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In recent years, the explosive deployment of Wireless LANs worldwide has brought the entire gamut of Web Applications to our fingertips.

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Title: In recent years, the explosive deployment of Wireless LANs worldwide has brought the entire gamut of Web Applications to our fingertips.


1
Introduction
  • In recent years, the explosive deployment of
    Wireless LANs worldwide has brought the entire
    gamut of Web Applications to our fingertips.
  • Although existing systems provide a wide range of
    services, they are highly inefficient.
  • The main goal of our ongoing research is to
    optimally allocate resources among users in
    Wireless Networks.
  • Bandwidth and Energy are primary resources of
    interest.
  • Our framework studies a general multi-hop
    network.
  • Cellular and Access networks are special cases.

2
Applications of our Work
  • Data Services in Cellular Networks, similar to
    IEEE802.11, HDR etc.
  • Access Networks Existing WLANs can greatly
    benefit by use of multiple hops to the
    base-station.
  • Sensor Networks
  • Military Applications Scatter smart devices over
    a field. Devices configure themselves to form a
    high speed network. An instant platform for
    communications in rough terrain.
  • Commercial Applications Use sensor nodes to
    gather information about traffic conditions,
    volcanoes, earthquakes etc.

3
Problem Scenario
4
Decentralized Control
5
Notations and Definition
  • N user network.
  • Routes between users are given.
  • A directed link e i,j is a valid signal
    transmission from node i to node j. Total of L
    links in the network.
  • A subset of L links is called a transmission
    mode.
  • There are 2N transmission modes for a N user
    system.
  • Link Scheduling Policy The time fractions
    associated with each transmission mode
    constitutes a scheduling policy.
  • Power Control Policy The power used by each node
    in each slot constitutes a power control policy.

6
User Objective Vs Network Objective
  • User Objective Each user would like to get the
    highest possible rate while using a limited
    amount of energy.
  • Network Objective The network may choose to
    maximize the total data transferred for a given
    amount of energy used.
  • Alternately, a network may choose to minimize the
    total amount of power consumed while meeting
    minimum rate guarantees per user.
  • We assume that the connections supported are long
    enough and users always have data to transmit.
    E.g. file transfers, video/audio streaming etc.
    For short-life connections we could employ
    lightweight heuristics instead.

7
Current Approaches
  • Most approaches decouple link scheduling from
    power control policy. We propose an integrated
    approach.
  • Links are scheduled such that the interference
    experienced by the scheduled link is negligible
    ( ambient noise).
  • Theme Schedule nodes that are far away from each
    other simultaneously.
  • Such approaches allow users to transmit at peak
    power.
  • These approaches exploit spatial diversity by
    allowing multiple transmissions concurrently.
  • Sub-optimal with respect to system capacity
    maximization. They do not exploit spatial
    diversity optimally.
  • Overhead Require limited processing of topology
    information but compute the link schedule using a
    centralized entity.

8
Conflict-Free Link Scheduling Policies
  • Graph Theoretic Approach.
  • If a node is transmitting is a slot, nodes in its
    vicinity cannot transmit in that slot.
  • The underlying assumption in most link scheduling
    algorithms is to limit interference to a very
    small value ( ambient noise)
  • For a N node multi-hop network The problem of
    computing the transmission schedules with optimal
    throughput is NP-complete? Computing a Maximum
    Independent Set in a Graph.
  • Polynomial time approximation algorithms have
    been proposed.

9
Previous Work on Link Scheduling
  • Work Done by Victor Li Ji-Her Ju
  • Consider a single channel TDMA network with N
    mobiles.
  • The TDMA frame consists of q sub-frames each q
    slots long.
  • Each user is allocated q slots, one in each
    sub-frame.
  • Problem Maximize the worst-case throughput of
    the network such that all users have at least 1
    slot in each sub-frame to transmit.
  • Throughput Defined as number of conflict-free
    transmissions in a frame.
  • Collision occurs when 2 or more nodes within a
    1-hop distance in connectivity graph transmit
    simultaneously.
  • A central controller allocates q slots per user
    so that there are at most k collisions between
    any two users in a frame.

10
Work on Link Scheduling Cont
  • Inputs to the Algorithm are
  • N total number of nodes
  • D Maximum Degree of any node in the network
  • Given N and D, the algorithm finds a suitable q
    and k so that the worst-case Tput, (q k
    D)/(q2) is maximized.
  • A centralized entity computes the schedule and
    broadcasts it to all the nodes in the network.
  • Pros Cons
  • Maximizes the Worst-case Scheduling
    policy is Centralized.
  • Throughput. Blind to Changing
    Channel
  • Simple to compute Schedules Conditions.
  • Schedules can be updated Fails to
    exploit the peak wireless
  • infrequently channel, unlike our approach,
    HDR
  • Topology Immune Algorithm N and D are slowly
    varying quantities, but assumes worst case
    scenario.

11
Joint Link and Packet Scheduling
  • Work done by Timely Research Group, UIUC.
  • Proposed a suite of Wireless Fair Queuing (WLFQ)
    algorithms to achieve packet level fairness in
    Cellular Networks.
  • WLFQ algorithms assumed that users could transmit
    in every slot, not the case in the multi-hop
    scenario.
  • Extend the notion of Wireless Fair Queuing to the
    multi-hop network paradigm.
  • They incorporate the effects of spatial diversity
    into fairness.
  • Link Schedules are determined based on a simple
    heuristic.
  • The WLFQ algorithm is augmented to account for
    link schedules.

12
Joint Link and Packet Scheduling Cont
  • A Centralized entity takes as input the flow
    weights at each node, and the set of neighbors to
    compute link schedules.
  • How do nodes communicate with the centralized
    entity?
  • A commonly proposed technique to communicate
    update information to a central entity is to
    maintain a minimum spanning tree among the N
    nodes
  • Nodes in the vicinity (2-hop) of each other have
    an edge in the flow contention graph G(V,E).
  • Packets whose deadlines are about to expire are
    scheduled first. The rest of the links are chosen
    based on MIS.
  • If flow i has been picked based on WLFQ, the
    remaining flows are picked such that they form a
    Maximum Independent Set (MIS) in the sub-graph G
    N(i), where N(i) is the set of nodes in the
    vicinity of flow i.
  • Computing an MIS is an NP-complete problem.

13
Maximum Independent Set
  • They resort to a polynomial time approximation
    algorithm to compute the MIS of the graph.
  • Given a graph G(V,E), a independent set S of a
    graph is a set of nodes in V such that if nodes
    i,j is an edge in the graph, then either i or j
    belong to S.
  • MIS Independent set with the highest
    cardinality.
  • Choose initial set of links L_i based on packet
    deadlines assigned by WLFQ at different nodes.
  • For each node i, pick node with the minimum
    degree in the sub-graph G N(i) for concurrent
    transmission.
  • The final set is the set of concurrent scheduled
    transmissions.
  • Approximation ratio (D 2)/3
  • Runtime O(N2)

14
System Model
  • N user network.
  • Slotted CDMA multiple access network.
  • Routes between users are given.
  • A link e i,j is a valid signal transmission
    from node i to node j. Total of L links in the
    network.
  • A subset of L links is called a transmission
    mode.
  • Nodes can transmit and receive in the same slot.
  • A node can transmit to multiple receivers at the
    same time.
  • Nodes can receive data from multiple
    transmitters concurrently.
  • Total amount of bandwidth available W

15
System Model Contd
  • Path-loss from node i to node j is G(i,j).
  • Assume a slow fading channel. i.e. G(i,j) changes
    slowly with time (frame length).
  • The power of signal transmission of node i to
    node j P(i,j)
  • The signal to interference-plus-noise ratio for
    node is transmission at node j is SIR(i,j)
  • The link capacity characterization C(i,j)
  • Shannon Capacity C(i,j) log(1 SIR(i,j) ) ?
    is not delay limited, assumes interference has a
    gaussian distribution.
  • We consider linear characterization C(i,j) W
    SIR(i,j)
  • Our characterization of capacity Loss-rate
    bounded and Delay limited and thus practical.
    This characterization is widely used in the
    research world.

16
System Model Contd.
  • Rate of link e i, j X(i,j) C(i,j)
  • The rate for link e in slot k is Xk(i,j) W
    SIRk(i,j)
  • The weight associated with link e is Ø(e)
  • Refer to the .pdf file for the continuation of
    this slide.

17
Mobile Ad Hoc Networks
Autonomous Distributed Systems All mobile nodes,
all wireless connections
18
What we have done?
  • Problem A Find a link scheduling and power
    control policy that maximizes the average
    multi-hop network capacity subject to peak power
    constraint per node and average power constraint
    per node (link).
  • Problem B Find a link scheduling and power
    control policy that minimizes the total power
    consumed in the multi-hop network subject to peak
    power constraints per node and minimum average
    rate guarantees per node (link).
  • We have developed algorithms to solve both the
    above problems

19
Observations
  • Some observations
  • Problem A and Problem B have objective functions
    with at most 2N variables and N1 constraints.
  • Solving problems of such size is hard, we use
    convex duality approach to solve the above
    problem.
  • The complexity of our algorithm is a low order
    polynomial in M where M is the number of allowed
    transmission modes. Algorithm converges in a
    finite number of steps.
  • Complexity of our algorithm can be further
    reduced if we assume that Nodes cannot transmit
    and receive at the same time.
  • The number of optimal transmission modes is
    typically (lt N1).
  • As the level of ambient noise increases, the
    number of concurrent transmissions increases as
    well.

20
Cellular Network Paradigm
  • Highly Practical for Cellular Networks For an
    N-user K- base station system with a total of M
    transmission modes per base station, the
    worst-case complexity of our algorithm is O(MK).
  • The average case (more realistic) complexity is
    significantly less.
  • Our algorithm is exactly the same for both uplink
    and downlink communications.
  • Alternately, one could use a powerful decoding
    scheme, Successive Interference Cancellation
    (SIC) to get high rates.
  • SIC is only effective if interference has a
    Gaussian distribution.
  • This is however not practical for multi-hop
    networks.

21
Simulation Environment
  • Six user single cell network.
  • Users have an average power Peak Power/4
  • Compare simulations with respect to K-TDMA
    policy.
  • K-TDMA transmission policy
  • The total number of concurrent transmissions is
    equal to K.
  • Each transmission mode gets to transmit for equal
    amounts of time.
  • This policy meets the average power constraint
    with equality.
  • HDR is a 1-TDMA policy
  • Our policy outperforms all other polices for all
    values of ambient noise (external interference).

22
Simulation Results
23
Simulation Results Cont
24
A Note on Energy Efficiency
  • Energy Efficiency Quotient (total data
    transferred/ total power consumed)
  • Solutions of Problem A and Problem B Near
    Optimal Energy Efficient.
  • Use the solution of Problem A as inputs to
    Problem B
  • Solution to Problem B is optimally energy
    efficient with respect to the network in the real
    sense of the word.
  • The final solution also maximizes system
    capacity, meets average minimum rate guarantees
    and average power constraints per node (link).

25
Conclusions
  • Solved two resource allocation problems in the
    realm of wireless networks.
  • Performs better than HDR for dense networks.
  • Unified Link Scheduling and Power Control
    Approach first known results.
  • Developed a suite of low-complexity near optimal
    heuristics.
  • Developed an online routing policy, not mentioned
    here.
  • General Idea Opportunistic in the short-term
    (slot-level), fair in the long term (frame or
    sub-frame-level).
  • Identified a number of applications for our
    results.
  • Would like to better understand the dynamics of
    the wireless channel using a real test-bed.

26
Current work on test-bed implementation
  • Monarch at Carnegie Mellon were the first to
    implement a multi-hop network test-bed.
  • They implemented dynamic source routing (DSR) for
    nodes in a multi-hop network.
  • Insignia at Columbia Univ. integrated an adaptive
    QoS module with Monarchs DSR module.
  • Code for both implementations is freely available
    for BSD Unix.
  • We must use their work to our advantage.

27
What we propose to do?
  • Set up a programmable test bed with a few laptops
    with IEEE802.11 cards.
  • Set up a few access-points campus wide.
  • Implement our integrated routing, link/packet
    scheduling and power control policy.
  • Short-term objective Study a network of
    stationary nodes.
  • Long-Term objective Study a network of mobile
    nodes.

28
Monarch Project
  • Among the first university-based multi-hop
    network test bed implementations.
  • Setting Outdoor Environments, Mobile nodes.
  • Constant topology changes causes need for dynamic
    routing policies.
  • 8 node multi-hop network
  • 2 stationary nodes, 5 mobile nodes and 1 roving
    node that monitors the others.
  • 8 Laptops equipped with
  • Lucent Wavelan-1 cards.
  • GPS receiver to track position Accurate to 1
    meter.
  • Laptops and accessories racked up in rented cars.
  • Implement Dynamic Source Routing Module on all
    laptops.

29
How does DSR Work?
  • Route Discovery and Route Maintenance.
  • A source node S queries its neighbors with a
    Route Request packet.
  • Neighbors with a route to destination D, send all
    path information from themselves to D, with a
    Route Reply packet.
  • Node S chooses one of many paths arbitrarily, but
    caches the all of them.
  • If no path can be found to node D, the query is
    flooded into the network.
  • If a route to D is then found, node S and all
    intermediate nodes cache this route.
  • The gateway also checks if D lies within its
    subnet or not.

30
How does DSR Work? Cont
  • If the gateway finds a route to D, it replies
    back to node S.
  • Else, it sends a Route Error packet to S.
  • How do nodes know who their neighbors are?
  • Through IEEE802.11s Link-layer ACK mechanism.
  • By reading GPS information embedded in data
    packets periodically.
  • If a path breaks due to node mobility, a Route
    Error message is sent to node S.
  • DSR On-Demand Routing.
  • Packets used the IPv6 extension header processing
    framework.
  • Aggressive querying of neighbors cache limits
    Route Request propagation.
  • Can be greatly improved with power control.

31
In-Lab Network Emulation
  • A useful tool for network emulation was a
    MAC-filter.
  • MAC-filter a filter that only processes
    legitimate packets.
  • Drops packets if their source IP addr. matches
    any on the forbidden src IP addr. list.
  • Nodes used a trace file of routing table entries
    and MAC-filter lists to perform their respective
    route discovery operations.
  • Enabled routing of physically close nodes through
    an intermediate node.
  • Allowed them to emulate a multi-hop network in a
    single room.
  • Enabled easier testing of a wide range of
    scenarios.
  • Easier debugging and validation of their code.

32
Logging and Support Utilities
  • Visualization Tools Track nodes using GPS and
    view 1- second interval snapshots at the field
    office.
  • Helpful in spotting unpredictable behavior.
  • Sieve out packet losses due to channel errors
    from those due to routing errors in post-run
    analysis.
  • Nodes use tcpdump() to record per-packet
    information.
  • They record signal strength (SIR) and signal
    quality (Frame error rate) for each received
    packet.
  • Packet arrival times and sequence numbers are
    also recorded.
  • Post-processing Compute the goodput of the
    system after each test-run.
  • Bottom Line Knowledge of node positions is
    necessary to diagnose network dynamics in
    real-time and non-real-time.

33
Lessons Learned
  • Lack of route diversity with 8 nodes.
  • Hard to construct multiple routes between nodes.
  • Our solution to that Use Power Control.
  • Multi-level priority queues are worth
    implementing.
  • Control Information should be given higher
    priority over data.
  • In-lab testing is critical for success.
  • Use of MAC-filter enabled them to test diverse
    scenarios.
  • Use of GPS receivers, provided valuable insight
    about the goings-on during test-runs.
  • Visualization tools aided in on-line and off-line
    diagnosis.
  • Wireless propagation not what you would expect.
  • Personnel management was not trivial.

34
The Insignia Project
Provides something better than
best effort service
for some flows, e.g., video, voice.
Hard QoS guarantee not possible in MANET
? Adaptive QoS
? Service Differentiation
e.g., QoS insensitive flows can be serviced in
best effort manner e-mail QoS sensitive flows
should be treated in better than best effort
manner
35
INSIGNIA Features
  • Approach
  • Adaptive QoS approach
  • Service Differentiation via packet prioritization

To provide adaptive QOS
? Fast Reservation
? Fast Restoration
? QoS reporting a feedback mechanism
? Adaptation according to network conditions
36
INSIGNIA Principles
In band signaling
? to be responsive
- requires only single packet on new path to
initiate the restoration after rerouting
- explicit out-of-band signaling is not
responsive enough and often fails to reach
the target mobile nodes
Soft-state ? for management and maintenance of
resource reservations
? first packet on new path create states (if
necessary) and subsequent packets
refresh the previous associated reservations
en-route
? outstanding reservations and states
automatically time out (i.e., typically in
seconds range.)
37
INSIGNIA IP option
SERVICE MODE adaptive (RES) service / best
effort service
PAYLOAD INDICATOR base quality (BQ) packet /
enhanced quality (EQ) packet
BANDWIDTH INDICATOR reflects the resource
availability en route
BANDWIDTH REQUEST indicates the max/min BW
requirements
38
Reservation Set-up
M2
MS
MD
M1
M3
M4
QOS report MAX reservation established
39
Re-routing / Restoration
M2
M2
MS
MD
M1
M3
Rerouting
Rerouting
M4
40
Re-routing / Degradation
MS
MD
M1
Rerouting
Rerouting
Rerouting
Rerouting
M4
bottleneck node
41
Adaptation Scale Up
MAX service re-initiated
MS
MD
M1
M5
bottleneck node
bottleneck node
M4
QOS report Scale Up
42
INSIGNIA Summary
INSIGNIA performs
? fast resource reservation
? responsive restorations and,
? timely adaptations.
QoS reports function as
- notification of flow set up completion,
- report for on-going service quality and
- also used for triggering adaptation process.
In band nature allows INSIGNIA to be responsive
enough to deal with the frequent rerouting
Soft state approach guarantees the release of
outstanding reservations on old path
43
How to Improve Network Performance
  • Monarch and Insignia are implemented on a
    IEEE802.11 WLAN.
  • The above projects test their network under
    light-to-moderate loads.
  • IEEE802.11 does not scale well with the number of
    users.
  • In dense neighborhoods, high contention for data
    slots results in frequent collisions in the
    reservation slots.
  • IEEE802.11 uses the point coordination function,
    servicing one node at a time under such
    circumstances.
  • We need the same functionality in a multi-hop
    network, i.e. link scheduling by the access
    point.
  • Our resource allocation policies achieve
    substantial improvements in network capacity,
    theoretically.
  • We would like to verify the profundity of our
    results in a real network.

44
Our Proposal
  • Implement Power Control.
  • Nodes can change their power levels depending on
    the population density around them.
  • Power control can be done using certain WLAN
    cards. e.g. 3Com cards.
  • Implement a variety of link and packet scheduling
    policies. This requires nodes in the network to
    be in sync with each other.
  • Nodes in an IEEE802.11 network are already
    synchronized to their Access Point (AP).
  • APs beacon the correct time to all the nodes in
    their domain.
  • APs could choose to flood the network with the
    correct time. This method might not be accurate
    enough.
  • Alternately, GPS receivers at nodes can
    synchronize them to desired accuracy
    (sub-millisecond).
  • Implement alternative routing strategies.
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