Title: In recent years, the explosive deployment of Wireless LANs worldwide has brought the entire gamut of Web Applications to our fingertips.
1Introduction
- 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.
2Applications 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.
3Problem Scenario
4Decentralized Control
5Notations 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.
6User 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.
7Current 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.
8Conflict-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.
9Previous 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.
10Work 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.
11Joint 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. -
12Joint 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.
-
13Maximum 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)
14System 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
15System 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.
16System 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.
17Mobile Ad Hoc Networks
Autonomous Distributed Systems All mobile nodes,
all wireless connections
18What 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
19Observations
- 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.
20Cellular 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.
21Simulation 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).
22Simulation Results
23Simulation Results Cont
24A 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).
25Conclusions
- 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.
26Current 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.
27What 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.
28Monarch 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.
29How 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.
30How 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.
31In-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.
32Logging 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.
33Lessons 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.
34The 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
35INSIGNIA 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
36INSIGNIA 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.)
37INSIGNIA 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
39Re-routing / Restoration
M2
M2
MS
MD
M1
M3
Rerouting
Rerouting
M4
40Re-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
42INSIGNIA 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
43How 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.
44Our 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.