i

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

xxii

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 /

xxiii

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-

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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.

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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).

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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(Internet)

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.

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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.

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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.

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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.

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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.

lxiii

CHAPTER FIVE

5.0 Recommendations

This project proposes a WSNs system, which is separated into control and data

paths. Each sensor node will provide a multiple sensor sensing capabilities such

that multiple nodes can communicate easily with the cloud either in a mesh or star

network topologies(12), and the save power efficiently by adaptive low power

control. It also recommends the use 2.4 GHz and 60 GHz to be our transceivers

frequencies, which would make our communication system more suitable for our

hierarchy architecture. We had already seen several researches completed the use

of thermal sensor groups and the 2.4 GHz communication system which proved

effective and partially reliable health wise. Much as this exists, there is a pressing

need for improving the ways in which the nodes are composed and sensors

communicate for effective health monitoring.

However it explores the use of cloud computing, cloud networks (servers,

databases and application) for effective and efficient health monitoring and

administration. In the future, we will accomplish other applications sensor group

for implementing the whole WSN system tapping all resources and opportunities

existing in the cloud.

The combination of intelligent data processing for clinical decision making

processes and subsequently alert agents and healthcare professionals alike is a

step towards optimization of dynamic healthcare monitoring services tailored

according to each individual user.

The proposed architecture based on multiple complementary wireless

communication access networks between the patient and the system, through the

Internet is a powerful system which warrants further consideration. The

lxiv

biomedical data can now be generated continually by the sensor nodes platform.

The proposed system can establish a distributed Health Monitoring System where

each patient is connected to the Home Health Monitoring Center. The territory will

be covered by a Visitor Health Monitoring Centers in a Wide Area Network

5.1 Conclusions

There are several important consequences of the design and design space as

discussed above. Clearly, a single hardware platform will most likely be sufficient

to support the wide range of possible applications for health monitoring. In order

to avoid the development of application-specific hardware, it would be desirable,

however, to have available a (small) set of platforms with different capabilities that

cover the design space. A modular approach, where the individual components of

a sensor node can be easily exchanged, might help to partially overcome this

difficulty. Principles and tools for selecting suitable hardware components for

particular applications would also be desirable.

As far as software is concerned, the situation becomes even more complex. As

with available and applicable hardware, one could try to cover the design space

with a (larger) set of different protocols, algorithms, and basic services. However, a

system developer would then still be faced with the complexity of the design

space, since each application would potentially require the use of software with

different or expanded interfaces and properties.

In conventional distributed systems, middleware has been introduced to hide such

complexity from the software developer by providing programming abstractions

that are applicable for a large class of applications. This raises the question of

whether appropriate abstractions and middleware concepts can be devised that

are applicable for a large portion of the sensor network design space. This is not an

lxv

easy task, since some of the design space dimensions (e.g., network connectivity)

are very hard to hide from the system developer. Moreover, exposing certain

application characteristics to the system and vice versa is a key approach for

achieving energy and resource efficiency in sensor networks. The interfaces would,

however, contain methods for exposing application characteristics to the system

and vice versa. Different points in the design space would then require different

implementations of these interfaces.

A modular software architecture would then be needed, together with tools that

would semi-automatically select the implementations that best fitted the

application and hardware requirements. One possible approach here is the

provision of a minimal fixed core functionality that would be dynamically extended

with appropriate software modules. I acknowledge that my design is somewhat

speculative.

However, research into software support for WSNs especially for multiple sensing

is still at an early stage and significant advances will be required to approach the

goal of easy and consistent programmability, testing, and deployment of

applications across the design space especially for single node with multiple

sensor.

The sensor proposed above will enable the sensing of all medical conditions that

develop lumps, alter the rate of heart beat, temperature and diffusion, pumping of

blood round the body, and detect areas of wrong concentrations at the early stage.

In addition to these more technical issues, the design space I advocate can

hopefully bring more clarity to the often somewhat diffuse discussions about the

typical or right characteristics and requirements of wireless sensor networks.

lxvi

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Wireless Sensor Design for Hospital Management and Applications │ AMINU Lookman Enitan │ lookman.a@luqcom.com │ @Lukamins │ Luqcom Informatics©