Biosci. Biotech. Res. Comm. 11(3): 523-534 (2018)
Real time deployments of sensors and sensing patients
vital parameters using secure autonomous WSN for
medical applications
Sandip Mandal and Rama Sushil
Department of Information Technology, DIT University, Dehradun, India
Wireless Medical Sensor Networks (WMSNs) have emerged as the most reliable technology for implementing a
pervasive paradigm, facilitating doctor to patient ef ciency and improving the quality of life. Sensor devices have
invaded the medical domain over a recent past with wide range of capability. WSN for healthcare enable remote
patient monitoring, timely exchange of health information, reminders and support thereby extending the reach of
healthcare assistance anywhere, anytime. In this paper, we analyze the performance of a scalable WSN infrastructure
with respect to medical applications and presents their response in scenarios which are simulated to mimic real-time
behavior. All simulations have been done in MATLAB. Proposed design of an autonomic WMSN in such a way that it
meets the requirements of various applications like sensed quantities, body sensor nodes autonomy, energy ef ciency
and reliable transmissions. The focus of the paper is on the overall network ef ciency since low energy consumption,
increased throughput and reliable transmission are the prerequisites for providing robust, reliable and long-lasting
unhindered operations in healthcare. From the results, it shows that our autonomic WSN tries to maintain optimal
amount of power for each node and also ensures that appropriate data communication occurs for all the nodes in
the network.
*Corresponding Author:
Received 11
July, 2018
Accepted after revision 23
Sep, 2018
BBRC Print ISSN: 0974-6455
Online ISSN: 2321-4007 CODEN: USA BBRCBA
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© A Society of Science and Nature Publication, Bhopal India
2018. All rights reserved.
Online Contents Available at: http//
DOI: 10.21786/bbrc/11.3/24
Sandip Mandal and Rama Sushil
Wireless medical sensor networks (WSNs) are among the
most promising technologies that enable enhanced sens-
ing capability, powerful data processing and increased
communication ability from human body, within vari-
ous environments and in different context. The tech-
nology offers a unique set of capabilities that produces
an exciting but complex design space which is often
dif cult to negotiate in an application context. Deploy-
ing sensing physical environments has its own set of
challenges, and can result into the system failure, thus
resulting in problems that can be dif cult to discover or
reproduce in simulation. Sustained efforts in the area
of wireless networked sensing over the last few decades
have resulted in a large number of theoretical develop-
ments, substantial practical achievements. In order to
bridge the gap between (on the one hand) a very large
scale, randomly deployed, autonomous networks and
(on the other) the actual performance of  elded systems,
we need to consider deployment as an essential com-
ponent in the process of developing sensor networks:
a process that serve speci c applications and end-user
needs. Incorporating deployment into the design process
reveals an entirely new and different set of considera-
tions and requirements whose solutions require innova-
tive thinking, strong involvement from end-user com-
munities and multidisciplinary teams.
WMSN face particular challenges. They are deployed
in hostile environments and are expected to operate
ef ciently within very con ned technological limita-
tions, not least of all in respect to power requirements.
Maintaining the operational lifespan of the WMSN is a
fundamental objective as we cannot afford to lose the
medical data being transmitted in the network. While
the use of intelligent techniques offer one approach, this
paper will advocate the adoption autonomic principles,
augmented with intelligent techniques, as the primary
means by which this objective is met. The paper is struc-
tured as follows: Section I provides a brief description
of WMSNs. In Section II, the notion of an autonomic
WMSN is examined, with particular emphasis on the
critical issues of intelligent power management, and
intelligent coverage and reliable packet transmissions
via message switching respectively. Section III describes
the overview of the technology used. Section IV, V, VI
provides the detailed motivation, architectural design
and implementation of our idea respectively.
The paper speci cally addresses issues of generic
importance for WMSN system designers:
(i) Autonomic Behavior, (ii) Energy Ef ciency, (iii)
Communication Availability and Quality, (iv) Timely
Data Transmission of the network designed. The main
focus is the deployment and deployment evaluation.
In this work, MATLAB has been used to simulate and
analyze the performance of a scalable WMSN infrastruc-
ture with respect to medical applications such as pulse
rate, body pressure, temperature etc. and  nd out how
such systems respond in scenarios which are simulated
to mimic real-time behavior.
With the dramatic increase in computing devices, their
increased computing capacity and complexity combined
with popularity of internet, there has been a phenom-
enal growth in heterogeneous networks and network
applications. Due to this increasing complexity, network
management issues and communication protocols have
reached a level beyond human ability to manage com-
puter systems. At the highest level, the solution is to
have computers manage themselves. This is achieved by
providing pre knowledge to a computing element about
its operation that it is capable of making informed deci-
sions leading to its automation, self- protection and self-
Based on the inspiration from the autonomic func-
tioning of human central nervous system, where auto-
nomic controls use motor neurons to send indirect
messages to organs at a sub conscious level, an initia-
tive was created by IBM towards Autonomic Comput-
ing for relieving humans from the burden of managing
computer systems which is enormously growing to the
extent of unmanageability.
FIGURE 1. Body Sensor Network (BSN) [A. Ruzzelli
et al, 2005]
Sandip Mandal and Rama Sushil
WSNs often lack robustness, are unreliable, have many
elements which interoperate in complex manners, and
are subject to much environmental variability. This
means that it would be extremely dif cult for a per-
son to effectively administer a WSN (if access is even
possible); therefore, system administrators would ben-
e t greatly by the implementation of autonomic princi-
ple to a system (Kephart et al., 2003 and Ruzzelli et al.,
To clarify the contribution that autonomic computing
can bring to WMSNs, we will provide a series of sce-
narios in which a common problem in WMSN operation
can be tackled using autonomic principles.
Self Con guration
Firstly, there is an issue of deployment. It is often assumed
that the nodes forming a network cannot be perfectly
positioned. Hence, a pre-programmed con guration for
the network will not work. Self-con guring nodes can
set up network connections, proceed to establish sensing
and communication schedules and evaluate if there are
any gaps in the WSN, either from a networking or sens-
ing viewpoint. This can be well understood by studying
a protocol (Ruzzelli et al, 2005) for automatically build-
ing a network out of randomly distributed nodes.
Self Protection
Secondly, there is the self-protection attribute. Sensor
nodes are usually exposed to harsher environmental
conditions and are thus, subjected to energy depletion
and incidental damage which can lead to gradual degra-
dation of the network as a whole. Network paths break
and gaps appear in the sensing coverage of an area. A
WSN needs to adapt to the changes in its topology con-
stantly throughout its lifetime.
Therefore, WSN needs to adapt to the changes, recover
from losses and be self- protected. This is achieved by
renegotiation of network routes, voltage level moni-
toring with the sensor nodes, control over each node
by base station and upon failure activating redundant
nodes to replace the damaged ones.
Self Healing
Thirdly, self-healing is the ability to detect and eliminate
the damage caused to the network transmissions due to
the addition of some unwanted elements. (e.g., sensor
transmission contaminated with some noise).
Self Optimization
Lastly, self-optimization is an important trait for WSN
protocols so that maximum energy ef ciency is gained
from the available energy as the energy at each sen-
sor node is limited. Given an application that uses the
network, energy savings can be achieved by reducing
FIGURE 2. Conceptual Demonastration of WSN for Medical Applications - Remote Patient Moni-
toring (Tynan et al., 2005).
Sandip Mandal and Rama Sushil
the overall performance of the network and because
redundant nodes can be put into a low-power sleep
mode, ready to be reactivated when the need arises. This
can prove to be the best effort in achieving maximum
energy ef ciency. In the following subsections, we will
expand on the details of the most important aspects of
a WSN, namely network integrity, sensing quality and
power management, and how autonomic principles aid
in their optimization.
Intelligent Power Management:
Due to the deployment of WSN in a potential hazard-
ous environment, it is of paramount importance that the
network operates for as long as possible without requir-
ing routine and frequent maintenance tasks. The most
common of these tasks is battery replacement. Thus, it is
vital that the nodes of the network manage their power
consumption in an intelligent manner to deliver the lon-
gevity required by the network. A node’s lifetime will be
proportional to the amount of time it is active. Therefore
limiting the nodes activity on a network wide basis will
increase the lifespan of the entire network and this has
proved to be one of the most effective power manage-
ment techniques for WSNs [F. Ye et al, 2003]. An inactive
node is termed as hibernating, a temporary state in which
negligible or no power is consumed by it. Hibernating
nodes are unable to report their sensed data and as such
leave a blind spot in the network, where no active sensor
is monitoring. The hibernation of a node must be per-
formed in context to a network wide quality of service
metric that must be maintained by the active nodes. For
power management, this is typically coverage. Each sen-
sor is associated with a sensing radius within which it
can sense and outside which it is dormant. Some tech-
niques use an inverse distance relationship between the
sensor and its ability to sense but there is still a limit
to its sensing capability. An area is deemed covered if
the union of all the active sensors sensing discs includes
every point within the sensed area. This is the constraint
under which nodes may be hibernated, thus managing
the networks power consumption intelligently while
maintaining a quality of service with regard to its surveil-
lance density. Algorithms based on this principle include
CCP [H. Zhang et al, 2005] and OGDC [F. Ye et al, 2003].
These techniques operate on an optimization of the prob-
lem, ensuring that the intersection of two sensing discs
is covered by another sensor and that every intersection
point of the sensors discs within the boundary area are
also covered by another sensor. Using this property the
algorithms manage the nodes activity and limit the power
consumption by the network. Another technique based
on the standard coverage maintenance is interpolation,
and more speci cally interpolation error [R.Tynan et al,
2005]. For a given sensor network meant for sensing tem-
perature for example, there will be a temperature distri-
bution function across the area of the network. Sensors
of the network are responsible for sampling this func-
tion at discrete locations within the area and report their
readings to a base station. Interpolation is a mathematical
technique for approximation of function values between
known values of that function. The known points of the
temperature function are at the locations of the sensors
within the context of sensor networks. Therefore, inter-
polation could be used to approximate the temperature
between the sensors. A set of sensors used to approximate
another sensor’s temperature reading and then compar-
ing it to the actual reading at the sensor’s location is the
interpolation error. Hibernating sensors whose interpo-
lation error is less than a particular de ned application
threshold, then the node is hibernated, thus conserving
further amounts of energy. This interpolation technique
is the  rst coverage technique that includes neighboring
node locations as well as neighboring node readings and
can deliver greater savings.
Intelligent Routing:
Forwarding of packets from a source to a destination is
the fundamental feature of multi-hop networks. In such
networks, a node should be capable of identifying the
best node to pass on the packet so that it reaches the
destination in time. The easiest way to do it would be
to  ood the entire network; however, this is an unfeasi-
ble approach due to the energy consumption and packet
overhead that the  ooding would cause. An ef cient
routing poses great challenges such as which node to
forward the packet among all neighbouring nodes and
the trade-off between lightweight (but with high delay)
reactive on-demand type routing and low latency (but
computational heavy) of proactive type routing. Proac-
tive protocols try to keep an accurate snapshot of the
networks working status and maintain full knowledge
of the system; hence they are suitable for high computa-
tional capability devices but the low processing capabil-
ity of sensors prevents them possessing a full proactive
routing protocol. On the contrary, on-demand routing
protocols exempt from maintain a routing table all the
time but only build it when a packet needs to be relayed
to a certain destination. This is not an easy task, espe-
cially when the destination is unknown, and the situa-
tion is exacerbated when sensor node energy constraints
do not permit continuous network  ooding of packets. A
secondary issue concerns global node addressing, which
causes to prevent functioning of nodes in two differ-
ent parts of the networks from having the same ID; an
undesirable situation which can lead to incorrect routing
for certain destinations. Apparently, this is not a trivial
problem, particularly when dealing with large scale sen-
sor networks that may comprise thousands of units. The
Sandip Mandal and Rama Sushil
literature survey reveals a profusion of routing proto-
cols, all tailored to wireless sensor networks. However,
before delving into the relevant approach proposed, it is
important to understand the necessary perquisites for a
routing algorithm to be apt for sensor networks:
1. Self-organization, as the expended nodes might or
might not be substituted with new ones; hence it
is important for the network to be prepared in ad-
vance for sudden changes of devices and possibly
their location.
2. Flexibility, external factors such as temporary or
permanent obstacles, might cause the network
topology to change continuously due to which
neighboring nodes may be disconnected. This can
result in the need for the identi cation of a new
routing path.
3. Scalability, a sensor network consists of a certain
number of nodes that may span from single  gures
to tens of thousands of devices.
4. Lightweight, due to limited processing capability
single devices may be prevented from having a
high computational load.
5. Energy-ef ciency, nodes run on batteries and need
may arise to scavenge energy from the environ-
ment. Furthermore, nodes might be deployed in
remote areas where it is impractical to recharge
6. Loop-free, the routing algorithm should ensure
that the packets are not routed endlessly around
the network without  nding the correct destina-
7. Reliability, the protocol should guarantee a high
percentage of correctly routed packets to the des-
8. Tolerable latency, some application need to receive
the packets requested within a certain deadline.
Thus, it would be preferable to have a protocol
that can autonomously trade off energy savings
for packet delay as per the application needs.
All the above characteristics imply the need for an
Intelligent Routing protocol for autonomic wireless sen-
sor networks (Ruzzelli et al., 2005).
Intelligent Coverage:
One of the most important aspects of a sensor network
is to have satisfactory coverage i.e. the ability to pro-
vide sensory data of suf cient quality to the application
which is using it, At the highest level, what concerns
the most is that the sampling frequency, both in spatial
and temporal terms, is high enough so that the phe-
nomena of interest can be observed in suf cient detail.
However, considering WSNs, we must also be aware of
the energy cost of any actions. As suggested earlier, it
is necessary to balance the level of detail the network is
providing to the client against the rate at which energy
is being consumed while gathering the data. Clearly, it
is preferable to tune the network automatically, rather
than doing it manually. Autonomic computing helps in
intelligent reasoning about the coverage in the network.
Various methods can be used to change the quality of
the sensory data the network produces. The simplest
among them is to reduce the rate at which the sensors
sample the environment. This increases the time period
the sensor nodes can spend in a low-power sleep state,
and is relatively easy to coordinate. A more complex
mechanism is to identify nodes that are not required
due to a high density of nodes in a particular area [H.
Zhang et al, 2005]. This needs coordination between the
nodes on a local level to decide which ones should sleep
and which ones should enter an active state. If the range
of the sensors is variable, then varying this parameter
can be put to use is one more way to  ne tune the
power/sensing relationship [D. Marsh et al, 2005]. When
ooded with pre-calculated knowledge of the relation-
ship between deployment densities, coverage levels and
sensor ranges, a node can alter these variables while
avoiding combinations that would have an adverse
effect on the network as a whole. Finally, mobile nodes
have the capability to reposition themselves as condi-
tions dictate [K. H. Low et al, 2006]. In a mixed net-
work of  xed and mobile sensors, the preservation of
coverage in areas where too many static sensors have
become depleted or damaged (often considered a fail-
ure condition for WSNs) is facilitated by this. In mobile
networks, nodes effectively occupy two or more spa-
tial locations once they switch positions faster than the
requisite temporal sampling rate, thus restricting down
on the number of nodes needed. A special case occurs
when an inherently static node with independent deci-
sion making is attached to a mobile object, for instance
a vehicle or person. In this instance coverage becomes
a probabilistic problem, since no actions on the WSN’s
part can in uence where the nodes will end up. In all
types of coverage that a WSN requires, there exist trade-
offs. It is often possible to reduce communication rates
for an increase in processing time. When mobile nodes
are equipped with an appropriate sensing modality,
sensing can be utilized to observe other nodes, rather
than using the radio to exchange positions (Low et al.,
2006). Thus, depending on the relative costs of sensing,
processing and radio communication, a sensor network
can dynamically choose to favour one method over
the other if it leads to signi cant energy savings. By
intelligently choosing the optimal alternative, an auto-
nomic sensor network can achieve energy savings to a
level beyond what could be expected from a standard
Sandip Mandal and Rama Sushil
Modern wireless sensor technologies enable power-
ful data processing, enhanced sensing capability, and
increased communication ability from human body, in
different context within various environments. With
increased prevalence of chronic diseases all over the
world, there is constant pressure on healthcare system
to  nd ways to deliver reliable healthcare solutions that
can provide same service at affordable cost and the ser-
vices that do not need intensive enrolment of medical
staff. In critical emergency response scenarios, design-
ing a medical sensor network that can deliver suitable
functionality (e.g. energy ef ciency, throughput, sensor
data transmission rate) to meet the evolving patient,
provider, and work ow needs is a critical challenge. The
use of wireless technologies in medical environment is
bringing major advancement to the existing healthcare
services. However, these have several key research chal-
lenges such as various types of network communication
infrastructure, fault-tolerance, data integrity, low-power
consumption, transmission delay, node failure, etc. Reli-
ability is one of the most important factors in a suc-
cessful healthcare system. To ensure this factor, system
designers have to care about adaptation of nodes when
its location, connection and link quality is changed. Dif-
ferent network communications infrastructure should be
used in appropriate situation. For example, with high-
risk patients, higher QoS services should be used. The
integrity of distributed data system and fault-tolerance
should be given a proper consideration also. Every
device operates differently at different times, especially
sensor devices. A node in a network can be fail at any-
time for number of reason including battery exhaus-
tion, human-related issues or natural issues. Ensuring a
seamless service during life time of the network could be
a big challenge. How to manage the transmission delay
of various types of communications in the network is
an undoubted challenge. With the system using WMSN,
data must undergo reduced hop counts before it reaches
the sink. In addition, these hops are sometime located
in very critical conditions, such as areas bearing inter-
ference of radio waves or magnetic  eld. As a result,
various transmission delays can occur and thus, require
extra effort of system designer to synchronize the whole
While deploying a WMSN all sensor nodes are com-
municating to the central server i.e. hub through a
wireless protocol. If the transmitted packets contain-
ing patient data are not routed properly due to rout-
ing congestion, there is likelihood that the packets con-
taining patient’s vital information will reach late or not
reach the desired destination at all. This leads to packet
delay which is strictly undesirable in WMSN as diagno-
sis delayed is as good as diagnosis not done. Also the
problem of packet delay is due to the coverage area as
medical sensor networks consists of millions and bil-
lions of sensor nodes deployed within a particular range.
Suppose the range is x metres and the nodes in this
particular range become so dense that communication
is effected to an extent that leads to packet delay. To
counter attack this situation our priority is to reduce the
effective area of the network by  nding a common bal-
ance between the number of nodes in a given region and
the number of packets they will transmit. Further, the
sensor nodes deployed in the network are continuously
communicating altogether at the same time. Thus, there
becomes a possibility that some of the nodes become
vulnerable and might get hacked.
We are introducing a dynamic medical sensor net-
work architectural paradigm, where any of the routing
protocol and security algorithms have the ability to tune
themselves to suit the usage scenario. The sensor net-
work can be statically con gured prior to deployment
and dynamically recon gured during operation.
As a solution for listed problems we have proposed and
created VWMSA – Veracious Wireless Medical Sensor
Architectonics The aim of VWMSA is to serve as a very
simple but robust wireless sensor architecture that can
help in designing a reliable, scalable and fault toler-
ant topology meant for WSN. An ef cient architecture
should be capable of offering:
• High throughput
• Small overhead
Low or manageable latency
Optimal power consumption
• Excellent ultra-low power performance in contin-
uous reliable transmission of data, and large data
traf c.
In order to accomplish all the above goals we create
a WSN topology – VWMSA which is intended to pro-
vide energy ef ciency communication, both for reliable
transmission of data, and for data streaming. It incor-
porates autonomic behavior which is very much desir-
able in modern WSN also a method of packet switching
known as message switching. Thus, it contains propri-
etary communication solution methods which are ideal
for medical applications as we can’t afford to lose any
data being transferred in the network.
VWMSA shows an autonomic behavior where the
central server can place the nodes into active and sleep
mode but once the nodes are active, they can take their
own decisions in real time. Also the nodes can com-
Sandip Mandal and Rama Sushil
FIGURE 3. Proposed Policy Based Autonomic Architecture for WMSN
municate with each other in real time through a packet
switching technique called message switching where the
packets are routed from the source node to the destina-
tion node, in their entirety, one hop at a time.
In message switching [Cohler et al, 1967], the source
and destination nodes are not involved in direct commu-
nications. Instead, the intermediary node (central server)
is responsible for transferring the data from one node to
the other by the process of activation/deactivation of the
source node. It is only after the source node is set up into
transmission mode i.e. when it receives the clearance
or acknowledgement from the destination node, pack-
ets are transmitted in just one go preventing malicious
packet to enter the network. As shown in Figure 4 data
packet P1 is transmitted from A – C and data packet P2
is transmitted from D – B. Data P1 follows the route A
I IIIIIC and P2 follows the route DIVIIB
depending upon the availability of the free path at that
particular moment.
The purpose of this prototype is to assess the perfor-
mance of a Wireless Sensor Network primarily targeted
for medical applications. Our primary concern is to verify
that all the nodes are always able to communicate their
data to the main server and that network performance is
fairly uniform over the entire duration of the operation
(de ned by simulation time). Our approach is to cre-
ate coherent wireless sensor network architecture with
arbitrary number of nodes assumed to be scattered in a
2-dimensional space. The next step is to de ne a proper
topology that can ensure that the data is always safely
transmitted and shared within the network. For this we
create an adaptive segmentation of regions each having
a  nite number of nodes i.e. continuous changes with
each time stamp to avoid security vulnerabilities. During
this region wise segmentation, all the nodes will be used
FIGURE 4. Message switching Policy for secure transactions
Sandip Mandal and Rama Sushil
to communicate with each other through packet trans-
mission. It is assumed that only one packet is transmit-
ted every time stamp. Fig.5 shows the clustering of the
various sensor nodes representing various hospital data.
The region wise segmentation is performed based on the
real-time data being transmitted and how important the
data is. These regions are guaranteed to have reliable
operation since network traf c is optimal. We intercon-
FIGURE 5. Clusters with cluster heads to communicate with hos-
pital end.
FIGURE 6. The average energy consumed during WMSN routing.
Sandip Mandal and Rama Sushil
nect these regions to form a global network thereby
assuring optimal performance of the entire WMSN by
overall reducing the effective area of the network.
Once the operation of the WMSN is simulated (using
event driven simulation) we collect several important
parametric data and use it as benchmarks for assess-
ment of the WMSN model created and check its reli-
ability in terms of energy ef ciency, clustering density
and throughput analysis. The ef ciency of the proposed
approach is based on the simulation studies that have
been performed using MATLAB
Energy Ef ciency
The average power consumption of the sensor nodes and
the network lifetime is related to the work-idle-work
intervals. Evidently, the longer the intervals lower the
power consumption. The adaptive working mode can be
applied in most of scenarios in WMSN. For increasing
the network lifetime, nodes are con gured to “Sleep”
or power saving mode in which the sensor nodes could
turn off most of the modules, greatly reducing quiescent
power consumption under idle state. In contrast, data-
intensive nodes are con gured to the “Continuous” or
“Standby” power saving mode in which the sensor nodes
work continuously, for data collecting and transmission.
When the communication process is done we have to
nd the measure of the network performance where the
FIGURE 7. Communications of data among patients’ nodes
communication is done satisfactorily and all the nodes
are performing well. This depends on the amount of
energy being consumed in the network and it should be
uniform across all the nodes for the communication to
take place reliably.
Cluster Density
Hierarchical based clustering seems to be ideal for
WMSNs. It ensures energy ef cient routing by forming
local clusters and transmitting the information to the
gateway nodes based on the events incurred in an adja-
cent area followed by data aggregation by means of the
gateway nodes. The clustering and re-clustering designs
are simple and reliable [Sung-Hwa Hong et al, 2013].
Cluster head selection is performed in a greedy manner
via the local exchange of node energy states. Each cluster
head determines when to abandon its role and become a
cluster member, depending only on its own energy state.
These local interactions and local decisions regard-
ing clustering and re-clustering increase scalability and
reduce control overhead at the cost of reduced optimality.
The clustered structure is not for the routing purposes.
Routing information is managed independently from
cluster structure. A cluster member transmits packets
only to its local cluster head, but a local cluster head can
transmit packets to any nodes that can route the packets
to the central server or the hub node which is where the
Sandip Mandal and Rama Sushil
FIGURE 8. The average density of clusters formed during active routing.
doctor has an access to the patient’s data. Fig.7 shows
the average density of clusters being formed while active
routing is being performed.
This has similarity to a random variable which is not
surprising as multiple routes can be used in the WMSN
for delivery of the packets.
The percentage of total packets received successfully, is
known as throughput of the network or packet delivery
ratio. It is expressed as:
In Fig.9, almost all nodes show uniform packet trans-
missions since, they send relatively greater number of
packets in the network. The plot shows the average
throughput of the sensor nodes in a given region speci-
fying how the data packets are uniformly distributed
with respect to the time.
Wireless sensor networks for healthcare certainly have
developed to a stage where their usefulness in health-
care application is undoubted, but the technology is still
at an early stage of development. Problems that need
to be solved in order to facilitate the use of the sensor
networks in medical environment are lack of standardi-
zation and therefore low interoperability. In this paper,
a relaying energy-ef cient heterogeneous WMSN archi-
tecture for patient monitoring is proposed. This architec-
ture compatibly incorporates various routing protocols
and security algorithms that de ne the minimum energy
parameters for the sensors to avoid damage to the
human lives. Main focus lies on the autonomic architec-
tural design of WSN for medical applications. The results
are satisfactory and clearly show that in terms of the
network lifetime and stability, our proposal is new, bet-
ter and ef cient when considering the energy ef ciency,
reliable packet transmissions, throughput and latency in
the network as compared with the various existing rout-
ing techniques.
In future we intend to implement the presented sce-
nario on human body with physical sensor nodes and
performing comparison it with other existing mechanism.
Sandip Mandal and Rama Sushil
FIGURE 9. The average throughput of the medical sensors in a given region
IBM (2005) An architectural blueprint for autonomic comput-
ing Autonomic Computing White Paper © Copyright IBM Cor-
poration 2005.
Demirkol, C. E., Alagoz (2006) Mac protocols for wireless sen-
sor networks IEEE Communication Magazine, Vol.06 Isuue 1,
pp 115–121.
Stankovic J.A, Cao Q, Doan T, Fang L., He Z, Kiran R (2005)
Wireless Sensor Networks for In-Home Healthcare: Potential
and Challenges in Proc. of High Con dence Medical Node
Software and Systems (HCMDSS) Workshop, Philadelphia.
Haenggi M, Andrews F, Baccelli F, Dousse O, and Franceschetti
O (2009) Stochastic geometry and random graphs for the anal-
ysis and design of wireless networks IEEE J. Sel. Areas Comm.,
Vol. 27 Issue 3, pp 1029–1046.
Kar S and Moura J (2008) Sensor networks with random links:
Topology design for distributed consensus ,IEEE Trans. Signal
Processing, Vol. 56, pp 3315– 3326.
Hung, K.; Zhang, Y.T. (2002) Usage of Bluetooth in wireless sen-
sors for telehealthcare, Engineering in Medicine and Biology,
2002, 24
Annual Conference of the IEEE. Vol. 3, pp 1881–1882.
Borromeo S., Rodriguez-Sanchez C., Machado F., Hernandez-
Tamames J.A. (2007), A Recon gurable, Wearable, Wireless
ECG System, Engineering in Medicine and Biology Soci-
ety 29
Annual International Conf. of the IEEE, pp 1659–
Kephart J.O. and Chess D.M. (2003), The vision of autonomic
computing, IEEE Computer, Vol. 36-1, pp 41-50.
Ruzzelli, O’Hare G.M.P, O’Grady M.J., Tynan R. (2005), Adap-
tive scheduling in wireless sensor networks 2nd IFIP Interna-
tional Workshop on Autonomic Communication, Vouliagmeni,
Athens, Greece.
Qi H, Kuruganti P.T., Xu Y. (2002), The development of local-
ized algorithms in wireless sensor networks ,Sensors, Vol. 2,
pp 286-293.
F. Ye, G. Zhong, J. Cheng, S. Lu, L. Zhang (2003), “ PEAS:
A Robust Energy Conserving Protocol for Long-lived Sensor
Networks”, Proceedings of the 23rd International Conference
on Distributed Computing Systems, IEEE Computer Society, pp
H. Zhang, J. C. Hou(2005), “Maintaining Sensing Coverage
and Connectivity in Large Sensor Networks”, Ad Hoc & Sensor
Wireless Networks, Vol. 1, pp 89-124.
R.Tynan, G.M.P. O’Hare, D.Marsh, D, O’Kane (2005), “Interpo-
lation for Wireless Sensor Network Coverage”, EmNetS-II:The
Second IEEE Workshop on Embedded Networked Sensors, Syd-
ney, Australia.
Sandip Mandal and Rama Sushil
Marsh D., Tynan R, O’Hare G. M. P., Ruzzelli A. (2005), The
effects of deployment irregularity on coverage in wireless
sensor networks, 2nd International Conference on Intelligent
Sensors, Sensor Networks and Information Processing (ISSNIP
2005), Melbourne, Australia.
Low K. H., Leow W. K., Ang M. H., (2006), Autonomic mobile
sensor network with self-coordinated task allocation and exe-
cution, IEEE Trans. on Systems, Man and Cybernetics – Part C:
Applications and Reviews, Vol. 36-3, pp 315-327.
Cohler, Edmund U., Rubinstein, Harvey (1967), A Multicom-
puter Message Switching Data Processing System, IEEE Trans.
on Communication Technology, – Part C: Applications and
Reviews, Vol. 15, pp 314-321.
Sung-Hwa Hong, Jeong-Min Park,Joon-Min Gil(2013), Perfor-
mance Evaluation of a Simple Cluster-Based Aggregation and
Routing in Wireless Sensor Networks, International Journal of
Distributed Sensor Networks, Vol. 2013, Article ID 501594, pp
Mandal S, Sushil R (2018) Enhanced Energy-Balanced Lifetime
Enhancing Clustering for WSN (EEBLEC), International Journal
of Applied Engineering Research ,Vol 13, Issue 16, pp. 12911-
12916 .
Mandal S, Sushil R (2018) Energy Saving Dynamic Clustering
Protocol for Wireless Sensor Network, International Journal of
Engineering Applied and Management Sciences Paradigms ,
Vol 53 , Issue 3, pp. 25-28.