Cognition is the scientific term for the
process involved in knowing, which includes perception and judgement. Such
process intelligently detects surrounding environment and makes decision based
on what is learnt. Recent advancements in sensor technologies coupled with
miniaturization and its huge potential to function without much human intervention
makes the deployment of an automated health care monitoring system feasible.
Body sensors (wearable, implantable, or portable) are fast emerging and are
envisioned to be a promising approach to monitor the physiological conditions
and disease progression. This enables monitoring physiologies without affecting
users’ normal day-to-day life. Many such body sensors have recently been proposed
and are already in the verge of commercial use. Wearable devices that measure
the vital signs of patients have drawn much research attention.
The end of the analog TV broadcasting opens
a broad spectrum bands in the 400 MHz and 700 MHz range for wireless
transmission for the public safety operations and other commercial services. A
wide range of applications will be trialed and competing for this band. The
excellent propagation characteristics of this band through buildings and other
obstructions makes it an ideal band for Multimedia Home Networks.
It can be considered a good medium for
at-home medical information collection and transmission. The transmission of
medical information
such as vital signs can be
performed using any or a combination of the existing wireless technologies,
e.g., cellular,WiFi, Zigbee, Bluetooth. Each of these technologies provides
different advantages in terms of coverage (e.g., cellular) or bandwidth (e.g., WiFi).
With the advent of heterogeneous wireless access networks, wireless service providers
(WSPs) can combine the complementary advantages of different wireless access
networks operating on both licensed and unlicensed bands to serve an increasing
amount of automated health monitoring demands. This, however, poses a unique
challenge of switching between different wireless networks to provide
ubiquitous connectivity.
When a CR senses the location of a device
that is transmitting in a frequency band that may cause interference between
them, it will switch its frequency band to avoid interference with that device.
Here, location information can be used to predict and model interference among
the functional medical devices and assist CR to make appropriate decision on
frequency band selection upon sensing possible interference. This requires the
CR to have location sensing capability of both itself and the other devices.
Since the monitoring devices are usually attached to patients who may not show
much mobility, the environment is rather static in a sense that the radio
environment may not change much. Therefore, spectrum and interference
measurement can be used to model spectrum availability and resource allocation.
An important criteria for the CR nodes to
decide when to switch access networks or frequency channels is the location of
the transmitting devices. In an automated health care monitoring system, it is
crucial to provide accurate location information of either health care
receivers or medical devices. This not only ensures timely response to emergent
situations by providing accurate location but also avoids interference among
the functioning medical devices by smartly sensing and switching transmission
channels.
Paramedics require real-time accurate
patient location information to provide immediate medical attention. E-911
service requires location information provided by service provider. Providing
location information of mobile medical devices ensures the choice of the proper
transmission frequency to avoid interference with other devices.
Also, devices that are close to each other
and transmitting using the same frequency band may cause interference to each
other. This may result in faulty data or service interruption. It is thus essential
to design a system that can switch between different access networks and different
frequency bands effectively in an intelligent manner. Cognitive radio (CR)
based on dynamic spectrum access (DSA) has the capability of dynamically
accessing different frequency bands depending on the environment and location
and thus is anticipated to enable dynamic switching between different wireless
networks.
How can the CR know when to switch frequency
band and what frequency band to use? In a hospital or senior health care center,
many medical devices are present and may be functioning at a certain frequency
band. These devices may be physically placed in a room or moved around. The CR
need to know the location of itself and the location of neighboring devices and
their frequency bands. When a CR senses the location of a device that is
transmitting in a frequency band that may cause interference between them, it
will switch its frequency band to avoid interference with that device. Here,
location information can be used to predict and model interference among the
functional medical devices and assist CR to make appropriate decision on frequency
band selection upon sensing possible interference. This requires the CR to have
location sensing capability of both itself and the other devices. Since the
monitoring devices are usually attached to patients who may not show much
mobility, the environment is rather static in a sense that the radio
environment may not change much. Therefore, spectrum and interference
measurement can be used to model spectrum availability and resource allocation.
Global positioning system (GPS) provides
outdoor location information; however, a major challenge in outdoor/wide area network
(WAN) environments lies in that the existing wireless network protocols often
do not support GPS. Moreover, for indoor locations, the situation is even worse
as it is hard for the commercially deployed GPS to provide accurate location
information due to Non-Line-of- Sight
(NLoS). In recent times, there have also been few research works focusing on IP
geolocation using commercial databases. However, the record in the database is often
not updated on time, therefore, makes it unreliable when the IP address of the network
devices changes constantly.
Why is location information important to
the system in an indoor environment? In a hospital or health care center,
various medical devices are present which are sensitive to interference. To
provide an untethered connection among different mobile health monitoring
devices and to the medical data center, the devices need to be able to flexibly
use a limited available wireless spectrum. It is mandatory that the devices do
not cause interference to each other as well as to other high-priority
equipments like MRI or surgical machines. Note that two devices cause
interference to each other when they are present within a certain physical
proximity and operate using the same frequency bands. The knowledge of the
location of such devices helps prevent this interference while making mobile devices
smart enough to avoid the interference. Intelligent mechanisms are therefore necessary
to avoid interference and provide high throughput at the same time.
When transmitting large amounts of medical
data over wireless channels using small CR devices, power consumption is of
great concern. It is also crucial to adopt lossless compression methods to
reduce storage space. Therefore, it is important to adopt an efficient data
compression and channel coding scheme to losslessly compress the transmitted
data on the monitoring devices.
In a CR health care automation network,
users could have tiny body sensors attached to them as shown in the
illustration below.
The body sensors collect necessary vital
signs information in a periodic or on-demand manner and transmit them to a
nearby CR node, which can be connected to a bed in a hospital or home
environment or even be worn to enable mobility. The CR node transfers the
information to any base station or access point (the choice of which depends on
several factors like location, wireless channel characteristics, wireless
resource availability). The base stations/access points update the information
at a health record database through a smart monitoring engine. The smart
monitoring engine is capable of performing complex computations if necessary.
