Jennifer C. Hou Department of Computer Science University of Illinois at Urbana-Champaign

1. DAWN: Dynamic Ad-hoc Wireless Networks Progress Report Presentation Jennifer C. Hou Department of Computer Science University of Illinois at Urbana-Champaign September 10, 2014

2. Energy Efficient Network TrackPower, CS Threshold, and Rate Control

3. PHY/MAC Control Knobs • To mitigate interference and maximize the network capacity, there are several control knobs: • Transmit power  power/topology control • Carrier sense threshold  trade-off between spatial reuse and interference level • Spatial diversity  scheduling consecutive transmission for interference-free connections • Channel diversity  use of non-overlapping channels

4. PowerControl • Definition: Each node adjusts its transmission power so as to maintain network connectivity using the minimum possible power. • Objectives: • Maintaining network connectivity • Reducing energy consumption • Mitigating MAC interference • Achieving network capacity and spatial reuse

5. Preliminary Work on Power Control Local minimum spanning tree (LMST) [INFOCOM’03, IEEE TWC, IEEE TPDS] Localized algorithm  Relies only on local information Preserves connectivity. Ensures bi-directional links. Handles node heterogeneity (i.e., nodes have different maximal transmit powers) Bounds the degree of any node by 6.

6. Preliminary Work on Total PowerRequired for K-Connectivity • Xi: location of node i • Ri : transmission range of node i • Link (ji) exists if Rj  | Xi– Xj |: • Transmission power of node i • Total power: • k-connectivity: requires to remove at least k nodes to disconnect the network • Critical total power Wc: minimum total power W for maintaining k-connectivity 1 Xj Rj Ri Xi 1 Poisson point process with density n in a unit-area square

7. Question To Ask • In what order does the critical total power Wc increase/decrease as the node density increases? • All nodes choose common power • [Gupta & Kumar 98] studied the critical transmission range rn for 1-connectivity • [Wan & Yi 04] for k-connectivity • All nodes choose different power • [Blough 02] critical total power for 1-connectivity • Based on the total weight of minimum spanning tree • Our study: critical total power or k-connectivity

8. Major Results • Main theory: [Infocom 2005, ACM/IEEE ToN 2006] • The critical total power for maintaining k-connectivity is with probability approaching 1 • Comparison with common power • The critical total power for k-connectivity with common power is • Allowing power control at each node reduces the total power by a factor of

9. Rescaling to Expanded Networks • Expanded networks • Node density  fixed • Side length L • Expected number of nodes n=  L2 • Allowing power control • Average power is bounded • Using common power • The common power grows as 1 1 L L

10. Cross Layer Aspects of Power Control Cross Layer Design Dynamic Topology Control w.r.t. Network Traffic Power Control Network Layer Network Capacity Network Lifetime Critical Power Analysis Effect of MAC-Layer Interference MAC Layer Incorporating Physical Layer Characteristics Incorporating Physical Layer Characteristics Physical Layer Physical Layer

11. When Power Control Meets SINR • All the topology control algorithms in literature defined the neighbor relation based on the protocol model • A link exists between nodes i and j if dij <= dmax. TC: L(n)  T(n) • The protocol model ignores the effect of SINR. • What is more appropriate to define a link is the use of physical model • A link exists between i and j if • TC: L(n) x C(n)  T(n) Topology that defines the neighbor relations Set of configurations (b, max/min transmit power) Set of node locations

12. When Power Control Meets SINR • All existing topology control algorithms fail (i.e., cannot maintain network connectivity) under the physical model. • We are re-investigating topology control under the physical model.

13. B D C A F E Signal Strength CS Threshold distance Controlling Carrier Sense Threshold • The contending area can also be adapted through tuning the carrier-sensing threshold

14. B D C A F E Signal Strength distance CS Threshold Controlling Carrier Sense Threshold • A large CS threshold leads to • smaller contending area • Less contending nodes within the contending area • More concurrent transmission • Higher interference • Transmission rate depends on Signal-to-Interference-Noise Ratio

15. Tradeoff Analysis • Spatial reuse can be achieved at the cost of higher interference level and lower transmissionrate What is the optimal CS Threshold? How does it relate to the transmit power? Low rate links High rate links

16. Network Capacity • Network capacity as a function of transmit power and carrier sense threshold [ACM Mobicom 2006]

17. r1 d2 D1 D2 d1 r2 Tx1 Tx2 Rx1 Rx2 Power Control vs. Data Rate SINR requirements

18. Tx1 Tx2 Rx1 Rx2 Power Control vs Data Rate r1 d2 D1 D2 d1 r2 SINR requirements

19. Power and Rate Control • PRC algorithm: • A localized algorithm that enables each transmitter to adapt to the interference level that it perceives and determines its transmit power. • The transmit power is so determined that the transmitter can sustain the highest possible data rate, while keeping the adverse interference effect on the other neighboring concurrent transmissions minimal.

20. Simulation Setup • Modified ns-2 Ver. 2.28 • The interference perceived at a receiver is the collective aggregate interference from all the concurrent transmissions • Each node uses physical carrier sense to determine if the medium is free • IEEE 802.11a radios supporting 8 discrete data rate (6 ~ 54 Mbps) • Random topology • 3, 10, 20, 30, and 50 transmitter-receiver pairs are randomly generated in a 300m X 300m area, and represent sparsely, moderately, and densely populated networks, respectively,. • Algorithms used for evaluations • Static • Greedy Power Control (GPC) • Power and Rate Control (PRC)

21. Simulation Results Performance gain mainly because of • Higher concurrent transmissions

22. Simulation Track1. Expediting Wireless Simulation2. Incorporating Model Checking into Simulation

23. Expediting Wireless Simulation • Our profiling work indicates more than 50% of the execution time is spent on event scheduling and channel related activity handling. • Can we expedite simulation by reducing the number of unnecessary events while not impairing the accuracy. Proportion of the execution time that is spent on event enqueueing in a 100-node ad hoc network over a 1000mX1000m field. There are 40 CBR connections carrying a total of 120 packets/sec. Traffic (pkt. Size = 512B) [1] Chunyu Hu and Jennifer C. Hou, ``A reactive channel model for expediting wireless network simulation,'' ACM SIGMETRICS, Banff, Alberta, Canada, June 2005

24. Reactive Channel Model • The channel only notifies nodes in the following sets of the signal-arrival event • Nodes in range R • Nodes in (range L but not R) that are registered • When does a node register? • Whenever it needs to monitor the channel status, e.g., when it would like to gain access to the channel or when it is in the process of receiving a signal L R

25. A Case Study: IEEE 802.11 MAC *: assuming half-duplex radio is used

26. RCM Performance (Execution Time) RCM

27. RCM Performance(Memory Consumption)

28. Integration of Real/Virtual Worlds Virtual Wireless Ad-Hoc Network (J-Sim) Champaign-Urbana Wireless Community Network (Currently 40 wireless nodes in downtown Urbana; expected to extend to 100 nodes providing full coverage of Champaign and Urbana). • Channel behavior modeling • Physical capacity analysis • Incentive-based resource • management • Multi-radio, multi-path routing • Cross-layer optimization

29. J-Sim w/ MC s4 s1 J-Sim s5 s2 s0 s4 s1 X s6 s5 s3 s7 s2 s0 X s6 s3 s7 Marriage of Modeling Checking and Simulation • Stateful on-the-fly explicit-state model checking into J-Sim • Explore the state space of a network protocol up to a (configurable) maximum depth of transitions • No changes to the core design and implementation of J-Sim [1] Ahmed Sobeih, Mahesh Viswanathan and Jennifer C. Hou, “Check and Simulate: A Case for Incorporating Model Checking in Network Simulation,” Proceedings of the ACM-IEEE International Conference on Formal Methods and Models for Codesign (ACM-IEEE MEMOCODE 2005), San Diego, CA, June 2005.

30. An Overview of Our Work • Build the model checker as a component in J-Sim J-Sim Component Next State Current State Initial State Port Communication via ports Model Checker Error Trace / No Error Pn P1 P2

31. Evaluation and Results • Two case studies: AODV and Directed Diffusion • Representative routing and data dissemination protocols • Reasonably complex network protocols • 1200 – 1400 LOC (excluding the J-Sim library) • Safety property: • The loop-free property of routing/data dissemination paths • Summary of our discoveries: • A previously unknown bug in the J-Sim implementation of AODV • A previously unknown deficiency in directed diffusion [2] Ahmed Sobeih, Mahesh Viswanathan, Darko Marinov and Jennifer C. Hou, “Finding Bugs in Network Protocols Using Simulation Code and Protocol-Specific Heuristics,” Proceedings of the International Conference on Formal Engineering Methods (ICFEM 2005), Springer-Verlag LNCS 3785, Manchester, United Kingdom, November 2005.

32. Guiding Model Checking with Network Properties • We have developed search heuristics that exploit properties inherent to the network protocol and the safety property being checked and better guide the model checker to discover counter examples. • An interesting and important research question is how to determine a suitable BeFS strategy for a specific network protocol.