How do Wireless Chains Behave? The Impact of MAC Interactions
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1 The Impact of MAC Interactions S. Razak 1 Vinay Kolar 2 N. Abu-Ghazaleh 1 K. Harras 1 1 Department of Computer Science Carnegie Mellon University, Qatar 2 Department of Wireless Networks RWTH Aachen University, Germany ACM MSWiM, 2009
2 Introduction Motivation Problem Statement Brief Introduction Multi-Hop Wireless Networks (MHWN) becoming increasingly important: Mesh networks, Sensor networks, Bluetooth,... But, MHWN performance is unpredictable Inefficient and well below analytical limits Interference has substantial and unpredictable impact at all network layers Analysis and design of systems difficult
3 High-level Motivation Introduction Motivation Problem Statement Interference in wireless systems are poorly understood Better understanding and characterizing is necessary Design interference-aware protocols Redesign complete layering schemes Interference effects at lower layers has a great impact on higher layers. MAC level interactions play a central role in governing performance of a route. Focus of the paper
4 High-level Motivation Introduction Motivation Problem Statement Interference in wireless systems are poorly understood Better understanding and characterizing is necessary Design interference-aware protocols Redesign complete layering schemes Interference effects at lower layers has a great impact on higher layers. MAC level interactions play a central role in governing performance of a route. Focus of the paper
5 Introduction Motivation Problem Statement Problem Statement Recently, a new approach for analyzing interference at the CSMA based MAC layer was proposed. Several interactions occur between any two links due to effect of interference. Our contribution: Analyze the interactions that occur in a single multi-hop chain. Chain is a sequence of nodes from source to destination.
6 Introduction Motivation Problem Statement Problem Statement Recently, a new approach for analyzing interference at the CSMA based MAC layer was proposed. Several interactions occur between any two links due to effect of interference. Our contribution: Analyze the interactions that occur in a single multi-hop chain. Chain is a sequence of nodes from source to destination.
7 Problem Statement and Motivation Introduction High-level Motivation Problem Statement Self-interference in chains Probability of occurrence Performance analysis Generalization for n-hop chains
8 Problem Statement and Motivation Introduction High-level Motivation Problem Statement Self-interference in chains Probability of occurrence Performance analysis Generalization for n-hop chains
9 CSMA interactions in MHWNs Two-flows under CSMA/CA Countable number of interaction patterns: 10 categories under SINR model [2] 4 promintent categories.
10 No Interaction NI Sender Connected SC D 1 S 1 S 2 D 2 D 1 S 1 S 2 D 2 Hidden Terminal HT (AIS) HT with Capture HTC D 1 S 1 D 2 S 2 D 1 S 1 D 2 S 2
11 No Interaction NI Sender Connected SC D 1 S 1 S 2 D 2 D 1 S 1 S 2 D 2 Hidden Terminal HT (AIS) HT with Capture HTC D 1 S 1 D 2 S 2 D 1 S 1 D 2 S 2
12 No Interaction NI Sender Connected SC D 1 S 1 S 2 D 2 D 1 S 1 S 2 D 2 Hidden Terminal HT (AIS) HT with Capture HTC D 1 S 1 D 2 S 2 D 1 S 1 D 2 S 2
13 No Interaction NI Sender Connected SC D 1 S 1 S 2 D 2 D 1 S 1 S 2 D 2 Hidden Terminal HT (AIS) HT with Capture HTC D 1 S 1 D 2 S 2 D 1 S 1 D 2 S 2
14 No Interaction NI Sender Connected SC D 1 S 1 S 2 D 2 D 1 S 1 S 2 D 2 Hidden Terminal HT (AIS) HT with Capture HTC D 1 S 1 D 2 S 2 D 1 S 1 D 2 S 2
15 Contributions Systematically classify all possible categories of isolated chains based on MAC interactions. Examine how frequently each category occurs. Evaluate the impact of MAC interactions on performance. Testbed and simulation.. Vulnerability of chains to cross-chain hidden terminals.
16 Factors that affect chain behavior MAC level interactions within a chain. Cross-chain interactions. Interactions that arise between links of two or more chains. Pipelining effect. Unfairness due to insufficient channel idle-periods.
17 Factors that affect chain behavior MAC level interactions within a chain. Cross-chain interactions. Interactions that arise between links of two or more chains. Pipelining effect. Unfairness due to insufficient channel idle-periods.
18 Pipelining H 1 H 2 H 3 H 4 Traffic on later hops depend on traffic transmitted by earlier hops. Implication: Effect of MAC interactions from later hops to earlier hops is controlled. Link Throughput (in bps) x Total transmitted Successfully transmitted H1 H2 H3 H4 Hops Figure: Throughput across chain pipeline
19 System description and notations We start with analysis of 4-hop chains. Minimum number of hops to observe non-trivial interactions. Extend it to n-hop chains later. Link-quality based routing (NADV) S H 1 H 2 H 3 H 4 I 1 I 2 I 3 D Three important self-interference interactions INT1 Between H1 and H4 INT2 Between H1 and H3 INT3 Between H2 and H4 Chain type nomenclature: INT1-INT2-INT3 chain (e.g. HT-SC-SC chain).
20 System description and notations We start with analysis of 4-hop chains. Minimum number of hops to observe non-trivial interactions. Extend it to n-hop chains later. Link-quality based routing (NADV) S H 1 H 2 H 3 H 4 I 1 I 2 I 3 D Three important self-interference interactions INT1 Between H1 and H4 INT2 Between H1 and H3 INT3 Between H2 and H4 Chain type nomenclature: INT1-INT2-INT3 chain (e.g. HT-SC-SC chain).
21 Probability of occurrence Performance analysis Generalization for n-hop chains Problem Statement and Motivation Introduction High-level Motivation Problem Statement Self-interference in chains Probability of occurrence Performance analysis Generalization for n-hop chains
22 Probability of occurrence Performance analysis Generalization for n-hop chains How often does each chain category occur? Simulations with random 4-hop chains in different node-densities. Vary carrier sensing range and observe interactions. For realistic carrier-sensing ranges: INT2 and INT3 are SCs: INT1 is the principal interaction. Only 3 type of chains are predominant. HTC/SC/SC chain (HTC-Chains) SC/SC/SC chain (SC-Chain) HT/SC/SC chain (HT-Chain)
23 Recap: Categories of isolated chains Probability of occurrence Performance analysis Generalization for n-hop chains Any two links on a chain have SC-interaction except H1-H4. H 1 H 2 H 3 H 4 S I 1 I 2 I 3 D Principal Interaction (PI) Three prominent categories based on PI: SC-Chain, HT-Chain, HTC-Chain.
24 Recap: Categories of isolated chains Probability of occurrence Performance analysis Generalization for n-hop chains Any two links on a chain have SC-interaction except H1-H4. H 1 H 2 H 3 H 4 S I 1 I 2 I 3 D Principal Interaction (PI) Three prominent categories based on PI: SC-Chain, HT-Chain, HTC-Chain.
25 Probability of occurrence Performance analysis Generalization for n-hop chains SC-Chain H 1 H 2 H 3 H 4 S I 1 I 2 I 3 D SC interaction HT-Chain H 1 H 2 H 3 H 4 S I 1 I 2 I 3 D HT interaction HTC-Chain H 1 H 2 H 3 H 4 S I 1 I 2 I 3 D HTC interaction
26 Probability of occurrence Performance analysis Generalization for n-hop chains SC-Chain H 1 H 2 H 3 H 4 S I 1 I 2 I 3 D SC interaction HT-Chain H 1 H 2 H 3 H 4 S I 1 I 2 I 3 D HT interaction HTC-Chain H 1 H 2 H 3 H 4 S I 1 I 2 I 3 D HTC interaction
27 Probability of occurrence Performance analysis Generalization for n-hop chains SC-Chain H 1 H 2 H 3 H 4 S I 1 I 2 I 3 D SC interaction HT-Chain H 1 H 2 H 3 H 4 S I 1 I 2 I 3 D HT interaction HTC-Chain H 1 H 2 H 3 H 4 S I 1 I 2 I 3 D HTC interaction
28 Probability of occurrence Performance analysis Generalization for n-hop chains SC-Chain H 1 H 2 H 3 H 4 S I 1 I 2 I 3 D SC interaction HT-Chain H 1 H 2 H 3 H 4 S I 1 I 2 I 3 D HT interaction HTC-Chain H 1 H 2 H 3 H 4 S I 1 I 2 I 3 D HTC interaction
29 Performance analysis: Testbed Probability of occurrence Performance analysis Generalization for n-hop chains All chains consume different overall capacity to obtain the same end-throughput. Inefficient capacity utilization in HT- and HTC-Chains: Substantial impact on overall consumed bandwidth. Current routing protocols do not consider MAC interaction based routing.
30 Probability of occurrence Performance analysis Generalization for n-hop chains Generalization to n-hop chains Number of interactions grow rapidly as n increases (10 n(n 1) 2 ) We eliminate infrequent interactions by following these rules: Three prominent MAC interactions (SC, HT, HTC). Realistic carrier-sensing ranges eliminate some interactions. Node locality constraint: Intermediate hops are along the line from source to destination. Num interactions = 3 (n 3)
31 Probability of occurrence Performance analysis Generalization for n-hop chains Performance of n-hop chains Summary: Performance results follows same trend as in 4-hop scenario. All chains have similar overall end-throughput; wasted capacity in chains with HT/HTC. Observations: Throughput of chain is independent of number of SC, HTC or HT interactions. Number of dropped packets due to HT/HTC is a function of the hop number. Earlier the hop with HT/HTC, greater is the wasted capacity. Chains with only SC perform better than HT- or HTC-Chains
32 Problem Statement and Motivation Introduction High-level Motivation Problem Statement Self-interference in chains Probability of occurrence Performance analysis Generalization for n-hop chains
33 Cross-chain interaction characteristics MAC interactions: Independent links. Collisions possible at any node. Isolated chain interactions: Constrained. Node-locality. Cross-chain interactions: Independent interactions between chains Larger number of interactions. Constrained self-interference interactions. Characterizing cross-chain scenarios are harder. S D
34 Cross-chain interaction characteristics MAC interactions: Independent links. Collisions possible at any node. Isolated chain interactions: Constrained. Node-locality. Cross-chain interactions: Independent interactions between chains Larger number of interactions. Constrained self-interference interactions. Characterizing cross-chain scenarios are harder. S D
35 Cross-chain interaction characteristics MAC interactions: Independent links. Collisions possible at any node. Isolated chain interactions: Constrained. Node-locality. Cross-chain interactions: Independent interactions between chains Larger number of interactions. Constrained self-interference interactions. Characterizing cross-chain scenarios are harder. S D
36 Cross-chain interaction characteristics MAC interactions: Independent links. Collisions possible at any node. Isolated chain interactions: Constrained. Node-locality. Cross-chain interactions: Independent interactions between chains Larger number of interactions. Constrained self-interference interactions. Characterizing cross-chain scenarios are harder. D S D S
37 Cross-chain interaction characteristics MAC interactions: Independent links. Collisions possible at any node. Isolated chain interactions: Constrained. Node-locality. Cross-chain interactions: Independent interactions between chains Larger number of interactions. Constrained self-interference interactions. Characterizing cross-chain scenarios are harder. D S D S
38 Cross-chain interaction characteristics MAC interactions: Independent links. Collisions possible at any node. D Isolated chain interactions: Constrained. Node-locality. D Cross-chain interactions: Independent interactions between chains Larger number of interactions. Constrained self-interference interactions. Characterizing cross-chain scenarios are harder. S HT S
39 Cross-chain interaction characteristics MAC interactions: Independent links. Collisions possible at any node. D D Isolated chain interactions: Constrained. Node-locality. Cross-chain interactions: Independent interactions between chains Larger number of interactions. Constrained self-interference interactions. S SC S Characterizing cross-chain scenarios are harder.
40 Cross-chain interactions There are 2 chains interacting with each other; and 3 categories of chains. Hence, 6 scenarios: 2 SC-chains, 2 HT-Chains, 2 HTC-Chains, SC- and HTC-Chains, SC- and HT-Chains, HT- and HTC-Chains We analyze cross-chain interactions in above scenarios: Does the type of chain affect its susceptibility to cross-chain interference? Priliminary analysis of the effect of HT and HTC on performance of chains.
41 Cross-chain interactions There are 2 chains interacting with each other; and 3 categories of chains. Hence, 6 scenarios: 2 SC-chains, 2 HT-Chains, 2 HTC-Chains, SC- and HTC-Chains, SC- and HT-Chains, HT- and HTC-Chains We analyze cross-chain interactions in above scenarios: Does the type of chain affect its susceptibility to cross-chain interference? Priliminary analysis of the effect of HT and HTC on performance of chains.
42 Vulnerability of chains to HT/HTC interactions HT-Chains and HTC-chains are more vulnerable to cross-chain HT and HTCs. 55% for SC-Chains and 80% for HT-/HTC-chains Routing hint: SC-Chains are more robust in dynamic environment.
43 Vulnerability of links to HT/HTC interactions We calculate the conditional probability that a link has cross-chain HT/HTC interaction, given a certain self-chain interaction. Links that had HT/HTC in self-chain are more vulnerable to cross-chain HT/HTC interactions.
44 Problem Statement and Motivation Introduction High-level Motivation Problem Statement Self-interference in chains Probability of occurrence Performance analysis Generalization for n-hop chains
45 Conclusion We classified the types of isolated chains based on MAC interactions. Empirically analyzed the occurrence probability and performance of chains. Testbed and simulation Three type of chains are most common: SC-, HT- and HTC-Chains. All chains have similar end-throughput, but capacity efficiency drastically differs. Examined the impact of cross-chain interactions. Stronger categories in self-chain are more robust under cross-chains.
46 Future work Recently, our paper on in-depth analysis of cross-chain behavior has been accepted [3]. Measurement-based interference estimation. Interaction based routing.
47 Thank you. For further information, please contact: Saquib Razak: Vinay Kolar:
48 References M. Garetto, J. Shi, and E. W. Knightly, Modeling media access in embedded two-flow topologies of multi-hop wireless networks, in MobiCom 05, S. Razak, N. B. Abu-Ghazaleh, and V. Kolar, Modeling of two-flow interactions under SINR model in multi-hop wireless networks, in Proc. LCN, 2008, pp V. Kolar, S. Razak, N. Abu-Ghazaleh, P. Mahonen and K. A. Harras, Interference across multi-hop wireless chains., IEEE WiMob, 2009.
49 Backup slide: Related work Enumerates impact of MAC interactions Experimental validation of results Studies empirical probability of occurrence of each type of interaction based on routing rules and node density n-hop chain Two-chain interaction study SINR propagation model
50 Backup slide: Effect of HT and contention unfairness
51 Backup slide: Effect of HT and contention unfairness
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