Optimal dynamic transport selection for wireless portable devices

WIRELESS COMMUNICATIONS AND MOBILE COMPUTINGWirel. Commun. Mob. Comput. 2007; 7:9–21Published online 9 January 2006 in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/wcm.296

Optimal dynamic transport selection for wirelessportable devices

Mohamed Younis*,y, Amit Sardesai and Yaacov Yesha

Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore

County, Baltimore, M.D. 21250, U.S.A.

Summary

Recent technological advances in mobile computing and wireless communication have made portable devices,

such as PDA, laptops, and wireless modems to be very compact and affordable. On the other hand, wireless

networks have gained such wide popularity that new network infrastructure is continually introduced. It is thus

likely that many of the future portable devices will be equipped with multiple wireless modems such as Bluetooth

and 802.11 WLAN, in order to increase device inter-operability. The availability of multiple modems can leverage

the performance of the communication traffic generated by the applications, for example Internet access. We

envision a tool for managing the device connection through these modems. At the core of this tool is an

optimization engine that splits packet traffic across a subset of the available transports so that user’s performance

metrics are maximized. This paper describes a mathematical model for such an optimization problem considering

its applicability to small portable devices. Relevant quality of service (QoS) parameters such as bandwidth,

average delay, and energy consumption are covered in the model. The mathematical formulation is validated using

a simulated environment. The experimental results have demonstrated the effectiveness of our model and captured

the inter-relationship among the quality parameters. Copyright # 2006 John Wiley & Sons, Ltd.

1. Introduction

Technological advances in microelectronics and the

growing level of integration allowed wireless modems

to be energy-efficient and very small in size. Such

advances have made these modems to be widely

available and affordable for both traditional and

portable computing devices. It is thus expected that

the future laptop computer and some digital personal

assistants to be equipped with multiple types of

wireless modems, such as Bluetooth and 802.11

wireless LAN, in order to increase their versatility

and adaptability to different networks and environ-

ments. However, the availability of multiple modems

will require the development of a methodology for the

selection of the most suitable transport for a particular

application when more than one transport are feasible

candidates. In addition, the simultaneous use of multi-

ple transports can have a positive impact on the

response time since packets can be split and sent in

parallel over them.

Quality of service (QoS) of the network, which

means providing consistent, predictable data delivery

service at an acceptable cost. It also means the good-

ness a certain operation is performed with service

cost, throughput, energy efficiency, response time, and

connection reliability are the QoS metrics that are

affected by the transport selection. Therefore, there

might be a tradeoff between the services provided by

different networks. For example, some transports

*Correspondence to: Mohamed Younis, Department of Computer Science and Electrical Engineering, University of Maryland,Baltimore County, Baltimore, M.D. 21250, U.S.A.yE-mail: [email protected]

Contract/grant sponsor: Aether Systems, Inc.

Copyright # 2006 John Wiley & Sons, Ltd.

might ensure good throughput while others might

provide reliable connections. Thus, the selection of

whether a particular network is better than the other is

decided by what the user values the most. These

metrics are a function of the different parameters,z

like bandwidth, delay, jitter, energy consumption,

error rate, etc. Since a network may have different

parameters, the availability of multiple networks will

allow more choices and increase the feasibility of

attaining the desired levels of transmission quality. In

addition, the simultaneous use of multiple transports

will introduce parallelism in the data transmission and

thus increase the speed of the communication.

For example consider a user downloading a video

file or participating in an online conference in a

multimedia environment. Typically, the delay per

packet should be less than 150ms and the delay jitter

should not exceed 10ms in order to avoid static

frames and maintain lip synchronization. The user

will have a choice among the different transports

available to him to meet the quality requirements. If

none of the transports available to the user can meet

such requirements, packets can be split among multi-

ple transports in order to overcome some of the

performance shortcoming of some of these transports

when individually used. For example, parallel packets

transmission on multiple transports would make the

effective delay and jitter acceptable.

The simultaneous use of multiple wireless transport

raises two important issues. The first deals with the

methodology of selecting a subset of the transport for

consideration based on optimality criteria and subject

to user minimum expectation for achieved quality.

The second issue is related to supporting the use of

multiple connections for transmission and reception

of data of a single application. This includes dealing

with packet ordering and other related issues in the

communication stack. In this paper, we are only

concerned with the optimality of transport selection.

Other work has addressed the handling of multiple

connections [1].

In this paper, we develop a model for calculating

the optimal splitting of packets among the available

networks by considering the load on the network and

the dynamic nature of the different QoS parameters.

Optimality is achieved when the highest possible

levels of user’s valued QoS metrics are attained. We

characterize the model as a function of the number of

packets that are to be passed through each network.

The model is further simplified to a linear integer’s

programming problem. Such simplification is highly

desired in order to suit the energy and resource

constraints of portable wireless devices, which do

not afford to consume much of their resources for

solving a complex optimization problem in a very

dynamic networking environment.

Since the traffic condition of the networks is con-

stantly changing due to load and other factors such as

radio interference, the values of network QoS para-

meters also vary. In our framework, we monitor the

variability in the network QoS parameters such as

delay at the user node. In other words, we rely on the

user perceived values of the network quality para-

meters in order to track deviations from theoretical or

published figures. Such approach ensures the consid-

eration of current network load and interference while

optimally dividing user requests among the available

networks.

The paper is organized as follows. In Section 2, we

discuss related work. Section 3 describes the tool

design and the problem formulation. Section 4 dis-

cusses the experimental validation and the analysis of

the results. Finally, Section 5 concludes the paper and

points out future research directions.

2. Related Work

Supporting QoS through adaptive resource manage-

ment has received attention in multiple research areas,

most notably the work in the communication and

distributed computing community. While, in the com-

munication community, QoS is usually used to mean

throughput, reliability, end-to-end transmission delay,

etc., the distributed computing community has ex-

tended the notion to include computation-related

metric such as timeliness.

The bulk of the work on supporting QoS metrics in

communication networks has considered the issues in

just one network. When one network is considered, it

is conceivable to manage the different network re-

sources in order to optimize overall network perfor-

mance or even the performance experienced by a set of

users. Multiple techniques can be implemented within

a single network to support communication-based QoS

requirements [2–8]. However, non-communication

based QoS metrics such as cost and energy consump-

tion cannot be managed. Our model deals with multi-

ple transports and thus the performance that the user

experiences is the aggregate function of the perfor-

mance of multiple networks. In addition, the links tozWe will refer to them there after as QoS parameters.

10 M. YOUNIS, A. SARDESAI AND Y. YESHA

Copyright # 2006 John Wiley & Sons, Ltd. Wirel. Commun. Mob. Comput. 2007; 7:9–21

the different networks would have varying character-

istics and QoS parameters, thus making the packet

splitting problem more complex compared with just

single-network-based QoS provisioning.

The most famous techniques for supporting QoS

routing in networks are the differentiated service and

bandwidth reservation. Both techniques control one

QoS parameter, namely the bandwidth, in order to

ensure the achievement of the level of QoS required

by the application. Applying differentiated service

requires careful queue management at all the nodes

on the selected route [9–11]. On the other hand,

bandwidth reservation keeps aside enough resources

at every node on the route for the user connection [12–

18]. Both techniques are applicable only within the

same network and support only communication-based

QoS metrics such as end-to-end delay. In our model,

we do not control the resources of each of the avail-

able networks. Instead, we dynamically adjust the

usage profile of these networks, from the user pro-

spective, in order to dynamically cope with changes in

the network load and in user demands. In addition, we

support QoS metrics such as cost and energy con-

sumption, which are not traditionally considered.

The scope of QoS has been extended for large

distributed networks to accommodate processing

based quality (performance) metrics. The RTARM

project is an example of such work [19]. In RTARM,

the real-time performance has been added to the QoS

metrics, both on the communication and computation

level. The approach pursued relies on a middle ware

that is employed at every node in order to manage

local resources and collaborate with other computing

nodes on controlling network-level resources. The

middle ware continually monitors resource usage

and verifies constraints. If needed, tasks are reallo-

cated among the different computing nodes.

3. Optimal Transport Selection

The problem of selecting the optimal capacity usage

of multiple transports is a typical resource allocation

problem faced in many engineering designs. In most

cases, formulation of the allocation problem using a

mathematical model requires the most attention. Se-

lecting an optimization algorithm to solve the math-

ematical model depends on the nature of the model.

The model is usually classified based on the nature of

both the objective function and the constraints. Mod-

els with non-linear objective functions and constraints

are the most time consuming to solve.

This section describes our approach to optimally

divide the packets among the networks. We develop a

mathematical model for such optimization problem

and analyze the model complexity. We further sim-

plify the model to better suit devices with limited

computing and energy resources. First, we discuss the

big picture and where our work fits.

3.1. The Big Picture

We envision a tool that monitors the performance of

the different transports and adjust the packet splitting

ratio depending on the past experience with the net-

works. Since the user computing/communication de-

vice, when connected through one transport, is just a

node in the network, the values of QoS parameters of

that network cannot be exactly known. The tool would

document the user’s experience. Such experience is to

be used by the optimization module in adjusting the

split ratio in order to meet optimality criteria. The user

interface allows for changing user’s priorities for the

different quality parameters. The packet router en-

forces the packet splitting ratio generated by the

optimization module. Figure 1 depicts the interaction

between the different modules.

Routing packets of a particular source to a destina-

tion through multiple connections are not trivial since

most widely used network protocols associate the

address of the connection to the source. Packets sent

from source to destination with different source ad-

dresses than the established connection are likely to be

OptimizationModule

Model manager

Packet routerTran sport

quality monitor

User interface

Transport qualitymeasurements

Desired quality of serviceparameters

Tran

spor

tqu

ality

mea

sure

men

ts

Optim

altransportutilization

Quality

interest

Achievable

quality

Fig. 1. Interactions among the proposed software modules.

TRANSPORT SELECTION FOR WIRELESS PORTABLE DEVICES 11


dropped. We assume the availability of a network

layer protocol that handles such packet routing issue,

for example Reference [1]. The optimization process

can be envisioned as part of the protocol application

layer. This paper is only concerned with the model

manager and the optimization module of the tool.

3.2. Model Formulation

In this subsection, we formulize the problem of packet

splitting among different transports as an optimization

problem. The objective of the optimization model is to

maximize the QoS metrics as a function of the packets

allocated to every transport. We further simplify the

model in order to limit the computational complexity

of the optimization algorithm to fit a mobile comput-

ing environment in which a limited number of com-

pute cycles are available. While in the discussion we

focus on packet transmission, the formulation is

equally applicable to packet reception.

Before generating the overall objective function

and the constraints for optimal transport selection,

let us define the parameters and notations used.

m : Number of transport services available.

n : Number of QoS parameters considered.

T : Total number of packets.

Wj : User defined weighting factors for a QoS

parameter j (0�Wj� 1.0 andPm

j¼1

Wj ¼ 1).

Pj : Different QoS parameters considered by

the user with j ranging from 1 to n.

UPj : User specified bound value for each QoS

parameter with j ranging from 1 to n.

Pij : Actual QoS parameter value with i ranging

from 1 to m and j ranging from 1 to n.

meanij : Average value of a particular QoS para-

meter j over a particular transport i.

�ij : Standard deviation value of QoS parameter

j on transport i.

The problem can be formulated as finding the optimal

splitting of the T packets into a1, a2, . . . ,am packets to

be transmitted through transport 1 to m, respectively.

A precise formulation of the objective function would

involve the values of QoS metrics, such as throughput

for each network at the time of the packet splitting.

Obtaining an exact measure of these QoS metrics for a

particular transport would require a complete knowl-

edge and consistent monitoring of the entire network,

something a mobile user node cannot perform. There-

fore, we have decided to capture the effect of the QoS

parameters instead.

The effect of the QoS parameters on performance

can be contradicting. For example, to reduce the

transmission error rate the device should transmit

packets at high power, and thus increase the energy

consumption. Given such difficulty in controlling all

of the QoS parameters to achieve a positive impact

on all metrics, we decided to formulate an objective

function ‘F’ that is a weighted average of parameter-

specific functions. Since, enhancing the quality of the

communication requires minimizing most parameters

such delay, energy, etc., we formulate the problem as a

minimization problem. Objective functions for para-

meters such as bandwidth that need to be maximized

are transformed into an equivalent minimization for-

mulation. If F1, F2, . . . , Fn are the objective functions

for each QoS parameter, the overall objective function

can be expressed as:

Minimize

FðP; a1; ... ;mÞ ¼ W1 � F1ðP1; a1; ... ;mÞ þ � � � þWn

� FnðPn; a1; ... ;mÞ

The minimization of this function is constrained by

the bounds on the values of the QoS parameters

specified by the user.

Xmi¼1

aiPij � UPj

Xmi¼1

ai ¼ T ðTotal number of packets in the jobÞ

ai � 0 8 1 � i � m; and ai are all integers

Given the diverse nature of the QoS parameters, the

objective function of every parameter has to be unit-

less and normalized so that it would take values in

(0, 1]. The objective functions for the QoS parameters

are defined as follows:

Function for Bandwidth: Bandwidth reflects the data

transmission rate of a particular transport. If we ignore

collisions among the different transports, the band-

width can be considered an additive quantity. Thus,

the objective function for bandwidth can be expressed

as:

F1ðP1; a1;...;mÞ ¼Xmi¼1

BWi � ai



Where BWi is the bandwidth offered by transport

‘i’ The normalized and unitless function can be

obtained by dividing by BWT, which is the sum of

the bandwidths of all transports, that is BWT ¼Pmi¼1 BWi

F1ðP1; a1;...;mÞ ¼ ð1=TÞXmi¼1

BWi

BWT

� �� ai

The optimal value is obtained by maximizing F1.

Since the packet splitting formulation is a minimiza-

tion problem, F1 has to be transformed in order to fit

into overall objective function. F1 can be expressed as

minimization function as follows:

F1ðP1; a1;...;mÞ ¼ 1� ð1=TÞXmi¼1

BWi

BWT

� �� ai ð1Þ

Function for Latency: Assuming that the latency on a

particular transport obeys a normal distribution func-

tion and that the delays on the different transports are

independent, the latency of the split packets can be

expressed as a normal distribution with average means

and variance of all transports.

F2ðP2; a1;...;mÞ ¼ 1ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi2��Pm

i¼1 a2i �

2Li

p ðbound0

� expx�Pm

i¼1 ai�meanLi

�2�ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiPm

i¼1 a2i �

2Li

p !

dx

Since such formulation of F2 introduces unwanted

complexity to the objective function, we simplify it by

considering only the mean latency of every transport.

The contribution of the variance will be captured by

the jitter function. Thus, using the mean of combined

normal distribution:


meanLi � ai

The normalized and unitless function can be obtained

by dividing by meanLT, which is the sum of all mean

value of latency of all transports, that is

meanLT ¼Pmi¼1 meanLi


meanLi

meanLT

� �� ai ð2Þ

Function for Jitter: Similar to the latency, the jitter of

the split packets is expressed as a normal distribution.

As we have done with the latency function, the

simplified and normalized the objective function for

jitter is


meanJi

meanJT

� �� ai ð3Þ

Function for cost: Similar to bandwidth, cost is

considered additive and incurred on a per packet basis.

Following similar analysis to the bandwidth, the

function for cost can be expressed as:


Ci

CT

� �� ai ð4Þ

Function for Energy Consumption: Similar to cost and

bandwidth, energy is consumed on a per packet basis

and is thus additive.


Eti

ET

� �� ai ð5Þ

Since the energy consumed per packet for transmis-

sion is significantly different from the case of recep-

tion, the energy per packet Eti has to be adjusted

accordingly.

Function for Error Rate: Assuming transmission (re-

ception) error obeys a Poisson distribution and errors

on the different transports are independent, the com-

bined error distribution will be again a Poisson dis-

tribution with additive means.

F6ðP6; a1;...;mÞ ¼Xboundx¼0

1

x!

Xmi¼1

ai �meani

!x

� e�Pm

i¼1ai�meani

The function F6 can be further simplified by con-

sidering the mean error rates for all transports and

normalized similar to the latency function.


meanerri

meanerrT

� �� ai ð6Þ

The overall objective function can thus be ex-

pressed from Equations (1) to (6) as



Minimize

FðP; a1;...;mÞ ¼ W1 � ð1� ð1=TÞXmi¼1

BWi

BWT

� �� aiÞ

þW2 � ð1=TÞXmi¼1

meanLi

meanLT

� �� ai


meanJi

meanJT

� �� ai


Ci

CT

� �� ai


Eti

ET

� �� ai


meanerri

meanerrT

� �� ai

subject to the following constraints

Xmi¼1

aiPij � UPj;Xmi¼1

ai ¼ T

ai � 0 8 1 � i � m; and ai are all integers

The function F can be characterized as a linear

objective function of ai’s with linear inequality con-

straints. Since the solution has to be integers, the

optimization problem is classified as an integer linear

programming problem. Many techniques have been

proposed in the literature to solve such problem very

efficiently [20].

Given the dynamic nature of the network, it would

be beneficial to perform the packet splitting on the

smallest possible T in order to ensure the freshness of

the measures of the QoS parameters on which the

optimization is based. On the other hand, the proposed

tool is expected to run on resource-constrained por-

table devices and thus high frequency of running the

optimization algorithm can be an issue. Ideally,

although not practical due to the excessive overhead,

the optimization is performed on a per-packet basis.

We envision the number of packets T, which achieves

the best gains through splitting on multiple transports

with acceptable overhead, would highly depend on the

device capabilities and the available transports.

4. Experimental Validation

We have validated the mathematical model described

in the previous section through simulation. Nine

different wireless transports have been considered.

In the simulation, the network behavior and the

dynamic changes of network parameters are modeled

using the standard specifications and published per-

formance measurements of the considered transports.

The main goals of the simulation-based experiments

are:

� To validate the correctness of the formulation and

the capability of the model in capturing the effect of

the most important parameters.

� To show how the model reacts to changes in user’s

priorities. Such study can guide the choice of the

weighting factors by quantifying the relative impact

on the performance caused by a change in one of

the parameters.

� To uncover any dependencies among the QoS

parameters. Such investigation can point out

weighting factors that possibly lead to equivalent

effects and unexpected anomalies caused by certain

priority settings.

4.1. Simulation Design

For every transport considered, a network is simulated.

The simulation is based on load-performance relation-

ship for the underlying network. Since a single terminal

node cannot predict the internal structure of a network,

we believe that the only choice for a node is to reflect

on its perception of the network performance relative to

the load, to which the node is also contributing.

The workload on a network is modeled by the

number of user nodes connected to the network.

User arrival and departure follow a Poisson process,

that is exponential inter-arrival and departure time.

Every user generate packets on the network following

an exponential distribution. The number of packet

generated is picked using a uniform distribution.

A network is simulated by means of a single event

queue. Events include the generation of new set of

packets, the arrival of packets to their destination, a

new user joining the network and the departure of an

existing user. Events are pre-scheduled using the

inter-arrival time. For example, every time a user

joins a network the inter-arrival time for the next

user is calculated using the exponential distribution

and inserted in the event queue.

Packet delay is predicted at the time of packet

generation based on the current network load. That

delay is used to schedule packet arrival at their

destination and a delete event is attached to the queue.



For a packet delete event, the load is adjusted and the

next packet generation is scheduled. Figure 2 shows a

snapshot of the event queue having different delay

values at various instants depending on network load.

Figure 3 outlines the queue management procedure.

The simulation time and the time unit are parameters

of choice. Every time increment, the current time is

adjusted and the event queue is checked. In case of

matching the schedule of an event, the appropriate

action is taken and the process is repeated. It should be

noted that there is an event queue for every transport

that is considered in the simulation.

4.2. Experiment Setup

We have validated our mathematical model using nine

transports, namely Bluetooth, IEEE 802.11 Wireless

LAN, GSM, GPRS, UMTS, WCDMA, TDMA,

TETRA, and Ricochet. The values of the different

transport QoS parameters and how these values

change with load are based on published performance

studies found in References [21–29].

The parameter setting in our experiments are listed

in Table I. Energy consumption is estimated based on

the average distance between hops in different wire-

less networks. In our experiments, the selection of

the user specified bounds for network parameters are

based on multimedia environment where voice or data

No. Packets Delay Time Next Arrival Time Entry

15 0.00025 0.0005 "insert"


30 0.000375 0.0007 "insert"


10 .... 0.0012 "delete"


20 0.00045 0.0010 "insert"


15 .... 0.0008 "delete"

Fig. 2. Snapshot of the event queue.

Start

Initialize current time to zero andset all the QoS parameters to

values consistent with initial load

current time < simulation time

current time > arrival time

current time > delay time

Increase total load by current # packets,generate new users and packets andcalculate next arrival and delay time

Decrease total load by current # packets,generate new users and packets, andcalculate next arrival and delay time

current time = current time + increment

current time > next userarrival time

Stop

Generate new users andpackets and calculatearrival and delay time

Ye s

Ye s

Ye s

Ye s

No

No

No

No

Fig. 3. Flow diagram for simulator design.

Table I. Transport parameter used in the experiment.

Networks Bandwidth Delay Jitter Cost Energy Error User arrival Initial load(bit/s) (s) (s) (mJ) rate rate (# users)

Bluetooth 9600 0.6375 1E-09 0.07 1.445 0.05 10 20WLAN 11M 1.18 0.094 0.005 0.2 0.0001 40 40GSM 9600 0.47 1E-08 0.019 1.65 0.0005 10 20RICOCHET 128K 0.47 0.012 0.0512 0.3 0.001 10 20GPRS 113K 0.47 0.08 0.019 0.4 0.01 10 20UMTS 2M 0.67 25E-13 0.01 1.9 0.00001 15 15WCDMA 2.4M 0.47 1E-11 0.01 2 0.00001 20 40DECT 2M 0.2 6E-08 0.005 2.5 0.001 40 50TETRA 28800 0.8 5E-09 0.07 1.8 0.05 30 10



packets are streamed through the different networks.

The values of the bounds for delay, error rate, cost,

and energy are 0.75 s, 0.0512 packet/s, $0.64, and

1800mJ respectively [13,18,30–32].

4.3. Simulation Results

We have considered the prime choices for the weight-

ing factors to show how performance parameters

change with number of packets. The weighting factors

of each parameter for each setup are specified in the

caption of the respective graph. The performances

resulting from packet splitting is referred to as com-

bined network.

Figures 4 and 6 represent the curves for average

delay versus the number of packets, while Figures 5

and 7 represent the curves for energy consumption

versus the number of packets. For these Figures, the

weighting factors for only two QoS parameters are

considered while resetting the weighting factors of the

other parameters to zero. The graphs of Figures 4 and

5 have less priority for delay than for energy con-

sumption. Figure 4 shows that the delay for the case of

packet splitting is less than the delay incurred using

any of the other networks individually. This is mainly

due to parallelism in packet transmission. In Figure 5,

the curve for combined energy consumption is less

than most of the other curves. This is because energy

consumption is given high priority in Figure 5.

Comparing Figures 4 and 6, we see that delay curve

is lower in Figure 6 since more priority is given to

delay, and the networks with the best delay value are

selected. Meanwhile, the curve for energy consump-

tion in Figure 7 is higher than that of Figure 5 since

priority for the energy consumption factor is lowered.

In the case of Figure 5, most packets are sent through

the WLAN network, which has the least energy while

in the case of Figure 7, most packets are sent through

the DECT network, which has least delay. We can see

that the delay curve of the combined network in both

cases is lower than most other curves due to paralle-

lism. Even though we give more priority to other

parameters, the delay curve is always less. Therefore,

delay can be assigned low weight since it is enhanced

anyway by parallelism. It is worth noting that similar

observations could be made when we considered

delay and cost [33].

Figure 8 shows the change in the average delay with

respect to the number of packets while considering

only the weighting factors for delay and bandwidth.

Even though the values of the weighting factors of

0

0.5

1

1.5

2

2.5

0 200 400 600 800 1000

Number of packets

Ave

rag

ed

elay

(s)

Bluetooth

WLAN

GSM

GPRS

UMTS

WCDMA

TDMA

TETRA

Ricochet

Combined

Fig. 4. Average delay versus # packets, weighting factorsdelay¼ 0.2, energy¼ 0.8, rest are 0.0’s.

0

500

1000

1500

2000

2500

200 0 400 600 800 1000

Number of packets

En

erg

y C

on

sum

pti

on

(mJ)

Bluetooth

WLAN

GSM

GPRS

UMTS

WCDMA

TDMA

TETRA

Ricochet

Combined

Fig. 5. Energy consumption versus # packets, weightingfactors delay¼ 0.2, energy¼ 0.8, rest are 0.0’s.

0

0.5

1

1.5

2

2.5

0 200 400 600 800 1000

Number of packets

Ave

rag

e d

elay

(s)

Bluetooth

WLAN

GSM

GPRS

UMTS

WCDMA

TDMA

TETRA

Ricochet

Combined

Fig. 6. Average delay versus # packets, weighting factorsdelay¼ 0.8, energy¼ 0.2, rest are 0.0’s.

0

500

1000

1500

2000

2500

200 0 400 600 800 1000

Number of packets

En

erg

y C

on

sum

pti

on

( mJ)

Bluetooth

WLAN

GSM

GPRS

UMTS

WCDMA

TDMA

TETRA

Ricochet

Combined

Fig. 7. Energy consumption versus # packets, weightingfactors delay¼ 0.8, energy¼ 0.2, rest are 0.0’s.



bandwidth and delay were changed, we have founded

that the average delay experienced is almost the same.

Given the closeness of the results obtained only one

figure is included. Such behavior stays consistent as

long as the delay factor is considered, that is using

non-zero weight. A non-zero weight for the delay

forces the use of multiple transports and enhances the

response time through parallelism. Such results sug-

gest the inter-dependency between the delay and

bandwidth parameters and thus the weighting factor

of the bandwidth factor can be set appropriately as

long as the delay parameter is randomly assigned non-

zero value.

Figures 9 and 10 capture the change in the values of

average delay and jitter with respect to the number of

packets. From these figures, we see that even though

the weighting factors of jitter and delay are inter-

changed, the impact on both jitter and delay with

respect to the number of packets remains the same.

This indicates the inert-dependency between the jitter

and delay parameters delay, similar to the earlier case

of delay and bandwidth.

Considering the bandwidth and jitter parameters,

Figures 11 and 13 show the relationship between

average delay and the number of packets, while

Figures 12 and 14 represent the curves for jitter versus

the number of packets. In the case of jitter, not all

curves are shown because there is a large variation in

values of jitter for the different networks. It is clear in

Figures 11 and 13 that the delay curve for the

combined network is less than the delay curve for

other networks. The reduction in delay is expected

given the packets splitting among multiple transports.

0

0.5

1

1.5

2

2.5

0 200 400 600 800 1000

Number of packets

Ave

rag

e d

elay

(s)

Bluetooth

WLAN

GSM

GPRS

UMTS

WCDMA

TDMA

TETRA

Ricochet

Combined

Fig. 8. Average delay versus # packets for weight factorsdelay¼ 0.2, bandwidth¼ 0.8, rest are 0.0’s, delay¼ 0.6,

bandwidth¼ 0.4, rest are 0.0’s.

0

0.5

1

1.5

2

2.5

200 0 400 600 800 1000

Numberof packets

Ave

rag

e d

elay

(s)

Bluetooth

WLAN

GSM

GPRS

UMTS

WCDMA

TDMA

TETRA

Ricochet

Combined

Fig. 9. Average delay versus # packets for weighting factorsjitter¼ 0.2, delay¼ 0.4, rest are 0.1’s, jitter¼ 0.4,

delay¼ 0.2, rest are 0.1’s.

0

2

4

6

8

0 200 400 600 800 1000

Numberof packets

Jitt

er (

s)

Bluetooth

WLAN

GSM

GPRS

UMTS

WCDMA

TDMA

TETRA

Ricochet

Combined

Fig. 10. Average Jitter versus # packets for weighting factorsjitter¼ 0.2, delay¼ 0.4, rest are 0.1’s, jitter¼ 0.4,

delay¼ 0.2, rest are 0.1’s.

0

0.5

1

1.5

2

2.5

0 200 400 600 800 1000

Numberof packets

Ave

rag

e d

elay

(s)

Bluetooth

WLAN

GSM

GPRS

UMTS

WCDMA

TDMA

TETRA

Ricochet

Combined

Fig. 11. Average delay versus # packets, weighting factorsbandwidth¼ 0.4, jitter¼ 0.2, rest are 0.1’s.

-2

0

2

4

6

8

0 200 400 600 800 1000

Numberof packets

Jitt

er (

s)

Bluetooth

WLAN

GSM

GPRS

UMTS

WCDMA

TDMA

TETRA

Ricochet

Combined

Fig. 12. Jitter versus # packets, weighting factorsbandwidth¼ 0.4, jitter¼ 0.2, rest are 0.1’s.



The delay curve in Figure 13 is a little bit higher than

Figure 11 showing the superior effectiveness of ele-

vating the bandwidth to the jitter priority in enhancing

the average delay.

Comparing Figures 12 and 14, we find that the jitter

curve has increased when more priority is given to the

jitter parameter. This is opposite to what is expected

since the jitter curve should have decreases when the

priority of jitter is increased. Such unexpected perfor-

mance is because less number of networks is selected

when the bandwidth factor is 0.2 (jitter factor¼ 0.4)

compared to the case of 0.4 (jitter factor¼ 0.2). Since

fewer networks are selected, the net delay and jitter of

the combined network increases.

For additional experimental results involving other

combinations of parameters settings, the reader is

referred to Reference [33].

4.4. Parameters Setting

Based on our experience with the experiments and

conclusions drawn from the simulation results, we can

make the following remarks about QoS measure-

ments, parameters setting, and handling conflicting

goals in practice:

� From the experiments, we conclude that the weigh-

ing factor for the delay parameter and that of either

the bandwidth or the jitter parameter would have

equivalent effect on the delay metric. That is to say

focusing on the jitter or the bandwidth would have

positive impact on delay, with the bandwidth factor

demonstrating more effectiveness. However, it

should be noted that favoring the delay factor

does not necessarily enhance both the bandwidth

and the jitter.

� It is recommended to manipulate the priority of the

bandwidth parameter when the device connects to

new transports since there would be no experience

with the transport at that moment. During the use of

the transport the device will establish statistics

regarding the transport and controlling the priority

of the other parameters would be more appropriate.

� Collecting the statistics about a particular transport

can be tricky. Given the dynamic nature of traffic in

wireless ad hoc and cellular infrastructure, a node

perception about a particular transport does not

usually hold for long duration and continual assess-

ment would be needed. On the other hand, collect-

ing statistics imposes overhead and requires

experiencing all transports, even those for which

connections are not established. Thus, the fre-

quency of re-assessing a transport is subject to a

tradeoff and is expected to depend heavily on the

node and available transports.

� For applications that would favor the consideration

of the cost or energy parameters, the delay factor

can be given lower priority relying on the simulta-

neous packet transmission in minimizing the aver-

age packet delay. To force packet splitting and

avoid going with the least cost or energy transport,

a delay constraint should be imposed or a very

small weight can be assigned to the delay factor.

� Contradicting factors such as energy and error rate

can be effectively managed with the inclusion of

appropriate constraints and the use of equal weights

or picking only one factor for consideration (zero

weight for the other factor). Our model then will

cope with the minimal requirements and pursue

transport selection to optimize the valued factors.

5. Conclusions and Future Work

Technological advancements in the mobile com-

puting have foiled the development of new wireless

modems in order to connect such devices to network

0

0.5

1

1.5

2

2.5

200 0 400 600 800 1000

Numberof packets

Ave

rag

e de

lay(

s)

Bluetooth

WLAN

GSM

GPRS

UMTS

WCDMA

TDMA

TETRA

Ricochet

Combined

Fig. 13. Average delay versus # packets, weighting factorsbandwidth¼ 0.2, jitter¼ 0.4, rest are 0.1’s.

0

2

4

6

8

0 200 400 600 800 1000

Number of packets

Jitt

er(s

)

Bluetooth

WLAN

GSM

GPRS

UMTS

WCDMA

TDMA

TETRA

Ricochet

Combined

c

Fig. 14. Jitter versus # packets, weighting factorsbandwidth¼ 0.2, jitter¼ 0.4, rest are 0.1’s.



infrastructure while the user is on the move. Many

wireless transports are currently available such the

IEEE 802.11 wireless LAN and Bluetooth, and more

are being developed. The cost and size of these

modems are decreasing so rapidly that it is expected

for future portable devices to be equipped with multi-

ple of these modems in order to ensure user access to

the diverse network infrastructure.

The availability of these modems presents an op-

portunity for better user experience with applications

that requires network access. Multiple of these trans-

ports can be simultaneously used to meet and even

exceed user expectation regarding the quality of the

communication. In this paper, we have developed a

mathematical model for packet splitting across multi-

ple transports. The model captures the effect of

common parameters that control the quality of service

attained from a typical network. These parameters

include bandwidth, average delay, delay jitter, etc.

The model is further simplified to suit the energy and

computationally constrained portable devices.

The model is validated through simulation. The

simulation results have demonstrated the effectiveness

of our approach and the performance gains that the

user application can achieve. The experiments clearly

have indicated that the average delay is consistently

better than the case of using a single transport. Such

significant delay reduction is mainly due to the paral-

lel usage of multiple transports. In addition, the

experiments have captured dependency among the

different QoS parameters and provided guidelines on

how priorities can be assigned.

The work presented in this paper can be extended

by taking into consideration the resources consumed

by the optimization software module itself into the

model. Since portable devices are constrained in

energy supply and computation capacity, the gain

achieved by the optimization algorithm has to be

qualified using the resources consumed. Another

possible extension is by investigating the frequency

of running the optimization. Given the dynamic en-

vironment that portable devices operate in, changes in

the network quality parameters can be very often and

there will be a tradeoff between the frequency of

running the algorithm to adapt to these changes and

the overhead incurred when running the optimizer.

Acknowledgment

The authors are indebted to Aether Systems, Inc., for

funding this research work and to Professor D. Phatak

for his constructive comments.

References

1. Phatak DS, Goff T. A novel mechanism for data streamingacross multiple IP links for improving throughput and relia-bility in mobile environments. In Proceedings of the IEEEINFOCOM’02, New York, NY, June 2002.

2. ChiangM, O’Neill D, Julian D, Boyd S. Resource Allocation forQoS provisioning in Wireless Adhoc networks. In Proceedingsof IEEE GLOBECOM, San Antonio, TX, November 2001.

3. Rajkumar R, Lee C, Lehoczky J, Siewiorek D. Practicalsolutions for QoS-based resource allocation problems. InProceedings of the IEEE Real-Time Systems Symposium,Madrid, Spain, December 1998.

4. Lee C, Lehoczky J, Rajkumar R, Siewiorek D. On quality ofservice optimization with discrete QoS Options. In Proceed-ings of the IEEE Real-time Technology and ApplicationsSymposium, Vancouver, Canada, June 1999.

5. Qiu L, Bahl P, Adya A. The effect of first-hop wirelessbandwidth allocation on end-to-end network performance. InProceedings of the 12th International Workshop on Networkand Operating Systems Support for Digital Audio and Video(NOSSDAV), Miami Beach, FL, May 2002.

6. Naik A, Siegel H, Chong E. Dynamic resource allocation forclasses of prioritized session and data requests in preemptiveheterogeneous networks. In Proceedings of the InternationalConference on Parallel and Distributed Processing Technolo-gies and Applications (PDPTA 2001), Las Vegas, NV,June 2001.

7. Choi S, Shin K. Predictive and adaptive bandwidth reservationfor handoffs in QoS-sensitive cellular networks. In Proceedingsof the ACM SIGCOMM’98, Vancouver, Canada, August 1998.

8. Das S, Perkins C, Royer E. Performance comparison of twoon-demand routing protocols for ad hoc networks. In Proceed-ings of the IEEE Conference on Computer Communications(INFOCOM), March 2000.

9. Ogawa J, Nomura Y. A simple resource management architec-ture for differentiated services. In Proceedings of the InternetConference (INET 2000), Yokohama, Japan, July 2000.

10. Mahajan M, Parashar M. Managing QoS for multimedia appli-cations in a differentiated services environment. In Proceedingsof the 4th Annual International Workshop on Active MiddlewareServices (AMS’02), Edinburgh, United Kingdom, July 2002.

11. Fulp E, Reeves D. Optimal provisioning and pricing ofdifferentiated services using QoS class promotion. In Proceed-ings of the INFORMATIK Workshop on Advanced InternetCharging and QoS Technology (ICQT’01), 2001.

12. Pandey V, Ghosal D, Mukherjee B. Comparison of channelpartitioning strategies in single- and two-tier cellular networks.In Proceeding of IEEE Conference on Computer Communica-tions (INFOCOM’98), San Francisco, CA, 1998.

13. Popovici E, Borcoci E, Constantin A. Multimedia oriented—transport architectures and QoS management: models andsimulation studies in estelle. Research Report No: 000 06—LOR, Telecommunication Networks Department, Universityof Bucharest, September 2000. (http://www-lor.int-evry.fr/idemcop/uk/real-cs/mm-taqm/download/mm-exp-estelle.pdf)

14. Hoo G, Johnston W. QoS as middleware: bandwidth reserva-tion system design. In Proceedings of the 8th IEEE Interna-tional Symposium on High Performance DistributedComputing, Redondo Beach, CA, August 1999.

15. Das SK, Jayaram R, Kakani NK, Sen SK. A call admission andcontrol scheme for quality of service (QoS) provisioning innext generation wireless networks. Wireless Networks 2000;6(1): 17–30.

16. Cheng ST, Chen IR, Chen CM. A study of self-adjustingquality of service control schemes. In Proceedings of theWinter Simulation Conference (WSC’98), Washington DC,December 1998.



17. Prakash R, Shivaratri NG, Singhal M. Distributed dynamicchannel allocation for mobile computing. In Proceedings ofthe 14th ACM Symposium on Principles of Distributed Com-puting (PODC), Ottawa, Canada, August 1995.

18. Havinga P, Smit G. Mobile multimedia systems. In Chapter 15of Electronic Business and Education, Recent Advances inInternet Infrastructures, ChinWet al. (eds). Kluwer AcademicPublishers: Boston, 2001; 319–360.

19. Rosu ID, Schwan K, Yalamanchili S, Jha R. On adaptiveresource allocation for complex real-time applications. InProceedings of the 18th IEEE Real-Time Systems Symposium,San Francisco, December 1997.

20. Nemhauser G, Wolsey L. Integer and Combinatorial Optimi-zation. Wiley: New York, 1999.

21. Bluetoothi. Golmie N, Chevrollier N. Techniques to improve Bluetooth

performance in interference environments. In Proceedings ofMILCOM 2001, McLean, VA, October 2001.

ii. Lim Y, Kim J, Min SL, Ma JS. Performance Evaluation of theBluetooth based Public Internet Access Point. In Proceedingsof the 15th International Conference on Information Network-ing (ICOIN’01), Beppu, Japan, February 2001.

iii. http://www.us.design-reuse.com/news/news4288.html

22. 802.11 Wireless LANi. Anastasi G, De Stefano E, Lenzini L. QoS provided by the

IEEE 802.11 wireless LAN to advanced data applications: aSimulation Analysis. ACM/Baltzer Wireless Networks 2000;6(2): 99–108.

ii. Chow C, Leung V. Performance of IEEE 802.11 mediumaccess control protocol over a wireless local area networkwith distributed radio bridges. In Proceedings of the WirelessCommunication Networks Conference (WCNC), New Orleans,LA, September 1999.

iii. Ng CH, Chow J, Trajkovic L. Performance evaluation ofthe TCP over WLAN 802.11 with the Snoop performanceenhancing proxy. In Proceedings of OPNETWORK 2002,Washington, DC, August 2002.

iv. Leang L, Liew J, Seah W. Experimentation of TCP Schemesover GPRS & WLAN. In Proceedings of the 4th IEEEConference on Mobile and Wireless CommunicationsNetworks (MWCN 2002), Stockholm, Sweden, September2002.

v. Birkeland T, Nilsson F. Limitations in performance for WLANtechnologies. Master thesis in Information and Communica-tion Technology, Agder University College, Norway, May2002.

vi. http://www.tno.nl/instit/fel/ts/resources/simulation_ofWLAN.pdf

23. Ricocheti. http://www.wherry.com/gadgets/ricochet/ii. http://daedalus.cs.berkeley.edu/talks/retreat.6.96/Metri-

com.pdfiii. Stemm M, Katz RH. Measuring and reducing energy con-

sumption of network interfaces in hand-held devices. IEICETransactions on Communications 1997; E80-B(8): 1125–1131.

24. UMTSi. http://www.nt.tuwien.ac.at/mobile/projects/UMTS/en/ii. http://www.ftw.at/Dokumente/011130a.pdf

25. TETRAi. http://www.acterna.com/technical_resources/downloads/data-

sheets/4032tetrapol_ds_ae.pdfii. http://www.tetrapol.com

iii. Kuypers D, Sievering P, Steppler M. Traffic performanceevaluation of TETRA and TETRAPOL. In Proceedings ofthe 10th Aachen Symposium on Signal Theory, Aachen,Germany, September 2001.

26. GPRSi. http://www.gsm.org.uk/gprs.htmii. Araujo H, Costa J, Correia L. Analysis of a Traffic Model for

GSM/GPRS. In Proceedings of the 3rd Conference on Tele-communications, Figueira da Foz, Portugal, April 2001.

iii. Stuckmann P, Ehlers N, Wouters B. GPRS traffic performancemeasurements. In Proceedings of the IEEE Vehicular Technol-ogy Conference (VTC 2002), Vancouver, Canada, September2002.

iv. Saija D, Toniatti T. Performance evaluation of GPRS (GenericPacket Radio Service) radio access with quality of serviceprovision. In Proceedings of the 21st International Conferenceon Distributed Computing Systems (ICDCS), Phoenix, Ari-zona, April 2001.

v. Foh C, et al. Modeling and Performance Evaluation of GPRS.In Proceedings of IEEE Vehicular Technology Conference(VTC 2001), Rhodes, Greece, May 2001.

27. GSMi. Ajib W, Godlewski P. Service disciplines performance for

best-effort policies in packet-switching wireless cellular net-works. In Proceedings of IEEE Annual Vehicular TechnologyConference (VTC 2000), Tokyo, Japan, May 2000.

ii. http://wireless.agilent.com/rfcomms/refdocs/gsm/hpib_fetch_berror.html

iii. http://www.tele-servizi.com/janus/engfield2.html

28. DECTi. Zhang H, Yum TP. A dynamic reservation protocol for

prioritized multirate mobile data services based on DECT airinterface. IEEE transactions on Vehicular Technology 2000;49(2): 672–676.

ii. http://www.digitaltalkback.com/netscape/intromain.htmiii. http://www.comlab.hut.fi/opetus/260/111215data.pdf

29. WCDMAi. Matis K. Multilevel simulation of WCDMA systems for third-

generation wireless applications. Technical report, ICUCOMCorporation, http://www.sss-mag.com/pdf/wcdma.pdf

ii. Latva-aho M. Bit error probability analysis for FRAMESWCDMA downlink receivers. IEEE Transactions on VehicularTechnology 1998; 47(4): 1119–1133.

iii. Gu X, Olafsson S. A simplified and accurate method to analysea code division multiple-access performance. In Proceedingsof the Annual London Communication Symposium (LC 2000),September 2000.

iv. http://www.datum.com/pdfs/datum.pdf30. Chandra S. Wireless network interface energy consumption

implications of popular streaming formats. In Proceedings ofthe Symposium on Multimedia Computing and Networking(MMCN), San Jose, CA, January 2002.

31. Krashinsky R, Balakrishnan H. Minimizing energy for wire-less web access with bounded slowdown. In Proceedings ofACM MobiCom 2002, Atlanta, GA, September 2002.

32. Yuan W, Nahrstedt K, Gu X. Coordinating energy awareadaptation of multimedia applications and hardware resource.In Proceedings of 9th the ACM Multimedia Middleware Work-shop, Ottawa, Canada, October 2001.

33. Sardesai A. Optimal dynamic transport selection for mobilecomputing. MS Thesis, Department of Computer Science andElectrical Engineering, University of Maryland BaltimoreCounty, 2002.



Authors’ Biographies

Mohamed F. Younis received his B.S.degree in Computer Science and M.S.in Engineering Mathematics fromAlexandria University in Egypt in1987 and 1992, respectively. In 1996,he received his Ph.D. in ComputerScience from New Jersey Institute ofTechnology. He is currently an assis-tant professor in the Department ofComputer Science and Electrical Engi-

neering at the University of Maryland Baltimore County(UMBC). Before joining UMBC, he was with the AdvancedSystems Technology Group, an Aerospace Electronic Sys-tems R&D organization of Honeywell International, Inc.While at Honeywell, he led multiple projects for buildingintegrated fault tolerant avionics in which a novel architec-ture and an operating system were developed. This newtechnology has been incorporated by Honeywell in multipleproducts and has received worldwide recognition by boththe research and the engineering communities. He alsoparticipated in the development of the redundancy manage-ment system, which is a key component of the Vehicle andMission Computer for NASA X-33 space launch vehicle. DrYounis’ technical interest includes network architecturesand protocols, embedded systems, fault tolerant computing,and distributed real-time systems. Dr Younis has four

granted and three pending patents. He served on multipletechnical committees and published over 60 technicalpapers in refereed conferences and journals.

Amit Sardesai received his bachelordegree in Computer Science from theUniversity of Mumbai, India and hisM.S. degree in Computer Science fromthe University of Maryland BaltimoreCounty. He is currently pursing hisPh.D. in Computer Science at theUniversity of Florida. His researchinterests include wireless networks,distributed computation, web services,and databases.

Yaacov Yesha is a professor at theDepartment of Computer Science andElectrical Engineering at the Univer-sity of Maryland Baltimore County.He received his Ph.D. in ComputerScience in 1979 from the WeizmannInstitute of Science. His interestsinclude mobile computing, wirelessnetworks, and software testing.Yaacov Yesha was a program vice

chair or a program committee member for several scientificconferences.



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Optimal dynamic transport selection for wireless portable devices