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A Deferral Set Framework of Multi Hop Wireless Ad Hoc Networks

A Deferral Set Framework of Multi Hop Wireless Ad Hoc Networks. Presenting By: Yue Fang Advisor: A. Bruce McDonald The Reconfigurable Wireless Networking and Communications Laboratory (RWIN Lab) Department of Electrical and Computer Engineering

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A Deferral Set Framework of Multi Hop Wireless Ad Hoc Networks

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  1. A Deferral Set Framework of Multi Hop Wireless Ad Hoc Networks Presenting By: Yue Fang Advisor: A. Bruce McDonald The Reconfigurable Wireless Networking and Communications Laboratory(RWIN Lab) Department of Electrical and Computer Engineering Northeastern University, Boston, MA, 02115

  2. Outline of the Presentation • Motivation, philosophy and contribution etc. of the work. • Background: wireless ad hoc networks and related medium access control (MAC) protocols • Deferral set framework overview: components and related technology. • Application 1: channel capacity analysis of multi-hop 802.11 network using distributed coordination function. (DCF) • Application 2: network capacity analysis of multi-hop 802.11 network using distributed coordination function. (DCF) • Summary and future work Advisor: A.Bruce McDonald

  3. Introduction Advisor: A.Bruce McDonald

  4. Introduction: Motivation of the Work • Wireless ad hoc network tends to have less throughput and more dynamic behavior than the wired counterparts. • Like Shannon capacity in wired communication, we would like to derive the capacity limit for the wireless scenario. • Up to now, current works regarding to the performance analysis of wireless ad hoc network has not provided a satisfactory scheme/result of this problem, especially for multi-hop scenarios. • The knowledge of multi-hop wireless ad hoc network capacity can serve as a reference metric for routing algorithm design and improvement . Advisor: A.Bruce McDonald

  5. Introduction: Philosophy • There should be an universal model to investigate the capacity of wireless network in spite of the MAC layer specifications. • Different characteristics of wireless networks are caused by the “broadcast nature” of wireless communication. • Node prospective is more suitable than the widely used channel prospective in multi-hop scenario. • Different layer from the OSI 7 layer model should not be treated as independently as they were in the wired environment, “cross-layer interaction’’ information should be utilized when decisions of routing, scheduling are made in wireless ad hoc network Advisor: A.Bruce McDonald

  6. Introduction: Contribution of the Work • Provided a performance evaluation guide like Shannon capacity law for wireless networks. • A framework which is universal applicable in any kind of wireless networks. • Aimed at more immediate practical advance in the area of cross-layer design, more directly, in routing. • The results offer an accurate and validated characterization of complex interaction. • To our best knowledge, this is first work that notice and formally address the difference between node and channel prospective approach. Advisor: A.Bruce McDonald

  7. Background Advisor: A.Bruce McDonald

  8. Background: Wireless Ad Hoc Network • What is wireless ad hoc network? • Wireless ad hoc network is a self-organizing, rapid deployable networks consisting of a set of “cooperative” wireless mobile hosts. Each node serves as a “router” in the network. • Characteristics of wireless ad hoc network • Available resource such as bandwidth, power, etc.for each node is less than the wired counterparts. • There are several dynamic/difficult to predict characteristics such as channel quality, nodes mobility, fading , etc • Applications of wireless ad hoc network • military/scientific operations where wired communication is impossible • exhibitions/mobile conference, serves as an important supplement of wired network Advisor: A.Bruce McDonald

  9. Background: MAC Protocols • Random Access • Carrier sense multiple access (CSMA) • Multiple access with collision avoidance (MACA) • Floor acquisition multiple access (FAMA), etc • Pre-assigned Access • Time division medium access (TDMA) • Frequency division medium access (FDMA) • Code division medium access (CDMA) • Cluster Based Access • Pre-assigned inside cluster, random access among clusters Advisor: A.Bruce McDonald

  10. Background: IEEE 802.11 --- Overview • Limited in scope to physical (PHY) and medium access control (MAC) sub-layer. • A popular MAC layer protocol for the wireless ad hoc network related applications and research • “Infrastructure” mode and “ad hoc” mode. Advisor: A.Bruce McDonald

  11. Background: IEEE 802.11--- PHY layer • Direct Sequence Spread Spectrum (DSSS) • 2.4 GHz Industrial, Scientific and Medical (ISM) band • 1Mbps (DBPSK), 2Mbps (DQPSK) • 5.5Mbps, 11Mbps (optional) • Frequency Hopping Spread Spectrum (FHSS) • 2.4 GHz Industrial, Scientific and Medical (ISM) band • 1Mbps (2 level GFSK), 2Mbps (4 level GFSK) • Infra Red (IR) • Designed for indoor use only. • 1Mbps (16-PPM), 2Mbps (4-PPM) Advisor: A.Bruce McDonald

  12. Background: IEEE 802.11 --- MAC sub-layer • Distributed Coordination Function (DCF), “ad hoc” mode and Point Coordination Function (PCF), “infrastructure” mode. • DCF is suitable for best effort delivery of data; PCF is primarily designed for delay-sensitive traffic. • Only DCF is investigated in this work: • With basic access scheme and optional RTS/CTS access scheme. • Medium access scheme: carrier sensing multiple access/collision avoidance (CSMA/CA) • Both physical carrier sensing and virtual carrier sensing are employed • Exponential back-off scheme to prevent channel capture. Advisor: A.Bruce McDonald

  13. Background: IEEE 802.11 DCF with RTS/CTS SIFS DIFS RTS DATA SRC Source Destination RTS CTS ACK DEST DATA CTS ACK Other (No defer) Other (Defer) NAV(RTS) OTHER NAV(CTS) DIFS Advisor: A.Bruce McDonald

  14. Background: Previous Art • Theoretical maximum throughput of arbitrary channel [1] • Asymptotic throughput of a single node [2] • Saturation throughput of a fully connected ad hoc network [3] • Simulation of end-to-end throughput of ad hoc network [4] • Throughput of multi-hop ad hoc network and cross-layer interaction [5] Advisor: A.Bruce McDonald

  15. Deferral Set Framework Overview Advisor: A.Bruce McDonald

  16. Deferral Set Overview: Components • An appropriate abstract model which can characterize the features of multi-hop wireless communications. For example, the “deferral set” model and “equivalent competitor” concept. • An algorithm to calculate the probability of transmission of each single node in the network • Node behavior model contains information from above components to analyze performance. Advisor: A.Bruce McDonald

  17. { } { } { } { , } { , } Deferral Set Overview: Abstract Model level-1 interference node set level-2 interference node set deferral node set level-1 interference link set level-2 interference link set { } deferral link set Advisor: A.Bruce McDonald

  18. Deferral Set Overview: Equivalent Competitor • Observation: • Any communication originated from the nodes in level-1 interference node set will definitely affect current communication. • Only the communication originated from nodes in level-2 interference node set toward nodes in level-1 interference set will affect current communication. • “Equivalent competitor” reflects the overall competition the current communication will encounter in terms of “direct neighbors’’ of the ongoing communication. Advisor: A.Bruce McDonald

  19. Deferral Set Overview: Probability of Transmission • The probability of collision p is memory-less. It only depends on the network configuration and no matter how many transmission attempt a packet has been through, p is same. • Depends on the specific MAC protocol, basically a two-dimension Markov chain model can be derived to model the back-off procedure. • The probability of transmission is the sum of all the probability of transmission from all the possible back-off states. Advisor: A.Bruce McDonald

  20. Deferral Set Overview: Node Behavior Model • For single and multi hop network, there are two different channel behavior model. • Node behavior will follow the same rule no matter the network is single or multi hop . • Node’s behavior only depends on the underlying MAC protocol. • The value of parameters reflect the network configuration. Advisor: A.Bruce McDonald

  21. Application 1: Channel Capacity Analysis Advisor: A.Bruce McDonald

  22. Channel Capacity: Equivalent Competitor D • Node being two hop neighbor depends on whether it has a neighbor which is direct neighbor of ongoing communication. • Only the communication from two hop neighbor to one hop neighbor will affect the ongoing communication. • Integrate over level-2 interference region C E X F A G B X I H Only communication between C and F will affect the communication between A and B, thus the equivalent competitor is 1/3. Advisor: A.Bruce McDonald

  23. ... ... ... ... ... ... ... ... ... ... ... ... Channel Capacity: Probability of Transmission (1-p)/W0 1 (1-p) 1 1 ... 1 0,0 0,1 0,2 0,W0-2 0,W0-1 (1-p) i-1,0 p/Wi (1-p) 1 1 1 ... 1 i,0 i,1 i,2 i,Wi-2 i,Wi-1 p/Wi+1 p/WRL 1 1 1 1 ... 1 RL,0 RL,1 RL,2 RL,WRL-2 RL,WRL-1 Advisor: A.Bruce McDonald

  24. RL 1-p RL+1 b 0,0 i=0 1-p Channel Capacity: Probability of Transmission 2  = b i,0= p=(1- ) where:  is the probability of transmission, p is the probability of collision, and N’ is the “equivalent competitor” of the node N’ Advisor: A.Bruce McDonald

  25. p/t p/t collision NAV Start p/t p/t p/t RTS sent RTS recvd Backoff 1 p/t p/t CTS sent Backoff 2 p/t p/t CTS recvd p/t Backoff m p/t success p/t Backoff m’ Backoff 0 Discard Channel Capacity: Node Behavior model • p and t are derived based on the network configuration • timespent in back-off scheme depends on number of attempt and the back-off stage m • by studying back-off scheme the model can be simplified Advisor: A.Bruce McDonald

  26. Channel Capacity: Node and Channel Capacity • Node capacity E(data exchanged during transmission attempt) S node = E(time needed to complete transmission attempt) E(data exchanged during transmission attempt) = P success T success+P fail T fail • Channel capacity S channel =S node*2/navg • Mean service time (packet that got transmitted) T service = T success Advisor: A.Bruce McDonald

  27. Channel Capacity: Model Validation 1 • fixed packet length • propagation delay is assumed to be negligible • collision is assumed to be the most significant cause of packet corruption • node mobility is negligible here • work at saturation condition Example: find the saturation point Advisor: A.Bruce McDonald

  28. Channel Capacity: Model Validation 2 Model Validation: Channel Capacity Model Validation: Mean Service Time Advisor: A.Bruce McDonald

  29. Channel Capacity: Statistical Analysis Channel Capacity v.s. Retry Limit Channel Capacity v.s. Backoff stage m Advisor: A.Bruce McDonald

  30. Application 2: Network Capacity Analysis Advisor: A.Bruce McDonald

  31. Network Capacity: Introduction • Network Capacity • Network capacity reflect the ability of data exchange the whole network can bear at any time. No universal semantic is available. • Two interpretations of network capacity • Maximum instantaneous capacity (MIC): Total number of bits that can be transmitted per unit time by all the links in the network under ideal condition. It is the performance upper limit. • Network saturation capacity (NSC): The amount of bits exchanged by all the links in the network under realistic condition. It provides a roughly accurate estimation of the network performance in real world. ( No beforehand scheduling is present.) Advisor: A.Bruce McDonald

  32. Network Capacity: Topology Generation Network topology is generated by repeating specific patterns to avoid unnecessary randomness. navg=3 navg=6 Advisor: A.Bruce McDonald

  33. Network Capacity: Network Saturation Capacity • Intuitively, network saturation capacity can be obtained by summing up the capacity of all the nodes/links in the network. • From simulation, the real capacity is higher than expectation, the reason is: • Boundary Condition • When network is not large enough, the nodes that resides in the boundary of network will not has as many neighbors as the node in the center of network. • Boundary zone is defined as the outer doughnut area of network wherein nodes are closer to the edge of network than the range the interference set it is in. Advisor: A.Bruce McDonald

  34. A 2r-d B α d Network Capacity, NSC: Phantom Node • Nodes in the boundary zone (A, B) tend to have higher capacity than the nodes in the center of the network. • Nodes in the shaded area are called “phantom” nodes • In order to have an accurate estimation of network saturation capacity, the percentage of “phantom node” to the number of nodes in the network should below a threshold. Advisor: A.Bruce McDonald

  35. Network Capacity, NSC: How big is big? • Boundary condition effect can be regarded as negligible when then network radius is at least 10 times of transmission range. • Current simulation environment is not capable of simulating such big networks Percentage of phantom nodes to Num Number of phantom nodes vs. R/r Advisor: A.Bruce McDonald

  36. Network Capacity: Maximum Instantaneous Capacity • Maximum instantaneous capacity (MIC) can only be achieved when all the links that can transmit actually do transmit. • Maximum instantaneous capacity is the minimal maximal number of concurrent active links in the network at any given time. • To find this maximum number is a NP-complete problem, thus a sub-optimal greedy algorithm is proposed. Bottleneck is the MIC: c3 c1 simultaneous links c2 simultaneous links c3 simultaneous links c1>= c2>=c3 Advisor: A.Bruce McDonald

  37. 1 (1,2) 2 (3,4) (4.5) 3 5 (2,3) (2,5) 4 Network Capacity, MIC: NP completeness • By appropriate means, the problem of find maximum number of concurrent active links in the network can be transformed to classic maximum independent set problem, which is NP-complete. [6] Advisor: A.Bruce McDonald

  38. Network Capacity, MIC: The greedy Algorithm • List the degree of all the possible deferral sets in a ascending order. • Pick the first deferral set in the list , transmission along corresponding link can be granted. • Update the candidate deferral list. • Update the link degree of the remaining candidates and resort the list • If two deferral set have some link degree, the tie is broken by picking the one with less Euclidean distance. • Repeat 2-5 until the candidate deferral set list is empty. Advisor: A.Bruce McDonald

  39. n avg Network Capacity, MIC: Random Selection • Another possible approach to get the number of concurrent links in the network is by random selection. In our network topology, there will be no big discrepancy of the results from greedy algorithm and random selection. Advisor: A.Bruce McDonald

  40. n avg Network Capacity, MIC: Bottleneck of Network • In order to find the network capacity, all the links in the network has to be covered at least once. The round that has minimum concurrent active link is the bottleneck. Advisor: A.Bruce McDonald

  41. Network Capacity, MIC: Discussion • From Gupta and Kumar [2], using protocol model, the capacity of a random network is • Number of concurrent active link in a saturated network can be approximated by the number of non-overlapping level-1 interference sets. Which can be obtained by: • Network capacity then is: Advisor: A.Bruce McDonald

  42. Summary & Research Plan Advisor: A.Bruce McDonald

  43. Research Plan (1) • Study the network saturation capacity (NSC) problem for small and medium size network. • Network saturation capacity can be obtained by taking the sum of the capacities of all the nodes in the network • The capacity of an arbitrary node also depends on the location of the node to which the node is communicating. • Currently, it is believed that the expected node capacity can be obtained by probability approach. • Plan to complete this part in 3 weeks. Advisor: A.Bruce McDonald

  44. Research Plan (2) • Proposal a simply adaptive routing algorithm that can utilize the “cross-layer” information. • Try to use the existing results in coding theory in routing decisions. • However, in this work, our goal is not to provide the definition of ``distance'' with accurate characterization, but to point out the possible approach to utilize the ``cross-layer interaction'' to improve routing performance. • Design the centralized version of the ``coding routing algorithm''. • Plan to finish the algorithm design in 2weeks. Advisor: A.Bruce McDonald

  45. Research Plan (3) • Implement the proposed algorithm in one of the common used simulators, and apply the result of routing algorithm in network simulations. • Implement the algorithm in matlab, retrieve the information then imported it into network simulation environments. • Plan to finish in 2 weeks. Advisor: A.Bruce McDonald

  46. Research Plan (4) • Compare the performance of proposed routing protocol to the some popular routing algorithms used in wireless ad hoc network and data analysis • If there is improve in performance • Not try to predict the improvement quantitively • Under what situation the improvement is significant, and what situation the improvement is negligible or no improvement at all • what's the relationship between improvement and network density, traffic load, etc, • Can we improve throughput and delay at the same time? • Plan to finish in 6 weeks. Advisor: A.Bruce McDonald

  47. Questions? Advisor: A.Bruce McDonald

  48. References 1. P. Peddabachagari, J. Jun and M. Sichitiu, “Theoretical Maximum Throughput of IEEE 802.11 and Its Applications”; 2. P. Gupta and P.R. Kumar, “The Capacity of Wireless Networks”; 3. G. Bianchi, “ Performance Analysis of the IEEE 802.11 Distributed Coordination Function”; 4. J. Li, C. Blake et.al, “Capacity of Ad Hoc Wireless Networks”; 5. Y. Fang and A.B McDonald, “Cross-layer Performance Effects of Path Coupling in Wireless Ad Hoc Networks: Power and Throughput Implications of IEEE 802.11 MAC”; 6. A. Trojanowski and R. Tarjan, “Finding a Maximum Independent Set” Advisor: A.Bruce McDonald

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