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Title: Cross-Layer Network Planning and Performance Optimization Algorithms for Wireless Networks


1
Cross-Layer Network Planning and Performance
Optimization Algorithms for Wireless Networks
Dissertation Proposal
  • Yean-Fu Wen
  • Advisor Frank Yeong-Sung Lin
  • Department of Information Management,
  • National Taiwan University
  • 2007/4/24

2
Agenda
Agenda
Ch.2
Introduction
Conclusion
Ch. 3
Ch. 4
Ch.3
Ch.5
Ch.6,7
  • Introduction
  • Wi-Fi Hotspots (Ch. 2)
  • System Throughput Maximization Subject to Time
    Fairness Constraints
  • Wireless Mesh Networks (Ch. 3 and Ch. 4)
  • Fair Throughput and End-to-End Delay with
    Resource Allocation
  • Fair Inter-TAP Routing and Backhaul Assignment
  • Fair End-to-End Delay and Load-Balanced Routing
  • Ad Hoc Networks (Ch. 5)
  • A Minimum Power Broadcast (or Multicast)
  • Wireless Sensor Networks (Ch. 6 and Ch. 7)
  • Dynamic Radius, Duty Cycle Scheduling, Routing,
    Data Aggregation, and Multi-Sink (Clusters)
  • Conclusion Future Work

3
Background
Agenda
Ch.2
Introduction
Conclusion
Ch. 3
Ch. 4
Ch.3
Ch.5
Ch.6,7
  • 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
Ch.2
Introduction
Conclusion
Ch. 3
Ch. 4
Ch.3
Ch.5
Ch.6,7
Fairness model
Wire Network (Fiber, T3etc.)
AP5
AP1
Network
MAC layer
AP2
AP6
PHY layer
Application
AP7
Network
AP3
AP8
Mesh Networks
MAC layer
AP9
AP4
PHY layer
MDE
MDA
MDD
MAC layer
MDC
MDB
PHY layer
BS-oriented
Ad Hoc, Sensor or Hybrid Networks
5
Motivation
Agenda
Ch.2
Introduction
Conclusion
Ch. 3
Ch. 4
Ch.3
Ch.5
Ch.6,7
  • Fairness
  • to ensure the allocated resources are sufficient
    for all MDs to achieve equivalent throughput or
    channel access time, and minimize end-to-end
    delay
  • to distribute and balance the traffic load or on
    related links
  • to solve 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-rates (capacity)
  • Multi-rate
  • causes performance anomalies Heusse03

6
Motivation
Agenda
Ch.2
Introduction
Conclusion
Ch. 3
Ch. 4
Ch.3
Ch.5
Ch.6,7
  • Multi-hop
  • causes throughput and end-to-end delay fairness
    issues in WMNs
  • causes inefficient energy usage in WSNs
  • 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 vs. Multi-access
  • 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 a 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
Objective
Agenda
Ch.2
Introduction
Conclusion
Ch. 3
Ch. 4
Ch.3
Ch.5
Ch.6,7
  • How to achieve 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
    relay nodes to reduce end-to-end delay and among
    sensor nodes to increase the sensor networks
    lifetime.

8
Research Approaches
Agenda
Ch.2
Introduction
Conclusion
Ch. 3
Ch. 4
Ch.3
Ch.5
Ch.6,7
  • Discrete event simulation Harrell92
  • NS2 Fall99
  • Analytic heuristic modeling Harrell92
  • MATLAB
  • Lagrangean Relaxation (LR) Ahuja93 Fisher81

?0.5
?0.25
?0.125
?1
?2
9
Agenda
Agenda
Ch.2
Introduction
Conclusion
Ch. 3
Ch. 4
Ch.3
Ch.5
Ch.6,7
  • Introduction
  • Wi-Fi Hotspots (Ch. 2)
  • System Throughput Maximization Subject to Time
    Fairness Constraints
  • Wireless Mesh Networks (Ch. 3 and Ch. 4)
  • Fair Throughput and End-to-End Delay with
    Resource Allocation
  • Fair Inter-TAP Routing and Backhaul Assignment
  • Fair End-to-End Delay and Load-Balanced Routing
  • Ad Hoc Networks (Ch. 5)
  • A Minimum Power Broadcast Algorithm
  • Wireless Sensor Networks (Ch. 6 and Ch. 7)
  • Dynamic Radius, Duty Cycle Scheduling, Routing,
    Data Aggregation, and Multi-Sink (Clusters)
  • Conclusion Future Work

Publication List 1 Y.F. Wen, Frank Y.S. Lin,
and K.W. Lai, "System Throughput Maximization
Subject to Delay and Time Fairness Constraints in
802.11 WLANs," in Proc. of IEEE ICPADS, Fukuoka
Institute of Technology (FIT), Fukuoka, Japan,
Jul. 2005. (EI) 2 Yu-Liang Kuo, Kun-Wai. Lai,
Frank Yeong-Sung Lin, Yean-Fu Wen, Eric
Hsiao-kuang Wu, and Gen-Huey Chen, "Multi-Rate
Throughput Optimization with Fairness Constraints
in Wireless Local Area Networks," IEEE
Transactions on Vehicular Technology, Dec. 2006
(major revised).
10
System Throughput Maximization Subject to Time
Fairness Constraints in WLANs
Agenda
Ch.2
Introduction
Conclusion
Ch. 3
Ch. 4
Ch.3
Ch.5
Ch.6,7
  • 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
    Heusse03.

Given
11
System Throughput Maximization Subject to Time
Fairness Constraints in WLANs
Agenda
Ch.2
Introduction
Conclusion
Ch. 3
Ch. 4
Ch.3
Ch.5
Ch.6,7
  • Objective
  • to maximize system throughput.
  • Subject to
  • initial contention window size
  • packet size
  • multiple back-to-back packets
  • maximum cycle time (delay)
  • 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 Bk in a block within one transmission
    cycle

a great deal of computing time non-convex
problem
T(N)
SIFS
DIFS
SLOT
data
ACK
t
backoff time
12
System Throughput Maximization Subject to Time
Fairness Constraints in WLANs
Agenda
Ch.2
Introduction
Conclusion
Ch. 3
Ch. 4
Ch.3
Ch.5
Ch.6,7
  • Proposed algorithm
  • Modified binary search (Unimodal curve interval
    based on fairness index constraints Jain84 )
  • 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 lt xk xj.
  • remains the same iff ?x xk xj.
  • decreases iff ?x gt xk xj.

13
System Throughput Maximization Subject to Time
Fairness Constraints in WLANs
Agenda
Ch.2
Introduction
Conclusion
Ch. 3
Ch. 4
Ch.3
Ch.5
Ch.6,7
  • Experiment results
  • although the problem has been shown to be
    NP-complete Kuo05, 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 Wen06c.
  • 21 improvement in system throughput over the
    original MAC protocol.

14
System Throughput Maximization Subject to Time
Fairness Constraints in WLANs
Agenda
Ch.2
Introduction
Conclusion
Ch. 3
Ch. 4
Ch.3
Ch.5
Ch.6,7
  • Related work
  • performance anomaly Heusse03 (Grenoble)
  • 802.11 system throughput analysis Bianchi00
  • performance analysis under a finite load and
    improvements for multirate 802.11b Cantieni05
    (Brunel)
  • to discuss the issues of cycle time (delay)
    Wang03, Wu02, Chatzimisios03, and
    Raptis05
  • Jains Fairness Index (FI) model Jain84
  • integer programming Lai04 Kuo05 (NTU)
  • an uplink solution with packet size or burst
    packets Tan04a Tan04b (MIT)
  • simulate a high quality signal with multiple
    back-to-back packets Sadeghi02 (Rice)
    Sheu02 (NCU)

15
Agenda
Agenda
Ch.2
Introduction
Conclusion
Ch. 3
Ch. 4
Ch.3
Ch.5
Ch.6,7
  • Introduction
  • Wi-Fi Hotspots (Ch. 2)
  • System Throughput Maximization Subject to Time
    Fairness Constraints
  • Wireless Mesh Networks (Ch. 3 and Ch. 4)
  • Fair Throughput and End-to-End Delay with
    Resource Allocation
  • Fair Inter-TAP Routing and Backhaul Assignment
  • Fair End-to-End Delay and Load-Balanced Routing
  • Ad Hoc Networks (Ch. 5)
  • A Minimum Power Broadcast Algorithm
  • Wireless Sensor Networks (Ch. 6 and Ch. 7)
  • Dynamic Radius, Duty Cycle Scheduling, Routing,
    Data Aggregation, and Multi-Sink (Clusters)
  • Conclusion Future Work

Publication List 1 Yean-Fu Wen and Frank
Yeong-Sung Lin, "Fair Bandwidth Allocation and
End-to-End Delay Routing Algorithms in Wireless
Mesh Networks," Communications, IEICE
Transactions on, E90-B(5), pp. xxxx, May 2007.
(SCI, EI)
16
Fair Throughput and End-to-End Delay with
Resource Allocation for WMNs
Agenda
Ch.2
Introduction
Conclusion
Ch. 3
Ch. 4
Ch.3
Ch.5
Ch.6,7
  • We discuss the scenario where many clients use
    the same backhaul to access the Internet.
    Consequently, throughput depends on each clients
    distance from the gateway node. Karrer04
    Gambiroza04

Given
17
Fair Throughput and End-to-End Delay with
Resource Allocation for WMNs
Agenda
Ch.2
Introduction
Conclusion
Ch. 3
Ch. 4
Ch.3
Ch.5
Ch.6,7
  • To determine
  • the resources cs(u,v) that should be allocated to
    the selected links of a TAP node s.
  • the end-to-end delay on the selected path of a
    TAP node.
  • the maximum end-to-end delay d of the WMN.
  • Objective
  • to minimize the maximal end-to-end delay of the
    WMN.
  • Subject to
  • capacity
  • delay

18
Fair Throughput and End-to-End Delay with
Resource Allocation for WMNs
Agenda
Ch.2
Introduction
Conclusion
Ch. 3
Ch. 4
Ch.3
Ch.5
Ch.6,7
  • Lemma 3-1 fair end-to-end delay is achievable
  • monotonic increases in f(u,v)
  • the delay time approaches 8, when f(u,v) ? C(u,v)
  • the delay function is a convex function

19
Fair Throughput and End-to-End Delay with
Resource Allocation for WMNs
Agenda
Ch.2
Introduction
Conclusion
Ch. 3
Ch. 4
Ch.3
Ch.5
Ch.6,7
  • Experiment results

20
Fair Throughput and End-to-End Delay with
Resource Allocation for WMNs
Agenda
Ch.2
Introduction
Conclusion
Ch. 3
Ch. 4
Ch.3
Ch.5
Ch.6,7
  • Related work
  • wireless mesh networks a survey Akyildiz05
    (GIT)
  • describe 10 challenging issues Karrer04 (Rice
    university)
  • spatial bias fairness temporal fairness
    Gambiroza04 (Rice university)
  • average delay, end-to-end delay routing and
    capacity assignment for virtual circuit networks
    Cheng95 Yen01 (NTU)
  • to maximize spatial reuse of a spectrum by
    maintaining basic fairness among contending flows
    Li05 (Toronto)
  • hierarchically aggregated fair queuing (HAFQ) for
    per-flow fair bandwidth allocation Maki06
    (Osaka)

21
Agenda
Agenda
Ch.2
Introduction
Conclusion
Ch. 3
Ch. 4
Ch.3
Ch.5
Ch.6,7
  • Introduction
  • Wi-Fi Hotspots (Ch. 2)
  • System Throughput Maximization Subject to Time
    Fairness Constraints
  • Wireless Mesh Networks (Ch. 3 and Ch. 4)
  • Fair Throughput and End-to-End Delay with
    Resource Allocation
  • Fair Inter-TAP Routing and Backhaul Assignment
  • Fair End-to-End Delay and Load-Balanced Routing
  • Ad Hoc Networks (Ch. 5)
  • A Minimum Power Broadcast Algorithm
  • Wireless Sensor Networks (Ch. 6 and Ch. 7)
  • Dynamic Radius, Duty Cycle Scheduling, Routing,
    Data Aggregation, and Multi-Sink (Clusters)
  • Conclusion Future Work

Publication List 1 Frank Yeong-Sung Lin and
Yean-Fu Wen, "Fair Inter-TAP Routing and Backhaul
Assignment in Wireless Mesh Networks," was
submitted to Journal of WCMC, Oct. 2006. (under
review) 2 Y.F. Wen and Frank Y.S. Lin, "The Top
Load Balancing Forest Routing in Mesh Networks,"
in Proc. of IEEE CCNC, Las Vegas, NV, Jan. 2006.
(EI)
22
Fair Inter-TAP Routing and Backhaul Assignment
Algorithms for WMNs
Agenda
Ch.2
Introduction
Conclusion
Ch. 3
Ch. 4
Ch.3
Ch.5
Ch.6,7
  • How to cluster backbone mesh networks efficiently
    so that the load-balanced routing is concentrated
    on given and to-be-determined backhauls.
  • Problem

Given
backhaul
TAP
link
23
Fair Inter-TAP Routing and Backhaul Assignment
Algorithms for WMNs
Agenda
Ch.2
Introduction
Conclusion
Ch. 3
Ch. 4
Ch.3
Ch.5
Ch.6,7
  • Objective
  • to minimize the sum of the aggregated flows of
    selected links
  • Subject to
  • budget ?
  • backhaul assignment B
  • backhaul selection
  • routing Psb
  • link L, ?p(u,v), Hs
  • capacity Cuv
  • load balancing ?, ?
  • To determine
  • which TAP should be selected to be a backhaul ?b
  • which backhaul should be selected for each TAP to
    transmit its data zsb.
  • The routing path from a TAP to a backhaul xp.
  • whether a link should be selected for the routing
    path y(u,v).
  • aggregated flow on top-level selected link
    f(u,b).
  • aggregated flow on each backhaul ?b.
  • a top-level load-balanced forest.

24
Fair Inter-TAP Routing and Backhaul Assignment
Algorithms for WMNs
Agenda
Ch.2
Introduction
Conclusion
Ch. 3
Ch. 4
Ch.3
Ch.5
Ch.6,7
(9)
(1)
(2)
(10)
BAMCP NP-complete
(3)
(11)
(4)
(12)
(5)
(14)
(6)
(7)
(13)
(8)
(15)
25
Fair Inter-TAP Routing and Backhaul Assignment
Algorithms for WMNs
Agenda
Ch.2
Introduction
Conclusion
Ch. 3
Ch. 4
Ch.3
Ch.5
Ch.6,7
  • Proposed algorithm
  • weighted backhaul assignment (WBA) algorithm
  • greedy load-balanced routing (GLBR) algorithm

26
Fair Inter-TAP Routing and Backhaul Assignment
Algorithms for WMNs
Agenda
Ch.2
Introduction
Conclusion
Ch. 3
Ch. 4
Ch.3
Ch.5
Ch.6,7
  • 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

27
Fair Inter-TAP Routing and Backhaul Assignment
Algorithms for WMNs
Agenda
Ch.2
Introduction
Conclusion
Ch. 3
Ch. 4
Ch.3
Ch.5
Ch.6,7
  • Related work
  • traditional AP assignment focuses on coverage of
    the service area Tutschku99, Unbehaun03,
    Mathar00, and Fortune95
  • cluster-head assignment methods, such as max-min
    d-hop cluster Amis00, LCA Baker81
  • multi-constrained path problem (MCP) is an
    NP-complete problem Wang96 (London)
  • a single sink to balance the traffic load on the
    incoming link of an egress node
  • a general tree structure Hsiao01 (Harvard)
  • sensor networks Dai03 (Colorado)

28
Agenda
Agenda
Ch.2
Introduction
Conclusion
Ch. 3
Ch. 4
Ch.3
Ch.5
Ch.6,7
  • Introduction
  • Wi-Fi Hotspots (Ch. 2)
  • System Throughput Maximization Subject to Time
    Fairness Constraints
  • Wireless Mesh Networks (Ch. 3 and Ch. 4)
  • Fair Throughput and End-to-End Delay with
    Resource Allocation
  • Fair Inter-TAP Routing and Backhaul Assignment
  • Fair End-to-End Delay and Load-Balanced Routing
  • Ad Hoc Networks (Ch. 5)
  • A Minimum Power Broadcast Algorithm
  • Wireless Sensor Networks (Ch. 6 and Ch. 7)
  • Dynamic Radius, Duty Cycle Scheduling, Routing,
    Data Aggregation, and Multi-Sink (Clusters)
  • Conclusion Future Work

Publication List 1 Frank Yeong-Sung Lin and
Yean-Fu Wen, "Fair Inter-TAP Routing and Backhaul
Assignment in Wireless Mesh Networks," was
submitted to Journal of WCMC, Oct. 2006. (under
review) 2 Yean-Fu Wen and Frank Yeong-Sung Lin,
"Fair Bandwidth Allocation and End-to-End Delay
Routing Algorithms in Wireless Mesh Networks,"
Communications, IEICE Transactions on, E90-B(5),
pp. xxxx, May 2007. (SCI, EI)
29
Fair End-to-End Delay and Load-Balanced Routing
Agenda
Ch.2
Introduction
Conclusion
Ch. 3
Ch. 4
Ch.3
Ch.5
Ch.6,7
  • How to cluster backbone mesh networks efficiently
    so that the load-balanced routing and fair
    end-to-end delay are concentrated on given
    backhauls.
  • Objective
  • to minimize the maximum end-to-end delay
  • Subject to
  • routing Ps
  • link (tree or mesh) L, ?p(u,v)
  • resource allocation C(u,v)
  • delay (including end-to-end delay)
  • To determine
  • The routing path from a TAP to a backhaul xp.
  • whether a link should be selected for the routing
    path y(u,v).
  • the resource that should be allocated to the
    selected links of a TAP node. cs(u,v)
  • the maximum end-to-end delay d of the WMN.

30
Fair End-to-End Delay and Load-Balanced Routing
Agenda
Ch.2
Introduction
Conclusion
Ch. 3
Ch. 4
Ch.3
Ch.5
Ch.6,7
objective function
Tree structure
Mesh structure
subject to
Steiner tree Knapsack Problem NP-complete
31
Fair End-to-End Delay and Load-Balanced Routing
Agenda
Ch.2
Introduction
Conclusion
Ch. 3
Ch. 4
Ch.3
Ch.5
Ch.6,7
  • Lagrangean Relaxation (LR3-2)
  • Lagrangean dual problem (D3-2)

Tree structure
(LR3-2)
subject to (3-2.1), (3-2.3), (3-2.4), (3-2.5),
and (3-2.7).
objective function
(D3-2)
subject to
32
Fair End-to-End Delay and Load-Balanced Routing
Agenda
Ch.2
Introduction
Conclusion
Ch. 3
Ch. 4
Ch.3
Ch.5
Ch.6,7
  • Lagrangean Relaxation (LR3-3)
  • Lagrangean dual problem (D3-3)

Mesh structure
(LR3-3)
subject to (3-3.1), (3-3.3), (3-3.4), (3-3.5),
and (3-3.7).
objective function
(D3-3)
subject to
33
Fair End-to-End Delay and Load-Balanced Routing
Agenda
Ch.2
Introduction
Conclusion
Ch. 3
Ch. 4
Ch.3
Ch.5
Ch.6,7
  • Sub-problem (SUB3-2.1) is related to decision
    variable xp.

objective function
(SUB3-2.1)
subject to (3-2.1)
Each sub-problem of OD-pair, xp, is a shortest
path problem solved by considering the link
weight
(3-2.1)
a top-level load-balanced problem
34
Fair End-to-End Delay and Load-Balanced Routing
Agenda
Ch.2
Introduction
Conclusion
Ch. 3
Ch. 4
Ch.3
Ch.5
Ch.6,7
  • Sub-problem , related to
    decision variable y(u,v), ys(u,v), and cs(u,v).

(SUB3-2.2)
(SUB3-3.2)
Mesh structure
Tree structure
objective function
(SUB3-2.2)
(SUB3-3.2)
subject to (3-2.3), (3-2.4), (3-2.5), and (3-2.7).
subject to (3-3.3), (3-3.4), (3-3.5), and (3-3.7).
we consider the delay function is M/M/1
35
Fair End-to-End Delay and Load-Balanced Routing
Agenda
Ch.2
Introduction
Conclusion
Ch. 3
Ch. 4
Ch.3
Ch.5
Ch.6,7
  • Sub-problem , related to
    decision variable d.

(SUB3-3.3)
(SUB3-2.3)
objective function
Mesh structure
Tree structure
(SUB3-2.3)
(SUB3-3.3)
subject to the lower and upper bound of d.
Lemma 3-3 how to determine the upper bound and
lower bound of d?
As described in LR approach, the getting primal
feasible solution gets the upper bound of this
problem.
The lower bound is calculated by the fully
distributed the traffic load, aggregated from the
higher level around the level of each node. (BFS)
36
Fair End-to-End Delay and Load-Balanced Routing
Agenda
Ch.2
Introduction
Conclusion
Ch. 3
Ch. 4
Ch.3
Ch.5
Ch.6,7
  • Getting primal feasible solution
  • the incoming link costs of the backhaul are set
    to delay function ?(u,v) ?s?S D(u,v)(C(u,v),?s)
    (a ), where (u,v) ? L.
  • greedy load-balanced routing (GLBR) Wen06a
  • resource allocation scheme (EDTB) Wen07

37
Agenda
Agenda
Ch.2
Introduction
Conclusion
Ch. 3
Ch. 4
Ch.3
Ch.5
Ch.6,7
  • Introduction
  • Wi-Fi Hotspots (Ch. 2)
  • System Throughput Maximization Subject to Time
    Fairness Constraints
  • Wireless Mesh Networks (Ch. 3 and Ch. 4)
  • Fair Throughput and End-to-End Delay with
    Resource Allocation
  • Fair Inter-TAP Routing and Backhaul Assignment
    Algorithms
  • Fair End-to-End Delay and Load-balanced Routing
  • Ad Hoc Networks (Ch. 5)
  • A Minimum Power Broadcast Algorithm
  • Wireless Sensor Networks (Ch. 6 and Ch. 7)
  • Dynamic Radius, Duty Cycle Scheduling, Routing,
    Data Aggregation, and Multi-Sink (Clusters)
  • Conclusion Future Work

Publication List 1 Frank Yeong-Sung Lin and
Yean-Fu Wen, "A Path-Based Minimum Power
Broadcast Algorithm in Wireless Networks," was
submitted to ACM Baltzer Mobile Networks and
Applications (MONET), Mar. 2007. (under
review) 2 Frank Y.S. Lin, Y. F. Wen, L.C. Fu,
and S.P. Lin, A Path-Based Minimum Power
Broadcast Problem in Wireless Networks," in Proc.
of IEEE TENCON, Melbourne, Australia, Nov. 2005.
(EI)
38
A Minimum Power Broadcast Algorithm for Ad-hoc
(Sensor) Networks
Agenda
Ch.2
Introduction
Conclusion
Ch. 3
Ch. 4
Ch.3
Ch.5
Ch.6,7
  • We discuss how to construct a multicast tree that
    minimizes power consumption with the multicast
    wireless advantage.
  • Problem


Given
8
ev(rv)


5

9




(
1
,
2
)
2

Power


10
1

ev(rv)rv? a

(
1
,
3
)

consumption
6


(normalized)
3


11
rv

7
Power range
4

12

39
A Minimum Power Broadcast Algorithm for Ad-hoc
(Sensor) Networks
Agenda
Ch.2
Introduction
Conclusion
Ch. 3
Ch. 4
Ch.3
Ch.5
Ch.6,7
  • Objective
  • to minimize the total broadcast power consumption
  • Subject to
  • routing
  • tree
  • radius
  • To determine
  • the routing path from each source to the
    destination, denoted as an OD-pair xp.
  • whether a link should be on the multicast tree
    y(u,v) .
  • a multicast tree.
  • transmission radius for each MD ru.

a multicast tree which is also a Steiner
tree NP-complete
40
A Minimum Power Broadcast Algorithm for Ad-hoc
(Sensor) Networks
Agenda
Ch.2
Introduction
Conclusion
Ch. 3
Ch. 4
Ch.3
Ch.5
Ch.6,7
  • Proposed algorithm
  • a minimum power broadcast algorithm
  • Experiment results

41
A Minimum Power Broadcast Algorithm for Ad-hoc
(Sensor) Networks
Agenda
Ch.2
Introduction
Conclusion
Ch. 3
Ch. 4
Ch.3
Ch.5
Ch.6,7
  • Related work
  • power range and topology control Salhieh01
    Bettstetter02 Santi01
  • to build minimum energy networks via the shortest
    path tree (SPT) algorithm, measuring the cost of
    the edge by its power level Salhieh01,
    Dowell01, Montemanni04, and Li01
  • node-based solutions in static all-wireless
    networks in terms of a trade-off a node can
    reach more nodes in a single hop by using higher
    transmission power Ahluwalia05,
    Wieselthier00 and Cagalj02
  • link-based solutions Das03a
  • a series of heuristics (e.g., BIP) to solve this
    problem Wieselthier00, Wieselthier01, and
    Wieselthier02
  • r-shrink Das03b

42
Agenda
Agenda
Ch.2
Introduction
Conclusion
Ch. 3
Ch. 4
Ch.3
Ch.5
Ch.6,7
Publication List 1 Frank Yeong-Sung Lin and
Yean-Fu Wen, "Multi-sink Data Aggregation Routing
and Scheduling with Dynamic Radii in WSNs," IEEE
Communications Letters, 10(10), pp. 692694, Oct.
2006. (SCI, EI) 2 Yean-Fu Wen and Frank
Yeong-Sung. Lin, "Cross-Layer Duty Cycle
Scheduling with Data Aggregation Routing in
Wireless Sensor Networks," Lecture Notes Computer
Science (LNCS), vol. 4096, pp. 894903.
(Proceedings of IFIP EUC 2006) (SCI, EI) 3Y.F.
Wen, Frank Y.S. Lin, and W.C. Kuo, "A Tree-based
Energy-efficient Algorithm for Data-Centric
Wireless Sensor Networks," in Proc. of IEEE AINA,
Ontario, Canada, May 2007. (EI) 4Y.F. Wen and
Frank Y.S. Lin, "Energy-Efficient Data
Aggregation Routing and Duty-Cycle Scheduling in
Cluster-based Sensor Networks," in Proc. of IEEE
CCNC, Las Vegas, NV, Jan. 2007. (EI)
  • Introduction
  • Wi-Fi Hotspots (Ch. 2)
  • System Throughput Maximization Subject to Time
    Fairness Constraints
  • Wireless Mesh Networks (Ch. 3 and Ch. 4)
  • Fair Throughput and End-to-End Delay with
    Resource Allocation
  • Fair Inter-TAP Routing and Backhaul Assignment
  • Fair End-to-End Delay and Load-Balanced Routing
  • Ad Hoc Networks (Ch. 5)
  • A Minimum Power Broadcast Algorithm
  • Wireless Sensor Networks (Ch. 6 and Ch. 7)
  • Dynamic Radius, Duty Cycle Scheduling, Routing,
    Data Aggregation, and Multi-Sink (Clusters)
  • Conclusion Future Work

43
Cross-Layer Duty Cycle Scheduling with Data
Aggregation Routing for WSNs
Agenda
Ch.2
Introduction
Conclusion
Ch. 3
Ch. 4
Ch.3
Ch.5
Ch.6,7
  • We discuss how to increase the battery lifetime
    and energy consumption efficiency of a network
    from the Physical layer to the Application layer
    in terms of the following issues
  • single/multi-sink
  • 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
44
Cross-Layer Duty Cycle Scheduling with Data
Aggregation Routing for WSNs
Agenda
Ch.2
Introduction
Conclusion
Ch. 3
Ch. 4
Ch.3
Ch.5
Ch.6,7
  • Objective
  • minimize the total energy consumed by a target
    transmission to one of sink nodes
  • Subject to
  • sink selection D
  • restrictions on the structure of trees in the
    form of three link constraints PsD, L, ?p(u,v),
    Hs
  • duty cycle scheduling
  • the time for node-to-node communication
    Shiou05
  • dynamic radius ?uv, Ru
  • To determine
  • The sink node that a source node bsg will route
    to
  • a routing path xp and link y(u,v) from the source
    node to the sink node
  • the earliest time nu at which a node wakes up and
    begins aggregating data and
  • the time mu at which aggregation of sub-tree data
    will be completed
  • the time ?uv needed for a successful node-to-node
    transmission.
  • the power range ru of each node

a type of reverse-multicast tree which is also a
Steiner tree MCP NP-complete
45
Cross-Layer Duty Cycle Scheduling with Data
Aggregation Routing for WSNs
Agenda
Ch.2
Introduction
Conclusion
Ch. 3
Ch. 4
Ch.3
Ch.5
Ch.6,7
  • Proposed algorithm single sink

0
0
?
3, 50
D
1
0
2
0
3, 41
2
1
0
0, 32
0, 31
3
4
1
3
2
0, 01
5
0, 03
0, 02
7
6
3
S1
S2
0, 03
S3
8
0
0
8
8
S4
0
8
0
8
O
46
Energy-Efficient Data Aggregation Routing and
Duty-Cycle Scheduling for Multi-Sink WSNs
Agenda
Ch.2
Introduction
Conclusion
Ch. 3
Ch. 4
Ch.3
Ch.5
Ch.6,7
  • Proposed algorithm multi-sink
  • We discuss how to increase the lifetime of the
    networks already discussed with a multiple sink
    structure (outgoing information gateways)

47
Cross-Layer Duty Cycle Scheduling with Data
Aggregation Routing for WSNs
Agenda
Ch.2
Introduction
Conclusion
Ch. 3
Ch. 4
Ch.3
Ch.5
Ch.6,7
  • Experiment results single sink

48
Energy-Efficient Data Aggregation Routing and
Duty-Cycle Scheduling for Multi-Sink WSNs
Agenda
Ch.2
Introduction
Conclusion
Ch. 3
Ch. 4
Ch.3
Ch.5
Ch.6,7
  • Experiment results multi-sink

49
Cross-Layer Duty Cycle Scheduling with Data
Aggregation Routing for WSNs
Agenda
Ch.2
Introduction
Conclusion
Ch. 3
Ch. 4
Ch.3
Ch.5
Ch.6,7
  • Related work
  • backhaul selection Wen07 (NTU)
  • multi-sink Yuen06 (Toronto), Kalantari06
    (Maryland), Kim06 (Seoul)
  • three aggregation heuristics, namely, the
    Shortest Paths Tree (SPT), Center at Nearest
    Source (CNS), and the Greedy Incremental Tree
    (GIT) Krishnamachari02 (USC)
  • the tradeoff between power consumption and
    coverage of transmission nodes Carle04
  • S-MAC Ye02, T-MAC Dam03, D-MAC Lu04a
    Lu04b
  • retransmission Shiou05 Bianchi00 Sheu03
    Wen06c
  • radius (refer to Ch. 5)

50
Agenda
Agenda
Ch.2
Introduction
Conclusion
Ch. 3
Ch. 4
Ch.3
Ch.5
Ch.6,7
  • Introduction
  • Wi-Fi Hotspots (Ch. 2)
  • System Throughput Maximization Subject to Time
    Fairness Constraints
  • Wireless Mesh Networks (Ch. 3 and Ch. 4)
  • Fair Throughput and End-to-End Delay with
    Resource Allocation
  • Fair Inter-TAP Routing and Backhaul Assignment
  • Fair End-to-End Delay and Load-Balanced Routing
  • Ad Hoc Networks (Ch. 5)
  • A Minimum Power Broadcast Algorithm
  • Wireless Sensor Networks (Ch. 6 and Ch. 7)
  • Dynamic Radius, Duty Cycle Scheduling, Routing,
    Data Aggregation, and Multi-Sink (Clusters)
  • Conclusion Future Work

51
Conclusion Future Work
Agenda
Ch.2
Introduction
Conclusion
Ch. 3
Ch. 4
Ch.3
Ch.5
Ch.6,7
  • For hot-spot networks
  • system throughput maximization subject to time
    fairness constraints
  • For mesh networks
  • fair throughput and end-to-end delay with
    resource allocation
  • fair inter-TAP routing and backhaul assignment
  • fair end-to-end delay and load balanced routing
  • For ad hoc networks
  • message broadcasting
  • dynamic adjustment of the transmission radius

52
Conclusion Future Work
Agenda
Ch.2
Introduction
Conclusion
Ch. 3
Ch. 4
Ch.3
Ch.5
Ch.6,7
  • For wireless sensor networks
  • data aggregation
  • tree structure routing
  • duty-cycle scheduling
  • node-to-node communication time
  • retransmissions
  • dynamic radius
  • multi-sink
  • cluster

53
Conclusion Future Work
Agenda
Ch.2
Introduction
Conclusion
Ch. 3
Ch. 4
Ch.3
Ch.5
Ch.6,7
  • Hot-spot and Mesh Networks
  • channel assignment
  • Ad hoc and Sensor Networks
  • the proposed maximization of mobile network
    lifetime is extended to include balanced 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.

54
Agenda
Ch.2
Introduction
Conclusion
Ch. 3
Ch. 4
Ch.3
Ch.5
Ch.6,7
  • THANK YOU FOR YOUR ATTENTION

55
Agenda
Ch.2
Introduction
Conclusion
Ch. 3
Ch. 4
Ch.3
Ch.5
Ch.6,7
  • Journal Papers
  • Yean-Fu Wen and Frank Yeong-Sung Lin, Fair
    Bandwidth Allocation and End-to-End Delay Routing
    Algorithms in Wireless Mesh Networks,
    Communications, IEICE Transactions on, E90-B(5),
    pp. xx-xx, May. 2007. (SCI, EI) (in press)
  • Frank Yeong-Sung Lin and Yean-Fu Wen, Multi-sink
    Data Aggregation Routing and Scheduling with
    Dynamic Radii in WSNs, IEEE Communications
    Letters, 10(10), pp. 692-694, Oct. 2006. (SCI,
    EI)
  • Under revision
  • Yu-Liang Kuo, Kun-Wai. Lai, Frank Yeong-Sung Lin,
    Yean-Fu Wen, Eric Hsiao-kuang Wu, Gen-Huey Chen,
    Multi-Rate Throughput Optimization with Fairness
    Constraints in Wireless Local Area Networks,
    IEEE Transactions on Vehicular Technology, Dec.
    2006 (major revised).
  • Under review
  • Frank Yeong-Sung Lin and Yean-Fu Wen, Fair
    Inter-TAP Routing and Backhaul Assignment in
    Wireless Mesh Networks, was submitted to Journal
    of WCMC, Oct. 2006.
  • Frank Yeong-Sung Lin and Yean-Fu Wen, A
    Path-Based Minimum Power Broadcast Algorithm in
    Wireless Networks, was submitted to ACM Baltzer
    Mobile Networks and Applications (MONET), Mar.
    2007.
  • Book Chapters
  • Yean-Fu Wen and Frank Yeong-Sung. Lin,
    Cross-Layer Duty Cycle Scheduling with Data
    Aggregation Routing in Wireless Sensor Networks,
    Lecture Notes Computer Science (LNCS), vol. 4096,
    pp. 894-903. (Proceedings of IFIP EUC 2006) (SCI,
    EI)

56
Agenda
Ch.2
Introduction
Conclusion
Ch. 3
Ch. 4
Ch.3
Ch.5
Ch.6,7
  • Conference Papers
  • Y.F. Wen and Frank Y.S. Lin, W.C. Kuo, A
    Tree-based Energy-efficient Algorithm for
    Data-Centric Wireless Sensor Networks, was
    accepted to appear in IEEE AINA 2007, Ontario,
    Canada, May 2007. (EI)
  • Y.F. Wen and Frank Y.S. Lin, Energy-Efficient
    Data Aggregation Routing and Duty-Cycle
    Scheduling in Cluster-based Sensor Networks,
    IEEE CCNC 2007, Las Vegas, NV, Jan. 2007. (EI)
  • C.D. Lee, Frank Y.S. Lin and Y.F. Wen, An
    Efficient Object Tracking Algorithm in Wireless
    Sensor Networks, JCIS, Kaohsiung Taiwan, 8-11
    October, 2006. (EI)
  • Y.F. Wen and Frank Y.S. Lin, Cross-Layer Duty
    Cycle Scheduling with Data Aggregation Routing in
    Wireless Sensor Networks, IFIP EUC 2006, Seoul
    Korea, Aug. 1-4, 2006. (EI)
  • Frank Y.S. Lin, H.H Yen, S.P. Lin, and Y.F. Wen,
    MAC Aware Energy-Efficient Data-Centric Routing
    in Wireless Sensor Networks, IEEE ICC 2006,
    Istanbul, TURKEY, Jun. 2006. (EI)
  • Y.F. Wen and Frank Y.S. Lin, The Top Load
    Balancing Forest Routing in Mesh Networks, IEEE
    CCNC 2006, Las Vegas, NV, Jan. 2006. (EI)
  • Frank Y.S. Lin, Y. F. Wen, L.C. Fu, and S.P. Lin,
    Path-Based Minimum Power Broadcast Problem in
    Wireless Networks, IEEE TENCON 2005, Melbourne,
    Australia, Nov. 2005. (EI)

57
Agenda
Ch.2
Introduction
Conclusion
Ch. 3
Ch. 4
Ch.3
Ch.5
Ch.6,7
  • Y.L. Kuo, K.W. Lai, Frank Y.S. Lin, Y.F. Wen,
    H.K. Wu, G.H. Chen, Multi-Rate Throughput
    Optimization for Wireless Local Area Network
    Anomaly Problem, IEEE/ICST BroadNets 2005,
    Boston, MA, USA, Oct. 2005. (EI)
  • Y.F. Wen, Frank Y.S. Lin, and K.W. Lai, System
    Throughput Maximization Subject to Delay and Time
    Fairness Constraints in 802.11 WLANs, IEEE
    ICPADS 2005, Fukuoka Institute of Technology
    (FIT), Fukuoka, Japan, Jul. 2005. (EI)
  • C.W. Shiou, Frank Y.S. Lin, H.C. Cheng, and Y.F.
    Wen, Optimal Energy-Efficient Routing for
    Wireless Sensor Networks, IEEE AINA 2005,
    Tamkang Taiwan, Mar. 2005. (EI)
  • Y.F. Wen and Frank Y.S. Lin, Minimum Energy
    Consumption Routing Protocol in Multi-Rate
    Non-Infrastructure Wireless Network, ICS 2004,
    Taipei Taiwan, Dec. 2004.
  • Y.F. Wen, Frank Y.S. Lin, and K.W. Lai,
    Maximization of System Throughput Subject to
    Access Time Fairness Constraints in Multi-Rate
    802.11 WLANs, ICT 2004, Bang Na Thailand, Nov.
    2004, pp. 99-108.
  • Y.F. Wen, Frank Y.S. Lin, and K.W. Lai, Access
    Delay and Throughput Evaluation of Block ACK
    under 802.11 WLAN, IASTED CCN 2004, MIT,
    Cambridge, USA, Nov. 2004.

58
Fair Throughput and End-to-End Delay with
Resource Allocation for WMNs
Agenda
Ch.2
Introduction
Conclusion
Ch. 3
Ch. 4
Ch.3
Ch.5
Ch.6,7
Sender-based Wen07 Receiver-based Cheng95
Network type wireless network virtual circuit networks
Given traffic requirement ?s link capacity traffic requirement ?s total capacity
Resource control an outgoing link by resource allocation incoming links by capacity assignment
If link capacity is given fully utilize the resources of each selected link partially utilize the capacity to achieve minimax delay
Sources any node must be a leaf node
Perfect end-to-end delay achieved by the next hop from the sink node. achieved in the sink tree
The of usage queues the of queues is equal to the of branches. the of queues is equal to the of branches.
Routing dynamically adjustable. fixed with capacity assignment otherwise, partial usage
59
To increase a sensor networks lifetime
Agenda
Ch.2
Introduction
Conclusion
Ch. 3
Ch. 4
Ch.3
Ch.5
Ch.6,7
Origin
Destination
Wen07
60
Cross-Layer Duty Cycle Scheduling with Data
Aggregation Routing for WSNs
Agenda
Ch.2
Introduction
Conclusion
Ch. 3
Ch. 4
Ch.3
Ch.5
Ch.6,7
Objective function
Subject to path constraints
(1)
(2)
(3)
1
5
6
Xp 1
4
S
2
...
?
.
...
7
Xp 0
3
61
Cross-Layer Duty Cycle Scheduling with Data
Aggregation Routing for WSNs
Agenda
Ch.2
Introduction
Conclusion
Ch. 3
Ch. 4
Ch.3
Ch.5
Ch.6,7
link constraints
(4)
(5)
(6)
1
5
6
4
S
2
...
?
(7)
.
...
7
3
S
(8)
Note that (8) is added to the LR approach
62
Cross-Layer Duty Cycle Scheduling with Data
Aggregation Routing for WSNs
Agenda
Ch.2
Introduction
Conclusion
Ch. 3
Ch. 4
Ch.3
Ch.5
Ch.6,7
node-to-node transmission time constraints
(9)
(10)
(11)
(12)
1
luv
5
(13)
6
4
S
2
(14)
...
?
.
...
7
(15)
3
S
63
Cross-Layer Duty Cycle Scheduling with Data
Aggregation Routing for WSNs
Agenda
Ch.2
Introduction
Conclusion
Ch. 3
Ch. 4
Ch.3
Ch.5
Ch.6,7
the number of retransmission constraints
Shiou05
64
Cross-Layer Duty Cycle Scheduling with Data
Aggregation Routing for WSNs
Agenda
Ch.2
Introduction
Conclusion
Ch. 3
Ch. 4
Ch.3
Ch.5
Ch.6,7
scheduling constraints
(18)
(19)
(20)
(21)
65
Cross-Layer Duty Cycle Scheduling with Data
Aggregation Routing for WSNs
Agenda
Ch.2
Introduction
Conclusion
Ch. 3
Ch. 4
Ch.3
Ch.5
Ch.6,7
  • LR-based approach

66
Agenda
Ch.2
Introduction
Conclusion
Ch. 3
Ch. 4
Ch.3
Ch.5
Ch.6,7







subject to (1), (2), (4), (5), (6), (7), (8), (10), (11), (14), (15), (17), (19) and (21).
67
Energy-Efficient Data Aggregation Routing and
Duty-Cycle Scheduling in Cluster-based WSNs
Agenda
Ch.2
Introduction
Conclusion
Ch. 3
Ch. 4
Ch.3
Ch.5
Ch.6,7
  • Problem
  • we discuss how to increase the lifetime of a
    network (including the previous issues) with a
    multiple sink structure (outgoing information
    gateways) and a cluster structure (source nodes
    message must be forwarded to the cluster-head
    first)
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