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