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Heterogeneous wireless network management


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Heterogeneous wireless network management
W. Qadeer*, T. Simunic*^, J.Ankcorn^, V. Krishnan^ and G. De Micheli*
*Stanford University, ^HP Labs
AbstractTodays wireless networks are highly heterogeneous
with diverse range and QoS. The maintenance of a wireless link
by a mobile device requires support of multiple network
interfaces. Since the battery lifetime is limited, power
management of their interfaces without any significant
degradation in performance has become essential. In our
research we developed an integrated approach for the
management of power and performance of mobile devices in
heterogeneous wireless environments. Our policy decides what
wireless network interface (WNIC) to employ for a given
application and how to optimize the WNIC usage. This decision is
governed by the current power and performance needs of the
system. The policy dynamically switches between interfaces
during program execution if data communication requirements
and/or network conditions change. For the verification of our
power and performance management algorithm, we have
experimentally characterized Bluetooth and 802.11b wireless
interfaces. We implemented our policy on HPs IPAQ portable
device that is communicating with HPs HotSpot server [14].
The applications we tested range from MPEG video to email.
The results show that our policy offers

a large

improvement in
power savings as compared to singly using 802.11b or Bluetooth
while enhancing performance.
I.
I
NTRODUCTION

Mobile communications today has heterogeneous wireless
networks providing varying coverage and QoS. Various
communication services are available. The infrastructure
enables mobile devices to run applications with diverse
bandwidth and network connectivity requirements, such as
distributed speech recognition, video streaming, gaming etc.
To satisfy the bandwidth and QoS constraints of the
applications, the mobile devices need to allow seamless
switching among various wireless network interfaces..

Additionally, the high communication and computation cost
of applications is a burden on the battery life of portable
devices. Capacity of a battery has not increased tremendously.
Improvements of only a factor of 2-4 have been observed
during the past 30 years. The ever-increasing need for battery
lifetime in mobile devices demands a tighter control over its
energy consumption.

Although low-power circuit design forms the basis of
power management in a mobile device, higher-level
management of power dissipation offers many more
advantages. These techniques allow seamless integration
between user applications and power management policy
design thus allowing energy consumption to be reduced while
maintaining a desired QoS. During program execution
communication interfaces are placed in low-power states
depending upon their acces patterns and application
performance needs. Various components of the mobile
system, such as the user, the wireless channel and the
individual interfaces can be modeled using a state based
abstraction.

The techniques developed to date for the enhancement of
heterogeneous networks concentrate on improving their
accessibility and QoS. These methods enable mobile devices
to communicate with each other by introducing changes in the
network protocol stack. They also allow establishment and
maintenance of connections between mobile hosts using any
available links to improve robustness and performance.
However, none of the techniques adequately addresses power
management. Power reduction methodologies presented in
the past largely focus on improving energy consumption of
one single device e.g. WaveLAN, CPU etc. Policies for the
reduction of power dissipation range from simple time-out
methods to complex techniques based upon stochastic models.

This work presents a new methodology for managing
power and performance of mobile devices consisting of
heterogeneous WNICs. The policy formulated decides what
network interface to employ on a portable device for a given
pattern of usage. The decision is governed by the current
power dissipation and QoS requirements of the system. The
maximum likelihood estimator is employed for tracking
system changes. It detects variations in the average throughput
of available wireless interfaces and the data usage patterns.
The policy for power and performance management (PPM)
decides:

a. What wireless network interface card to use
b. What low-power state to employ
c. Transition times between active and low-power states
d. Buffer size to use for good application QoS

We implemented the policy on HPs IPAQ portable device
that is communicating with HPs HotSpot server [14] via
Bluetooth and 802.11b. The applications we tested range
from MPEG video to email. Our results show both large
savings in power when using a single WNIC, as well as
seamless switching with concurrent power savings among
WNICs.



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II. RELATED WORK

Mobile devices require wireless communication interfaces
to facilitate connectivity with Internet and with the other
devices. A mechanism is required for forwarding packets
between different wireless networks due to increasing device
mobility. Mobile IP [1] provides one example of such
mechanism. Changes are introduced in the network and link
layers of the network protocol stack that assist the hosts home
network in forwarding packets to its network of residence.
However, with mobile IP even if communicating devices are
in the same wireless network, data needs to traverse a multi-
hop path. In order to perform localized communication
between devices, which are one hop distance away, Contact
Networking [2] has been proposed. By allowing seamless
switching between multiple diverse interfaces, this technique
enhances robustness and QoS of the network.

Mobile hosts experience varying data rates during
communication in part due to lossy nature of the wireless link.
In order to avoid disruptions, a distributed file system has
been developed [3], [4]. It allows application aware and
application independent adaptation to a temporary loss or
degradation of the wireless link thus enhancing robustness. A
method for improving hand-offs proposes buffering data on
multiple base stations in close proximity to the mobile host [5]
thus achieving seamless switching between base stations.
Telephony and data services spanning diverse access networks
have been integrated in [6]. However, the focus of these
techniques has been on the enhancement of performance and
QoS of heterogeneous networks. Power management of
communicating hosts has been mainly overlooked.

Several techniques have been proposed to efficiently
manage power dissipation in portable devices. These methods
employ diverse mechanisms to predict periods of inactivity
during communication. Based upon these predictions the
mobile device is put into a low-power state. The most basic
power management policy is a time-out. If the device remains
idle for a certain period, it is put into a low power state.
Similarly, a device can enter low-power mode when idleness
is being anticipated in a connection [7]. However, incorrect
estimates cause performance and power penalties. In contrast,
stochastic models derive provably optimal power management
policies. Pure Markov decision processes [8], [9] employ
either discrete or continuous time memory-less distributions.
However, discrepancies have been observed in predicted and
actual power savings owing to history dependent nature of
real world processes. Time-indexed semi Markov decision
processes [10] are based upon history based distributions.
This technique has demonstrated energy savings in real-world
applications. The power management techniques presented to
date mostly focus on the reduction of power dissipation in one
WNIC. This leads to inefficient power management for
portables with multiple diverse communication interfaces.

Methods being employed for the performance enhancement
of homogeneous networks put a lot of emphasis on power
management. IEEE 802.11 [11] standard implements power
management by sending a traffic indication map (TIM) with
the beacon to the client. It enables the client to enter doze
mode if no more data is available. Since the device still has to
wake up after every beacon interval for TIM, a new technique
proposes decoupling of control and data channels [12]. The
control channel uses low-power radio and wakes up the device
whenever data is present. Application level information is
used for power management in [13]. In our work we
developed an integrated policy for power and performance
ma