hears a message or collision • Phase 3 : • Any node that decides to veto broadcasts a veto • If any node performs a veto, nodes start from phase 1

20. Consensus for No CF