Security Issues in Biomedical Wireless Sensor Networks Applications: A Survey

International Journal of Advanced Research in Technology, Engineering and Science (A Bimonthly Open Access Online
Journal) Volume2, Issue6, November-December, 2015.ISSN:2349-7173(Online)
All Rights Reserved © 2015 IJARTES visit: www.ijartes.org Page 1
Security Issues in Biomedical Wireless Sensor
Networks Applications: A Survey
S. P. Predeep Kumar1
, Dr. E. Babu Raj2
, Dr. M. Chithirai Pon Selvan3
_____________________________________________________
Abstract The use of wireless sensor networks in healthcare
applications is growing in a fast pace. Numerous applications
such as heart rate monitor, blood pressure monitor and
endoscopic capsule are already in use. To address the growing
use of sensor technology in this area, a new field known as
wireless body area networks has emerged. As most devices
and their applications are wireless in nature, security and
privacy concerns are among major areas of concern. Body
area networks can collect information about an individual’s
health, fitness and energy expenditure. Comprising body
sensors that communicate wirelessly with the patients
control device for monitoring and external communication.
This paper provides the challenges of using the wireless
sensor network in biomedical field and how to solve most of
these issues. To analyze the different security strategies in
Wireless Sensor Networks and propose this system to give
highest quality medical care with full security in their
reliability________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________
_
Keywords: Wireless Sensor; Biomedical Sensor; Biomedical
Security; Patient; Privacy; Confidentiality__________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________
I. INTRODUCTION
Wireless sensor networks are rapidly growing as they have
many outstanding characteristics such as the low power
consumption, remote location sensing, low cost wirelessly
communication and mobility. There has been increase in
using wireless sensor networks in the biomedical and
healthcare. The pervasive interconnection of autonomous
and wireless sensor devices has given birth to a broad class of
exciting new applications in several areas of our lives, with
health care being one of the most important and rapidly
growing one. The emergence of low-power, single-chip
radios has allowed the design of small, wearable, truly
networked medical sensors. These tiny sensors on each
patient can form an ad hoc network, relaying continuous vital
sign data to multiple receiving devices, like PDAs carried by
physicians, or laptop base stations in ambulances [1].
First Author’s Name: S. P. Predeep Kumar, Research Scholar, St.
Peter’s University, Chennai, Tamilnadu, India. E-
mail: sppredeep@gmail.com
Second Author’s Name: Dr. E. Babu Raj , Principal, N S
College of Engineering, Kanyakumari Dist., Tamilnadu, India
Third Author’s Name: Dr. M. Chithirai Pon Selvan, Associate
Professor, Amity University, Dubai, UAE.
The benefit of using wireless sensors in health care is two fold:
First, they allow monitoring of the patient at home, so that the
elderly or patients with chronic diseases can enjoy treatment
and medical monitoring in their own environment. Second,
they substantially increase the efficiency of treatments inside
the hospital environment. Today biomedical sensors are wired,
attaching patients to machines, in order to read different values
of vital data. The implementation of a more flexible wireless
technology can lead to improved data quality, data resolution
and increase of patient’s mobility outside thesurgery room.
This results in enhanced decision making for diagnostics,
observation and patient treatment.
Fig. 1. Overview of WSN applications [1]
Wireless sensor networks in healthcare are deployed
outside or inside the patient body. Outside the body which is
involves placing the sensors around the body. At the other
hand, inside the body involves implanting the wireless sensor
network inside the patient body. When placing several
sensors in or outside the body they form a network called
BAN (Body Area Network) or PAN (Personal Area
Network). The collected data can be sent to a cluster head
device for aggregation or passed to another node to be
forwarded to the base station. Dependent on the application
the wireless sensors network functions and operations will be
determined [2]. For instance, data collection, aggregation,
processing, forwarding, and analyzing [3].The following
Table 1 shows the different between WSN and BAN.

Table 1. Some Differences between WSNs and BANs
Wireless Sensor
Networks
Body Area
Networks
Scale Wide area
coverage (up to
several
kilometres)
Limited by the
human body (in
centimetres)
Node
number
Huge number of
nodes for
coverage
Limited number of
pervasive nodes
Accuracy Compensated by
the redundancy
Accurate
measurements are
required by each
node
Failure Nodes often
disposable
Difficult replacement
of implanted nodes
Energy Solar or wind
power
Motion or body heat
II. RESEARCH ISSUES IN BIOLOGICAL
APPLICATIONS
The WSN based applications have made tremendous
impact for biological problems. Some of these include
biological task mapping and scheduling, biomedical signal
monitoring etc. A brief description of these applications has
been presented in this section. WSNs find widespread
applications in the area of biological sensing. Specifically,
there is recent research going on in the concept of “labs on a
chip”, supported by latest technologies like Nano-techniques.
The use of WSNs for biological applications have been
accelerated due to the advancements in Micro Electro-
Mechanical Systems (MEMS), embedded systems,
microcontrollers and various wireless communication
technologies.
Y.E.M. Hamada and C. Phillips [4] presented a
BTMS (Biological Task Mapping and Scheduling) algorithm,
in which a group of nodes was used to execute an application.
In this work, it was assumed that the application could be
broken down into smaller tasks with different weights and
hence a general model was considered for complex
applications. In order to achieve and enhance the desired
performance objectives, assigning of resources to tasks is
known as Task mapping and the sequence of execution of the
tasks is known as task scheduling. Task mapping and
scheduling are of much importance in high performance
computing. A near-optimal solution for task mapping can be
obtained using heuristic techniques. But the constrained
resources of WSNs require the design objectives to be
different. However the simulation model that was built was
applicable only if the nodes in the WSN were separated with a
distance set to 150m.
WSNs are very efficient in supporting various day-to-day
applications. WSN based technologies have revolutionized
home and elderly healthcare applications. Physiological
parameters of patients can be monitored remotely by
physicians and caretakers without affecting the patients’
activities. This has resulted in reduction of costs,
improvement of equipment and better management of patients
reaping huge commercial benefits. These technologies have
significantly minimized human errors, allowed better
understanding into origin of diseases and has helped in
devising methods for rehabilitation, recovery and the impacts
of drug therapy. The recent developments in the application of
WSN in healthcare are being presented. The implementation
and analysis of a WSN based e-Health application has been
described in [5]. The main research issue to be addressed is to
increase the degree of awareness of home assistants,
caregivers, primary healthcare centers, to understand the
patients’ health and activity status to quickly discern and
decide on the required action. A simple localization
algorithm based on sensor data and Received Signal Strength
Indicator (RSSI) was presented. This algorithm was proved
experimentally to work fine in home environment. However,
the use of multi-sensor analysis,
Which is expected to give better accuracy, is an area yet to be
explored.
A qualitative research on the perceptions and
acceptance of elderly persons regarding the usage of WSN for
assisting their healthcare is done in [6]. A light-weight, low-
cost WSN based home healthcare monitor has been developed
in [7]. An attempt to integrate the WSN technology and public
communication networks in order to develop a healthcare
system for elderly people at home without disturbing their
routine activities has been presented in [8]. Improved
performance with minimum decision delay and good accuracy
using Hidden Markov Model is yet to be addressed. A WSN
based home healthcare application is developed in [9]. The
main issue that was considered in this research is the
development of a working model of home healthcare
monitoring system with efficient power, reliability and
bandwidth.
A WSN based prototype sensor network for
monitoring of health, with sensors for heart activity, using
802.15.4 complaint network nodes is described in [10]. The
issues regarding its implementation have also been discussed.
The paper also describes the hardware and software
organization of the presented system and provides solutions
for synchronization of time, management of power and on-
chip signal processing. However, the areas that are yet to be
addressed are improvement in QoS of wireless
communication, standardization of interfaces and
interoperability. Specific limitations and new applications of
the technology can be determined by in-depth study of
different medical conditions in clinical and ambulatory
settings. The micro Subscription Management System
(µSMS) middleware using an event-based service model.
This novel approach meets the design constraints of limited
resources, efficiency, scalability, dependability and low power

consumption by implementing a dynamic memory kernel and
a mechanism of variable payload multiplexing for the
information events to provide better services. It was
observed that application of this approach yielded best results
for e-health applications.
For continuous and real-time monitoring of health
Y.D. Lee and W. Y. Chung developed a smart shirt which
measured ECG (Electro Cardio Gram) and acceleration
signals [23]. The shirt was made up of conductive fabrics to
obtain the body signal as electrodes and consisted of sensors
for online health data monitoring. The observed and
measured data are transmitted in an ad-hoc network for
remote monitoring. A key establishment method to secure
communications in biomedical sensor networks has emerged
to be biometrics [11]. It advocates the use of the body itself as
a means of managing cryptographic keys for symmetric
cryptography. For sensors attached on the same body, if
they measure a previously agreed physiological value
simultaneously and use this value to generate a pseudo-
random number, this number will be the same. Then it can be
used to encrypt and decrypt the symmetric key to distribute it
securely. The physiological value to be used should be chosen
care-fully, as it must exhibit proper time variance and
randomness.
In different case the whole scheme can be vulnerable
to brute force attacks. For example, blood glucose, blood
pressure or heart rate are not appropriate. On the other hand,
ECG (electrocardiogram) has been shown to be appropriate
[12]. However, an important requirement is to have accurate
time synchronization, so that sensors take their measurements
at the same time and produce the same value. To do that, a
time synchronization protocol is needed, using reference
broadcasts. Such protocols have been shown to be susceptible
to attacks [17] and securing them will require even more of
the mote’s resources. Another disadvantage of this method is
that only biosensors in and on the body can measure
biometrics, so it cannot be applied for securing the
communication of other sensor nodes in the general
architecture. Moreover, this method assumes that there is a
specific pre-defined biometric that all biosensors can measure,
which is not necessarily true.
A . Description of Body Area Networks
The technology behind the WSNs is still under
development. However, the research on WSNs does not deal
with the challenges associated with human body1 monitoring.
For this reason, a new generation of WSNs has emerged: the
Body Area Networks2 (BANs). BANs provide a new
paradigm for the WSNs technology in the physiological
biosensor, the MAC layer, the network layer, the routing
strategy, etc.
A BAN is a network of wearable on-body computing
devices. It can also include in vivo implanted biosensor de-
vices. According to the IEEE 802.15, a BAN consists of “low
power devices operating on, in or around the human body (but
not limited to humans) to serve a variety of applications
including medical, consumer electronics / personal
entertainment and other”. BANs have a wide range of
applications, as, for instance, in gaming or virtual reality.
However, the main challenges in terms of research or
engineering remain in the biomedical and healthcare
monitoring applications. Indeed, evolution of BANs should
follow the ever-increasing development in the medical
domain; its main objective being to ensure constant vigilant
and pervasive monitoring of patients at home or at work.
B . Requirements in body area networks
Although BANs are essentially WSNs, they take the
WSNs to their extreme in many directions. Indeed, BANs for
instance are limited to the human body where very few
nodes are deployed. Having few sensing devices, data loss in
BANs could be significant, as opposed to WSNs, where nodes
yield redundant information. Therefore, each node needs to
provide accurate measurements, especially for nodes giving
vital information, such as electrocardiogram (ECG)
measurements. This may require additional measurements to
ensure quality-of-service and real-time data delivery, since
reliability of measurements is a must in the medical domain.
Moreover, BANs often consist of networks with devices
performing di- verse tasks. Examples of these devices include
ECG, blood pressure, pulse oximetry, temperature,
respiratory, etc. Such heterogeneous characteristics require
different frequency acquisition rates as well as different
transmission rates.
On the other hand, some devices of the BAN need to
be implanted, in vivo, inside the human body. Implanted
biosensors should be biocompatible, robust, as replacement is
difficult, and with low energy requirements, since energy is
hard to supply. In addition, in BAN, nodes are in close
proximity to or inside the human body. This may lead to high
absorption of the electromagnetic radio-frequency waves, and
thus to an increase of the body temperature. According to the
safety limits for exposure to radio-frequency energy,
communications should be uniformly distributed among all
the nodes. Moreover, as the human body is not rigid, frequent
changes in nodes positions should be considered in the
network topology. For these reasons, a special attention
should be paid to the design of the BAN, including the routing
strategy.
III. ENERGY SCAVENGING
The energy consumption in BANs is crucial,
especially in implanted biosensors, since they are inaccessible
and difficult to replace. For instance, a pacemaker battery
usually lasts for 5 to 10 years. Since the battery is sealed
inside the pacemaker, replacing the battery leads to the
replacement of the entire system. In order to increase the
lifetime of battery-powered devices, extra amount of energy
can be delivered by energy harvesting, i.e., energy scavenging
[13]. As opposed to WSNs, where a great attention is paid to

solar energy, BANs introduced a new nature of energy
scavenging. Devices can scavenge power from the human
body using body heat and body vibrations. Although heat and
motion of the human body bring new opportunities, they also
introduce new challenges, as shown in the following.
A . Body motion
Electrical power can be generated from motion and
vibration, with the so-called inertial-power scavenging.
Natural human body motion may be easily converted into
electrical power. This is illustrated by electronic self-winding
watches based on moving mechanics, such as the ETA Auto--
quartz and the Seiko Automatic Generating System. More
promising approaches are based on piezoelectric or capacitive
generators. An example of electric power generators is the
electric shoes [14]. For instance, electric shoes to provide
autonomy to artificial organs. While motion may provide
power to BANs, it introduces new challenges. In fact, as the
human body is not rigid, thus the devices can move, at least
relatively to each others. Thus, frequent changes in the
network topology should be considered in the design of the
network architecture. BANs should be robust to these
changes, for instance by adapting the routing strategy, and the
self-organization of the nodes.
B . Body heat
Temperature difference can be directly converted
into electrical power, as given by the Peltier-Seebeck effect.
As any heat engine, the efficiency of thermoelectric energy is
limited by the Carnot’s theorem. In practice, large thermal
gradients are required to create satisfactory levels of voltage
and power. This limits the application of thermoelectric
energy in BANs, provided by body heat over ambient
temperature. Nevertheless, recent works are very encouraging,
as illustrated by the Seiko Thermic watch.
While environment temperature cannot be controlled,
human body temperature is essentially3 37 ◦C (98.6 ◦F). In
BANs, the temperature is likely to be different than this
common value. In fact, radio-frequency transmission of
wireless nodes located on-body or implanted contribute to
radiation absorption, which may result into thermal effects.
For safety reasons, the radiation absorption should be reduced,
by con- trolling the Specific Absorption Rate (SAR) [15] and
using an appropriate routing protocol.
Wireless Sensors have limited energy and usually
they operate using batteries. These batteries are required to be
changed or recharged after a period of time. However, there
are some of attacks that target the wireless sensor to exhaust
the device so it lose its powers and die. There for, a
protection is needed from some of the unauthorized
connections that aim to exhaust the wireless sensor are
required.
IV. LIMITATION RESOURCES
To solve the energy limitation in wireless sensor network
many researchers have conducted studies. In fact the limited
resources such as in energy have a great effect on the wireless
sensor security. That because when adding security measures
such as encryption extra processing and data transmission will
be added as an overhead cost which will affect the sensor
energy consumption and shortage it’s functioning life.
Moreover, a study in [16] aimed to find the best encryption
algorithm that fits the wireless sensors networks in
biomedical. Many algorithms were studied based on their
performance and the energy consumption and concluded by
finding MISTY1 encryption algorithm is the best to be used.
Considering the routing protocol in wireless sensor networks
and their ability to play a major role in reserving the sensor
resource, protocols such is Hybrid Indirect Transmission [17]
is designed to lower the resources when transmitting data.
HIT is based on clustering and utilize data fusion. Moreover,
HIT has different mechanisms such as Carrier Sense Medium
Access with Collision Detection where the sender after
finding the medium is free sends the data and listen in the
medium in case of collision happen. HIT also use Time
Division Multiple access where each sensor can occupy the
whole medium for certain amount of time. Finally, HIT uses
mechanism from LEACH protocol (Low Energy Adaptive
Clustering Hierarchy) [18]. Overcoming the resources
limitation in wireless sensors network is essential when
implementing security measures are required to reduce the
effect of the security overhead costs.
Because of the nature of the data the wireless sensor
is sending, it’s important to make sure that the data is accurate
as the healthcare provider depend of these data to diagnose the
patient. However, faults in wireless sensors are provoked by
the lack of energy or during the transmission. In fact, the
failure could be in some of the transmitted data or in all the
data. Other causes of failure could data congestion in the
network and an attack on the data during transmission. In
[19] a study have been done to identify the threats related to
fault tolerance and interferences. As a result, the authors
indicated that these issues are still open in wireless sensors
network and there is no protocol currently solving them.
Speaking about interference and jamming, FHSS (frequency
hopping spread spectrum) and DSSS (Direct sequence spread
spectrum) are traditional solutions. However, there is cost of
implementing these techniques in term of power consumption.
A study was conducted to address routing issues, power
consumption, where a signal jamming and node faults using a
signal with priority properties to detect the jamming location
was proposed in [20]. Further, detecting the interference in
Body Area Network using a coordinator was proposed in [21]
where this coordinator checks for the existing of the
interfering packets.
A role based system was proposed to overcome
different types of security issues related to wireless sensor
network. Where the system after detecting the compromised
node, it stops from having to function in the network.
Another technique that uses different process aiming to

mitigate the effect of the compromised node, a solution was
proposed based on having communication with neighbors to
avoid having a communication failure in the network in case
of an attack on the network. As the neighbor nodes store the
packets it they listen and track the connection until it fully
received by the destination node.
V. CONCLUSION
There are many benefits we can utilize using the new
technologies however, it always introduce new challenges.
Wireless sensor networks biomedical in new emerging
technology but has some challenges that need to be addressed.
In this paper we have provided a solution to one of these
challenges where the wireless sensor network needs to be
secured to provide protection for the patient and the healthcare
provider. Where the data must be encrypted and transmitted
using low energy network. As a result, that will make the
patient assuring the confidentiality, privacy and prolong the
need to maintain the wireless sensor itself.
REFERENCES
[1] D. Cypher, N. Chevrollier, N. Montavont, and N. Goldie, “Prevailing
over wires in healthcare environments: benefits and challenges,” IEEE
Communications Magazine, vol. 44, no. 4, pp. 56–63, 2006.
[2] B. Warneke, K. S. J, and S. Dust, “Smart dust: Communicating with a
cubic-millimeter computer,” Classical Papers on Computational Logic, vol. 1,
pp. 372–383, 2001.
[3] G.-Z. Yang, Body Sensor Networks. Secaucus, NJ, USA: Springer-
Verlag New York, Inc., 2006.
[4] J. A. Paradiso and T. Starner, “Energy scavenging for mobile and wire-
less electronics,” Pervasive Computing, IEEE, vol. 4, no. 1, pp. 18–27, Mar.
2005.
[5] J. F. Antaki, G. E. Bertocci, E. C. Green, A. Nadeem, T. Rintoul, R. L.
Kormos, and B. P. Griffith, “A gait-powered autologous battery charging
system for artificial organs.” ASAIO J., vol. 41, no. 3, pp. M588–95,1995.
[6] N. S. Shenck and J. A. Paradiso, “Energy scavenging with shoe- mounted
piezoelectrics,” IEEE Micro, vol. 21, pp. 30–42, May 2011.
[7] J. W. Stevens, “Optimal design of small ∆T thermoelectric generation
systems,” Energy Conversion and Management, vol. 42, pp. 709–720, 2011.
[8] H. Ren and M. Meng, “Rate control to reduce bio-effects in wireless
biomedical sensor networks,” in Mobile and Ubiquitous Systems, Annual
International Conference on. Los Alamitos, CA, USA: IEEE Computer
Society, July 2006, pp. 1–7.
[9] W. R. Heinzelman, A. Chandrakasan, and H. Balakrishnan, “Energy-
efficient communication protocol for wireless microsensor networks,” in
Proceedings of the 33rd Hawaii International Conference on System
Sciences-Volume 8 - Volume 8, ser. HICSS ’00.Washington, DC, USA:
IEEE Computer Society, 2009.
[10] O. Younis, M. Krunz, and S. Ramasubramanian, “Node clustering in
wireless sensor networks: Recent developments and deployment challenges,”
IEEE Network Magazine, vol. 20, pp. 20–25, 2006.
[11] T. Watteyne, I. Auge´-Blum, M. Dohler, and D. Barthel, “Anybody: a
self-organization protocol for body area networks,” in Proceedings of the
ICST 2nd international conference on Body area networks, ser. BodyNets ’07.
ICST, Brussels, Belgium, Belgium: Institute for Computer Sciences, Social-
Informatics and Telecommunications Engineering, 2007, pp. 6:1–6:7.
[12] B. J. Culpepper, L. Dung, and M. Moh, “Design and analysis of hybrid
indirect transmissions (hit) for data gathering in wireless micro sensor
networks,” SIGMOBILE Mob. Computer. Communication. Rev., vol. 8, pp.
61–83, January 2004.
[13] M. Moh, B. Culpepper, L. Dung, T.-S. Moh, T. Hamada, and C.-F. Su,
“On data gathering protocols for in-body biomedical sensor networks,” in
IEEE Global Telecommunications Conference (GLOBECOM’05), St. Louis,
MO, USA, Dec. 2005.
[14] Q. Tang, N. Tummala, E. K. S. Gupta, L. Schwiebert, S. Member, and
S. Member, “Communication scheduling to minimize thermal effects of
implanted biosensor networks in homogeneous tissue,” IEEE Tran.
Biomedical Eng, vol. 52, pp. 1285–1294, 2005.
[15] A. Bag and M. Bassiouni, “Energy efficient thermal aware routing
algorithms for embedded biomedical sensor networks,” IEEE International
Conference on Mobile Adhoc and Sensor Systems Conference, vol. 0, pp.
604–609, 2006.
[16] D. Takahashi, Y. Xiao, F. Hu, J. Chen, and Y. Sun, “Temperature-aware
routing for telemedicine applications in embedded biomedical sensor
networks,” EURASIP J. Wireless Communication. N/ws.,
vol. 2008, pp. 26:1–26:26, January 2008.
[17] “A practical guide to the determination of human exposure to
radiofrequency fields,” NCRP, Tech. Rep. 119, 1993.
[18] B. Zhen, K. Takizawa, T. Aoyagi, and R. Kohno, “A body surface
coordinator for implanted biosensor networks,” in Proceedings of the
2009IEEE international conference on Communications, ser. ICC’09. Pis-
cataway, NJ, USA: IEEE Press, 2013, pp. 475–479.
[19] E. Stuart, M. Moh, and T.-S. Moh, “Privacy and security in biomedical
applications of wireless sensor networks,” in First International Symposium
on Applied Sciences on Biomedical and Communication Technologies
ISABEL ’08, Aalborg, Oct. 2008, pp. 1–5.
[20] L. W. Hanlen, D. Smith, J. A. Zhang, and D. Lewis, “Key-sharing via
channel randomness in narrowband body area networks: is everyday
movement sufficient?” in Proceedings of the Fourth International Conference
on Body Area Networks, ser. BodyNets ’09. Brussels, Belgium, Belgium:
Institute for Computer Sciences, Social-Informatics and Telecommunications
Engineering, 2012, pp. 17:1–17:6.
[21] S. Ullah, H. Higgins, B. Braem, B. Latre, C. Blondia, I. Moerman, S.
Saleem, Z. Rahman, and K. Kwak, “A comprehensive survey of wireless
body area networks,” Journal of Medical Systems, pp. 1–30, Aug. 2013.

Security Issues in Biomedical Wireless Sensor Networks Applications: A Survey

More Related Content

What's hot (20)

Similar to Security Issues in Biomedical Wireless Sensor Networks Applications: A Survey (20)

More from IJARTES (20)

Recently uploaded (20)

Security Issues in Biomedical Wireless Sensor Networks Applications: A Survey