1. Jennifer C. Hou Department of Computer Science University of Illinois at Urbana-Champaign June 11, 2015June 11, 2015June 11, 2015 Jennifer C. Hou Department of Computer Science University of Illinois at Urbana-Champaign June 11, 2015June 11, 2015June 11, 2015 DAWN: Dynamic Ad-hoc Wireless Networks Progress Report Presentation DAWN: Dynamic Ad-hoc Wireless Networks Progress Report Presentation

2. Energy Efficient Network Track Power, CS Threshold, and Rate Control

3. DEPARTMENT OF COMPUTER SCIENCE UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN Jennifer Hou 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. DEPARTMENT OF COMPUTER SCIENCE UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN Jennifer Hou Power Control  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. DEPARTMENT OF COMPUTER SCIENCE UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN Jennifer Hou 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. DEPARTMENT OF COMPUTER SCIENCE UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN Jennifer Hou X i : location of node i R i : transmission range of node i Link (j  i) exists if R j  | X i – X j |: Transmission power of node i Total power: k-connectivity: requires to remove at least k nodes to disconnect the network Critical total power W c : minimum total power W for maintaining k- connectivity 1 1 RiRi RjRj XjXj XiXi Preliminary Work on Total Power Required for K-Connectivity Poisson point process with density n in a unit- area square

7. DEPARTMENT OF COMPUTER SCIENCE UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN Jennifer Hou Question To Ask In what order does the critical total power W c increase/decrease as the node density increases? All nodes choose common power [Gupta & Kumar 98] studied the critical transmission range r n 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. DEPARTMENT OF COMPUTER SCIENCE UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN Jennifer Hou 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. DEPARTMENT OF COMPUTER SCIENCE UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN Jennifer Hou Rescaling to Expanded Networks Expanded networks Node density fixed Side length L  Expected number of nodes n= L 2 Allowing power control Average power is bounded Using common power The common power grows as 1 1 L L

10. DEPARTMENT OF COMPUTER SCIENCE UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN Jennifer Hou Cross Layer Aspects of Power Control Physical Layer MAC Layer Network Layer Power Control Incorporating Physical Layer Characteristics Cross Layer Design Effect of MAC- Layer Interference Dynamic Topology Control w.r.t. Network Traffic Network Capacity Network Lifetime Critical Power Analysis Network Capacity Network Lifetime Critical Power Analysis Physical Layer Incorporating Physical Layer Characteristics

11. DEPARTMENT OF COMPUTER SCIENCE UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN Jennifer Hou 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 d ij <= d max. 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) Set of node locations Set of configurations ( , max/min transmit power) Topology that defines the neighbor relations

12. DEPARTMENT OF COMPUTER SCIENCE UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN Jennifer Hou 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. DEPARTMENT OF COMPUTER SCIENCE UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN Jennifer Hou Controlling Carrier Sense Threshold The contending area can also be adapted through tuning the carrier-sensing threshold A B C D distance Signal Strength CS Threshold E F

14. DEPARTMENT OF COMPUTER SCIENCE UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN Jennifer Hou 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 Controlling Carrier Sense Threshold A B C D distance Signal Strength CS Threshold E F

15. DEPARTMENT OF COMPUTER SCIENCE UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN Jennifer Hou Tradeoff Analysis Spatial reuse can be achieved at the cost of higher interference level and lower transmission rate High rate links Low rate links What is the optimal CS Threshold? How does it relate to the transmit power?

16. DEPARTMENT OF COMPUTER SCIENCE UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN Jennifer Hou Network Capacity Network capacity as a function of transmit power and carrier sense threshold [ACM Mobicom 2006]

17. DEPARTMENT OF COMPUTER SCIENCE UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN Jennifer Hou Power Control vs. Data Rate Tx1 Tx2 Rx 1 Rx 2 d1 r2 r1 d2 D1 D2 SINR requirements

18. DEPARTMENT OF COMPUTER SCIENCE UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN Jennifer Hou Power Control vs Data Rate Tx1Tx2Rx 1 Rx 2 d1 r2 r1 d2 D1 D2 SINR requirements

19. DEPARTMENT OF COMPUTER SCIENCE UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN Jennifer Hou 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. DEPARTMENT OF COMPUTER SCIENCE UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN Jennifer Hou 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. DEPARTMENT OF COMPUTER SCIENCE UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN Jennifer Hou Simulation Results Performance gain mainly because of Higher concurrent transmissions

22. Simulation Track 1. Expediting Wireless Simulation 2. Incorporating Model Checking into Simulation

23. DEPARTMENT OF COMPUTER SCIENCE UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN Jennifer Hou 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. DEPARTMENT OF COMPUTER SCIENCE UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN Jennifer Hou Reactive Channel Model R L 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

25. DEPARTMENT OF COMPUTER SCIENCE UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN Jennifer Hou A Case Study: IEEE 802.11 MAC StateTo register? transmitting * ☒ receiving ☑ idle backing-off ☑ deferring ☑ -- ☒ sleep ☒ turn-off ☒ *: assuming half-duplex radio is used

26. DEPARTMENT OF COMPUTER SCIENCE UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN Jennifer Hou RCM Performance (Execution Time) RCM

27. DEPARTMENT OF COMPUTER SCIENCE UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN Jennifer Hou RCM Performance (Memory Consumption)

28. DEPARTMENT OF COMPUTER SCIENCE UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN Jennifer Hou 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). Integration of Real/Virtual Worlds Channel behavior modeling Physical capacity analysis Incentive-based resource management Multi-radio, multi-path routing Cross-layer optimization

29. DEPARTMENT OF COMPUTER SCIENCE UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN Jennifer Hou Marriage of Modeling Checking and Simulation s0s0 s1s1 s2s2 s3s3 s4s4 s5s5 s6s6 s7s7 X 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. s0s0 s1s1 s2s2 s3s3 s4s4 s5s5 s6s6 s7s7 X J-Sim w/ MC 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

30. DEPARTMENT OF COMPUTER SCIENCE UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN Jennifer Hou An Overview of Our Work Build the model checker as a component in J-Sim P1P1 P2P2 PnPn Model Checker J-Sim Error Trace / No Error Initial State Current State Next State Component Port Communication via ports

31. DEPARTMENT OF COMPUTER SCIENCE UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN Jennifer Hou 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 [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. Summary of our discoveries: A previously unknown bug in the J-Sim implementation of AODV A previously unknown deficiency in directed diffusion

32. DEPARTMENT OF COMPUTER SCIENCE UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN Jennifer Hou 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.