ArticlePDF Available

Abstract and Figures

In this work, the author has evaluated the propagation of electromagnetic waves inside the human tissue such as blood, skin and fat for single-path and multi-path layers according to nano sensor transmit power calculations. In particular, the propagation characteristics of the Intra-Body Nano-Network communication channel are calculated using a theoretical approach. The analysis in this paper provides an evaluation related to the path loss, bit error rate, signal to noise ratio and the channel capacity. The model is evaluated for each single-path effect and multi-path effect. The effects of human tissue for each blood, skin and fat for single-path effect and multi-path are included in the analysis. The model frequency range is chosen from 0.01 to 1.5 THz frequencies, which are ideal for designing nano sensors antennae and using THz range for communication. This paper will also guide other researchers who are working on the electromagnetic radiation performance of Intra-Body Nano-Network and Nano sensors designed at the THz range.
This content is subject to copyright. Terms and conditions apply.
Wireless Personal Communications (2021) 118:3129–3143
1 3
Nano‑Sensor Modelling forIntra‑Body Nano‑Networks
Accepted: 28 January 2021 / Published online: 11 February 2021
© The Author(s) 2021
In this work, the author has evaluated the propagation of electromagnetic waves inside the
human tissue such as blood, skin and fat for single-path and multi-path layers according
to nano sensor transmit power calculations. In particular, the propagation characteristics
of the Intra-Body Nano-Network communication channel are calculated using a theoreti-
cal approach. The analysis in this paper provides an evaluation related to the path loss,
bit error rate, signal to noise ratio and the channel capacity. The model is evaluated for
each single-path effect and multi-path effect. The effects of human tissue for each blood,
skin and fat for single-path effect and multi-path are included in the analysis. The model
frequency range is chosen from 0.01 to 1.5 THz frequencies, which are ideal for designing
nano sensors antennae and using THz range for communication. This paper will also guide
other researchers who are working on the electromagnetic radiation performance of Intra-
Body Nano-Network and Nano sensors designed at the THz range.
Keywords Intra-body nano-networks· Human tissue· Path loss· Nano-communication·
Terahertz· Channel analysis
1 Introduction
Next generation wearable technologies, which is also supported with Internet of Things
(IoT) and Nano-technology have to be in miniature size. Therefore, the designers need to
work on higher frequencies such as 0.1–10 THz to reduce antenna size [1]. With the help
of Nano-technology, nano-communication and THz waves, nano or micro size machines
can communicate with each other [2, 3]. Since nano-technology was put forward in 1959,
it has not only gained great attention in body-centric applications, but it has also gained
great attention in many other fields [4]. Nano-technology, nano-networks and nano-com-
munication will greatly affect human life and health. Nano-machines which are especially
designed for the human body can be placed inside the body or surface-mounted on the
body. With the help of these technologies, patient data can be sent to monitoring centers
independent of the patient location [5].
* Mustafa Alper Akkaş
1 Department ofComputer Engineering, Bolu Abant Izzet Baysal University, 14280Bolu, Turkey
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
1 3
One of the most important parts to achieve nano-technology is improving nano-
machines without battery. Nano-machines are nano sized nodes which are used for commu-
nication, sensing, computation etc. [6]. In Intra-Body Nano-Networks, communication is
done by nano-machines which function like nano-nodes [1]. The communication between
nano-nodes in Intra-Body Nano-Networks is still an open issue and there are challenges to
be solved [7]. So far, two communication methods have been used for Intra-Body Nano-
Networks. These are Electromagnetic Communication (EMC) and Molecular Communi-
cation (MC). EMC communication uses EM waves for communication and transmission
of information. MC systems are different from EMC, forming a new and interdisciplinary
research area, which use the absence or presence of a selected type of molecule to digi-
tally encode messages [8]. Molecules are used as a communication carrier in MC systems.
MC is a new, open and interdisciplinary research area, and there are many challenges to
be solved. These challenges are definition of MC channel model, characterization of MC
mechanisms, development of its architectures and the networks protocols [9].
As shown in Fig.1, the magnitude of the node needs to be in the nanometer size because
the place where nano-nodes are placed is too small in biomedical applications. Nano nodes
require THz antennas for their dimensions in EM communication. In THz band communi-
cation, phase shifting effects and path loss fluctuates according to the environment. There-
fore electromagnetic (EM) waves need to communicate where phase shifting effects and
path loss fluctuates are minimum. In EMC transmission distance between nodes can be
increased by using the bandwidths where absorption and path loss is minimum.
We know that battery dependent machines are limited to use. This rule is also valid for
nano-machines. That is why alternative energy methods should be developed like changing
vibrational movement, mechanical movement or hydraulic energy into electrical energy.
Another alternative energy method is charging batteries wirelessly but it’s not easy to
implement. Whence, nano-machines transmit power is very important and is covered in
this paper [10, 11]. Part of this work was presented in [12] and an extended version of the
article is given in this study.
In this paper, the author has carried out calculations of the Path Loss, bit error rate
(BER), signal to noise ratio (SNR) and Channel Capacity effect based on the channel model
Fig. 1 A schematic network
architecture for intra-body nano-
networks with nano-sensors
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Nano-Sensor Modelling forIntra-Body Nano-Networks
1 3
for each single-path effect and multi-path effect, shown in Fig.2. The effect of the human
tissues according to Path Loss, BER, SNR and the Channel Capacity for each blood, skin
and fat for single-path effect and multi-path effect are included in the all analysis.
This paper is organized as follows. In Sect.2, related work is investigated. In Sect.3,
models for intra-body nano-networks for single and multi-layers are given. In Sect. 4,
graphs of the theoretical model are shown. Conclusions are drawn in the last section.
2 Related Work
Akyıldız etal. [13] present an overview of two main alternatives for nano-communication,
namely Electromagnetic Communication and Molecular Communication in the THz Band.
The aim of the study is to provide a better understanding of current research topics in this
important field and pave the way for future studies in nano-networks.
Yang etal. [14] modeled the human tissue with a 3-D numerical model at the THz range
but they did not consider multi-layers according to the nano-sensor transmit power calcula-
tion. They also specify this lack in their conclusion part.
Pratap Singh etal. [8] analyzed the probability density function of radiation absorption
noise and included the properties of different tissues of the human body to demonstrate its
applicability. Also, the closed form expression of error probability for MNC under radia-
tion noise is derived. Numerical analysis is shown in different tissues of the human body:
The polarization factor of the incoming EM radiation is shown as well as the skin, brain
and blood.
Again, Pratap Singh etal. [15] proposes a more general and appropriate noise model as
the Gaussian distribution to derive a new closed form expression of the conditional error
probability for the nano communication system. They have compared their noise model
with different models in the literature. Finally, with respect to the conditional error prob-
ability, closed form statements derived for average bit error rate, the Weibull-Gamma and
Mixture Gamma were derived from fading channels.
Fig. 2 Multi-path channel model
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
1 3
Hadeel Elayan etal. [16] analyzed the photo thermal effects of the THz range inside the
human body as a heat diffusion mathematical model. Shortly they have analyzed EM waves
release energy as a heat to their environment.
Zhang etal. [17] investigates the mathematical model for invivo nano networks at the
THz range including the information speed and the noise of link. In their analytical model,
they have investigated signal-to-noise ratio according to different information and power
allocation for body-centric nano-networks.
In their paper, Piro etal. [18] present the range of transmission and the channel capacity
for intra-body systems for general healthcare applications. Again, Piro etal. [19] has stud-
ied the communication capabilities of a body area nano-network by carefully taking into
account the inhomogeneous and disordered structure offered by biological tissues.
However, most of the works presented above consider the human tissue as a single level.
In this work, a multi-layer communications method has been proposed. In addition, the
reflection properties between blood, fat and skin are investigated. This work also calcu-
lates the propagation of electromagnetic waves inside the human tissue containing blood,
skin and fat for single and multi-layers according to nano-sensor transmit power calcula-
tion. Transmit power calculation is an innovative topic which has not been investigated in
detail before as it is in this paper. Hence, this work investigates Intra-Body Nano-Network
communication propagation channel characteristics which are calculated using a theoreti-
cal approach that is modeled providing an evaluation about the losses, capacity, BER and
SNR considering the multipath effect of the channel according to the nano-sensor transmit
power calculation.
3 Model forIntra‑Body Nano‑Networks forSingle andMulti‑layers
The Friis Transmission Equation is used to calculate received power from an antenna to
another antenna at some distance given a transmission frequency and antenna gains. Friis
Equation is used to find the ideal power received at an antenna from basic information
about the transmission [20]. For the propagation in human tissue, noise power (NP), ther-
mal noise and additional losses (Lmedium) at the receiver which are caused by blood, skin
and fat are added to Friis equation in formula (1). To calculate the NP, the Bandwidth (B)
and ambient temperature (T), which is taken as body temperature of 310.15K, need to be
calculated. Consequently, the received signal in the Friis equation can be updated as [21]:
Table1 shows the values in equations.
In Eq.(1) LNP calculated as 10log10(103 × kB × T × B). Lmedium equals to:
Lmedium (2) is a combination of Lβ and Lα. Lα which is 8.96αd(dB) is the transmission
loss caused by attenuation with attenuation constant α. Lβ is the attenuation loss due to the
difference of the wavelength of the signal in medium, λ, compared to the wavelength in
free space, λ0. So Lβ can be also written as 20log(λ0/ λ). Here, in this formula λ = 2π/β and
LFSPL(dB)LNP (dBm)Lmedium(dB
System Loss
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Nano-Sensor Modelling forIntra-Body Nano-Networks
1 3
λ0 = c/f (Here c is speed of light) then Lβ can be written as 154 − 20log(f) + 20log(β) as dB.
Then Lmedium which is our body in this work becomes:
where parameters and constants are also given in Table1. Note that Lmedium in (2) depends
on the β, α of the human body [19]. The human body’s dielectric properties in this paper
are obtained from [14].
In these analyzes, the communication channel is modeled as an independent Rayleigh
distributed random variable, Xi, i {1,2} [22, 23]. The single-path model received energy
spectral density is given by (4) and has a distribution of (5).
The received signal is modeled as the addition of two independent Rayleigh distributed
random variables.
Consequently, the composite attenuation constant, X, for the multi-path model is given
by [22, 23]:
(dB)=6.4 +20 log(d)+20 log(𝛽)+8.69𝛼d
SNR exp
X2Γexp (𝛼Δ(r))
22X1X2Γexp (𝛼Δ(r)) ×cos
Table 1 Constants and parameters
Symbol Quantity Units Symbol Quantity Units
PrReceiving antenna’s power dBm α Attenuation constant 1/m
PtTransmitting antenna’s power dBm β Phase shifting constant rad/m
GtTransmitting antenna gain dB d Distance between nano-sensors m
GrReceiving antenna gain dB PtTransmit power dBm
LFSPL Free space path loss dB LfTotal path loss dBm
c Speed of ligh m/s PnNoise energy dBm
LNP Receiver’s noise power dBm LNP Noise power dBm
B Bandwidth Hz C Capacity bits/s
k Boltzmann constant j/K Γ Amplitude of the reflection coef-
T Ambient temperature 310.15K ϕ Phase angle of the reflection coef-
LβAttenuation loss dB α Attenuation constant
LαTransmission loss dB X1, X2Single and multi path channel model
independent Rayleigh distributed
random variables envelope
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
1 3
The SNR is given by SNR = PtLfPn in paper [21]. In this paper Pt assumes 15 to 5
dBm which are low enough for nano-node [7]. LNP is given by (7) as dBm [24]:
According to paper [23] 2PSK modulation has more range when we compare with other
modulations. For this reason, in this paper 2PSK modulation is considered. The BER rate
for 2 PSK is 0.5erfc((SNR)1/2) in additive white Gaussian noise (AWGN) [23].
The multi-path channel model in blood, fat and skin is shown in Fig.2. The reflections
are the same in the other human tissue because according to the papers [25, 26] the relative
magnetic permeability is 1 in all parts of the body. The single path is the direct path, which
is shown with the red line between the two sensors in Fig.2. The medium all around the
sensor nodes can be considered homogeneous for instance the model is suitable for higher
The multi-path channel model is given by (8) [20, 21, 25]
where human tissue is the path loss due to the single path given in (4) and the second part
of the equation is the second path’s attenuation factor which is unit in dB [22, 23, 27].
Capacity is the highest data rate that can be delivered reliably over a channel. The
resulting capacity is measured in bits/s because the logarithm is taken in base 2 in Eq.(9)
[14]. The unit of the bandwidth of the channel (B) is hertz. The signal and noise powers
are S and N. The ratio between S and N is called SNR. The detailed model of the system is
shown step by step in Fig.3 to make this section more easily readable.
4 Numerical Results
In this part the proposed channel model’s path loss, BER, attenuation factor, channel
capacity and SNR values are given.
Figure 4a gives the values of path loss for blood, skin and fat. Figure4b gives a 3D
version of Fig.4a. In Fig.4a and in the following figures the red lines show the blood, the
black lines show the skin and the blue lines show the fat. The lines style at figures are the
same in the following figures that is why lines style legend is not given in some following
figures not to make them complicated. Figure4 shows that when the frequency and dis-
tance increases path loss is increased. Path loss is directly proportional to frequency and
distance. Figure4 also shows that blood has higher path loss than skin and fat. The reason
why the blood has the highest path loss is that the amount of water in blood is more than
in skin and fat. The human blood contains about 45% of erythrocytes and 54.3% of plasma
by volume. The plasma contains about 92% water, while the erythrocytes, about 64% by
weight. These papers [2830] also prove why the water has higher absorption and path
f(dB)=LHuman Tissue(dB)−10 log
=1+(Γ×exp (𝛼Δr))22Γexp (𝛼Δr)2×cos 𝜋𝜙2𝜋f
Δr=rd,r=r1+r2(in Fig.2)
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Nano-Sensor Modelling forIntra-Body Nano-Networks
1 3
Figure5a shows BER vs. distance for 0.5–1.5 THz. Figure 5b examines the BER for
blood in the case of 15 to 5 dBm transmit power and frequency at 0.5 THz. The results
show that BER of the 0.5–1.5 THz operating frequencies in blood, skin and fat for the
single path channel model increases between 1 and 3mm for blood, 1–5mm for skin and
2–7mm for fat at minimum received signal power of 5 dBm. The millimeter size com-
munication distance increments are very important for nano-nodes inside the body. Fig-
ure5 proves that the communication range depends on the value of the dielectric loss of the
human body, remaining power of the node and the operating frequency. In Fig.5b shows
that each 5 dBm increment in Pt increases the communication distance around 0.1 mm.
Figure5c, d gives the values of capacity and SNR respectively that have been calculated
from (8). Figure5c shows that path loss increments cause less capacity and Fig.5d shows
that when the frequency decreases SNR increases and when the path loss increases SNR
decreases that is why 0.5 THz fat has the highest SNR.
Figure6a, b give the values of path loss at 0.5–1.5 THz for multi-path channel accord-
ing to distance and depth respectively. When we compare Fig.6a with Fig.4a the path loss
is around 80–300dB in one-path model and the path loss is around 100–300dB in multi-
path model. Figure6a also shows that in the multi-path model, communication distance at
path loss 100dB is up to 0.8mm, 1.4 mm and 2 mm in blood, skin and fat respectively.
Also in one-path model, the range is increased by 0.2mm, 0.4mm and 0.6mm in blood,
skin and fat respectively at 0.5 THz. Added reflection component of the signals do not help
to increase the communication distance in the multi-path model because there is not much
reflection in human tissue as seen in Fig.6b. Figure6b also shows that path loss values
depend on human tissue distance and depth. Fluctuations at Fig. 6b decreases when the
depth increases and almost disappears after distance around 0.2mm. Figure6c shows path
loss, distance and depth relation in 3D dimensions at 0.5 THz. The 3D graph shows that
at depths smaller than 0.2mm there is a wave, which is too small to affect communication
distance. In Fig.6d attenuation factor is given which is the second part of the Eq.7. As
seen in the Fig.6d attenuation factor decreases at depths smaller than 0.2mm this caused
the increased path loss in multi-path model and decreased the communication distance.
Fig. 3 Detailed model of the system
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
1 3
This is also one reason why the multi-path channel model has smaller communication dis-
tance than one path-channel model.
Figure7 shows BER vs. distance for 0.5–1.5 THz operating frequencies for the multi-
path channel model. BER versus depth has not been given because there is almost zero
BER at all depths. Figure7a shows that the increment in the path loss has small effect on
BER in multi-path channel model. The BER rate is directly proportional to the distance.
Figure7b examines the BER for blood in the case of 15 to 5 dBm transmit power Pt at
frequency 0.5–1.5 THz. When we compare Figs.5b with 7b we see that there is no much
difference between one-path and multi-path channel models. Only in the multi-path chan-
nel model, the transmission distance decreases around 1mm at 0.5 THz.
Figure8 shows Capacity versus distance and depth for 0.5–1.5 THz operating frequen-
cies for the multi-path channel model. Figure8a gives capacity values according to the
distance at 5 dBm transmit power. Figure8b gives capacity values according to depth
at 5 dBm transmit power. Figure8c gives capacity values according to distance at 15
Fig. 4 Path loss vs. distance for
0.5 to 1.5THz in single-path
channel model. a 2D version, b
3D version
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Nano-Sensor Modelling forIntra-Body Nano-Networks
1 3
to 5 dBm from 0.5 to 1.5 THz. From Fig.8a we can understand that capacity and path
loss inversely proportional to each other as expected. Figure8b shows the capacity at 5
dBm transmit power according to depth. Fluctuations at Fig.8b decreases when the depth
increases and almost disappears after distance around 0.2mm as in the Fig.6b. Figure8c
gives capacity values according to distance at 15 to 5 dBm transmit power in the blood.
Figure8c also shows that frequency and capacity inversely proportional to each other and
transmit power increases the capacity as expected.
Figure9 shows SNR vs. distance and depth for 0.5–1.5 THz operating frequencies for
the multi-path channel model. Figure 8a gives SNR values according to distance at 5
dBm transmit power. Figure8b gives SNR values according to depth at 5 dBm transmit
power. Figure8c gives SNR values according to distance at 15 to 5 dBm from 0.5 to 1.5
THz. From Fig.9a we can understand that SNR and path loss are inversely proportional to
each other as expected. Figure9b shows the SNR at 5 dBm transmit power according to
depth. Fluctuations at Fig.8b decreases when the depth increases and almost disappears
after distance around 0.4mm but see the effect at capacity and path loss up to 0.2 mm.
Figure9c gives SNR values according to distance at 15 to 5 dBm transmit power. Fig-
ure9c also shows that frequency and SNR inversely proportional to each other and trans-
mit power increases the capacity as expected. At Fig.9c SNR values of 1 THz and 1.5
Fig. 5 BER, capacity and SNR versus distance for 0.5 to 1.5THz in single-path channel model. a BER ver-
sus distance, b BER for blood, c capacity, d SNR
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
1 3
THz frequencies are not given because they are around the zero level. SNR values can help
other researchers who are working on terahertz intra body networks. SNR is also affected
from distance, frequency depth and transmit power. Figure9 also reminds us that SNR is
indirect proportional to frequency and direct proportional to transmit power.
5 Conclusion
Due to the small communication range inside the human body EM waves do not prop-
agate easily especially in THz Bands. This paper examines the path loss, BER, channel
capacity and SNR of nano-sensors propagating THz EM waves inside the blood, skin and
fat according to transmit power and channel type. Briefly, the paper sets the theoretical
background for the propagation of THz EM waves in blood, skin and fat in the THz range
and determines the incurred path loss, BER, capacity and SNR of nano-sensors in single-
channel and multi-channel. The paper also shows the reasons for why the multi-path chan-
nel model has smaller communication distance than one path-channel model. Numerical
evaluations show that data communication is possible over the 0.01–1.5 THz band at trans-
mit power 15 to 5 dBm but to reach more communication distance, a new communication
Fig. 6 Path loss vs. distance and depth for 0.5–1.5THz in multi-path channel model. a Path loss versus dis-
tance, b Path loss versus depth. c Path Loss, Distance and Depth Relation. d Attenuation
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Nano-Sensor Modelling forIntra-Body Nano-Networks
1 3
model needs to be investigated. Theoretical results show that wireless nano-sensor can
communicate through the human body but thermal noise is too high to use the THz waves
inside the human body. That is why new techniques need to be develop not to harm the
body at THz range. The results in this paper also aim to guide other researchers that will
be working in the area of the intra-body nano-networks. In the future, experiments can be
done by using spectroscopy at THz range to validate the numerical findings.
Fig. 7 BER versus distance
for 0.5–1.5THz in multi-path
channel model. a BER versus
distance, b BER for blood
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
1 3
Fig. 8 Capacity vs. distance and
depth for 0.5 to 1.5THz in multi-
path channel model. a Capacity
versus distance. b Capacity
versus depth. c Capacity in the
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Nano-Sensor Modelling forIntra-Body Nano-Networks
1 3
Fig. 9 SNR versus distance
and depth for 0.5 to 1.5THz in
multi-path channel model. a SNR
versus distance. b SNR versus
depth. c SNR in the Blood
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
1 3
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,
which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long
as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Com-
mons licence, and indicate if changes were made. The images or other third party material in this article
are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the
material. If material is not included in the article’s Creative Commons licence and your intended use is not
permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly
from the copyright holder. To view a copy of this licence, visit http://creat iveco mmons .org/licen ses/by/4.0/.
1. Akyildiz, I. F., & Jornet, J. M. (2010). Electromagnetic wireless nanosensor networks. Nano Commu-
nication Networks, 1(1), 3–19.
2. Akyildiz, I. F., Brunetti, F., & Blázquez, C. (2008). Nanonetworks: A new communication paradigm.
Computer Networks, 52(12), 2260–2279.
3. Chopra, N., etal. (2014). Understanding and characterizing nanonetworks for healthcare monitor-
ing applications. In 2014 IEEE MTT-S international microwave workshop series on RF and wire-
less technologies for biomedical and healthcare applications (IMWS-Bio). IEEE.
4. Feynman, R. P. (1992). There’s plenty of room at the bottom [data storage]. Journal of Microelec-
tromechanical Systems, 1(1), 60–66.
5. Abdelaziz, A. F., etal. (2015) Terahertz signal propagation analysis inside the human skin. In 2015
IEEE 11th International Conference on Wireless and Mobile Computing, Networking and Commu-
nications (WiMob). IEEE.
6. Bush, S. F. (2010). Nanoscale Communication Networks. Norwood: Artech House.
7. Dressler, F., & Fischer, S. (2015). Connecting in-body nano communication with body area net-
works: Challenges and opportunities of the Internet of Nano Things. Nano Communication Net-
works, 6(2), 29–38.
8. Singh, S. P., Singh, S., Guo, W., Mishra, S., & Kumar, S. (2020). Radiation absorption noise for
molecular information transfer. IEEE Access, 8, 6379–6387.
9. Pierobon, M., & Akyildiz, I. F. (2010). A physical end-to-end model for molecular communication
in nanonetworks. IEEE Journal on Selected Areas in Communications, 28(4), 602–611.
10. Lee, S. J., etal. (2015). Design of wireless nanosensor networks for intrabody application. Interna-
tional Journal of Distributed Sensor Networks, 2015, 90.
11. Akkaş, M. A. (2016). A comparative review of mote size and communication method for wireless
sensor network. In Applied mechanics and materials (Vol. 850). Trans Tech Publications.
12. Akkaş, M. A. (2016). Nano-sensor capacity and SNR calculation according to transmit power esti-
mation for body-centric nano-communications. In 2016 3rd international symposium on wireless
systems within the conferences on intelligent data acquisition and advanced computing systems
13. Akyildiz, I. F., Jornet, J. M., & Pierobon, M. (2011). Nanonetworks: A new frontier in communica-
tions. Communications of the ACM, 54(11), 84–89.
14. Yang, K., etal. (2015). Numerical analysis and characterization of THz propagation channel for
body-centric nano-communications. IEEE Transactions on Terahertz Science and Technology, 5(3),
15. Singh, S. P., Kumar, A., & Kumar, S. (2017). Novel expressions for CEP/BEP under GGD noise for
nano communication system. International Journal of Electronics Letters, 5(4), 463–474.
16. Elayan, H., Johari, P., Shubair, R. M., & Jornet, J. M. (2017). ’Photothermal modeling and analy-
sis of intrabody terahertz nanoscale communication. IEEE Transactions on NanoBioscience, 16(8),
17. Zhang, R., Yang, K., Abbasi, Q. H., Qaraqe, K. A., & Alomainy, A. (2018). Analytical modelling
of the effect of noise on the terahertz in-vivo communication channel for body-centric nano-net-
works. Nano Communication Networks, 15, 59–68.
18. Piro, G., etal. (2015). Terahertz communications in human tissues at the nano-scale for healthcare
applications. IEEE Transactions on Nanotechnology, 14(3), 404–406.
19. Piro, G., et al. (2016). Terahertz electromagnetic field propagation in human tissues: A study on
communication capabilities. Nano Communication Networks, 10, 51–59.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Nano-Sensor Modelling forIntra-Body Nano-Networks
1 3
20. Friis, H. T. (1946). A note on a simple transmission formula. Proceedings of IRE, 34, 254–256.
https :// c.
21. Akkaş, M. A. (2018). Using wireless underground sensor networks for mine and miner safety. Wire-
less Networks, 24, 1–10.
22. Vuran, M. C., & Silva, A. R. (2010) Communication through soil in wireless underground sensor
networks–theory and practice. In Sensor networks (309–347). Berlin: Springer.
23. Akyildiz, I. F., Sun, Z., & Vuran, M. C. (2009). Signal propagation techniques for wireless under-
ground communication networks. Physical Communication, 2(3), 167–183.
24. Couch, I. I., & Leon, W. (1994). Modern communication systems: Principles and applications.
Upper Saddle River: Prentice Hall.
25. Pethig, R., & Kell, D. B. (1987). The passive electrical properties of biological systems: Their
significance in physiology, biophysics and biotechnology. Physics in Medicine and Biology, 32(8),
26. Collins, C. M., etal. (2002). Numerical calculations of the static magnetic field in three-dimensional
multi-tissue models of the human head. Magnetic Resonance Imaging, 20(5), 413–424.
27. Li, L., Vuran, M.C., & Akyildiz, I. F. (2007). Characteristics of underground channel for wireless
underground sensor networks. In Proceedings of Med-Hoc-Net’07.
28. Akkaş, M. A., & Sokullu, R. (2015). Channel modeling and analysis for wireless underground sen-
sor networks in water medium using electromagnetic waves in the 300–700 MHz range. Wireless
Personal Communications, 84(2), 1449–1468.
29. Akkaş, M. A., Akyildiz, I. F., & Sokullu, R. (2012). Terahertz channel modeling of underground sen-
sor networks in oil reservoirs. In Global communications conference (GLOBECOM), 2012 IEEE.
30. Akkaş, M. A. (2019). Terahertz wireless data communication. Wireless Networks, 25, 1–11.
Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and
institutional affiliations.
Mustafa Alper Akkaş received his B.S. degree in Electrical and Elec-
tronics Engineering from Erciyes University in 2006. He received his
Ph.D. degree in the Department of Electrical Engineering and Elec-
tronics at Ege University in 2014. Currently, he is an associate profes-
sor at the Department of Computer Engineering at Bolu Abant Izzet
Baysal University. His research interests include Wireless underground
communication networks, Terahertz-band communication networks,
Intra-body wireless nanosensor networks and Internet of Things. He
was a visiting student at Georgia Institute of Technology, in the Broad-
band Wireless Networking Lab under the supervision of Prof. Ian F.
Akyildiz from September 2011 to May 2012.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Terms and Conditions
Springer Nature journal content, brought to you courtesy of Springer Nature Customer Service Center
GmbH (“Springer Nature”).
Springer Nature supports a reasonable amount of sharing of research papers by authors, subscribers
and authorised users (“Users”), for small-scale personal, non-commercial use provided that all
copyright, trade and service marks and other proprietary notices are maintained. By accessing,
sharing, receiving or otherwise using the Springer Nature journal content you agree to these terms of
use (“Terms”). For these purposes, Springer Nature considers academic use (by researchers and
students) to be non-commercial.
These Terms are supplementary and will apply in addition to any applicable website terms and
conditions, a relevant site licence or a personal subscription. These Terms will prevail over any
conflict or ambiguity with regards to the relevant terms, a site licence or a personal subscription (to
the extent of the conflict or ambiguity only). For Creative Commons-licensed articles, the terms of
the Creative Commons license used will apply.
We collect and use personal data to provide access to the Springer Nature journal content. We may
also use these personal data internally within ResearchGate and Springer Nature and as agreed share
it, in an anonymised way, for purposes of tracking, analysis and reporting. We will not otherwise
disclose your personal data outside the ResearchGate or the Springer Nature group of companies
unless we have your permission as detailed in the Privacy Policy.
While Users may use the Springer Nature journal content for small scale, personal non-commercial
use, it is important to note that Users may not:
use such content for the purpose of providing other users with access on a regular or large scale
basis or as a means to circumvent access control;
use such content where to do so would be considered a criminal or statutory offence in any
jurisdiction, or gives rise to civil liability, or is otherwise unlawful;
falsely or misleadingly imply or suggest endorsement, approval , sponsorship, or association
unless explicitly agreed to by Springer Nature in writing;
use bots or other automated methods to access the content or redirect messages
override any security feature or exclusionary protocol; or
share the content in order to create substitute for Springer Nature products or services or a
systematic database of Springer Nature journal content.
In line with the restriction against commercial use, Springer Nature does not permit the creation of a
product or service that creates revenue, royalties, rent or income from our content or its inclusion as
part of a paid for service or for other commercial gain. Springer Nature journal content cannot be
used for inter-library loans and librarians may not upload Springer Nature journal content on a large
scale into their, or any other, institutional repository.
These terms of use are reviewed regularly and may be amended at any time. Springer Nature is not
obligated to publish any information or content on this website and may remove it or features or
functionality at our sole discretion, at any time with or without notice. Springer Nature may revoke
this licence to you at any time and remove access to any copies of the Springer Nature journal content
which have been saved.
To the fullest extent permitted by law, Springer Nature makes no warranties, representations or
guarantees to Users, either express or implied with respect to the Springer nature journal content and
all parties disclaim and waive any implied warranties or warranties imposed by law, including
merchantability or fitness for any particular purpose.
Please note that these rights do not automatically extend to content, data or other material published
by Springer Nature that may be licensed from third parties.
If you would like to use or distribute our Springer Nature journal content to a wider audience or on a
regular basis or in any other manner not expressly permitted by these Terms, please contact Springer
Nature at
... These nanoperipherals can be combined to form a nanosensor cable to detect small-scale events and stimulate small-scale actions. To produce a complete nanosensor, different peripherals are required [19]. Details of each peripheral are provided as follows: ...
... The nanosensor applications can be categorized into diseases diagnoses and treatment applications. The disease diagnosis applications allow nanosensors to be released inside the human body to sense and detect various diseases or health related indicators [19]. The chemical, biological, or physical changes in nanomaterials allow nanosensors to observe certain elements or substances inside the human body. ...
Full-text available
Nanomaterials such as graphene have allowed the manufacturing of different types of nanosensors. These nanosensors will sense and stimulate action, store, and process the data and transmit electromagnetic signals in the terahertz range. Enabling communication will allow nanosensors to form a network that will lead to various applications in different fields, especially in advanced manufacturing technologies and biomedical. However, the signal propagated by nanoantennas in terahertz gets attenuated by molecules present in the medium, such as the human body. This paper presents a simple fading model for wireless nanosensor networks for advanced manufacturing technologies and applications that incorporates molecular absorption and molecular noise. The model estimates the log-distance path loss model with random attenuation caused by the molecules present in the medium. It has been observed that the water molecule present inside the human body produces significant attenuation to terahertz frequencies. The path loss model presented has been used to identify certain frequency windows within the terahertz band that can be used for communication purposes in nanoapplications. For instance, a 400-GHz wideband from 100 to 500 GHz has been identified with a mean attenuation of 0.021 dB/cm inside the human body. Furthermore, channel capacity is computed for these bands that shows the specified bands achieve better throughput than those with more significant molecular attenuation.
... Similarly, proteins, their assemblies and other biological materials have demonstrated high frequency oscillations and spectral resonances at around THz frequency [16]- [18], which can be the source of EM emission and absorption. Besides natural THz emission by biological organs, the THz frequency band has also been examined extensively for intra-body communication networks and sensing applications [19], [20]. This is mainly due to the available bandwidth and the relatively small wavelength size which can be leveraged to realize efficient nano-scale antennas utilizing surface plasmonic concepts and novel materials including graphene [21]. ...
... Nanofibrils length distribution is considered to be uniform, L ∼ U (0.1, 6)µm. Using those previously used values of the mass density (ρ), cross section (A) and bending rigidity (EI ), and setting nanofibrils dipole charges [29] q = 3e − (e − is the electron charge value), (19) and (20) are solved for the resonant frequency (communication frequency) as well as the vibration amplitude of all nanofibrils in response to one initially-disturbed nanofibril. The quality factor for a cantilever beam vibrating in water (biofilm background medium) is set to be Q ∼ 4 (see Appendix B). ...
Full-text available
This paper presents a model that describes a possible mechanism for electromagnetic (EM) signal transmission and reception by bacterial cells within their biofilm communities. Bacterial cells in biofilms are embedded into a complex extracellular matrix containing, among other components, charged helical nanofibrils from amyloid-forming peptides. Based on the current knowledge about the nanoscale structure and dynamics of the amyloids, we explore a hypothetical model that the mechanical vibration of these nanofibrils allows the cells to transmit EM signals to their neighboring cells and the surrounding environment. For the reception, the induced electric field can either exert force on the charges of adjacent nanofibrils associated with the neighboring cells or affect the placement/conformation of a certain charged messenger protein within the cell. The proposed model is based on a coupled system of electrical and mechanical nanoscale structures, which predicts signal transmission and reception within kHz-GHz frequency ranges. Different mechanisms for generating EM signals at various frequency bands related to the structure of the cell and their biofilm constituents are discussed.
... That development of battery-free nanomachines is one of the most crucial steps toward achieving nanotechnology. Nano-sized nodes that are utilised for communication, sensing, processing, etc. are known as nano-machines (Akkaş, 2021). The selectivity of the sensor renders generic sensor array pattern recognition methods useless, and the conductance of the sensor is exactly proportional to the concentration of a particular analyte (Gouma et al., 2009). ...
One of the areas of science which has immensely advanced in the recent years is nanotechnology. This area broadly revolves around matter at scales between 1 and 100 nm, where peculiar phenomena make way for cutting-edge applications. Today, nanotechnology has a daily impact on human life with numerous and varied possible advantages. Nanosensors are one of the products of nanotechnology and any sensor that uses nanoscale phenomena qualifies to be known as a nanosensor. Nanosensors have proven very useful in a number of sectors including medical applications, food quality analysis and agricultural controlling process, etc. One of the major human healthcare applications of nanosensors is for disease diagnosis. With the aid of nanosensors, numerous neurodegenerative disorders and inflammatory diseases are commonly identified and treated of late. Alzheimer's disease (AD) and inflammatory bowel disease fall under the categories of neurodegenerative illnesses and inflammatory diseases. There are more than 20 million cases of (AD) making it the most prevalent neurological condition globally and "inflammatory bowel disease" (IBD) refers to a variety of conditions that cause persistent inflammation of the digestive tract. Here we present a comprehensive account on the utility of nanosensors for the diagnosis and treatment of (AD) and (IBD).
... A propagation model for terahertz communications has been proposed based on radiative transfer theory by taking into consideration the absorption and spreading path loss as well as the molecular absorption noise and the effect of the molecular composition of the medium [2]. Terahertz transmission loss due to the signal wavelength variation in single and multi-path channels has been examined inside blood, skin, and fat [23]. A numerical model has been presented by considering spreading and absorption path loss in blood, skin, and fat tissue and by calculating the system's capacity [21]. ...
Communication among nanodevices in the human body provides accurate and localized disease detection as well as biosensing applications. Terahertz waves as a promising candidate for wireless intrabody communications could be subjected to scattering effects by different cells and particles during propagation. In this paper, we examined the intrabody scattering coefficient and path loss for three biological layers of blood, epidermis, and dermis. The results are presented and compared in two air-enclosed and tissue-enclosed approaches. We also take into account the size and shape of the particles, as well as the difference in complex refractive indices of the host medium and its scatterers. Our results emphasize how the proper assumption of the complex refractive indices of the tissue's surrounding medium could prevent the overestimation of the scattering loss and, consequently, lead to more realistic anticipation of the scattering effects.
... The accurate knowledge of frequency-dependent dielectric susceptibility is also relevant in microwave computational dosimetry, dielectric spectroscopy and imaging for early-stage cancer diagnostics and treatment, as well as in the study of the materials involved in the production and processing of crops and food of agricultural origin [43,44]. Furthermore, dielectric properties play an important role in the analysis of body area networks, nano-networks in living biological tissues, and for in-body electromagnetic communications [45][46][47][48]. ...
Full-text available
The use of fractional derivatives and integrals has been steadily increasing thanks to their ability to capture effects and describe several natural phenomena in a better and systematic manner. Considering that the study of fractional calculus theory opens the mind to new branches of thought, in this paper, we illustrate that such concepts can be successfully implemented in electromagnetic theory, leading to the generalizations of the Maxwell’s equations. We give a brief review of the fractional vector calculus including the generalization of fractional gradient, divergence, curl, and Laplacian operators, as well as the Green, Stokes, Gauss, and Helmholtz theorems. Then, we review the physical and mathematical aspects of dielectric relaxation processes exhibiting non-exponential decay in time, focusing the attention on the time-harmonic relative permittivity function based on a general fractional polynomial series approximation. The different topics pertaining to the incorporation of the power-law dielectric response in the FDTD algorithm are explained, too. In particular, we discuss in detail a home-made fractional calculus-based FDTD scheme, also considering key issues concerning the bounding of the computational domain and the numerical stability. Finally, some examples involving different dispersive dielectrics are presented with the aim to demonstrate the usefulness and reliability of the developed FDTD scheme.
... The perspective can be explained as follows. Akka s et al. theoretically examined the propagation of EM waves inside the human body cell such as blood, skin, and fat for single-path and multi-path layers [23]. The frequency range was considered from 0.01 to 1.5 THz frequencies, and the calculation was associated with the path-loss, bit error rate, signal to noise ratio, and channel capacity. ...
Full-text available
Nanoscale devices, also called nanomachines, form communication networks and cooperate with each other so that they can be used to perform complicated tasks. Such networks of nanodevices, named nanonetworks, envisioned to serve the functionality and performance of today’s Internet. This paper presents a comparative review of the state-of-the-art electromagnetic (EM) nanonetworks highlighting their potentials and challenges in a comprehensive manner. We first introduce the promising areas of applications of nanonetworks; therein, we explain how it can be useful in biomedical fields, environmental domains, consumer products, military systems, and on-chip wireless communications. Then, the survey focuses on the basic principles of fundamental physical layer issues enabling nanonetworks; the discussion includes frequency bands, modulation and demodulation, and EM properties of nanoparticles. Subsequently, the study provides an overview of transmission characteristics including channels, channel coding, and energy constraint nature of nanonetworks. Furthermore, we provide an in-depth discussion on nanoantenna highlighting its variants and their characteristics; give an overview of network layer issues; and discuss the security issues in EM nanonetworks. The study argues that despite the significant recent development of EM communication as one of the most desired modes of nano communications, the limited capabilities of nanomachines introduce a new set of challenges and unique requirements and pose unseen characteristics that need to be deliberately addressed. On that, the review finally provides a critical discussion on the applicability of EM mode for nano communication networks highlighting the future challenges with a set of perspectives of possible solutions.
Internet of nano things (IoNT) is growing at an exponential rate due to a growing population, more communication between devices in networks, sensors, actuators, and so on. This rise shows up in many ways, such as volume, speed, diversity, honesty, and value. Getting important information and insights is hard work and a very important issue. One of the most important ways to solve a problem is to come to a conclusion based on a number of different criteria. This can help you choose the best solution from a number of options. AI-enabled algorithms and decision making that takes into account multiple factors can be useful in big data sets. During the deduction process, AI-enabled algorithms and evaluations based on multiple criteria are used. Because it works well and has a lot of potential, it is used in many different areas, such as computer science and information technology, agriculture, and business.
Electromagnetic nano communication (EMNC) is an evolving field of modern communication technology. Terahertz imaging, sensing, food safety and security, quality control and simultaneous localisation and mapping, which all together make it a suitable use case for 6 G, are the most lively applications of EMNC. This manuscript proposes unified expressions of error rate under different modulation schemes for EMNC. For this, mixture gamma (MG) fading distribution, including almost all the fading channels, under dual selection combining (SC) diversity is developed and employed. Also, generalised Gaussian distribution (GGD), which is appropriate for the EMNC system under an aqueous medium, is employed in the analysis. Specifically, analytical expressions for BER of BPSK, M-PAM and M-QAM schemes over MG fading under dual SC diversity are derived in the presence of GGD noise. Proposed analytical expressions are numerically compared with those available in the literature without SC diversity. Comparison shows that irrespective of the value of fading severity ‘m’ and/or noise shaping parameter ‘p’, dual SC offers a significant improvement in BER. It is most important to mention that proposed expressions are unified in nature because a variety of results can be reproduced for different values of ‘N’ and ‘p’.
Full-text available
Molecular signaling is ubiquitous across scales in nature and finds useful applications in precision medicine and heavy industry. Characterizing noise in communication systems is essential to understanding its information capacity. To date, research in molecular nano communication (MNC) primarily considers the molecular dynamics within the medium, where various forms of stochastic effects generate noise. However, in many real-world scenarios, external effects can also influence molecular dynamics and cause noise. Here, the noise due to the temperature fluctuations from incident electromagnetic (EM) radiation is considered, with applications ranging from cell signaling to chemical engineering. EM radiation and subsequent molecular absorption cause temperature fluctuations which affect molecular dynamics and can be considered as an exogenous noise source for MNC. In this paper, the probability density function of the radiation absorption noise (RAN) is analyzed and to demonstrate applicability, we include characteristics of different tissues of the human body. Furthermore, the closed-form expression of error probability (EP) for MNC under the radiation noise is derived. Numerical analysis is demonstrated on different tissues of the human body: skin, brain, and blood, as well as the polarization factor of incident EM radiation is demonstrated. The coupling relationship between the radiation frequency and the intrinsic impedance of the human body on the PDF of radiation absorption noise is presented. This is useful for understanding how mutual information changes with external radiation sources.
Full-text available
Wireless communication among implanted nanobiosensors will enable transformative smart health monitoring and diagnosis systems. The state of the art of nano-electronics and nano-photonics points to the Terahertz (THz) band (0.1-10 THz) and the optical frequency bands (infrared, 30-400 THz, and visible, 400-750 THz) as the frequency range for communication among nano-biosensors. Recently, several propagation models have been developed to study and assess the feasibility of intrabody electromagnetic (EM) nanoscale communication. These works have been mainly focused on understanding the propagation of EM signals through biological media, but do not capture the resulting photothermal effects and their impact both on the communication as well as on the body itself. In this paper, a novel thermal noise model for intra-body communication based on the diffusive heat flow theory is developed. In particular, an analytical framework is presented to illustrate how molecules in the human body absorb energy from EM fields and subsequently release this energy as heat to their immediate surroundings. As a result, a change in temperature is witnessed from which the molecular absorption noise can be computed. Such analysis has a dual benefit from a health as well as a communication perspective. For the medical community, the presented methodology allows the quantization of the temperature increase resulting from THz frequency absorption. For communication purposes, the complete understanding of the intra-body medium opens the door towards developing modulations suited for the capabilities of nano-machines and tailored to the peculiarities of the THz band channel as well as the optical window.
Full-text available
In recent years Terahertz (THz) Band communications have gained even greater interest and higher expectations to meet an ever increasing demand for the speed of wireless communications. This paper provides the characteristics of electromagnetic waves propagating in the THz Band, which is one of the key technology to satisfy the increasing demand for Terahertz Wireless Data Communication (ThWDC). The performance of future terabit super channels implemented using bipolar phase-shift-keying which gives the best BER (Bit Error Rate) with today’s technology is investigated through the simulations for ThWDC. The objective of this paper is to describe the important issues related to the transmission in of ThWDC in air environment and to determine the best transmission windows in the THz range. In particular, ThWDC channel is modeled considering effects like capacity, channel performance and BER is investigated through simulation. The simulation results and the theoretical analysis show that data communication is possible from 0.01 to 0.5 THz frequency range and the best transmission window in this range have been found ω1 = [0.01–0.05 THz], ω2 = [0.06–0.16 THz] and ω3 = [0.2–0.3 THz] in this paper.
Full-text available
Wireless sensor networks (WSNs) are a specific type of networks that link sensors and have the potential to greatly benefit monitoring of coal mine applications, underground mine safety and localization of miners. Such systems can monitor the underground environment in real-time, provide information about the localization of miners and production parameters thus enabling early warning. In this paper, the possibilities and limitations of using WSNs that can effectively operate in coal environments are investigated. In particular, the coal communication channel is modelled considering the propagation of electromagnetic (EM) waves in coal, the multipath effect and providing an evaluation about the bit error rate of the modelled channel depending on the coal type and depth. The propagation characteristics are investigated using a theoretical approach. More specifically, the paper sets the theoretical background for examining the path loss of EM waves propagating in coal in the MHz range and determines the incurred path loss. As a result the frequency window, which provides the best performance, has been determined in the 615 MHz band since compared to the 2.216 Ghz band it has a weaker dependence on both the molecular composition of the medium and the transmission distance in coal medium.
The paper presents an analytical model of the terahertz (THz) communication channel (0.1 - 10 THz) for in-vivo nano-networks by considering the effect of noise on link quality and information rate. The molecular absorption noise model for in-vivo nano-networks is developed based on the physical mechanisms of the noise present in the medium, which takes into account both the radiation of the medium and the molecular absorption from the transmitted signal. The signal-to-noise ratio (SNR) of the communication channel is investigated for different power allocation schemes and the maximum achievable information rate is studied to explore the potential of THz communication inside the human body. The obtained results show that the information rate is inversely proportional to the transmission distance. Based on the studies on channel performance, it can be concluded that the achievable transmission distance of in-vivo THz nano-networks should be restrained to approximately 2 mm maximum, while the operation band of in-vivo THz nano-networks should be limited to the lower band of the THz band. This motivates the utilisation of hierarchical/cooperative networking concepts and hybrid communication techniques using molecular and electromagnetic methods for future body-centric nano-networks.
A newly emerging branch of communication system is nano communication (NC). The NC has enabled numerous important applications, such as immune system support, drug delivery systems, nuclear, biological and chemical defence and air pollution control. However, like in any communication system in NC system also, the medium through which communication takes place introduces various impairments such as fading and noise. In this research paper, channel model 1, i.e. CM1 scenario is analysed over suitable fading channels and noise distribution. First and foremost, a new approximation for Weibull-Gamma (WG) fading distribution that is suitable for CM1 scenario is proposed. Further, suitable noise model for NC is proposed as; generalized Gaussian distribution under CM1 scenario. Furthermore, a novel expression for conditional error probability (CEP) is also presented. Finally, following the proposed CEP closed-form expressions for average bit error rate is derived over WG and Mixture Gamma fading channel in terms of Meijer’s G function. To the best of our knowledge results are novel and never reported in the literature of NC system.
A Body Area Nano-NETwork represents a system of biomedical nano-devices that, equipped with sensing, computing, and communication capabilities, can be implanted, ingested, or worn by humans for collecting diagnostic information and tuning medical treatments. The communication among these nano-devices can be enabled by graphene-based nano-antennas, which generate electromagnetic waves in the Terahertz band. However, from a perspective of the electromagnetic field propagation, human tissues generally introduce high losses that significantly impair the communication process, thus limiting communication ranges. In this context, the aim of this contribution is to study the communication capabilities of a Body Area Nano-NETwork, by carefully taking into account the inhomogeneous and disordered structure offered by biological tissues. To this end, the propagation of Pulsed Electric Fields in a stratified media stack made up by stratum corneum, epidermis, dermis, and fat has been carefully modeled. First, electric and magnetic fields, as well as the Poynting vector, have been calculated through an accurate Finite-Difference Time-Domain dispersive modeling based on the fractional derivative operator. Second, path loss and molecular absorption noise temperature have been evaluated. Finally, channel capacity and the related transmission ranges have been estimated by using some baseline physical interfaces. Moreover, the comparison with respect to reference values already available in the literature is presented too. Obtained results clearly highlight that new research efforts are needed to ensure the considered communications due to the severe impairment suffered by electromagnetic waves.
In the near future, WSNs (Wireless Sensor Networks) have grown from a theoretical concept to a burgeoning modern technology. In this paper, it is presented a comparative review of wireless sensor network according to their size and communication methods. The main contributions of this paper are: comparing the mote size and the communication channel model considering the propagation properties of EM wave, magnetic induction, molecular communication, optical communication and acoustic communication; show the feasible range, speed and bandwidth of the channel of nodes for soil, water and air. In this respect the work presents a guideline for other researches to choose the right node and communication method for their application.