Wireless Sensor Design for Hospital Managements and Applications; By AMINU Lookman Enitan

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LAGOS STATE UNIVERSITY
FACULTY OF SCIENCE
DEPARTMENT OF COMPUTER SCIENCE
WIRELESS SENSOR DESIGN FOR HOSPITAL
MANAGEMENT AND APPLICATIONS
AMINU LOOKMAN ENITAN
080591087
A PROJECT WORK SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS
FOR THE AWARD OF THE DEGREE OF BACHELOR OF SCIENCE (B.Sc) FACULTY OF
SCIENCE, DEPARTMENT OF COMPUTER SCIENCE, LAGOS STATE UNIVERSITY, OJO,
LAGOS STATE, NIGERIA
SEPTEMBER, 2012

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CERTIFICATION
This is to certify that this project work was carried out by AMINU LOOKMAN ENITAN
with matriculation number 080591087 under my supervision in the department of
Science for the award of the degree of Bachelor of Science in Computer Science at Lagos
State University, Ojo, Lagos State.
_________________________ __________
MR. TOYIN ENIKUOMEHIN DATE
Supervisor
_________________________ __________
DR. RAHMAN DATE
Head, Department of
Computer Science
_________________________ __________
EXTERNAL SUPERVISOR DATE:

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DEDICATION
I dedicate this work to the Almighty ALLAH, the creator of the heavens, earth and all that
exists, may his peace and blessings be upon the generality of mankind.
In addition, to all comrades in the struggle for a free and just society, humanitarian
workers, volunteers and all those working to make the world a healthier and better place.

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ACKNOWLEDGEMENT
I use this opportunity to specially thank my parents Alhaji. B. O Aminu and my beloved
mummy Alhaja. A. A. Aminu, my kith and kins, my supervisor Mr. Toyin Enikuomehin for
keeping me in the field of Robotics (wireless sensor device for hospital …), my lecturers
and friends (both in AOCOED and LASU), professional in the InfoTech industry globally.
I also acknowledge the immense contribution of all those whose research works and
experiments have inspired me in life and in InfoTech, especially those young chaps from
Europe, Asia and Egypt whose practices of science and technology inspires me, they make
me feel like I am about to arrive and that the world is waiting for my work.
Also, the contributions of my mentors, fellow competitors at RYPOTIA, friends at other
schools NACOSSites, NANS and MSSN.

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CONTENT
Cover page ------------------------------------------------------------------------------------------- i
Certification ------------------------------------------------------------------------------------------ ii
Dedication -------------------------------------------------------------------------------------------- iii
Acknowledgement --------------------------------------------------------------------------------- iv
Content ------------------------------------------------------------------------------------------------ v
Abstract ----------------------------------------------------------------------------------------------- viii
Chapter One ----------------------------------------------------------------------------------------- 1
1.0 Introduction ------------------------------------------------------------------------ 1
1.1 Problem Definition --------------------------------------------------------------- 3
1.2 Aim and Objectives --------------------------------------------------------------- 3
1.3 Research Design ------------------------------------------------------------------- 4
1.4 Scope of Study --------------------------------------------------------------------- 5
1.5 Definition of terms --------------------------------------------------------------- 5
1.5.1 An Antenna ------------------------------------------------------------------------- 5
1.5.2 Adaptive Self-Configuring Sensor Networks Topologies (ASCENT) - 5
1.5.3 Base-Station Controlled Dynamic Clustering Protocol (BCDCP) ----- 5
1.5.4 Body Sensor Networks (BNSs) ------------------------------------------------- 6
1.5.5 Cloud Databases (Cloud) -------------------------------------------------------- 6
1.5.6 CodeBlue -----------------------------------------------------------------------------6
1.5.7 Central Database -------------------------------------------------------------------6
1.5.8 Free-Space optical (FSO) --------------------------------------------------------- 6
1.5.9 Healthcare Provider IT Systems (HPITS) ------------------------------------ 6
1.5.10 Intelligent Medical Server [IMS] ----------------------------------------------- 7
1.5.11 Medium access control (MAC) ---------------------------------------- 7
1.5.12 MobiHealth ------------------------------------------------------------- 7
1.5.13 Motion Sensor --------------------------------------------------------------------- 7
1.5.14 Patient's Personal Home Server [PPHS] ------------------------------ 8
1.5.15 Radio Frequency Identification (RFID) --------------------------------- 8
1.5.16 Tags ---------------------------------------------------------------------- 8
1.5.17 Tiny Microchip ---------------------------------------------------------- 8

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1.5.18 Transponder ------------------------------------------------------------- 8
1.5.19 Wireless Body Area Network (WBAN) --------------------------------- 9
1.5.20 Wireless Personal Area Network (WPAN) ----------------------------- 9
1.5.21 Wireless Local Area Networks (WLAN) -------------------------------- 9
1.5.22 Wireless Fidelity’ (WiFi) ------------------------------------------------ 9
1.6 Significance of Study --------------------------------------------------------------10
Chapter Two ------------------------------------------------------------------------------------------ 11
2.0 Literature Review ----------------------------------------------------------- 11
2.1 Sensor and Nodes ------------------------------------------------------ 11
2.2 Data Collection ---------------------------------------------------------- 13
2.3.0 Sensor Network Design ----------------------------------------------------- 15
2.3.1 Design Scenario -------------------------------------------------------------- 17
2.3.2 Application Designs --------------------------------------------------------- 20
2.4 Intelligent / Expert Medical System ------------------------------------------ 21
2.5 Success Stories --------------------------------------------------------------------- 23
2.6 Existing Hardware Platforms --------------------------------------------------- 25
2.7 Power Consumption and Optimization ----------------------------------------- 28
2.8 Conclusion --------------------------------------------------------------- 30
Chapter Three ----------------------------------------------------------------------------------------31
3.0 Requirements Analysis and Design ------------------------------------------- 31
3.1 Hardware Architecture -----------------------------------------------------------31
3.2.0 Software Architecture ------------------------------------------------------------33
3.2.1 Network Coordinator -------------------------------------------------------------34
3.2.2 Telos Software ----------------------------------------------------------------------34
3.2.3 Zigbee -------------------------------------------------------------------------------- 35
3.2.4 Bluetooth ---------------------------------------------------------------------------- 36
3.2.5 Ultra WideBand -------------------------------------------------------------------- 37
3.2.6 Sensor Software --------------------------------------------------------------------39
3.3.0 Other QoS Requirements --------------------------------------------------------39
Chapter Four ------------------------------------------------------------------------------------------42
4.0 The Sensors Node ----------------------------------------------------------------- 42

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4.0.1 Wearability --------------------------------------------------------------------------42
4.0.2 Interoperability -------------------------------------------------------------------- 42
4.1.0 Micro Control Unit (MCU) ------------------------------------------------------ 44
4.1.1 Sensors -------------------------------------------------------------------------------44
4.1.2 ADC, DSP, and MDCC and CPP ------------------------------------------------- 44
4.1.3 2.4GHz RF Transceiver and Antennas --------------------------------------- 45
4.1.4 Cost, Size, Resources, and Energy -------------------------------------------- 46
4.2.0 Connectivity ------------------------------------------------------------------------ 47
4.2.1 Reliable Communication -------------------------------------------------------- 47
4.2.2 Mobility ------------------------------------------------------------------------------ 47
4.2.3 Base Station -------------------------------------------------------------------------48
4.2.4 Gateway Server (Hospital) ------------------------------------------------------ 49
4.2.5 Data Synchronization ------------------------------------------------------------- 49
4.2.6 Antenna ------------------------------------------------------------------------------52
4.2.7 Radio Frequency Identification ------------------------------------------------ 53
Chapter Five ------------------------------------------------------------------------------------------ 55
5.0 Recommendations ---------------------------------------------------------------- 55
5.1 Conclusions ------------------------------------------------------------------------- 56
Appendix I ---------------------------------------------------------------------------------------------57
Table 1 Commercial Modules for WSN ------------------------------------------------37
Table 2 Body Area Networks Projects ------------------------------------------------- 41
Figure 1 An Implanted WSND ------------------------------------------------------------2
Figure 2 A Wearable WSND -------------------------------------------------------------- 2
Figure 3 Architecture of a Sensor Node ---------------------------------------------- 13
Figure 4 Wireless Sensor for Habitat Monitoring ---------------------------------- 16
Figure 5 Intelligent Mobile Health Monitoring System (IMHMS) -------------- 23
Figure 6 Designed Communication Paths ---------------------------------------------43
Figure 7 A Diagram of a Sensor Node -------------------------------------------------44
Figure 8 A Diagram of a Sensor Node (Improved) --------------------------------- 44

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ABSTRACT
This research work takes a critical study of the usefulness of the devices we use on a daily
bases, additional features that can simply be modified and adapted it into, and the need
of designing a devices that will improve your health and ensure humanity lives a life of
joy. The research will look into available technology and areas in which they can be
useful, design and architecture of both sensor and communication path, the type of
sensor devices that are most ideal for monitoring most common degenerative medical
conditions, type of databases required batched-online real-time update, data storage and
monitoring in cloud, the trigger system and the crucial parts of the body the need regular
sensing and monitoring. It shall also look into solutions to solving various constraints
faced by the development of an efficient design.

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CHAPTER ONE
1.0 Introduction
The title wireless sensor design devices for hospital managements and applications brings
to mind the aim of most technological, research and advancements in the history of man.
These devices bring to mind the need to better enhance the life of man and ensure man
lives a better life monitoring his health status as he goes along with is day to day
activities, ensuring that every change in body composition is detected at the early stage
and every ailment is treated at the very early stage before things goes out of hand.
Imagine a world where technology aids longevity and a healthy life style, together we can
make the world a better place.
The design and architecture for such wireless sensor to be used in health monitoring, data
capturing, data management and in the diagnosis of various ailments that attack man.
The reason for any changes in the internal organs of the human body can be enormous, it
can be from a slight change in environmental temperature or gases contained in the
environment, a funny reaction in the intestine due to food or fluid consumed, irregular
locomotive activity and even a small quantity of fluid injected via stings from mosquitoes,
spiders and scorpions.
Most wireless sensors used in the health sector are designed to be small and use battery
to power the sensor, as scientist continue to develop more sophisticated equipment’s to
perform these purpose and in some cases multiple monitoring purposes, the challenge of
the efficiency, durability and reliability of the device become an issue of concern for the
more the efficiency the more the quantity of power required to maintain the device.
Though some devices have been developed to function without the use of battery,
devices are usually implanted wireless sensors which require the use of a radio frequency

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identification device (RFID) to transmit power to them. The electric signals are
transmitted via the blood vessels as tiny packet of signals to the device.
Figure 1: An Implanted WSND (1)
Figure 2: A Wearable WSND(8)
Moreover, such devices are usually wearable wireless sensor(8)
that are designed like a
small wrist watch, embedded within it is a small radio transmitter/transponder that
transmits signal and update information to a online real-time database, an antenna for
reaching out to the external network, a tiny rice-sized micro-chip which uses an
automated data-capture technology to keep track of change, identify the device, keep
basic records of the patient, allergies and other important and relevant details, a battery
and an accelerometer for logging protocols. This information’s are usually inputted by the
hospital administrators and are security with a password to protect the data in it. These
devices are usually connected via the wireless network to a Hospital Patient Management
System (HSPS) which is a database monitored by intelligent systems that can trigger
alarms and alert both the doctors, hospital staffs and patient when the need arises before
it degenerates into an emergency.
However, these devices can appear in a variety of designs; from head gear, wrist bands,
rib band, Personal digital assistants (PDAs)(13)
, smart phones, simple temperature,
location and motion sensors, pulse-oximeter and lots more. More has been done in this
area of research but a lot more needs to be done, why? because little has been achieved

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the collaboration among various hospital managements, the database and their patients
will help improve the effectiveness of the aim of the technology as signal of some critical
attacks to patient occur when the patient is far from the medical facility and needs urgent
attention in a nearby medical facility.
These signals are more effective if they are transmitted over a Wide Area Network
(WAN), and the database is located in the cloud. This enables the ease is accessibility to
data by the hospital managements and health professionals, and in some cases aid the
transfer of short messages as first aid remedy for the patient. The applications used to
run such and monitor such devices are operated with several interfaces which is most
suitable for the personnel accessing the data, ensure data security and the integrity of the
information delivered by the signal. The interfaces provide for basic and best
information’s required by the technician, the system analyst, the technical and non-
medical personnel’s involved in the health care services as well as the patient himself.
1.1 Problem Definition
I. Determining other areas of application of WSN in medical practices and diagnoses
II. How to reduce the size and power consumption of WSN as well as increasing
efficiency. are you sure you can resolve this part
III. Determine the effectiveness of the online real-time databases located in the cloud
on the exchange and interpretation on monitored data.
1.2 Aim and Objectives
I. To examine the scalability and reliability of wireless sensor networks for early
diagnoses
II. Create a more efficient (less power consuming and multipurpose) wireless sensor
design for hospital managements and applications

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III. Determine other diseases that can be diagnosed early via Wireless Sensor
Networks and as well develop application (Intelligent Systems) that can provide
first aid treatments offline.
1.3 Research Design
Wireless Sensor Network Architecture and Design
The first step in designing a sensor network is deciding on the physical quantities to
measure. Although sensing displacement is possible using global positioning system GPS
technology, for the reliability, accuracy, and sampling rate are not yet sufficient for many
applications, particularly those needing high-frequency sampling of small changes. Strain
measurements could provide a direct measure of damage, but the identification,
installation, operation, and interpretation of reliable strain sensors on a large structure is
difficult. Consequently, accelerations provide useful information about structural
vibration characteristics, so they are adequate for the primary goal of this investigation.
The network must be designed for fast sampling rates for temporal scalability, and
reliable command dissemination and data to a database running a online real-time
update system.
Mesh, the least utilized network architecture to date, but by some accounts the most
promising. A mesh would appear to be the most expensive architecture to build—though
manufacturers of mesh equipment dispute this—because each node requires a router. It
also appears to be the most robust because each node has multiple pathways available to
it. Since deployments have been so few, it is difficult to ascertain the real business case
for mesh architectures in hospital management and applications

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1.4 Scope of Study
I. This study will focus more of wearable wireless sensors and limit itself from other
sensors uses during medical operations or surgery processes.
II. It will include the ability of the sensor to sense and send signals (triggers) as alert
for both medical professionals and feedback to patients for first aid purposes.
III. The study will also monitor habits of the patients that could trigger an attack or
make the medical condition deteriorate.
IV. The study will also focus more on efficiency of design and not the efficiency of the
physical sensor.
1.5 Definition of terms
1.5.1 An Antenna – These are receivers for efficient system and relaying small amounts
of important data between the sensor and the data HPITS with minimal power
consumption. Some use wireless LAN while others use RFID for transmitting data.
1.5.2 Adaptive Self-Configuring Sensor Networks Topologies (ASCENT) - that operates
between routing and link layers. Any routing or data dissemination protocol can
use ASCENT to manage node redundancy. In ASCENT, nodes monitor their
connectivity and decide whether to become active and participate in the multi-hop
networking. Moreover, nodes other than active nodes remain in a passive state
until they receive a request from active nodes.
1.5.3 Base-Station Controlled Dynamic Clustering Protocol (BCDCP) - The protocol tries
to distribute the network energy evenly among all the network nodes and
proposes a specialized approach for selecting the cluster heads. The BCDCP divides
the network into clusters that are each managed by a base station assigned CH.
1.5.4 Body Sensor Networks (BNSs) and issues such as power optimization, battery life
performance and radio design are key.

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1.5.5 Cloud Databases (Cloud) These are databases that typically runs using a platform
called “cloud computing”. Such databases deployed using a virtual machine image
and can be coded using most database languages such as SQL, MSSqle.t.c
1.5.6 CodeBlue(2)
- CodeBlue is a sensor networks based medical research project being
developed at Harvard. Specific goals for this project include pre-hospital care and
in-hospital emergency care, stroke patient rehabilitation and disaster response.
Research from this project has potentials for resuscitative care, realtime triage
decisions and long term patient observations.
1.5.7 Central Database: This refers to a database created and designed on a server
which is located in the cloud
1.5.8 Free-Space optical (FSO) – are transmission systems. Both FSO and EHF are
vulnerable to adverse weather conditions— FSO to heavy fog and EHF to rain and
snow—but, on the other hand, EHF is not much troubled by fog, and FSO is not
obstructed by raindrops. Thus, used together, the two systems can achieve an
availability figure that is at least an order of magnitude better than either alone
over a given distance. And because the normal operating distances of the two
technologies are similar and because both transmit tightly focused line-of-sight
beams, the two work well in tandem.
1.5.9 Healthcare Provider IT Systems (HPITS) - RFID is powered by his own battery. This
enables higher signal strength and extended communication range of up to 100
meters; but the implementation of active communication requires larger batteries
and more electronic components The antenna picks up radio-waves or
electromagnetic energy beamed at it from a reader device and enables the chip to
transmit patients unique ID to the reader device, allowing the patient to be
remotely identified. The reader converts the radio waves reflected back from the

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patient wristband into digital information that can then be passed onto HPITS for
processing.
Patient’s basic important data (e.g., patient ID, name, age, location, drug allergies,
blood group, drugs that the patient is on today) can be stored in the patient’s back-
end databases for processing. Patient databases can also be linked through
Internet into other hospitals databases
1.5.10 Intelligent Medical Server [IMS](15)
receives data from all the PPHS. It is the
backbone of this entire architecture. It is capable of learning patient specific
thresholds. It can learn from previous treatment records of a patient. Whenever a
doctor or specialist examines a patient, the examination and treatment results are
stored in the central database. IMS mines these data by using state-of-the-art data
mining techniques such as neural nets, association rules, decision trees depending
on the nature and distribution of the data.
1.5.11 Medium access control (MAC) protocol in wireless networks has an important role
to enable the successful operation of the network. One fundamental task of the
MAC protocol is to avoid collisions so that two interfering nodes do not transmit at
the same time
1.5.12 MobiHealth - Mobihealth is a project based on a European initiative to create a
generic platform for home healthcare using BAN-based sensors and wireless
telephony technology. They are using GPRS/UMTS wireless communication
technology for transferring data. Some of the capabilities include measurement
and transmission of vital signs and other bio signals. In this project they developed
a Body Area Network (BAN) and a service platform for patients and healthcare
professionals

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1.5.13 Motion Sensor(4)
- Sensing motion with infrared detectors has been a staple
feature of security systems for decades. Integrating this functionality into an easily
deployed mesh network would allow intrusion detection over a large area. For a
security application, we expect that motion would be the most reliable sign of
intrusion.
1.5.14 Patient's Personal Home Server [PPHS](6)
The patient's personal home server can
be a personal computer or mobile devices such as cell phone/PDA. We suggest
mobile devices because it will be more suitable for the users to use their mobile
devices for this purpose. PPHS collects information from the central controller of
the WBSN.
1.5.15 Radio Frequency Identification (RFID) - This technology mainly consists of a
Transponder (smart tag), a Reader and Healthcare Provider IT Systems (HPITS).
Each tag attached to and are embedded in the patient wristband contains an
antenna and a tiny microchip smaller than a grain of sand.
1.5.16 Tags – tags are attached to the patient wristband contains an antenna and a tiny
microchip smaller than a grain of sand.
1.5.17 Tiny Microchip – This is a sand-like tiny microchip which runs a IMS(15)
and a small
database for temporary storage of data. It aids retention of packets and update
over a local database in case of update rollbacks.
1.5.18 Transponder - These are smart tags that are used to identify individual tag, the
reader or transceiver that reads and writes data to a transponder, and the
computer containing database and information management software. RFID tags
can be active, passive or semi-passive. Passive and semi-passive RFID send their
data by reflection or modulation of the electromagnetic field that was emitted by
the Reader. The typical reading range is between 10 cm and 3 m. The battery of

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semi passive RFID is only used to power the sensor and recording logic. The
communication of active RFID is powered by his own battery. This enables higher
signal strength and extended communication range of up to 100 meters; but the
implementation of active communication requires larger batteries and more
electronic components
1.5.19 Wireless Body Area Network (WBAN)(1)
Recent advances in sensors, lower
powered integrated circuits and wireless networks have brought ideas in
developing low powered physiological sensor platforms that can be integrated in
Body Area Networks (BANS). These sensor networks have extremely low power
requirements which make them fit for integrating them in day to day wearable’s.
1.5.20 Wireless Personal Area Network (WPAN)(1)
WPANS using IEEE 802.15.4 or
Bluetooth have potential uses in the medical fields. These are short range
networks that can be deployed for example, within a patient’s room. Nurses are
able to monitor patients in real-time without having to visit them frequently. This
saves them time, and gives them the opportunity to take care of more patients.
Other uses of WPANs are in interfacing multiple expensive and large devices within
the hospital. Data from one device can be forwarded directly from one machine to
another without doctors wasting time by transferring this information by hand.
Bluetooth is also a good technology for short range communication
1.5.21 Wireless Local Area Networks (WLAN)(5)
that allows healthcare provider (e.g.,
hospitals) to deploy a network more quickly, at lower cost, and with greater
flexibility than a wired system.
1.5.22 Wireless Fidelity’ (WiFi)- Mobile devices such as PDA (Personal Digital Assistant),
laptops and other mobile devices. WiFi (wireless fidelity’) – These enables Personal
Digital Assistance (PDA), smart phones / cell phone (e.g., 2G, 2.5G, 3G) that
connects directly to the medical server.

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1.6 Significance of Study
I. To identify other areas of application of wireless sensors in medical applications.
II. To optimize the use of wireless sensor in the measurement of temperature, heart
rate, glucose level, blood pressure, patients record changes in body functions such
as range of motion, pain, fatigue, sleep, headache, irritability, sleep disorders etc.
III. To optimizes the diagnoses and treatment of Alzheimer, Parkinson and diagnoses
of other degenerative ailments like rheumatic diseases e.t.c.

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CHAPTER TWO
2.0 Literature Review
Implementing a sensor network to gather biometric data could help medical
practitioner’s overcome a lot of stress and pay attention to patients at required
time before ailments that could be treated at early state degenerates into a
medical attack. Randy S. Tolentino and Sungwon (2010), observed that "Advances
in telecommunication technology have made possible data transmission over the
wireless system. This has enabled remote patient monitoring which collects
disease-specific metrics from wireless biomedical devices used by patients in their
homes or other settings outside of a clinical facility. Remote monitoring systems
typically collect patient readings and then transmit it to a remote server for storage
and later examination by the healthcare professionals."
Also, according to Daniele Puccinelli and Martin Haenggi (2005), "Sensor networks
offer a powerful combination of distributed sensing, computing and
communication. They lend themselves to countless applications and, at the same
time, offer numerous challenges due to their peculiarities, primarily the stringent
energy constraints to which sensing nodes are typically subjected." and that
"Sensor networks provide endless opportunities, but at the same time pose
formidable challenges, such as the fact that energy is a scarce and usually non-
renewable resource" Daniele Puccinelli and Martin Haenggi (2005)
2.1 Sensor and Nodes
A node can represent a point of communication of a common date. Daniele
Puccinelli and Martin Haenggi (2005), in their study stated that "sensor networks
have a direct impact on the hardware design of the nodes" and also that a node
have "at least four levels: the power source, processor, communication hardware,

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and sensors" ,"Most sensor networks use radio communication, even if alternative
solutions are offered by laser and infrared."
Also, a node can be regarded as "a BioNoc" while "A Wireless BioNoC is a Network-
on-Chip with medical sensors and processing cores that support wire and wireless
communications" Iyad Al Khatib, et. al (2007).
Furthermore, Joseph Robert Polastre (2003), in his research project in an attempt
to measure the frequency of transmitting from a node stated "The sensor nodes in
burrows are transmit only with a low duty-cycle; they sample about once per
minute. The gateway mote is fully powered by solar power, so it is always on and
relaying packets to the base station."
However, Daniele Puccinelli and Martin Haenggi (2005), noted that "a large
number of small sensing self-powered nodes which gather information or detect
special events and communicate in a wireless fashion, with the end goal of handing
their processed data to a base station. Sensing, processing and communication are
three key elements whose combination in one tiny device gives rise to a vast
number of applications." also refering the quality of data minned form individval
sensor Feng Xia et. Al (2007) "...with mobile (actuator) nodes, the distance between
the mobile actuator and the sensor that are involved in transmitting the sensed
data will change over time, thus aggravating the variability of link quality. This is
why we pay a special attention to examining how the link quality varies over
distance."
Joseph Robert Polastre (2003), also stated that "for every sensor we can bound the
cost of taking a single sample. By analyzing the requirements we can place a bound
on the energy spent on data acquisition".

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Figure 3: Architecture of a sensor node developed by Lennart Y. et.al(2005)(11)
2.2 Data Collection
Data mining form sensors will be of significant value if data collection capabilities
are reliable, according to Randy S. Tolentino and Sungwon Park (2010) "A loss in
medical data typical of that seen in wireless transmissions cannot be tolerated each
piece of cardiac data could carry important medical information. For example, a
sudden heart failure may produce an abnormal ECG signal that lasts for only a few
seconds. The transmission error of such a segment of ECG data is disastrous to the
capture of, and response to, sudden heart failure events."
It is also important to know how data is collected over existing sensor networks if
one is to determine its efficiency. In a work by Daniele Puccinelli and Martin
Haenggi (2005), "Data collection is related to network connectivity and coverage.
An interesting solution is the use of ubiquitous mobile agents that randomly move
around to gather data bridging sensor nodes and access points, whimsically named
data MULEs (Mobile Ubiquitous LAN Extensions)."
Ameer Ahmed Abbasi and Mohamed Younis (2007), in their study realized that
"Since sensor nodes might generate significant redundant data, similar packets
from multiple nodes can be aggregated so that the number of transmissions would
be reduced. Data aggregation combines data from different sources by using
functions such as suppression (eliminating duplicates), min, max and average.
Some of these functions can be performed either partially or fully in each sensor
node, by allowing sensor nodes to conduct in-network data reduction." Daniele

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Puccinelli and Martin Haenggi (2005), "...mobility of the data sink can be used to
save power, as nodes can learn its schedule."
In the work of Joseph Robert Polastre (2003), he stated "To compare the difference
between transmitting the raw data and the cost of compression, we can analyze a
dataset collected from indoor monitoring networks. We must decide whether it is
worthwhile to log data and compress it, or is it more efficient to push each sample
into the network." In furtherance of the above he noted that "... place a bound on
the energy spent on data acquisition. We trade the cost of data processing and
compression against the cost of datatransmission. Results can be transmitted as
raw data, transmitted data, filtered data, and in-network aggregated data." It
could also be observed that if "all data are relayed to a base station, but this form
of centralized data collection may shorten network lifetime. Relaying data to a data
sink causes non-uniform power consumption patterns that may overburden
forwarding nodes" Daniele Puccinelli and Martin Haenggi (2005).
Ameer Ahmed Abbasi and Mohamed Younis (2007), however noted that "...
computation would be less energy consuming than communication, substantial
energy savings can be obtained through data aggregation. This technique has been
used to achieve energy efficiency and traffic optimization in a number of routing
protocols."
However, data collected from a sensor is transmitted through a base station to the
databases where it becomes accessible to health professionals. "The base station
currently uses a Postgres SQL database. The database stores time stamped
readings from the sensors, health status of individual sensors (e.g., battery status)
and the network as a whole (e.g., connectivity and routing information) as well as
metadata (e.g., sensor locations). All of this information is specified by the
database schema, which adds the ability to record both raw and compensated /

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calibrated sensor readings in addition to the attributes above." Joseph Robert
Polastre (2003), Daniele Puccinelli and Martin Haenggi (2005)
2.3.0 Sensor Network Design
A design developed by Feng Xia and Yu-Chu Tian et. Al (2007) which was a
modification of some existing designs with "distributed ad-hoc network algorithms
to facilitate executing control procedures in a distributed manner. Prototyped a
light monitoring and control application as a case study of wireless sensor area
networks (WSANs)." Also illustrate the main challenges in developing real-time
control systems for pursuit-evasion games using a large-scale sensor network. A
mixed model for design, analysis, and synthesis of control algorithms within sensor
networks has been presented by (Korber et al.) dealt with some of the design issues
of a highly modular and scalable implementation of a WSAN for factory
automation applications. Considering networked control systems (NCSs) over
WSNs," another design considered "developed a gain scheduler to cope with time-
varying delay induced by dynamic changes in the number of hops in multi-hop
communications."(7)
(Witrant et. al.) Also considered the effect of time-varying
delay caused by multi-hop communication, and proposed a predictive control
scheme with a delay estimator.", "the impact of packet loss as a result of unreliable
communications in WSANs, particularly those with mobile nodes, on the
performance of the control applications remains an open issue, and needs to be
investigated systematically."
"...to provide remote access to the habitat monitoring network, the collection of
sensor network patches is connected to the Internet through the transit network.
The base station connects the transit network to the wide-area network. On Great
Duck Island, we implemented the base station’s Internet connection through a two-

xxiv
way satellite connection provided by Hughes and similar to a DirecTV system." Feng
Xia 1,2, Yu-Chu Tian et. Al (2007) in his work on habitat monitoring wireless sensor.
The ability to act and respond intelligently is also considered by Rifat Shahriyar et.
Al (2009) "Intelligent Medical Server [IMS] receives data from all the Patients
Personal Home Server (PPHS). It is the backbone of this entire architecture. It is
capable of learning patient specific thresholds. It can learn from previous treatment
records of a patient. Whenever a doctor or specialist examines a patient, the
examination and treatment results are stored in the central database. IMS(15)
mines
these data by using state-of-the-art data mining techniques such as neural nets,
association rules, decision trees depending on the nature and distribution of the
data. After processing the information it provides feedback to the PPHS or informs
medical authority in critical situations. PPHS displays the feedback to the patients.
Medical authority can take necessary measures."
Figure 4:
Wireless sensor for habitat monitoring designed by Joseph Robert Polastre (2003)

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2.3.1. Design Senerio
I. ECG Sensor Design Specification.
The cardiac signals measured by the ECG sensors should undergo a signal
conditioning and preprocessing session to gain a suitable voltage level and signal to
noise ratio (SNR). The main difficulty is that the voltage levels are very low, in order
of 0.5-5Mv. The front end of the ECG sensor must be able to deal with the
extremely weak nature of this signal in it measuring. This is the specification in
design front end of the ECG Sensors which was designed by Nor Syahidatul Nadiah
Ismail and Sharifah H. S. Ariffin et.al(2008):
"• Capability to sense low amplitude signals in the range of 0.05 – 10mV
• Very high input impedance, > 5 Mega-ohms(MΩ)
• Very low input leakage current, < 1 micro-Amp (μA)
• Flat frequency response of 0.05 – 150 Hz
• High Common Mode Rejection Ratio"
II. Nearly all radio-based platforms use COTS (Commercial Off-The-Shelf) components.
Popular choices include the TR1000 from RFM (used in the MICA motes) and the
CC1000 from Chipcon (chosen for the MICA2 platform).
More recent solutions use industry standards like IEEE 802.15.4 (MICAz and Telos
motes with CC2420 from Chipcon) or pseudo-standards like Bluetooth. Typically,
the transmit power ranges between −25 dBm and 10 dBm, while the receiver
sensitivity can be as good as −110 dBm Spread spectrum techniques increase the
channel reliability and the noise tolerance by spreading the signal over a wide

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range of frequencies. Frequency hopping (FH) is a spread spectrum technique used
by Bluetooth: the carrier frequency changes 1600 times per second on the basis of
a pseudo-random algorithm. However, channel synchronization, hopping sequence
search, and the high data rate increase power consumption; this is one of the
strongest caveats when using Bluetooth in sensor network nodes. In Direct
Sequence Spread Spectrum (DSSS), communication is carried out on a single carrier
frequency. The signal is multiplied by a higher rate pseudo-random sequence and
thus spread over a wide frequency range (typical DSSS radios have spreading
factors between 15 and 100)." Daniele Puccinelli and Martin Haenggi (2005).
III. "Ultra Wide Band (UWB) is of great interest for sensor networks since it meets
some of their main requirements. UWB is a particular carrier-free spread spectrum
technique where the RF signal is spread over a spectrum as large as several GHz.
This implies that UWB signals look like noise to conventional radios. Such signals
are produced using baseband pulses (for instance, Gaussian monopulses) whose
length ranges from 100 ps to 1 ns, and baseband transmission is generally carried
out by means of pulse position modulation (PPM). Modulation and demodulation
are indeed extremely cheap.
UWB provides built-in ranging capabilities (a wideband signal allows a good time
resolution and therefore a good location accuracy), allows a very low power
consumption, and performs well in the presence of multipath fading. Radios with
relatively low bit-rates (up to 100 kbps) are advantageous in terms of power
consumption. In most sensor networks, high data rates are not needed, even
though they allow shorter transmission times thus permitting lower duty cycles and
alleviating channel access contention. It is also desirable for a radio to quickly
switch from a sleep mode to an operational mode. Optical transceivers such as
lasers offer a strong power advantage, mainly due to their high directionality and
the fact that only baseband processing is required.

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Also, security is intrinsically guaranteed (intercepted signals are altered). However,
the need for a line of sight and precise localization makes this option impractical for
most applications." Daniele Puccinelli and Martin Haenggi (2005).
IV. "Data prioritization has been used for data routing in several protocols for WSNs.
This section reviews the existing approaches in using priorities in WSNs and states
the goals and the contribution of this project. In a priority-based routing path
selection mechanism is exploited for a proposed multi-path(14)
routing protocol
(PRIMP) which is based on the directed diffusion. Actually, sampled data items are
not prioritized in this protocol. Instead, each gradient is given a priority tag based
on its accumulated hop count to the sink or the remaining energy source of nodes
in that particular routing path. The source node then uses the priority tags of all
received gradients to select the best.
The Priority-based Dynamic Adaptive Routing (PDAR) protocol is proposed in
aiming to balance the energy consumption while providing better service for
significant information. The protocol is based on a former routing protocol for
multi-hop wireless ad hoc networks called Dynamic Source Routing (DSR) with the
emphasis on congestion prediction and priority scheduling for data routing.
Data packets are categorized into two classes of vital and common packets.
Accordingly, every node in the routing path(14)
maintains two separate data queues,
each dedicated to a certain class of packets. The packets in the higher-priority
queue (vital packets) are always sent before packets in the lower-priority queue
(common packets). Data priorities are supposed to be determined by the
application." Majid Nabi et. Al (2011)
V. In a recent work, "the Priority-based Hybrid Routing (PHR) mechanism is proposed
in which the characteristics of the sensed data determine its priority. An abrupt

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change in the data stream reveals the importance of the new data. Consequently, a
multi-path diffusion-based mechanism is used for forwarding the packets of high
importance to provide a more reliable and faster data delivery. A single-path
routing mechanism based on the known Ad-hoc On-demand Distance Vector
(AODV) approach, that is prone to data loss, is exploited for normal packets." Majid
Nabi et. Al (2011)
2.3.2. Application Designs
“For emergency medical care application CodeBlue(18)
, CodeBlue is designed to
provide routing, naming, discovery and security for wireless medical sensors, PDAs,
PCs, and other devices that may be used to monitor and threat patients in a range
medical setting. CodeBlue is designed to scale across a wide range of network
densities, ranging from sparse clinic and hospital development deployments to very
dense, ad hoc deployments at a mass casualty site. Wireless ECG sensor can be
used to capture real-time vital signs from patients in a moving ambulance, relay
the data to handheld computers carried by physicians for pre-hospital diagnosis.” -
Nor Syahidatul Nadiah Ismail and Sharifah H. S. Ariffin et.al(2008), Luis Fernández
et. al (2006).
“Another emergency medical care existing application is MOLEC. The MOLEC
Monitor is capable storing the ECG signal and embedded real-time system that
captures, processes, detects, analyzes and notifies possible dangerous
abnormalities to an alarm center through the network from anywhere and at any
time. The MOLEC Centre is the system part that manages the communication with
all the PDA monitors and updates the MOLEC Centre’s database with the new
information that receives from each of them. The Alarm Center receives all the risk
alarms detected into the PDA, in order to react and immediately provide proper
medical assistance. The proposed wireless ECG platform can be implementing on

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the MOLEC monitor and integrate with MOLEC centre and alarm centre.” Nor
Syahidatul Nadiah Ismail and Sharifah H. S. Ariffin et.al (2008)
2.4 Intelligent / Expert Medical System
"Intelligent Mobile Health Monitoring System (IMHMS), which can provide medical
feedback to the patients through mobile devices based on the biomedical and
environmental data collected by deployed sensors." Rifat Shahriyar et. Al. (2009)
“Moreover, so far there is no automated medical server used in any of the work
related to mobile health care. To maintain the server a large number of specialist
are needed for continuous monitoring. The presence of a large number of
specialists is not always possible. Moreover in the third world countries, specialist
without proper knowledge may provide incorrect prescription." "...an intelligent
medical server for mobile health care applications that will aid the specialists in the
health care. As a large amount of medical data is handled by the server, the server
will perform mine and analyze the data. With the result of mining, analysis and
suggestions and information provided by the specialists in the critical scenarios the
server can learn to provide feedback automatically. Moreover as time grows the
server will trained automatically by mining and analyzing data of all the possible
health care scenarios and become a real intelligent one." Rifat Shahriyar et. Al.
(2009)
"Wearable Body Sensor Network is formed with the wearable or implantable bio-
sensors in patient's body. These sensors collect necessary readings from patient's
body. For each organ there will be a group of sensors which will send their readings
to the group leader. The group leaders can communicate with each others. They
send the aggregated information to the central controller. The central controller is
responsible for transmitting patient's data to the personal computer or cell

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phone/PDA.", "...for wireless communication inside the human body, the tissue
medium acts as a channel through which the information is sent as electromagnetic
(EM) radio frequency (RF). So in WBSN, information is transmitted as
electromagnetic (EM) radio frequency (RF) waves. The central controller of the
WBSN communicates with the PPHS using any of the three wireless protocols:
Bluetooth, WLAN (802.11) or ZigBee. Bluetooth can be used for short range
distances between the central controller and PPHS. WLAN can be used to support
more distance between them. Now days ZigBee introduces itself as a specialized
wireless protocol suitable for pervasive and ubiquitous applications." Luis
Fernández et. al (2006), Rifat Shahriyar et. Al. (2009)
"The patient's personal home server can be a personal computer or mobile devices
such ascell phone/PDA. We suggest mobile devices because it will be more suitable
for the users to use their mobile devices for this purpose. PPHS collects information
from the central controller of the WBSN. PPHS sends information to the IMS. PPHS
contains logics in order to determine whether to send the information to the IMS or
not. Personal Computer based PPHS communicates with the IMS using Internet.
Mobile devices based PPHS communicates with the IMS using GPRS / Edge / SMS.
The best way to implement IMS is by Web Service or Servlet based architecture. The
IMS will act as the service provider and the patients PPHS will act as the service
requester. By providing these types of architecture, a large number of
heterogeneous environments can be supported with security. So personal computer
or cell phone/PDA can be connected easily to a single IMS without any problem."
Rifat Shahriyar et. Al. (2009)

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Figure 5:
Intelligent Mobile Health Monitoring System (IMHMS) Architecture by Rifat Shahriyar et. Al (2009)
According to Rifat Shahriyar et. Al. (2009) the characteristics of IMHMS are:
simplicity, cost effective, secure, flexible, capability to predict spread of diseases
and the capability to help authority to determine general health policies.
2.5 Success Stories
Several success stories were extracted from the works of Daniele Puccinelli and
Martin Haenggi (2005), Kay Romer and Friedemann Mattern (December 2004),
"possible applications of sensor networks are of interest to the most diverse fields.
Environmental monitoring, warfare, child education, surveillance, micro-surgery,
and agriculture are only a few examples. Through joint efforts of the University of
California at Berkeley and the College of the Atlantic, environmental monitoring is
carried out off the coast of Maine on Great Duck Island by means of a network of
Berkeley motes equipped with various sensors. The nodes send their data to a base
station which makes them available on the Internet. Since habitat monitoring is

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rather sensitive to human presence, the deployment of a sensor network provides
a noninvasive approach and a remarkable degree of granularity in data acquisition.
Also Daniele Puccinelli and Martin Haenggi (2005), “The same idea lies behind the
Pods project at the University of Hawaii at Manoa, where environmental data (air
temperature, light, wind, relative humidity and rainfall) are gathered by a network
of weather sensors embedded in the communication units deployed in the South-
West Rift Zone in Volcanoes National Park on the Big Island of Hawaii. A major
concern of the researchers was in this case camouflaging the sensors to make
them invisible to curious tourists.”
In Princeton’s Zebranet Project, a dynamic sensor network has been created by
attaching special collars equipped with a low-power GPS system to the necks of
zebras to monitor their moves and their behavior. Since the network is designed to
operate in an infrastructure-free environment, peer-to-peer swaps of information
are used to produce redundant databases so that researchers only have to
encounter a few zebras in order to collect the data. Sensor networks can also be
used to monitor and study natural phenomena which intrinsically discourage
human presence, such as hurricanes and forest fires.
Joint efforts between Harvard University, the University of New Hampshire, and
the University of North Carolina have recently led to the deployment of a wireless
sensor network to monitor eruptions at Volcán Tungurahua, an active volcano in
central Ecuador. A network of Berkeley motes monitored infrasonic signals during
eruptions, and data were transmitted over a 9 km wireless link to a base station at
the volcano observatory.
Intel’s Wireless Vineyard is an example of using ubiquitous computing for
agricultural monitoring. In this application, the network is expected not only to

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collect and interpret data, but also to use such data to make decisions aimed at
detecting the presence of parasites and enabling the use of the appropriate kind of
insecticide.
Data collection relies on data mules, small devices carried by people (or dogs) that
communicate with the nodes and collect data. In this project, the attention is
shifted from reliable information collection to active decision making based on
acquired data. Just as they can be used to monitor nature, sensor networks can
likewise be used to monitor human behavior.
In the Smart Kindergarten project at UCLA, wirelessly-networked, sensor-enhanced
toys and other classroom objects supervise the learning process of children and
allow unobtrusive monitoring by the teacher.
2.6 Existing Hardware Platforms
Berkeley motes, made commercially available by Crossbow, are by all means the
best known sensor node hardware implementation, used by more than 100
research organizations. They consist of an embedded microcontroller, low-power
radio, and a small memory, and they are powered by two AA batteries. MICA and
MICA2 are the most successful families of Berkeley motes. The MICA2 platform,
whose layout is shown in Figure 4, is equipped with an Atmel ATmega128L and has
a CC1000 transceiver.
A 51-pin expansion connector is available to interface sensors (commercial sensor
boards designed for this specific platform are available). Since the MCU is to
handle medium access and baseband processing, a fine-grained event-driven real-
time operating system (TinyOS) has been implemented to specifically address the
concurrency and resource management needs of sensor nodes. For applications
that require a better form factor, the circular MICA2Dot can be used: it has most of

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the resources of MICA2, but is only 2.5 cm in diameter. Berkeley motes up to the
MICA2 generation cannot interface with other wireless- enabled devices. However,
the newer generations MICAz and Telos support IEEE 802.15.4, which is part of the
802.15 Wireless Personal Area Network (WPAN) standard being developed by IEEE.
At this point, these devices represent a very good solution for generic sensing
nodes, even though their unit cost is still relatively high.
The proliferation of different lower end hardware platforms within the Berkeley
mote family has recently led to the development of a new version of TinyOS which
introduces a flexible hardware abstraction architecture to simplify multi-platform
support.
Intel has designed its own iMote to implement various improvements over
available mote designs, such as increased CPU processing power, increased main
memory size for on-board computing and improved radio reliability. In the iMote, a
powerful ARM7TDMI core is complemented by a large main memory and non-
volatile storage area; on the radio side, Bluetooth has been chosen.
Various platforms have been developed for the use of Berkeley motes in mobile
sensor networks to enable investigations into controlled mobility, which facilitates
deployment and network repair and provides possibilities for the implementation
of energy-harvesting. UCLA’s RoboMote, Notre Dame’s MicaBot and UC Berkeley’s
CotsBots are examples of efforts in this direction.
UCLA’s Medusa MK-2 sensor nodes, developed for the Smart Kindergarten project,
expand Berkeley motes with a second microcontroller. An on-board power
management and tracking unit monitors power consumption within the different
subsystems and selectively powers down unused parts of the node.

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UCLA has also developed iBadge, a wearable sensor node with sufficient
computational power to process the sensed data. Built around an ATMega128L
and a DSP, it features a localization unit designed to estimate the position of
iBadge in a room based on the presence of special nodes of known location
attached to the ceilings.”
MobiHealth “An European project has developed a customizable monitoring
system for vital signals, based on a BAN and an m-health service platform, utilizing
UMTS and GPRS networks. The prototype includes Bluetooth for the
communication intraBAN and the central device is a PDA(13)
.” Rifat Shahriyar et. Al.
(2009)
The EYES project (a joint effort among several European institutions) custom
nodes, have been developed to test and demonstrate energy-efficient networking
algorithms. On the software side, a proprietary operating system, PEEROS
(Preemptive EYES Real Time Operating System), has been implemented.
The Smart-Its project has investigated the possibility of embedding computational
power into objects, leading to the creation of three hardware platforms: DIY
Smart-its, Particle Computers and BTnodes.
The DIY Smart-its have been developed in the UK at Lancaster University; their
modular design is based on a core board that provides processing and
communication and can be extended with add-on boards. A typical setup of Smart-
its consists of one or more sensing nodes that broadcast their data to a base
station which consists of a standard core board connected to the serial port of a
PC. Simplicity and extensibility are the key features of this platform, which has
been developed for the creation of Smart Objects. An interesting application is the
Weight Table: four load cells placed underneath a coffee table form a Wheatstone

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bridge and are connected to a DIY node that observes load changes, determines
event types like placement and removal of objects or a person moving a finger
across the surface, and also retrieves the position of an object by correlating the
values of the individual load cells after the event type (removed or placed) has
been recognized.
Particle Computers have been developed at the University of Karlsruhe, Germany.
Similarly to the DIY platform, the Particle Smart-its are based on a core board
equipped with a Microchip PIC; they are optimized for energy efficiency, scalable
communication and small scale (17 mm × 30 mm). Particles communicate in an ad-
hoc fashion: as two Particles come close to one another, they are able to talk.
Additionally, if Particles come near a gateway device, they can be connected to
Internet-enabled devices and access services and information on the Internet as
well as provide information - Daniele Puccinelli and Martin Haenggi (2005).
2.7 Power Consumption and Optimization(11)
According to Daniele Puccinelli and Martin Haenggi (2005) "The BTnode hardware
from ETHZ is based on an Atmel ATmega128L microcontroller and a Bluetooth
module. Although advertised as a low-power technology, Bluetooth has relatively
high power consumption, as discussed before. It also has long connection setup
times and a lower degree of freedom with respect to possible network topologies.
On the other hand, it ensures interoperability between different devices, enables
application development through a standardized interface, and offers a
significantly higher bandwidth (about 1 Mbps) compared to many low-power
radios (about 50 Kbps).

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Moreover, Bluetooth support means that COTS hardware can be used to create a
gateway between a sensor network and an external network (e.g., the Internet), as
opposed to more costly proprietary solutions."
The ECG biomemedial sensor of Nor Syahidatul Nadiah Ismail and Sharifah H. S.
Ariffin et.al (2008) "60 Hz mains power line frequency and its harmonic
components are a common type of interference that occurs in biomedical signal.
These AC potentials are always present additive to the ECG signal and are in the
order of tens of volts. North filter is the suitable filter that provide high signal to
noise (SNR) ratio that can be used to eliminate this noise.
Joseph Robert Polastre (2003) stated that "to address the problems with AA
alkaline batteries, our next deployment features a node without a 3.3V boost
converter and will be powered by a lithium sulfur dioxide battery. The terminal
voltage of lithium batteries is very stable. The battery is able to maintain a
relatively constant terminal voltage until the last 15% of its life for significant
discharge rates. At lower discharge rates the terminal voltage will stay almost
constant until the last 5% of the battery’s life. This is in direct contrast to alkaline
batteries where the terminal voltage starts with a rapid drop as the internal
battery resistance climbs, wasting much of the remaining power. Lithium sulfur
dioxide batteries are typically used for outdoor military operations that require
long battery life in a small, light package."
In the works of Lennart Yseboodt et.al (2005) on power leakage "the large majority
of the leakage is in the memories. We tried four things to improve this leakage.
– Reduce the size of that data memory to 2kB. Since the ECG program only
requires 1.2kB and 120 bytes of stack this was possible. This reduced the leakage
to 65.6 µW, a 34.5% improvement.

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– By removing one of the three issue slots in the PearlRay processor and reducing
the size of the immediate, the width of the program memory could be reduced
from 128b to 64b. Due to the decrease of parallelism the instruction count was
increased with 27%, but the instruction width was reduced by 50%, allowing us to
reduce the program memory from 32kB to 16kB. This resulted in a reduction of
leakage power to 82µW, a 18% improvement."
2.8 Conclusion
According to Daniele Puccinelli and Martin Haenggi (2005), Luis Fernández et. al
(2006) "Medical research and healthcare can greatly benefit from sensor networks:
vital sign monitoring and accident recognition are the most natural applications. An
important issue is the care of the elderly, especially if they are affected by cognitive
decline: a network of sensors and actuators could monitor them and even assist
them in their daily routine. Smart appliances could help them organize their lives by
reminding them of their meals and medications. Sensors can be used to capture
vital signs from patients in real-time and relay the data to handheld computers
carried by medical personnel, and wearable sensor nodes can store patient data
such as identification, history, and treatments. With these ideas in mind, Harvard
University is cooperating with the School of Medicine at Boston University to
develop CodeBlue, an infrastructure designed to support wireless medical sensors,
PDAs, PCs, and other devices that may be used to monitor and treat patients in
various medical scenarios."

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CHAPTER THREE
3.0 Requirements Analysis and Design
3.1 Hardware Architecture
An architecture developed by the department of electrical and computer
engineering at the University of Alabama for monitoring Wireless Sensor Networks
for Personal Health Monitoring
Each ActiS node utilizes a commercially available wireless sensor platform Telos
from Moteiv and a custom intelligent signal processing daughter card attached to
the Telos platform. The daughter boards interface directly with physical sensors
and perform data sampling and in some cases preliminary signal processing. The
pre-processed data is then transferred to the Telos board. The Telos platform can
support more sophisticated real-time analysis and can perform additional filtering,
characterization, feature extraction, or pattern recognition. The Telos platform is
also responsible for time synchronization, communication with the network
coordinator, and secure data transmission.
Consider a block diagram of an ActiS with a Telos platform and a custom Intelligent
Signal Processing Module (ISPM) in the Medical Intelligent System (MIS). Telos is
powered by two AA batteries and features an ultralow power Texas Instruments
MSP430 microcontroller; a Chipcon CC2420 radio interface in the 2.4 GHz band; an
integrated onboard antenna with 50m range indoors/125m range outdoors; a USB
port for programming and communication; an external flash memory; and
integrated humidity, temperature, and light sensors. The MSP430 microcontroller
is based around a 16-bit RISC core integrated with RAM and flash memories,
analog and digital peripherals, and a flexible clock subsystem. It supports several

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low-power operating modes and consumes as low as 1 μA in a standby mode; it
also has very fast wake up time of no more than 6 μs.
Telos Revision A features a MS430F149 microcontroller with 2 KB RAM and 60 KB
flash memory; Telos Revision B (now TmoteSky) features a MSP430F1611 with 10
KB of RAM and 48 KB of flash memory.
The CC2240 wireless transceiver is IEEE 802.15.4 compliant and has programmable
output power, maximum data rate of 250 Kbs, and hardware support for error
correction and encryption. The CC2240 is controlled by the MSP430
microcontroller through the Serial Peripheral Interface (SPI) port and a series of
digital I/O lines with interrupt capabilities. The Telos platform features a 10-pin
expansion connector with one UART (Universal Asynchronous Receiver
Transmitter) and one I2C interface, two general-purpose I/O lines, and three
analog input lines.
Two custom boards were developed specifically for health monitoring applications,
an ISPM and an IAS (Intelligent Activity Sensor). The ISPM board extends the
capabilities of Telos by adding two perpendicular dual axis accelerometers (Analog
Devices ADXL202), a bioamplifier with signal conditioning circuit, and a
microcontroller MSP430F1232. The ISPM’s two ADXL202 accelerometers cover all
three axes of motion. One ADXL202 is mounted directly on the ISPM board and
collects data for the X and Y axes. The second ADXL202 is mounted on a card that
extends vertically from the ISPM and collects acceleration data on the Z axis. The
user’s physiological state can be monitored using an on-board bioamplifier
implemented with an instrumentation amplifier and signal conditioning circuit. The
bioamplifier could be used for electromyogram or electrocardiogram monitoring.
The output of the signal conditioning circuit is connected to the local
microcontroller as well as to the microcontroller on the Telos board via the

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expansion connector. The ISPM has its own MSP430F1232 processor for sampling
and low-level data processing, selected primarily for its compact size and excellent
MIPS/mW ratio. Other features that were desirable for this design were the 10-bit
ADC and the timer capture/compare registers that are used for acquisition of data
from accelerometers. The MSP430F1232 also has hardware UART that is used for
communications with the Telos board. The IAS board is a stripped-down version of
the ISPM with only accelerometer sensors and signal conditioning for a force-
sensing resistor that can be used as a foot switch.
3.2.0 Software Architecture
An architecture developed by the department of electrical and computer
engineering at the University of Alabama for monitoring Wireless Sensor Networks
for Personal Health Monitoring.
The system software is implemented in a TinyOS environment. TinyOS is a
lightweight open source operating system for wireless embedded sensors. It is
designed to use minimal resources, and its configuration is defined at compile time
by combining components from the TinyOS library and custom-developed
components. A TinyOS application is implemented as a set of component modules
written in nesC. The nesC language extends the C language with new constructs to
facilitate the component architecture and multitasking. By adding direct language
support for task synchronization and task management, it allows rapid
development and minimizes resource usage. The software architecture, from top
to bottom, it shows the network coordinator software, WWBAN node’s Telos
software, and WWBAN node’s daughter card software.

xlii
3.2.1 Network Coordinator:
The network coordinator is also implemented on a Telos platform. It feeds the PS
application through its USB connector and manages the WWBAN -- transmits the
messages from the PS that establish a session, assigns the individual sensor ID,
distributes keys if secure data are encrypted, and assigns communication slots. The
network coordinator autonomously emits beacon messages for time
synchronization. After the initial setup, it receives data from individual sensors,
aggregates the data, and forwards it to the PS application.
3.2.2 Telos Software:
The Telos application software is implemented as multiple TinyOS components
encompassing the following high-level functions: wireless communication,
extended flash storage, messaging software, board-to-board communications, and
signal feature extraction.
Telos serves as a master controller, and it requests data from the daughter sensor
card every 40 ms (25 Hz) by raising an interrupt request line. The daughter sensor
card sends preprocessed data via an asynchronous serial interface. The received
data can also be processed and analyzed. For example, motion sensors can analyze
acceleration signals to identify the moment when a step has been made. A step
detection event and the corresponding time stamp are sent to the personal server.
As an alternative, we can upload raw data from accelerometers at the price of
increased power consumption. The processed data set can be stored in an external
serial flash memory in the case of autonomous operation or if the wireless channel
is not available.
It should be noted that the flash memory, CC2420 radio interface, and the
daughter sensor card all share a single serial interface of the MSP30 on the Telos

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platform. This presented its own set of challenges since the Telos platform is
tasked with reliable communications to multiple devices using this single serial
interface. For example, to communicate with the daughter card, the software must
configure the serial interface as a UART running at 115.2 kbps. Once sensor data is
received, the serial interface is dynamically reconfigured for SPI at 500 kbps,
allowing communications to both the on-board radio and flash. Because events are
recognized asynchronously, accurate event time stamps can be made, but often
the messages must be buffered and queued for transmission when the serial
interface is available.
3.2.3 Zigbee
The Zigbee Alliance is an Industrial Consortium of chip manufacturers, OEM
manufactures, service providers, and users in the wireless sensor network market.
This Consortium is carrying out the task of association of specifying higher-layer
standards, based on IEEE 802.15.4. This includes network, security, and application
protocols.
The Zigbee standard specifies both mesh and star network topologies. There are
two physical device types for the lowest system cost to allow vendors to supply the
lowest possible cost devices: full function devices and reduced function devices.
The full function device (FFD):
 Can function in any topology.
 Capable of being the Network coordinator.
 Can talk to any other device.
The reduced function device (RFD).
 Limited to star topology.
 Cannot become a network coordinator.
 Talks only to a network coordinator.

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 Very simple implementation.
Zigbee is an incipient standard that covers the necessities of the network layer in
the wireless sensor networks, supported by important companies like Philips,
Samsung, Motorola, Mitsubishi Electric, Cisco Systems, Epson, etc.
3.2.4 Bluetooth
Bluetooth (BT) is a radio wave based technology for short-range wireless
connectivity among portable and/or fixed electronic devices, such as cellular
phones, headsets, personal digital assistants and laptops, as well as for their
connectivity to the Internet. The BT specification consists of the core and the
profile part: the core part defines how the BT technology works and the profile
part describes how the BT technology is used in specific scenarios and use cases.
The standard specifies from the physical to the application layer.
Radio frequency operation(16)
is in the unlicensed Industrial, Scientific and Medical
(ISM) band at 2.4 to 2.48 GHz, using a spread spectrum, frequency hopping, full-
duplex signal at up to 1600 hops/sec. The signal hops among 79 frequencies at 1
MHz intervals, to give a high degree of interference immunity. RF output is
specified for three different types:
 0 dBm (1 mW) for a communication range of less than 5 m.
 4 dBm (2.5 mW) for a communication range of 10-20 m.
 +20 dBm (100 mW) for a communication range of around 100 m.
The Bluetooth 1.1 specification defines a low power radio link capable of voice or
data transmission to a maximum capacity of 720 kbps per channel. The IEEE
802.15.1 Group Task provided a standard adaptation of the Bluetooth Specification
v1.1 Foundation MAC (L2CAP, LMP, and Baseband) and PHY (Radio).

xlv
The use of Bluetooth(12)
, although more extended that Zigbee is more focused on
the personal communications and essential problems, like the power consumption
or the synchronization in the BAN, could not be managed with this technology.
Table 1: Commercial Modules for WSN (5)
TelosA TmoteSky EM250 EM260 CC2430
Description Low power
wireless sensor
module with USB
(discontinued),
new version
called TmoteSky
Low power
wireless sensor
module with
USB
Chip with Zigbee
System-on-Chip
(micro and
transceiver)
Chip with Zigbee
System-on-Chip
(micro and
transceiver)
EmberZNet
Stack (mesh NW
included)
Chip with Zigbee
System-on-Chip
(micro and
transceiver) –
ZigbeeTM
protocol stack (Z-
StackTM)
Operating System TinyOS TinyOS Custom Custom Custom(Airbee)
Company Moteiv Moteiv Ember Ember Chipcon
Data Rate 250kbps 250kbps 250kbps 250kbps 250kbps
RF Transiever CC2420 CC2420 Custom Custom Cc2030
Frequency 2.4Ghz 2.4Ghz 2.4Ghz 2.4Ghz 2.4Ghz
MicroControlador TIMSP430 TIMSP430 embedded 16-bit
XAP2
microcontroller
16MHz 8051 8bit single-
cycle
microcontroller
Power
Requirement
19 mA (active-
radio on), 2.4uA
(sleep)
19 mA (active-
radio on), 5uA
(sleep)
1uA max (deep
sleep), 29mA
(RX+CPU)
1uA max (deep
sleep), 26mA
(RX+CPU)
RX+CPU->27 mA,
0.6 uA sleeping
Current
Deployment
State
Discontinued On sale Pre-Order Pre-Order Pre-Order
3.2.5 Ultra WideBand
Ultra-Wideband (UWB) technology has the objective of providing the convenience
and mobility of wireless communications; to high speed interconnect devices
throughout the digital home and office. Designed for short-range, wireless
personal area networks (WPANs)(9)
, UWB wants to become the leading technology

xlvi
for freeing people from wires, enabling wireless connection of multiple devices for
transmission of video, audio and other high-bandwidth data.
UWB, short-range radio technology, complements other longer range radio
technologies, such as WiFi, Wi-Max, and cellular wide area communications. It is
used to relay data from a host device to other devices in the immediate area (up to
10 meters).
A traditional UWB transmitter works by sending billions of pulses across a very
wide spectrum of frequencies, several GHz in bandwidth. The corresponding
receiver then translates the pulses into data by listening for a familiar pulse
sequence, sent by the transmitter. Specifically, UWB is defined as any radio
technology having a spectrum that occupies a bandwidth greater than 20 percent
of the centre frequency, or a bandwidth of at least 500 MHz. Modern UWB
systems use other modulation techniques, such as Orthogonal Frequency Division
Multiplexing (OFDM), to occupy these extremely wide bandwidths. In addition, the
use of multiple bands in combination with OFDM modulation, can provide
significant advantages to traditional UWB systems.
UWB's combination of broader spectrum and lower power improves speed and
reduces interference with other wireless spectra. In the United States, the Federal
Communications Commission (FCC) has mandated that UWB radio transmissions
can legally operate in the range from 3.1 GHz up to 10.6 GHz, at a limited transmit
power of -41dBm/MHz. Consequently, UWB provides dramatic channel capacity at
short range, that limits interference.
In June 2003, the MultiBand OFDM Alliance (MBOA) was formed, with many of the
most influential players in the consumer electronics, personal computing, home
entertainment, semiconductor, and digital imaging market segments. The goal of

xlvii
this organization is to develop the best technical solution for the emerging UWB
(IEEE 802.15.3a Task Group)
Physical and MAC specification for a diverse set of applications. To date, MBOA has
more than 60 participants that support a single technical proposal for UWB.
UWB is a technology in the physical level of the OSI stack. There are protocols as
Bluetooth, Zigbee, USB that are studying to include UWB in theirs physical layers.
3.2.6 Sensor Software(6)
:
The sensor boards handle acquisition of physiological signals and preprocessing.
For example, the ISPM samples three independent accelerometer axes each at a
rate of 200 Hz. The raw accelerometer data is filtered and preprocessed. The
filtering includes moving an average filter to eliminate high frequency movement
artifacts, and separation of low and high frequency components of the
acceleration signal. Sensor orientation can be calculated as the angle between low
frequency accelerometer components. User activity is estimated with a function
based on the sum of the integrals of the AC components in each channel.
3.3.0 Other QoS Requirements
Depending on the application, a sensor network must support certain quality-of-
service aspects such as real-time constraints (e.g., a physical event must be
reported within a certain period of time), robustness (i.e., the network should
remain operational even if certain well-defined failures occur), tamper-resistance
(i.e., the network should remain operational even when subject to deliberate
attacks), eavesdropping-resistance (i.e., external entities cannot eavesdrop on data
traffic), unobtrusiveness or stealth (i.e., the presence of the network must be hard

xlviii
to detect). These requirements may impact on other dimensions of the design
space such as coverage and resources.
The system consists of three major components: sensor nodes, transceivers, and a
central unit. Sensor nodes are connected to the power grid (at outlets or fuse
boxes) to measure power consumption and for their own power supply. Sensor
nodes directly transmit sensor readings to transceivers. The transceivers form a
multi-hop network and forward messages to the central unit. The central unit acts
as a gateway to the Internet and forwards sensor data to a database system.

xlix
Table: 2 Body Area Networks Projects (5)
WBAN-ETH MobilHealth PRIMA WBAN-
Alabam
My Heart Ubimon Basuma
Description WBAN of
non-invasive
sensor, the
RF
technology
is UWB
Customizable
BAN with
Bluetooth
and
GPRS/UMTS
WBAN for
portable
remote
monitoring
and
management
of at cardiac
risk patients
(ECG,
localization)
WBAN of
intelligent
motion
sensor for
computer
assisted
physical
rehabilitation
BAN of
intelligent
sensors using
smart-clothes
Ubiquitous
Monitoring
Environment
for wearable
and
Implantable
Sensors
Body Area
System for
Ubiquitous
Multimedia
Applications
Network
technology/
Protocol
UWB Bluetooth IEEE 802.15.4 IEEE 802.15.4 Wired BAN,
BAN->PDA
(Bluetooth),
PDA-
>Server(Intern
et)
IEEE 802.15.4 UWB-Frontend
and 802.15.3
(high Rate
Wireless PAN)
for MAC
Network and
Sensor Data
Rate
NA 115 kbps 259 kbps 250 kbps 56 kbaudps 250 kbps 20Mbps
Synchronization NA NA NA NA Hardware NA NA
RF transceiver NA Mobi
Bluetooth RC,
and
Bluetooth of
mobiles and
Iraq
(coordinator)
NA CC2420 RF NA CC2420 RF NA
Microcontroller
and OS
NA NA NA Telos
(MSP430)-
TinyOS
NA TI MSP430
ultra low
power
processor -
TinyOS
Leon2
Frequency 3Ghz–6Ghz 2.4Ghz 2.4Ghz 2.4Ghz 2.4Ghz (LAN) 2.4Ghz UWB
Power
Requirement
NA NA NA NA NA Active Mode
280uA at
1MHz 2.2v,
Standby Mode
1.6uA, Off
Mode 0.1uA
NA
Batteries NA NA NA NA 2 hours Low current
consumption
(RX 19.7mA TX
17.4mA)
NA
LAN/WAN
Technologies
NA GPRS/UMTS NA GPRS Bluetooth,
GPRS
Wi-Fi, GPRS NA
Current
Deployment
State
Current
work is
focused in
the effects
of body
blocking
with UWB
(1st
Publication
2003)
(2002-2003*)
Prototype
available -
*Delayed
Prima Project
is closed. The
spinoff
continues
with the R&D
Project 2004-
2007
Prototyping
Prototyping
available leads
ECG, 2-leads
ECG strip, and
Sp02---Future
ambient
sensors, data
mining
2004-2006
Developing
CHAPTER FOUR

l
4.0 The Sensors Node
Wireless medical sensors should satisfy the main requirements such as wearability,
reliability, security, and interoperability(1)
.
4.0.1 Wearability:
To achieve non-invasive and unobtrusive continuous health monitoring, wireless
medical sensors should be lightweight and small. The size and weight of sensors is
predominantly determined by the size and weight of batteries. But then, a
battery’s capacity is directly proportional to its size. We can expect that further
technology advances in miniaturization of integrated circuits and batteries will help
designers to improve medical sensor wearability and the user’s level of comfort.
4.0.2 Interoperability:
Wireless medical sensors should allow users to easily assemble a robust WSN
depending on the user's state of health. Standards that specify interoperability(1)
of
wireless medical sensors will promote vendor competition and eventually result in
more affordable sensor.

li
Figure 6: Designed Communication Paths
The sensor element is the lowest layer of the WSN system. As shown below, each
sensor element consists of
(1)MCU,
(2) The Sensors,
(3) ADC, DSP, MMDC, CPP, and
(4) 2.4GHz RF Transceiver and Antennas.

lii
Figure 7: A Diagram of a Sensor Node (17)
as proposed by Shih-Lun C. et. al. (2008) in
Wireless Sensor Network System by Separating Control and Data Path (SCDP) for Bio-
medical Applications
Figure 8: A Diagram of a Sensor Node (Improved)
4.1.0 Micro Control Unit (MCU)
MCU is a central controller of the sense element and it handles the controls and
data paths. Composed of finite state machine, the MCU receives commands from
sensor group layer and then decodes them to handle the power management
control and functional control paths in the sensor element. The MPC
(Measurement Power Control) and TPC (Transmission Power Control) are
efficiency control paths which ensure minimal fixed core functionality.

liii
4.1.1 Sensors
The sensors could get the various biomedical signals (blood pressure sensor,
temperature sensor, heart rate sensor, ultra sound, and ECG sensor) or and
environmental data (gas detection sensor, radiation level and photonic sensor) for
the measurements. Then they convert the measured data into digital signals by
ADC and it should be controlled by MCU for the difference measuring.
4.1.2 ADC, DSP, and MDCC and CPP
ADC (Analog to Digital Converter), DSP (Digital Signal Process), MMDC (Micro
Memory & Data Compressor) and CPP (Cell and Power Point) are four parts of data
process. When the data is measured by sensor, it would be converted into digital
signals by ADC. The converted digital signals are not completely suitable for
transmitting outside through wireless. The DSP would process the digital signals to
be suitable for compression. After the signal is processed by DSP, amount of digital
data would be compressed for power saving of wireless transmission. The data
compression is consisted of two parts predictor and entropy encoder. These two
compression circuits would compress the transmitting data effectively. The CPP
contains a tiny alarm and the cell.
In many biomedical or environment measure data, the most important is to
identify the unusual situations. If the sensing element receives an unusual data
from patients or environment, the sensor element would automatically promote
the resolution of ADC to a higher quality by CPP controlling which will trigger an
alarm to patients and doctors PDA(13)
and systems, the nurses’ logs and timely
analysis.

liv
4.1.3 2.4GHz RF Transceiver and Antennas
The wireless transceiver system in sensor element is for use in embedded
applications requiring low data rates and low power consumption. There is a highly
integrated 2.4 GHz RF transceiver(16)
, for control and data transmissive application.
The transceiver is composed of two parts: RF front-end and baseband. At the RF
front-end part of the receiver, the low noise amplifier (LNA) input for 2.4GHz is a
single-ended structure without external balun. The front-end gain of the receiver
could be adjusted through control pins with a variable gain amplifier (VGA), and
thus reduce the probability if bit errors caused by poor signal-to-noise ratio.
At the baseband part, the down- converted baseband signal is filtered by the low
pass filter, and then amplified by the VGA.
The frond-end of the transmitter part comprises a LPF and a VGA stage. A LPF is
realized to attenuate the undesired oversampling clock or spurious signals. There is
a power amplifier (PA) for 2.4GHz operation mode. The gain of the PA can be
adjusted by its bias current, which is controlled by MCU. The resulting network will
use very small amounts of power so individual devices might run for a year or two
using the originally installed battery.
4.1.4 Cost, Size, Resources(6)
, and Energy
Depending on the actual needs of the application, the form factor of a single
sensor node may vary from the size of a shoe box (e.g., a weather station) to a
microscopically small particle (e.g., mostly for military applications where sensor
nodes should be almost invisible). Similarly, the cost of every component in a
single device may vary from hundreds of thousands of Naira (for networks of very
few, but powerful nodes) to a few thousand Naira (for large-scale networks made
up of very simple nodes).

lv
Since sensor nodes are untethered autonomous devices, their energy and other
resources are limited by size and cost constraints. Varying size and cost constraints
directly result in corresponding varying limits on the energy available (i.e., size,
cost, and energy density of batteries or devices for energy scavenging), as well as
on computing, storage, and communication resources. Hence, the energy and
other resources available on a sensor node may also vary greatly from system to
system and minimized by minimal fixed core functionality paths. Power may be
either stored (e.g., in batteries) or scavenged from the environment (e.g., by solar
cells).
These resource constraints limit the complexity of the software executed on
sensor nodes. For our classification, we have partitioned sensor nodes roughly into
four classes based on their physical size.
Security: Another important issue is overall system security. The problem of
security arises at all three tiers of a WSN-based telemedical system. At the lowest
level, wireless medical sensors must meet privacy requirements mandated by the
law for all medical devices and must guarantee data integrity. Though key
establishment, authentication, and data integrity are challenging tasks in resource
constrained medical sensors, a relatively small number of nodes in a typical WSN
and short communication ranges make these tasks achievable.
4.2.0 Connectivity
4.2.1 Reliable Communication:
Reliable communication in WSNs is of utmost importance for medical applications
that rely on WSNs. The communication requirements of different medical sensors
vary with required sampling rates, from less than 1 Hz to 1000 Hz. One approach to
improve reliability is to move beyond telemetry by performing on-sensor signal

lvi
processing. For example, instead of transferring raw data from a sensor, we can
perform feature extraction on the sensor, and transfer only information about an
event (e.g., for ECG sensor, QRS features and the corresponding timestamp of R-
peak). In addition to reducing heavy demands for the communication channel, the
reduced communication requirements save on total energy expenditures, and
consequently increase battery life. A careful trade-off between communication
and computation is crucial for optimal system design.
4.2.2 Mobility
Sensor nodes may change their location after initial deployment. Mobility can
result from environmental influences such as wind or water, sensor nodes may be
attached to or carried by mobile entities, and sensor nodes may possess
automotive capabilities. In other words, mobility may be either an incidental side
effect, or it may be a desired property of the system (e.g., to move nodes to
interesting physical locations), in which case mobility may be either active (i.e.,
automotive) or passive (e.g., attached to a moving object not under the control of
the sensor node). Mobility may apply to all nodes within a network or only to
subsets of nodes. The degree of mobility may also vary from occasional movement
with long periods of immobility in between, to constant travel.
Mobility has a large impact on the expected degree of network dynamics and
hence influences the design of networking protocols and distributed algorithms(2)
.
The actual speed of movement may also have an impact, for example on the
amount of time during which nodes stay within communication range of each
other. Hence the network coverage range will span an area of 600 square meters
and may also vary based on the size of area of interest.

lvii
4.2.3 Base Station
The software running on the base station, an application distributed with TinyOS
called TOSBase, acts as a simple bridge between the serial and radio links. When a
server need to send out a message, TOSBase will forward this message to the radio
link and send it to motes with the same predefined group ID. Equivalently, it listens
to the radio link and filters out messages that do not contain the same group ID as
its own. TOSBase includes queues in both directions, with guarantee that once a
message enters a queue it will eventually exit on the other interface. Only when
the queue is full, new messages will be blocked until space is freed. By using a base
station that interfaces with both the wireless network and the wired local area
network, the system becomes modular, scalable and very flexible. One can imagine
that the Life Science Test bed can be extended to include home monitoring by
deploying a base station in a home, and link it to the central server at the hospital.
The server-system can then serves as a clinical decision support system and enable
the medical personnel to visualize the desired sensor data regardless of patient
location or serve as an early warning system.
4.2.4 Gateway Server (Hospital)
The central server can serve a large number of base stations which in turn serves a
number of sensors platforms. The server responsibility is to maintain a table
containing all the sensors and third party applications (clients) connected through
the system. This allows individual and remote configuration of motes. As new
clients, like a monitor, request data from a specific sensor, the server will provide
the client with the sensor properties necessary to configure itself for this sensor.

lviii
4.2.5 Data Synchronization
Timely data synchronization is a common requirement for WSNs since it allows
collective signal processing, sensor and source localization, data aggregation, and
effective distributed sampling. In wireless body area networks, synchronized time
stamps are critical for proper correlation of data coming from different sensors
and for an efficient sharing of the communication (18)
channel. For example, this
prototype needs to synchronize and time-stamp data from motion sensors and the
heart sensor and other sensors every seconds, and establish a protocol for sharing
the communication channel. The communication channel is a triangular path with
messages being sent simultaneously through a primary path, from sensor to the
cloud which is them transmitted to the hospital through its Base Transfer Station
(BTS) and the secondary path; directly to the BTS which is a temporary
transmission path which can also send temporary updates to the cloud when
unforeseen circumstances that could impede timely primary path update. Data
redundancy is reduced via an auto delete of data from secondary path whenever
primary path update is available.
Precise time stamping is also important in the case of intermittent communication
that can significantly postpone transmission of event messages.
A synchronization mechanism for a given application is determined by the
following:
(i) the high degree of precision needed,
(ii) the longevity of synchronization, that is, whether we need to stay
synchronized all the time or just when needed,
(iii) the resources available (clocks), and

lix
(iv) the power and time budget available for achieving time synchronization.
A number of protocols and clustering algorithms have been proposed
and implemented to provide time synchronization in computer networks
in several other designs. However, they are often ill-suited for wireless
sensor networks since they require significant computing resources and
do not offer fault-tolerant solutions. Several protocols have been
specifically developed for wireless sensor networks.
One of the key protocols for time synchronization in WSNs is the Flooding Time
Synchronization Protocol (FTSP) developed at Vanderbilt University. It features
MAC layer time stamping for increased precision and skew compensation with
linear regression to account for clock drift. The FTSP generates time
synchronization with periodic time sync messages. The network can dynamically
elect a root node. Whenever a node receives a time sync message, it rebroadcasts
the message, thus flooding the network with time sync messages through both
primary path (through cloud) and the secondary path (temporary transmission
path). The message itself contains a very precise timestamp of when the message
was sent. The receiving node takes an additional local timestamp when it receives
the message. Because the timestamps are taken deep in the radio stack, they
eliminate non-deterministic error sources and only include highly deterministic
events such as air propagation time, radio transmission, and radio reception time,
and required health sensing status.
Comparing the timestamps from the last several messages received, the node
computes a simple linear regression to allow it to account for the offset difference
in its clock from global time as well as the relative difference in frequency of local
oscillators.

lx
Many sensor applications need time correlated sensor readings and require an
underlying time synchronization mechanism. It has been shown that the accuracy
of distributed synchronization protocols is bounded by the unpredictable jitter on
communication times. Unlike in wide-area time synchronization protocols such as
NTP, we can determine all sources of communication delay. By exposing all sources
of delay up to the application, we are able to minimize the unknown jitter.
Additionally, by exploiting shared system timers, we are able to accurately assign
precise time stamps to incoming packets that can be exposed to applications.
The Mica platform was designed with the intention of using the internal, 16-bit
counter to act as the lower 16 bits of a continually running system time clock. This
high accuracy system clock is directly linked to the synchronization accelerator that
is used to capture the exact timing of the incoming packet. The synchronization
accelerator automatically timestamps each packet with the value of the system
timer, during transmission the communication stack can timestamp a packet with
this shared timer after all MAC delays have been determined. This allows the time
synchronization to be independent of MAC delay and back off. The time stamp
represents when the packet(7)
actually went over the radio and not when
communication was initiated. During periods of high contention, the MAC delay
may be hundreds of milliseconds. When hidden by external protocol engines, this
unknown delay significantly reduces time synchronization accuracy.
With our implementation, we are able to synchronize a pair of nodes to within 2
microseconds of each other. Our skew of +/-2us can be directly attributed to
several sources of jitter. The first is the raw RF transmission itself. When the
sending there is a jitter of +/- 1us on the transmission propagation due to the
internals of our radio. The arriving pulse is then captured by hardware with an
accuracy of +/- .25us. Finally, we must synchronize its clock based on the captured
value. This synchronization process introduces an additional +/- .625 us of jitter.

lxi
This implementation is only possible because we have a rich interface between
applications and protocols that allows us to exploit shared access to the high-
accuracy system timer. This provides a common reference for exchanging timing
information between the bottom of the network stack and the top of an
application.
4.2.6 Antenna
The most important purpose of data path is transmitting the measured data from
sensors to cloud and PC. In the data path design, the coexistence with dissimilar
systems (2.4/60 GHz) should be used as communication transceivers in the WSN.
The high data rates and high security of 60 GHz wireless system integrate with
available wireless technique such as Bluetooth(12)
and Zigbee to become the
telemetry system for healthcare monitoring.
The monitored biomedical signals are transmitted to sensor group layer and
received the control command using 2.4 GHz band and then transmitted the
merged signals in sensor group layer to computer through application layer using
60 GHz band.
In addition to the high data rates that can be accomplished in 60 GHz band, energy
propagation in the spectrum has unique characteristics that make possible many
other benefits such as excellent immunity to interference, high security, high data
rate and frequency re-use. The 60 GHz band enables complete system integration
including antenna. In addition, it allows small size nodes for WSN. However, the
isolation problem should be concerned. Using the frequency division duplex (FDD)
transceiver method, a distinct frequency channel is assigned for the transceiver
system with two antennas to improve the isolation problem. Accordingly, the
coexistence system should be used for bio-medical application to develop the
WSN.

lxii
4.2.7 Radio Frequency Identification
RFID(3)
is an emerging technology that makes use of wireless communication. The
protocol was originally developed for short-range product identification, typically
covering the 2 mm - 2 m read range, and has been promoted as the replacement
technology for the optical bar-code found, with the use of EPC (Electronic Product
Code). RFID has the ability to allow energy to penetrate certain goods and to read
a tag that is not visible. There are many distinct protocols used in the various RFID
systems, some using the lower end of the spectrum (135 KHz) and others using the
super high frequency (SHF) at 5.875 GHz:
There are various standards involved in RFID:
• ISO/IEC 7816 is the standard for contact chip cards.
• ISO/IEC 14443 is for contactless proximity cards operating at 13.56 MHz.
• ISO/IEC 15693 is for contactless vicinity cards operating at 13.56 MHz.
• ISO/IEC 18000 is for item management air interface, defining the parameters for
air interface in different frequencies: <135 kHz, 13.56 MHz, 2.45 GHz, 5.8 GHz, 860-
930 MHz and 433 MHz.
• ISO 11784, ISO 11785 and ISO 14223 are standards for the radio-frequency
identification of animals.
RFID systems are comprised of three main components: the tag or transponder,
the reader or transceiver that reads and writes data to a transponder, and the
computer containing database and information management software. RFID tags
can be active, passive or semi-passive. Passive and semi-passive RFID send their
data by reflection or modulation of the electromagnetic field that was emitted by
the Reader. The typical reading range is between 10 cm and 3 m. The battery of
semi passive RFID is only used to power the sensor and recording logic.

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