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

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

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

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

5. 5 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 constraints in three networks with different structures Multi-range  causes different levels of energy consumption  causes different bit-rate (capacity) Multi-rate  causes performance anomalies Agenda Ch. 2Introduction Conclusion Ch. 3 Ch. 4 Ch. 5 Ch. 6 Ch. 7

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

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

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

9. 9 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 time fairness in 802.11 WLANs. Problem  multiple bit rates cause performance anomalies. t FSlow MH TsTs TsTs TfTf TfTf F Throughput fairness vs. channel access time fairness Agenda Ch. 2Introduction Conclusion Ch. 3 Ch. 4 Ch. 5 Ch. 6 Ch. 7

10. 10 System Throughput Maximization Subject to Delay and Time Fairness Constraints in WLANs 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 t data ACK SIFS T(N) DIFS backoff time SLOT Agenda Ch. 2Introduction Conclusion Ch. 3 Ch. 4 Ch. 5 Ch. 6 Ch. 7

11. 11 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 < x k – x j. remains the same iff  x = x k – x j. decreases iff  x > x k – x j. Agenda Ch. 2Introduction Conclusion Ch. 3 Ch. 4 Ch. 5 Ch. 6 Ch. 7

12. 12 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 throughput over the original MAC protocol. Agenda Ch. 2Introduction Conclusion Ch. 3 Ch. 4 Ch. 5 Ch. 6 Ch. 7

13. 13 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. Agenda Ch. 2Introduction Conclusion Ch. 3 Ch. 4 Ch. 5 Ch. 6 Ch. 7

14. 14 Fair Throughput and End-to-end Delay with Capacity Assignment for WMNs Objective:  to minimize the maximal end-to-end delay of the WMN. Subject to:  capacity  link  delay To determine:  the capacity that should be allocated 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 Ch. 2Introduction Conclusion Ch. 3 Ch. 4 Ch. 5 Ch. 6 Ch. 7

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

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

17. 17 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 Agenda Ch. 2Introduction Conclusion Ch. 3 Ch. 4 Ch. 5 Ch. 6 Ch. 7 backhaul TAP link

18. 18 Fair Inter-TAP Routing and Backhaul Assignment Algorithms for WMNs 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 Ch. 2Introduction Conclusion Ch. 3 Ch. 4 Ch. 5 Ch. 6 Ch. 7

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

20. 20 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% Agenda Ch. 2Introduction Conclusion Ch. 3 Ch. 4 Ch. 5 Ch. 6 Ch. 7

21. 21 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 Agenda Ch. 2Introduction Conclusion Ch. 3 Ch. 4 Ch. 5 Ch. 6 Ch. 7

22. 22 A Path-based Minimum Power Broadcast Algorithm for Ad-hoc (Sensor) Networks 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 Ch. 2Introduction Conclusion Ch. 3 Ch. 4 Ch. 5 Ch. 6 Ch. 7

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

24. 24 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 Agenda Ch. 2Introduction Conclusion Ch. 3 Ch. 4 Ch. 5 Ch. 6 Ch. 7 Physical layer Application layer MAC layer Network layer

25. 25 Cross-Layer Duty Cycle Scheduling with Data Aggregation Routing for WSNs 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 Ch. 2Introduction Conclusion Ch. 3 Ch. 4 Ch. 5 Ch. 6 Ch. 7

26. 26 Cross-Layer Duty Cycle Scheduling with Data Aggregation Routing for WSNs S4 1 3 1 1 2 2 3 D κ S1 S2 S3 [0, 0+1] [0, 0+3] [0, 3+1] [3, 4+1] [3, 5+0] [0, 3+2] [0, 0+2] [0, 0+3] 2 34 7 8 6 5 O Proposed algorithm Agenda Ch. 2Introduction Conclusion Ch. 3 Ch. 4 Ch. 5 Ch. 6 Ch. 7 0 0 00 0 0 0 0 ∞∞ ∞ 0 ∞

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

28. 28 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) Agenda Ch. 2Introduction Conclusion Ch. 3 Ch. 4 Ch. 5 Ch. 6 Ch. 7

29. 29 Energy-Efficient Data Aggregation Routing and Duty-Cycle Scheduling for Multi-Sink WSNs 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 Ch. 2Introduction Conclusion Ch. 3 Ch. 4 Ch. 5 Ch. 6 Ch. 7

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

31. 31 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 Agenda Ch. 2Introduction Conclusion Ch. 3 Ch. 4 Ch. 5 Ch. 6 Ch. 7

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

33. 33 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. Agenda Ch. 2Introduction Conclusion Ch. 3 Ch. 4 Ch. 5 Ch. 6 Ch. 7

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

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

36. 36 Energy-Efficient Data Aggregation Routing and Duty-Cycle Scheduling in Cluster-based WSNs κ S1 S2 S3 S4 1 3 1 1 2 2 3 [n u, l uv ] [n 5,l 54 ] [0, 3+1] [3, 4+1] [3, 5+0] [0, 3+2] [0, 2] [0, 3] 2 34 7 8 6 5 [n u, m u ] of each node denote the earliest wake up time and the aggregated time successful transmission, respectively. [n u, max{m v } + l uv ] Agenda Ch. 2Introduction Conclusion Ch. 3 Ch. 4 Ch. 5 Ch. 6 Ch. 7

37. 37 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) Agenda Ch. 2Introduction Conclusion Ch. 3 Ch. 4 Ch. 5 Ch. 6 Ch. 7