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.

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.

Problem Scenario

Decentralized Control

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 2^N 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.

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.

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.

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.

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.

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)/(q^2) is maximized.

A centralized entity computes the schedule and broadcasts it to all the nodes in the network.

Pros ConsMaximizes 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 wirelessinfrequently channel, unlike our approach, HDR Topology Immune Algorithm: N and D are slowly varying quantities, but assumes worst case

scenario.

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.

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.

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(N^2)

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

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 i’s

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.

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.

Mobile Ad Hoc Networks

Autonomous Distributed Systems

All mobile nodes, all wireless connections

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

Observations

Some observations: Problem A and Problem B have objective functions with at most

2^N variables and N+1 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 (<= N+1). As the level of ambient noise increases, the number of concurrent

transmissions increases as well.

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.

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).

Simulation Results

Simulation Results Cont…

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).

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.

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 Monarch’s DSR module.

Code for both implementations is freely available for BSD Unix.

We must use their work to our advantage.

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.

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.

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.

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 IEEE802.11’s 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 neighbor’s cache limits Route Request

propagation. Can be greatly improved with power control.

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.

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.

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.

The Insignia Project

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

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

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

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.)

INSIGNIA IP option

SERVICEMODE

1 bit 1 bit 1 bit 16 bits

BANDWIDTHINDICATOR

BANDWIDTH REQUEST

PAYLOADINDICATOR

MAX MINBQ/EQRES/BE BW_IND

SERVICE MODE : adaptive (RES) service / best effort service

PAYLOAD INDICATOR : base quality (BQ) packet / enhanced quality (EQ) packetBANDWIDTH INDICATOR : reflects the resource availability en route

BANDWIDTH REQUEST : indicates the max/min BW requirements

Reservation Set-up

M1

M2

M3

M4

MS MD

RES/BQ packet

RES/EQ packet

Legend

BE packet

MAX reserved link

MIN reserved link

QOS report : MAX reservation established

RES BQ MAX Max_BW Min_BW

RES EQ MAX Max_BW Min_BW

Packets Received at Destination Mobile Node

Rerouting Rerouting

M2

M1M3

M4

MS MD

M2

immediate restoration

M2

RES/BQ packet

RES/EQ packet

Legend

BE packet

MAX reserved link

MIN reserved link

Re-routing / Restoration

M3

M5

RES/BQ packet

RES/EQ packet

Legend

BE packet

MAX reserved link

MIN reserved link

Rerouting Rerouting

Re-routing / Degradation

M1

M4

MS MD

Rerouting Rerouting

M3

M5

bottleneck node

EQ degradation: degraded to minimum service

M5

M3

RES BQ MIN Max_BW Min_BW

BE EQ - Max_BW Min_BW

Packets Received at Destination Mobile Node

bottleneck node

bottleneck node

Adaptation : Scale Up

M1

M4

MS MD

QOS report : Scale Up

constant resource availability detected

MAX service re-initiated

RES/BQ packet

RES/EQ packet

Legend

BE packet

MAX reserved link

MIN reserved link

M5

resource now available

QOS report : Scale Up

resource now available

constant resource availability detected

RES BQ MAX Max_BW Min_BW

Packets sent by Source Mobile Node in MIN service

RES BQ MAX Max_BW Min_BW

Pkts Received at Destination in MIN service

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

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.

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).

AP’s beacon the correct time to all the nodes in their domain. AP’s 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.