Cross-Layer Network Planning and Performance Optimization Algorithms for WLANs

1. Cross-Layer Network Planning and Performance Optimization Algorithms for WLANs Yean-Fu Wen Advisor: Frank Yeong-Sung Lin 2007/4/9

2. Agenda Introduction Ch. 2 Ch. 3 Ch. 4 Ch. 5 Ch. 6 Ch. 7 Conclusion Agenda • Introduction • Wi-Fi Hotspots(Ch. 2) • System Throughput Maximization Subject to Delay and Time Fairness Constraints • Wireless Mesh Networks(Ch. 3 and Ch. 4) • Fair Throughput and End-to-end Delay with Capacity Assignment • Fair Inter-TAP Routing and Backhaul Assignment Algorithms • Ad Hoc Networks(Ch. 5) • A Path-based Minimum Power Broadcast Algorithm • Wireless Sensor Networks(Ch. 6 and Ch. 7) • Dynamic Radius, Duty Cycle Scheduling, Routing, Data Aggregation, and Multi-Sink (Cluster) • Conclusions & Future Work

3. Agenda Introduction Ch. 2 Ch. 3 Ch. 4 Ch. 5 Ch. 6 Ch. 7 Conclusion Background • Wireless networks are the key to improving • person-to-person communications, • person-to-machine communications, and • machine-to-machine communications. • The research scope of this dissertation covers • various network architectures, and • various protocol layers [Ref: B3G Planning]

4. Agenda Introduction Ch. 2 Ch. 3 Ch. 4 Ch. 5 Ch. 6 Ch. 7 Conclusion

5. Agenda Introduction Ch. 2 Ch. 3 Ch. 4 Ch. 5 Ch. 6 Ch. 7 Conclusion Motivation • Fairness • to ensure the allocated resources are sufficient for all MDs to achieve equivalent throughput, channel access time, or end-to-end delay • to distribute and balance the traffic load or related links • to solve the fairness issues due to spatial bias or energy constraintsin three networks with different structures • Multi-range • causes different levels of energy consumption • causes different bit-rate (capacity) • Multi-rate • causes performance anomalies

6. Agenda Introduction Ch. 2 Ch. 3 Ch. 4 Ch. 5 Ch. 6 Ch. 7 Conclusion Motivation • Multi-hop • causes throughput and end-to-end delay fairness issuers • causes inefficient energy usage in data-centric networks • Multicast • reduce the number of duplicate packets in order to gain a “multicast wireless advantage” and thereby reach multiple relay nodes • reduce the number of duplicate packets in data-centric WSNs • Multi-channel • whether to use multi-channel to reduce the number of collisions • Multi-sink • in WMNs, find a TAP trade-off in routing to a backhaul via a shorter path or routing to light-load links and backhaul • in WSNs, find a source sensor trade-off between the shortest relay node or the sink node and the in-network process to reduce energy consumption

7. Agenda Introduction Ch. 2 Ch. 3 Ch. 4 Ch. 5 Ch. 6 Ch. 7 Conclusion Objective • How to achieve a throughput and channel access time fairness. • How to fairly allocate resources to solve the spatial bias problem in single hop or multi-hop wireless networks. • How to fairly distribute the traffic load among the relay nodes to reduce end-to-end delay and among the sensors to increase the sensor network’s lifetime.

8. Agenda Introduction Ch. 2 Ch. 3 Ch. 4 Ch. 5 Ch. 6 Ch. 7 Conclusion Solution Approach • NS2 + MATLAB • Lagrangean Relaxation (LR) =0.5 =0.25 =0.125 … =1 =2

9. Agenda Introduction Ch. 2 Ch. 3 Ch. 4 Ch. 5 Ch. 6 Ch. 7 Conclusion Ts Ts Tf Tf F Slow MH F Slow MH t Throughput fairness vs. channel access time fairness System Throughput Maximization Subject to Delay and Time Fairness Constraints in WLANs • We discuss how to achieve a trade-off between throughput fairness and channel access timefairness in 802.11 WLANs. • Problem • multiple bit rates cause performance anomalies.

10. Objective: maximize system throughput. Subject to: packet size; initial contention window size; multiple back-to-back packets; maximum cycle time time fairness; To determine: the initial contention window size for each bit rate class the packet size for each bit rate class the number of multiple back-to-back packets of class-k in a block within one transmission cycle Agenda Introduction Ch. 2 Ch. 3 Ch. 4 Ch. 5 Ch. 6 Ch. 7 Conclusion System Throughput Maximization Subject to Delay and Time Fairness Constraints in WLANs T(N) DIFS SLOT SIFS data ACK t backoff time

11. Agenda Introduction Ch. 2 Ch. 3 Ch. 4 Ch. 5 Ch. 6 Ch. 7 Conclusion System Throughput Maximization Subject to Delay and Time Fairness Constraints in WLANs • Proposed algorithm • modified binary search (Unimodal curve interval based on fairness index constraints) • theorem: If the time value x is deducted from a class-k MH, and it does not change any other class-j MHs, then the fairness: • increases iff x < xk – xj. • remains the same iff x = xk – xj. • decreases iff x > xk – xj.

12. Agenda Introduction Ch. 2 Ch. 3 Ch. 4 Ch. 5 Ch. 6 Ch. 7 Conclusion System Throughput Maximization Subject to Delay and Time Fairness Constraints in WLANs • Experiment results • Although the problem has been shown to be NP-complete, our numerical results reveal a simple unimodal feature • The relation between three MAC layer parameters (i.e., the initial contention window, packet size, and multiple back-to-back packets) and fairness achieves access time near-fairness and maximizes the system throughput with a simultaneous delay bound. • 20%improvement in system throughputover the original MAC protocol.

13. Agenda Introduction Ch. 2 Ch. 3 Ch. 4 Ch. 5 Ch. 6 Ch. 7 Conclusion Fair Throughput and End-to-end Delay with Capacity Assignment for WMNs • We discuss the scenario where many clients use the same backhaul to access the Internet. Consequently, throughput depends on each client’s distance from the gateway node.

14. Objective: to minimize the maximal end-to-end delay of the WMN. Subject to: capacity link delay To determine: the capacity that should beallocated to the selected links of a TAP node. the end-to-end delay on the selected path of a TAP node. the maximum end-to-end delay of the WMN. Agenda Introduction Ch. 2 Ch. 3 Ch. 4 Ch. 5 Ch. 6 Ch. 7 Conclusion Fair Throughput and End-to-end Delay with Capacity Assignment for WMNs

15. Agenda Introduction Ch. 2 Ch. 3 Ch. 4 Ch. 5 Ch. 6 Ch. 7 Conclusion Fair Throughput and End-to-end Delay with Capacity Assignment for WMNs • Proposed algorithm • monotonic increases inf(u,v) • the delay time approaching ∞, when f(u,v) C(u,v) • the delay function is a convex function

16. Agenda Introduction Ch. 2 Ch. 3 Ch. 4 Ch. 5 Ch. 6 Ch. 7 Conclusion Fair Throughput and End-to-end Delay with Capacity Assignment for WMNs • Experiment results

17. Agenda Introduction Ch. 2 Ch. 3 Ch. 4 Ch. 5 Ch. 6 Ch. 7 Conclusion Fair Inter-TAP Routing and Backhaul Assignment Algorithms for WMNs • How to cluster backbone mesh networks efficiently so that the load-balanced routing is concentrated on given and “to-be-determined” backhauls. • Problem backhaul TAP link

18. Objective: to minimize the sum of the aggregated flows of selected links Subject to: budget backhaul assignment backhaul selection routing link capacity load balancing To determine: which TAP should be selected to be a backhaul which backhaul should be selected for each TAP to transmit its data The routing path from a TAP to a backhaul. whether a link should be selected for the routing path. aggregated flow on top-level selected link. aggregated flow on each backhaul. a top-level load-balanced forest. Agenda Introduction Ch. 2 Ch. 3 Ch. 4 Ch. 5 Ch. 6 Ch. 7 Conclusion Fair Inter-TAP Routing and Backhaul Assignment Algorithms for WMNs

19. Agenda Introduction Ch. 2 Ch. 3 Ch. 4 Ch. 5 Ch. 6 Ch. 7 Conclusion Fair Inter-TAP Routing and Backhaul Assignment Algorithms for WMNs • Proposed algorithm • weighted backhaul assignment (WBA) algorithm • greedy load-balanced routing (GLBR) algorithm

20. Agenda Introduction Ch. 2 Ch. 3 Ch. 4 Ch. 5 Ch. 6 Ch. 7 Conclusion Fair Inter-TAP Routing and Backhaul Assignment Algorithms for WMNs • Experiment results • the load-balanced routing and backhaul assignment experiment results demonstrate that the GLBR plus WBA algorithms with the LR-based approach achieve a gap of 30% and outperform other algorithms by at least 10%

21. Agenda Introduction Ch. 2 Ch. 3 Ch. 4 Ch. 5 Ch. 6 Ch. 7 Conclusion A Path-based Minimum Power Broadcast Algorithm for Ad-hoc (Sensor) Networks • We discuss how to construct a multicast tree that minimizes power consumption with “multicast wireless advantage”. • Problem

22. Objective: to minimize the total broadcast power consumption Subject to: routing tree radius To determine: routing path from each source to the destination, denoted as an OD-pair. whether a link should be on the multicast tree. a multicast tree. transmission radius for each MD. Agenda Introduction Ch. 2 Ch. 3 Ch. 4 Ch. 5 Ch. 6 Ch. 7 Conclusion A Path-based Minimum Power Broadcast Algorithm for Ad-hoc (Sensor) Networks

23. Agenda Introduction Ch. 2 Ch. 3 Ch. 4 Ch. 5 Ch. 6 Ch. 7 Conclusion A Path-based Minimum Power Broadcast Algorithm for Ad-hoc (Sensor) Networks • Proposed algorithm • a path-based minimum power broadcast algorithm • Experiment results

24. Agenda Introduction Ch. 2 Ch. 3 Ch. 4 Ch. 5 Ch. 6 Ch. 7 Conclusion Cross-Layer Duty Cycle Scheduling with Data Aggregation Routing for WSNs • We discuss how to increase the battery lifetime and energy consumption efficiency of a network from the Physical layer to the Application layer in term of the following issues: • data aggregation • tree structure Routing • duty-cycle scheduling • node-to-node communication time • the number of retransmissions • dynamically adjusted radius Application layer Network layer MAC layer Physical layer

25. Objective: minimize the total energy consumed by a target transmission Subject to: restrictions on the structure of trees in the form of three link constraints duty cycle scheduling. the time for node-to-node communication dynamic radius To determine: a routing path from the source node to the sink node; the time at which aggregation of sub-tree data will be completed; the earliest time at which a node wakes up and begins aggregating data; and the time needed for a successful node-to-node transmission. the power range of each node; Agenda Introduction Ch. 2 Ch. 3 Ch. 4 Ch. 5 Ch. 6 Ch. 7 Conclusion Cross-Layer Duty Cycle Scheduling with Data Aggregation Routing for WSNs

26. Agenda Introduction Ch. 2 Ch. 3 Ch. 4 Ch. 5 Ch. 6 Ch. 7 Conclusion Cross-Layer Duty Cycle Scheduling with Data Aggregation Routing for WSNs • Proposed algorithm 0 0 κ [3, 5+0] D 1 0 2 0 2 [3, 4+1] 1 0 [0, 3+2] 4 [0, 3+1] 3 1 3 2 [0, 0+1] 5 [0, 0+3] [0, 0+2] 6 7 S1 3 S2 [0, 0+3] S3 8 0 ∞ ∞ 0 S4 0 ∞ 0 ∞ O

27. Agenda Introduction Ch. 2 Ch. 3 Ch. 4 Ch. 5 Ch. 6 Ch. 7 Conclusion Cross-Layer Duty Cycle Scheduling with Data Aggregation Routing for WSNs • Experiment results

28. Agenda Introduction Ch. 2 Ch. 3 Ch. 4 Ch. 5 Ch. 6 Ch. 7 Conclusion Energy-Efficient Data Aggregation Routing and Duty-Cycle Scheduling for Multi-Sink WSNs • Problem • We discuss how to increase the lifetime in the networks already discussed with a multiple sink structure (outgoing information gateways) and a cluster structure (source node’s message must forward to cluster-head first)

29. Objective: minimize the total energy consumed by a target transmission to one of the sink nodes. Subject to: sink selection ….(see the previous problem) To determine: The sink node that a source node will route to; ….(see the previous problem) Agenda Introduction Ch. 2 Ch. 3 Ch. 4 Ch. 5 Ch. 6 Ch. 7 Conclusion Energy-Efficient Data Aggregation Routing and Duty-Cycle Scheduling for Multi-Sink WSNs

30. Agenda Introduction Ch. 2 Ch. 3 Ch. 4 Ch. 5 Ch. 6 Ch. 7 Conclusion Energy-Efficient Data Aggregation Routing and Duty-Cycle Scheduling for Multi-Sink WSNs • Experiment results

31. Agenda Introduction Ch. 2 Ch. 3 Ch. 4 Ch. 5 Ch. 6 Ch. 7 Conclusion Conclusions & Future Work • For hot-spot networks • system throughput maximization subject to delay and time fairness constraints • For mesh networks • fair inter-TAP routing • fair inter-TAP routing & backhaul assignment algorithms • fair throughput and end-to-end delay routing • For ad hoc networks • message broadcasting • dynamic adjustment of the transmission radius

32. Agenda Introduction Ch. 2 Ch. 3 Ch. 4 Ch. 5 Ch. 6 Ch. 7 Conclusion Conclusions & Future Work • For wireless sensor networks • data aggregation • routing • duty cycle scheduling • node-to-node communication time • retransmissions • dynamic radius • multi-sink • cluster

33. Agenda Introduction Ch. 2 Ch. 3 Ch. 4 Ch. 5 Ch. 6 Ch. 7 Conclusion Conclusions & Future Work • Hot-spot & Mesh Networks • channel assignment • Ad hoc & Sensor Networks • the proposed maximization of mobile network lifetime is extended to include balancing power consumption among all nodes within a multiple session construction. • IEEE 802.16 BWA Networks • optimization of the related parameters and placing controls on scheduling and admissions to minimize delay and maximize performance under QoS considerations; • minimization of end-to-end delay with controls on scheduling in IEEE 802.16 mesh mode.

34. Agenda Introduction Ch. 2 Ch. 3 Ch. 4 Ch. 5 Ch. 6 Ch. 7 Conclusion THANK YOU FOR YOUR ATTENTION

35. Agenda Introduction Ch. 2 Ch. 3 Ch. 4 Ch. 5 Ch. 6 Ch. 7 Conclusion Appendix A: To increase a sensor network’s lifetime Origin Destination

36. Agenda Introduction Ch. 2 Ch. 3 Ch. 4 Ch. 5 Ch. 6 Ch. 7 Conclusion Energy-Efficient Data Aggregation Routing and Duty-Cycle Scheduling in Cluster-based WSNs [3, 5+0] κ 1 2 2 [3, 4+1] 1 [0, 3+2] [0, 3+1] [nu, max{mv} + luv] 4 3 1 3 6 [nu, luv] 2 S2 3 [n5,l54] [0, 2] 5 7 S1 S3 [nu, mu] of each node denote the earliest wake up time and the aggregated time successful transmission, respectively. [0, 3] 8 S4

37. Agenda Introduction Ch. 2 Ch. 3 Ch. 4 Ch. 5 Ch. 6 Ch. 7 Conclusion Energy-Efficient Data Aggregation Routing and Duty-Cycle Scheduling in Cluster-based WSNs • Problem • we discuss how to enlarge the lifetime in the previous issues with a multiple sink structure (outgoing information gateways) and a cluster structure (source node’s message must forward to cluster-head first)