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In vivo wireless body communications: State-of-the-art and future directions


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The emerging in vivo communication and networking system is a prospective component in advancing health care delivery and empowering the development of new applications and services. In vivo communications construct wirelessly networked cyber-physical systems of embedded devices to allow rapid, correct and cost-effective responses under various conditions. This paper surveys the existing research which investigates the state of art of the in vivo communication. It also focuses on characterizing and modeling the in vivo wireless channel and contrasting this channel with the other familiar channels. MIMO in vivo is as well regarded in this overview since it significantly enhances the performance gain and data rates. Furthermore, this paper addresses current challenges and future research areas for in vivo communications.
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In Vivo Wireless Body Communications:
State-of-the-Art and Future Directions
Raed M. Shubair 1,2and Hadeel Elayan 1
1Electrical and Computer Engineering Department, Khalifa University, UAE
2Research Laboratory of Electronics, Massachusetts Institute of Technology, USA
Abstract—The emerging in vivo communication and network-
ing system is a prospective component in advancing health care
delivery and empowering the development of new applications
and services. In vivo communications construct wirelessly net-
worked cyber-physical systems of embedded devices to allow
rapid, correct and cost-effective responses under various condi-
tions. This paper surveys the existing research which investigates
the state of art of the in vivo communication. It also focuses
on characterizing and modeling the in vivo wireless channel and
contrasting this channel with the other familiar channels. MIMO
in vivo is as well regarded in this overview since it significantly
enhances the performance gain and data rates. Furthermore, this
paper addresses current challenges and future research areas for
in vivo communications.
Keywords- In vivo communication, MIMO in vivo, WBAN.
Wireless Body Area Networks (WBANs) is an exciting
technology that promises to bring healthcare monitoring ap-
plications to a new level of personalization. The aim of these
applications is to ensure continuous monitoring of the patients’
vital parameters, while giving them the freedom of moving
thereby resulting in an enhanced quality of healthcare [1]. In
fact, a WBAN is a network of wearable computing devices
operating on, in, or around the body. It consists of a group
of tiny nodes that are equipped with biomedical sensors,
motion detectors, and wireless communication devices [2].
Actually, advanced health care delivery relies on both body
surface and internal sensors since they reduce the invasiveness
of a number of medical procedures [3]. Sensors such as
those shown in Fig. 1 transmit data to monitoring devices of
the hospital Information Technology (IT) infrastructure. Elec-
trocardiogram (ECG), electroencephalography (EEG), body
temperature, pulse oximetry (SpO2), and blood pressure are
evolving as long-term monitoring sensors for emergency and
risk patients [4].
Advanced sensors for chemical, physical and visual applica-
tions will become part of future monitoring platforms to check,
for example, insulin or hemoglobin. The benefit provided by
WBAN is obvious to the patients’ comfort especially for long-
term monitoring as well as complex monitoring during surgery
and medical examinations [4].
One attractive feature of the emerging Internet of Things
is to consider in vivo networking for WBANs as an impor-
tant application platform that facilitates continuous wirelessly
enabled healthcare [5]. In vivo communication, also known
Fig. 1. Sensor network of biomedical monitoring applications.
as Intra Body Communication (IBC), uses the human body
to transmit electrical signals, where the radiated energy is
mostly confined within the body [6]. Internal health monitoring
[7], internal drug administration [8], and minimally invasive
surgery [9] are examples of the pool of applications that
require communication from in vivo sensors/actuators to body
worn/surface nodes. However, the study of in vivo wireless
transmission, from inside the body to external transceivers is
still at its early stages.
Fig. 2. Simplified overview of the in vivo communication network.
Fig. 2 shows the modified network organization for in-
terconnecting the biomedical sensors. The data is not trans-
ferred directly from the biomedical sensors to the hospital
infrastructure as in Fig. 1. Indeed, the sensors send their data
via a suitable low-power and low-rate in vivo communication
link to the central link sensor (located on the body like all
other sensors). Any of the sensors may act as a relay sensor
between the desired sensor and the central link sensor if a
direct connection is limited. An external wireless link enables
the data exchange between the central link sensor and the
2015 Loughborough Antennas & Propagation Conference (LAPC)
978-1-4799-8943-0/15/$31.00 ©2015 IEEE
external hospital infrastructure [4].
This paper surveys the existing research which investigates
the state of art of the in vivo communication. It also focuses
on characterizing and modeling the in vivo wireless channel
and contrasting this channel with the other familiar channels.
MIMO in vivo is also regarded in this overview since it
significantly enhances the performance gain and data rates.
Furthermore, this paper proposes some future research areas.
The rest of the paper is organized as follows. In Section II, we
present the state of art of in vivo communication. Conducted
research on in vivo channel characterization is provided in
Section III. The MIMO in vivo system is described in Section
IV. In Section V, future research areas are presented. Finally,
we draw our conclusions and summarize the paper in Section
In vivo communication is a genuine signal transmission field
which utilizes the human body as a transmission medium for
electrical signals [10]. The body becomes a vital component
of the transmission system. Electrical current induction into
the human tissue is enabled through sophisticated transceivers
while smart data transmission is provided by advanced encod-
ing and compression. Fig. 3 below shows the main components
of an in vivo communication link.
Fig. 3. In vivo communication for data transmission between sensors enabled
by transmitter and receiver units.
A transmitter unit permits sensor data to be compressed
and encoded. It then conveys the data by a current-controlled
coupler unit. The human body acts as the transmission channel.
Electrical signals are coupled into the human tissue and
distributed over multiple body regions. On the other hand,
the receiver unit is composed of an analog detector unit
that amplifies the induced signal and digital entities for data
demodulation, decoding, and extraction [4].
Developing body transmission systems have shown the
viability of transmitting electrical signals through the human
body. Nonetheless, detailed characteristics of the human body
are lacking so far. Not a lot is known about the impact of
human tissue on electrical signal transmission. Actually, for
advanced transceiver designs, the effects and limits of the
tissue have to be cautiously taken into consideration [4] [11].
An in vivo channel is depicted as being both inhomogeneous
and very lossy [12]. Basically, in an in vivo channel, the
electromagnetic wave passes through various dissimilar media
that have different electrical properties, as displayed in Fig. 4.
This leads to the reduction in the wave propagation speed in
some organs and the stimulation of significant time dispersion
that differs with each organ and body tissue [13]. The previous
effect coupled with attenuation due absorption by the different
layers result in the degradation of the quality of the transmitted
signal in the in vivo channel.
Fig. 4. In vivo multi-path channel [3].
In addition, since the in vivo antennas are radiating into a
complex lossy medium, the radiating near fields will strongly
couple to the lossy environment. This signifies that the radiated
power relies on both the radial and angular positions; hence,
the near field effect has to be always taken into account when
functioning in an in vivo environment [14]. The electric and
magnetic fields behave differently in the radiating near field
compared to the far field. Therefore, the wireless channel
inside the body necessitates different link equations [15]. It
must be noted as well that both the delay spread and multi-
path scattering of a cellular network are not directly applicable
to near-field channels inside the body. The reason behind this
is the fact that the wavelength of the signal is much longer
than the propagation environment in the near field [12].
The authors in [3] used an accurate human body to investi-
gate the variation in signal loss at different radio frequencies
as a function of position around the body. They noticed
significant variations in the Received Signal Strength (RSS)
which occur with changing positions of the external receive
antenna at a fixed position from the internal antenna [3].
Nevertheless, their research did not take into account the basic
characterization of the in vivo channel. In [16], the authors
used an immersive visualization environment to characterize
RF propagation from medical implants. Based on 3-D elec-
tromagnetic simulations, an empirical path loss (PL) model is
developed in [17] to identify losses in homogeneous human
tissues. Further, numerical and experimental investigations of
biotelemetry radio channels and wave attenuation in human
subjects with ingested wireless implants are presented in [18].
Modeling the in vivo wireless channel including building a
phenomenological path loss model is one of the major research
goals in this field. A profound understanding of the channel
characteristics is required for defining the channel constraints
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and the subsequent systems’ constraints of a transceiver design
A. Path Loss
Path loss in in vivo channels can be investigated using either
a Hertzian-Dipole antenna or a monopole antenna. The former
case is presented in [12] in which path loss is examined with
minimal antenna effects. The length of the Hertzian-Dipole
is so small resulting in little interaction with its surrounding
environment. The path loss can be calculated as
P ath loss(r, θ, φ)=10log10(|E|2
r,θ, φ
where rrepresents the distance from the origin, i.e. the
radius in spherical coordinates, θis the polar angle and φ
is the azimuth angle. |E|2
r,θ, φ is the square of the magnitude
of the electric field at the measuring point and |E|2
r=0 is the
square of the magnitude of E field at the origin.
Due to the fact that the in vivo environment is an inhomoge-
neous medium, it is mandatory to measure the path loss in the
spherical coordinate system [12]. The setup of this approach
can be seen in Fig. 5 in which it includes the truncated human
body, the Hertzian-Dipole and the spherical coordinate system.
Fig. 5. Truncated human body with Hertizian-Dipole at the origin in spherical
coordinate system [12].
The latter case is presented in [3]. Actually, monopoles are
good choice of practical antennas since they are small in size,
simple and omnidirectional. The path loss can be measured by
scattering parameters (S parameters) that describe the input-
output relationship between ports (or terminals) in an electrical
system [3]. According to Fig. 6, if we set Port 1 on transmit
antenna and Port 2 on receive antenna, then S21 represents the
power gain of Port 1 to Port 2, that is
where Pris the received power and Ptis the transmitted
power. Therefore, we calculate the path loss by the formula
P ath loss(dB)=20 log10 |S21|(3)
Based on the simulations presented in [12], it can be ob-
served that there is a substantial difference in the behaviors of
Fig. 6. Simulation setup by using monopoles to measure the path loss [12].
the path loss between the in vivo and free space environment.
In fact, significant attenuation occurs inside the body resulting
in an in vivo path loss that can be up to 45 dB greater than the
free space path loss. Fluctuations in the out-of-body region is
experienced by the in vivo path loss. On the other hand, the
free space path loss increases smoothly. The inhomogeneous
medium results as well in angular dependent path loss [12].
B. Comparison of Ex Vivo and In Vivo Channels
The different characteristics between ex vivo and in vivo
channels are summarized in [13] as shown in Table 1 below.
IV. MIMO In Vivo
Due to the lossy nature of the in vivo medium, attaining
high data rates with reliable performance is considered a
challenge [5]. The reason behind this is that the in vivo
antenna performance may be affected by near-field coupling
as mentioned earlier and the signals level will be limited by a
specified Specific Absorption Rate (SAR) levels. The SAR is
a measurement of how much power is absorbed per unit mass
of conductive material, in our case, the human organs [19].
This measurement is limited by the Federal Communications
Commission (FCC) which in turns limits the transmission
power [19].
The authors in [13] analyzed the Bit Error Rate for a MIMO
in vivo system. Actually, by comparing their results to a 2×2
SISO in vivo, it was evident that significant performance gains
can be achieved when using a 2×2MIMO in vivo. This setup
allows maximum SAR levels to be met which results in the
possibility of achieving target data rates up to 100 Mbps if the
distance between the transmit (Tx) and receive (Rx) antennas
is within 9.5 cm [19].
Further, in [5], it was proved that not only MIMO in vivo
can achieve better performance in comparison to SISO systems
but also considerably better system capacity can be observed
when Rx antennas are placed at the side of the body. Fig. 7
compares the in vivo system capacity for front, right side, left
side, and back of the body. In addition, it was noticed that in
order to meet high data rate requirements of up to 100 Mbps
with a distance between the Tx and Rx antennas greater than
12 cm for a 20 MHz channel, relay or other similar cooperative
networked communications are necessary to be introduced into
the WBAN network [5].
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Fig. 7. 2×2MIMO and SISO in vivo system capacity comparison [5].
Features Ex vivo In vivo
Physical Wave
Constant speed
Multipath- reflection, scattering
and diffraction
Variable speed
Multipath and penetration
Attenuation and
Path Loss
Lossless medium
Decreases inversely with dis-
Very lossy medium
Angular (directional) depen-
Dispersion Multipath delays-time disper-
Multipath delays of
variable speed -frequency
dependency-time dispersion
Directionality Propagation essentially uniform Propagation varies with di-
Directionality of antennas
changes with position
Near Field
Deterministic near-field region
around the antenna
Inhomogeneous medium -
near field region changes
with angles and position in-
side the body
Average and Peak Plus specific absorption rate
Shadowing Follows a log normal distribu-
To be determined
Multipath Fading Flat fading and frequency selec-
tive fading
To be determined
Antenna Gains Constant Angular and positional de-
Gains highly attenuated
Wavelength The speed of light in free space
divided by frequency λ=c
at 2.4GHz, average dielec-
tric constant r=35,
which is roughly 6 times
smaller than the wavelength
in free space.
The major future research direction focuses on investigating
high throughput, efficient, and robust novel networking tech-
nologies that enable reliable information transmission between
devices. In order to achieve a system with such specifications,
great focus should be spent on architecting, realizing and
networking a family of wirelessly controlled in vivo devices.
In addition, electronic and mechanical miniaturization of com-
plex systems are necessitated. Basically, motion control, video
zoom, auto focus and LED illumination are some of the
various functions that require careful control of the in vivo
wireless devices.
The first aspect that should be regarded in order to ensure
improved future in vivo communication is frequency. Indeed,
the frequency range selected for such communications plays a
significant role in the design and performance of the system.
Actually, there is a direct relationship between frequency and
tissue warming in which the higher the frequency of the
electromagnetic signal, the higher is its absorption by the
tissue, thereby the greater the tissue warming. Thus, it is
favorable to use lower frequencies for communications. Yet,
the lower the frequency, the larger the antenna dimensions.
As a result, a tradeoff between antenna dimensions and greater
tissue warming should be achieved [20]. Moreover, limitations
exist when transmitting at high frequencies from in vivo
devices to ex vivo transceivers since the maximum transmit
power is restricted by the SAR safety guidelines [19]. Even
when operating under low noise conditions with moderate
BER requirements, reliable data transmission to an external
receiver can only be achieved when located very close to
the body. Therefore, when noise levels increase or the BER
becomes more stringent, a relay network or use of multiple
antennas is essential to achieve high data rates [19]. Based on
the simulations conducted by [19] , the maximum SAR levels
occur at points closest to the transmit antenna. Consequently,
it could be concluded that by placing the transmitter further
from organs, the power levels could be possibly increased to
obtain higher signal levels at the external receiver.
In addition, the minimum transmission power levels re-
quired to establish a reliable wireless data connection must
be recorded with the implants placed in different locations
inside the body. The first aim of this data is to compare the
power levels with international safety regulations, limiting the
human tissues exposure to radio frequency signals. The second
objective is to give reference levels for power consumption
during data transmission that can be useful to estimate battery
lifetime, since this task is one of the most energy demanding
for the implant, particularly, if compared with sensor data
Furthermore, a substantial feature in future in vivo research
is the development of parametric models for the in vivo
channel response which can have a beneficial effect on the
optimization of advanced communication techniques. This in-
volves the statistical characterization of the in vivo channel and
the usage of MIMO technology for improved communication
reliability and performance. Channel models are required to
achieve accurate link budget which aids in finding optimized
locations for placing the transmitter and receiver on and inside
the human body [21]. On the other hand, MIMO in vivo
capacity should be theoretically studied incorporating both the
near-and far field effects [13].
It must be noted as well that the channel at the head in
in vivo communication is of a particular interest since most
human communication organs such as mouth, ears and eyes
are located there. Therefore, it is suggested to consider the ear
to ear link in simulations, for it is the worst case scenario at
the head as it lacks the line of sight component [22].
As a result of the diversity found in the human body
including fluids, fats, bones, and muscles, variation in energy
absorption exists coupled with the possibility of tissue damage
due to heating by radiation. Such issues result in complicating
the design of biosensors and biosensor networks; therefore,
careful attention must be paid in future work to overcome
2015 Loughborough Antennas & Propagation Conference (LAPC)
such limitations [20]. Another particular concern is biocom-
patibility. Basically, any sensor must not only be biologically
inert, but also it should not perturb the normal biology its
interrogating [23].
Nanoscale in vivo devices are another major aspect of
biologically inspired network systems since they open up
amazing opportunities in health care. Indeed, these systems
can be introduced into the human body and could intervene
very closely with organs and cells. Nanodevices intervening at
the molecular level will allow novel healthcare solutions to be
developed which will be not only more efficient but also cost-
effective because of the possibility of large-scale production
Finally, testing using real animals to validate the simulation
results is one of the fundamental research directions [21] in
addition to hardware emulation with human body phantoms.
In this paper, an overview of the in vivo communication and
networking is provided. The overview focuses on the state of
art of the in vivo communication, the in vivo channel modeling
and characterization, and the concept of MIMO in vivo.The
paper also discusses few potential research areas covering the
development of parametric models and the in-depth study of
MIMO in vivo technology. Frequency range, power levels and
SAR requirements are few aspects that should be carefully
regarded in any upcoming research associated with this field.
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2015 Loughborough Antennas & Propagation Conference (LAPC)
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Computation offloading in multi-access edge computing (MEC) is an effective paradigm for enabling resource-intensive smart applications. However, when the wireless channel utilized for offloading computing activities is hostile, the proper advantages of MEC may not be completely realized. Intelligent reflecting surface (IRS) is a new technology that has recently attracted significant interest can optimize the wireless transmission environment in a programmable way and improving the connectivity between user equipment (UE) and base station (BS). In this paper, the performance of MEC architecture is analyzed considering both IRS-assisted and without IRS communication scenarios in the context of the urban micro cellular scenarios. The research obtained that the deployment of IRS can reduce the spectrum and energy consumption significantly.
... suggested using WNSN in the human body for constructing in vivo terahertz WNSNs (iWNSNs) 41 and suggested the potential system structure. 42,43 The iWNSNs might be applied for monitoring, analyzing, and warning major human body data. ...
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Wireless in vivo actuators and sensors are examples of sophisticated technologies. Another breakthrough is the use of in vivo wireless medical devices, which provide scalable and cost-effective solutions for wearable device integration. In vivo wireless body area networks devices reduce surgery invasiveness and provide continuous health monitoring. Also, patient data may be collected over a long period of time. Given the large fading in in vivo channels due to the signal path going through flesh, bones, skins, and blood, channel coding is considered a solution for increasing the efficiency and overcoming inter-symbol interference in wireless communications. Simulations are performed by using 50 MHz bandwidth at Ultra-Wideband frequencies (3.10-10.60 GHz). Optimal channel coding (Turbo codes, Convolutional codes, with the help of polar codes) improves data transmission performance over the in vivo channel in this research. Moreover, the results reveal that turbo codes outperform polar and convolutional codes in terms of bit error rate. Other approaches perform similarly when the information block length is increased. The simulation in this work indicates that the in vivo channel shows less performance than the Rayleigh channel due to the dense structure of the human body (flesh, skins, blood, bones, muscles, and fat).
The upcoming 6G communication network will revolutionize the scenario of customer services and related applications by building smart, autonomous systems. We first discuss here about 6G enabled smart applications like healthcare, smart city building, industrial IoTs, etc. We next provide a brief survey of the enabling technologies for achieving the required goals of 6G, such as terahertz communication, cell-free communication, holographic communication using beamforming, wireless power transfer, ultra-low latency communication, etc., with a view to tackle very large amounts of data traffic from several billions of smart interconnected devices while supporting ultra-low end-to-end communication latency. Finally, we present various research challenges involved in 6G communication towards building such smart systems.
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The book, Nanosensors for Futuristic Smart and Intelligent Healthcare Systems, presents a treatise on nanosensors technology including wearables, implantable devices and wireless tools. The recent pandemic (COVID-19) has changed the behaviour of people towards diagnosis of infectious diseases and monitoring remote patient health status in real-time. The main focus of this book is the basic concepts of nanomaterials and sensing paradigms for medical devices based on nanosensor technology. The book will be valuable to researchers, engineers and scientists interested in the field of healthcare for monitoring health status in real-time.
Intelligent reflecting surfaces (IRSs) with the ability to reconfigure inherent electromagnetic reflection and absorption characteristics in real-time provide unparalleled prospects to improve wireless connectivity in adverse circumstances. Unmanned aerial vehicles (UAV)-assisted wireless networks are evolved as a reliable solution to combat non-line of sight (NLoS) scenarios. Thereby, the IRS-empowered UAV-assisted cellular networks will be a significant role-player to improve the coverage and user experiences. The paper aimed to minimize the path loss and maximize the achievable data rate in IRS-UAV-assisted networks. In this context, the work analyzed path loss and achievable rate utilizing millimeter wave (mmWave) carrier considering the conventional UAV model and IRS-empowered UAV communication model. The research obtained that the IRSempowered UAV communications model can significantly minimize path loss and maximize the achievable data rate compared to the conventional UAV-assisted model.
Today’s nanotechnology progress in biomedical and biotechnology is to design novel materials with exclusive properties of nanoscale structures. The application of nano-structured materials into biomedical systems has received much attention due to its remarkable resolution in assisting diagnoses and treating medical difficulties. The variety of nanostructured materials produced could be easily controlled and manipulated. Moreover, they could be designed to provide a new property in a predictable manner, whereby the modified biological characteristic and functionalities are compatible with biomedical systems for various applications and purposes. All-inclusive, nanotechnology has an enormous impact on health care and undeniably shaping the future pathway. This paper reviews research methods in nanotechnology developments, which convey benefits to the biomedical application on nano-network and communication, biosensor, nanoprobe, drug delivery system and nano implants.
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We present the performance of MIMO for in vivo environments, using ANSYS HFSS and their complete human body model, to determine the maximum data rates that can be achieved using an IEEE 802.11n system. Due to the lossy nature of the in vivo medium, achieving high data rates with reliable performance will be a challenge, especially since the in vivo antenna performance is strongly affected by near field coupling to the lossy medium and the signals levels will be limited by specified specific absorption rate (SAR) levels. We analyzed the bit error rate (BER) of a MIMO system with one pair of antennas placed in vivo and the second pair placed inside and outside the body at various distances from the in vivo antennas. The results were compared to SISO simulations and showed that by using MIMO in vivo, significant performance gain can be achieved, and at least two times the data rate can be supported with SAR limited transmit power levels, making it possible to achieve target data rates in the 100 Mbps.
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In this paper we present the performance evaluation for a MIMO in vivo WBAN system, using ANSYS HFSS and the associated complete Human Body Model. We analyzed MIMO system capacity statistically and FER performance based upon an IEEE 802.11n system model, with receiver antennas placed at various angular positions around the human body. We also analyzed MIMO system capacity with receiver antennas at the front of the body at various distances from transmitter antennas. The results were compared to SISO arrangements and we demonstrate that by using 2x2 MIMO in vivo, better performance can be achieved, and significantly higher system capacity can be achieved when receiver antennas are located at the back of the body and in front of the body.
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Our long-term research goal is to model the in vivo wireless channel. As a first step towards this goal, in this paper we performed in vivo path loss measurements at 2.4GHz and make a comparison with free space path loss. We calculate the path loss by using the electric field radiated by a Hertzian-Dipole located inside the abdominal cavity. The simulations quantify and confirm that the path loss falls more rapidly inside the body than outside the body. We also observe fluctuations of the path loss caused by the inhomogeneity of the human body. In comparison with the path loss measured with monopole antennas, we conclude that the significant variations in Received Signal Strength is caused by both the angular dependent path loss and the significantly modified in vivo antenna effects.
Conference Paper
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We present a simulation method and results that utilizes accurate electromagnetic field simulations to study the maximum allowable transmitted power levels from in vivo devices to achieve a required bit error rates (BER) at the external node (receiver) while maintaining the specific absorption rate (SAR) under a required threshold. The BER of the communication can be calculated using the derived power threshold for a given modulation scheme. These results can be used to optimize the transmitted power levels while assuring that the safety guidelines in terms of the resulting SAR of transmitters placed in any location inside the human body are met. To evaluate the SAR and BER, a software-based test bench that allows an easy way to implement field solver solutions directly into system simulations was developed. To demonstrate the software-based test bench design, a complete OFDM-based communication (IEEE 802.11g) for the in vivo environment was simulated. Results showed that for cases when noise levels increase or the BER becomes more stringent, a relay network or the use of multiple receive antennas, such as in a MIMO system, will be become necessary to achieve high data rate communication.
Conference Paper
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In this paper we perform signal strength and channel impulse response simulations using an accurate human body model and we investigate the variation in signal loss at different RF frequencies as a function of position around the human body. It was observed that significant variations in received signal strength (RSS) occurred with changing position of the external receive antenna at a fixed distance from the internal antenna. The variations were even more profound at the highest frequency, where just a 5° of movement causes an increase of RSS up to 20 dB. Wideband scattering parameters were also obtained and the channel impulse response was calculated. A greater amount of dispersion through the abdomen has been observed than was expected based on human body geometry.
Conference Paper
Our long-term research goal is to model the in vivo wireless channel. As a first step towards this goal, in this paper we performed in vivo path loss measurements at 2.4 GHz and make a comparison with free space path loss. We calculate the path loss by using the electric field radiated by a Hertzian-Dipole located inside the abdominal cavity. The simulations quantify and confirm that the path loss falls more rapidly inside the body than outside the body. We also observe fluctuations of the path loss caused by the inhomogeneity of the human body. In comparison with the path loss measured with monopole antennas, we conclude that the significant variations in Received Signal Strength is caused by both the angular dependent path loss and the significantly modified in vivo antenna effects.
Conference Paper
In this paper, preliminary study on path loss characterization of in-vivo communication channel at 2.45 GHz using numerical modeling is presented. One of the antennas is implanted inside the chest and the second antenna is placed on the surface of body at certain distance. The on-body antenna is rotated at different angles with respect to the inbody antenna and path loss is calculated at certain fixed distance. The body-centric path loss is compared with the free space path loss as well. Results show that there is almost 10-15 dB variation in path loss in case of in-vivo channel as compared to free space. Also a degradation in path loss and channel response with respect to the orientation angle between implanted transmitter and on-body receiver is observed, which highlights the importance of carefully considering the location of on-body receiver for optimal system performance.
Conference Paper
A discussion on the main challenges designing efficient antennas for bio-implantable communication devices is presented, along with some of the main issues encountered in their characterization. Such devices are used in conjunction with health monitoring or health care systems. Implantable antennas are, by nature, electrically small, and difficulties linked to electrically small antenna design apply. But implants are also located in a lossy host body, which induces a major change of paradigm with classic Electrically Small Antennas (ESA), as the main design challenge for implantable antennas will be to reach an acceptable efficiency, and not a broad enough bandwidth. In this paper, we present first the main challenges to be met in designing implantable antennas, followed by suggestions for an efficient design procedure. Finally, the specific difficulties in characterizing implantable antennas are emphasized.
Bio-nanosensors and communication at the nanoscale are a promising paradigm and technology for the development of a new class of ehealth solutions. While recent communication technologies such as mobile and wireless combined with medical sensors have allowed new successful eHealth applications, another level of innovation is required to deliver scalable and cost-effective solutions via developing devices that operate and communicate directly inside the body. This work presents the application of nano technology for the development of miniaturized bio-nanosensors that are able to communicate and exchange information about sensed molecules or chemical compound concentration and therefore draw a global response in the case of health anomalies. Two communication techniques are reviewed: electromagnetic wireless communication in the terahertz band and molecular communication. The characteristics of these two modes of communication are highlighted, and a general architecture for bio-nanosensors is proposed along with examples of cooperation schemes. An implementation of the bio-nanosensor part of the nanomachine is presented along with some experimental results of sensing biomolecules. Finally, a general example of coordination among bio-nanomachines using both communication technologies is presented, and challenges in terms of communication protocols, data transmission, and coordination among nanomachines are discussed.