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Design of Wireless Nanosensor Networks for Intrabody Application

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Emerging nanotechnology presents great potential to change human society. Nanoscale devices are able to be included with Internet. This new communication paradigm, referred to as Internet of Nanothings (IoNT), demands very short-range connections among nanoscale devices. IoNT raises many challenges to realize it. Current network protocols and techniques may not be directly applied to communicate with nanosensors. Due to the very limited capability of nanodevices, the devices must have simple communication and simple medium sharing mechanism in order to collect the data effectively from nanosensors. Moreover, nanosensors may be deployed at organs of the human body, and they may produce large data. In this process, the data transmission from nanosensors to gateway should be controlled from the energy efficiency point of view. In this paper, we propose a wireless nanosensor network (WNSN) at the nanoscale that would be useful for intrabody disease detection. The proposed conceptual network model is based on On-Off Keying (OOK) protocol and TDMA framework. The model assumes hexagonal cell-based nanosensors deployed in cylindrical shape 3D hexagonal pole. We also present in this paper the analysis of the data transmission efficiency, for the various combinations of transmission methods, exploiting hybrid, direct, and multi-hop methods.
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Research Article
Design of Wireless Nanosensor Networks for
Intrabody Application
Suk Jin Lee,1Changyong (Andrew) Jung,2Kyusun Choi,3and Sungun Kim4
1Texas A&M University-Texarkana, Texarkana, TX 75503, USA
2Framingham State University, Framingham, MA 01701, USA
3Pennsylvania State University, University Park, PA 16802, USA
4Pukyong National University, Busan 608-737, Republic of Korea
Correspondence should be addressed to Sungun Kim; kimsu@pknu.ac.kr
Received  March ; Accepted June 
Academic Editor: Luis Orozco-Barbosa
Copyright ©  Suk Jin Lee et al. is is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Emerging nanotechnology presents great potential tochange human society. Nanoscale devices are able to be included with Internet.
is new communication paradigm, referred to as Internet of Nanothings (IoNT), demands very short-range connections among
nanoscale devices. IoNT raises many challenges to realize it. Current network protocols and techniques may not be directly applied
to communicate with nanosensors. Due to the very limited capability of nanodevices, the devices must have simple communication
and simple medium sharing mechanism in order to collect the data eectively from nanosensors. Moreover, nanosensors may be
deployed at organs of the human body, and they may produce large data. In this process, the data transmission from nanosensors
to gateway should be controlled from the energy eciency point of view. In this paper, we propose a wireless nanosensor network
(WNSN) at the nanoscale that would be useful for intrabody disease detection. e proposed conceptual network model is based
on On-O Keying (OOK) protocol and TDMA framework. e model assumes hexagonal cell-based nanosensors deployed in
cylindrical shape D hexagonal pole. We also present in this paper the analysis of the data transmission eciency, for the various
combinations of transmission methods, exploiting hybrid, direct, and multi-hop methods.
1. Introduction
Nanoscale devices require a new communication paradigm;
they perform simple tasks, share the collected data, and reach
unprecedented number of locations over the Internet. is
new network paradigm is called IoNT []. In the IoNT, the
new network architecture was proposed to accommodate
two potential applications: interconnect nanoscale devices
and interconnect oces. Our research work is focused on
the intrabody communications for healthcare providers to
develop the network system architecture for realizing IoNT
applications. Human body is made up of almost organs.
Here, the nanosensors may be implanted into the organs,
detecting specic symptom or virus and forwarding the
sensing data to the nanorouter. e nanorouter may collect
data from the nanosensors. e nanorouter then may send
the collected data to the outside of the body.
e intrabody wireless communications encounter some
diculties that do not appear in regular propagation condi-
tions because the human body has a lot of water. Firstly, in-
body path loss model for homogeneous human tissues was
investigated as a function of various parameters at . GHz
range []. In addition, it is also discussed that the terahertz
(THz) band can be the potential solution to operate the
future electromagnetic (EM) nanosensors []. Moreover, the
related studies reveal that the path loss in human tissues at
very short distances (several millimeters) is not signicant
to deal well with communications among nanosensors at
THz frequency range [,]. Recently, the numerical analysis
of EM wave propagation in the human body explains that
using of EM paradigm is favorable compared with the
molecular communication channel because the molecular
channel attenuation is considerably higher than the situation
usingTHzEMmechanismintermsofthepathlossversus
Hindawi Publishing Corporation
International Journal of Distributed Sensor Networks
Volume 2015, Article ID 176761, 12 pages
http://dx.doi.org/10.1155/2015/176761
International Journal of Distributed Sensor Networks
distance []. On the other hand, the nanosensor, equipped
with graphene-based nanopatch antennas, is envisaged to
allow the implementation of nano-EM communications [].
Nanonetworking is an emerging eld, communicating
among nanomachines and expanding the capability of a
single nanomachine. Moreover, WNSN at the nanoscale
may be useful for intrabody disease detection. For instance,
nanosensors deployed in WNSN, equipped with graphene-
based nanopatch antennas [], can detect symptoms or virus
by means of molecules []orbacteriabehaviors[]. In fact,
the large surface area and the excellent electrical conductivity
of graphene allow rapid electron transfer that facilitates
accurate and selective detection of biomolecules. According
to Kuila et al., the advancement of graphene-based biosensors
allows the application of graphene for the detection of glu-
cose, Cyt-c (Cytochrome-c), NADH (Nicotinamide Adenine
Dinucleotide Hydride), Hb (Hemoglobin), cholesterol, AA
(Amino Acid), UA (Uric Acid), and DA (Diamino Acid) [].
In general, nanosensors have the very limited amount
of energy in their batteries and it is not easy to replace
or recharge them. When the energy harvesting systems are
assumed in IoNT applications, the energy resources of nano-
sensors might be retained over time. at is because the nano-
scale power generator could convert mechanical movement,
vibrational movement, or hydraulic energy into the electrical
energy []. However, they are not easy to implement.
erefore, the data transmission method plays an important
role in the optimization of the energy consumption []. In
addition, the data transmission is one of the important factors
for realizing ecient IoNT applications due to large data
produced from nanosensors [].
For example, Pierobon et al. proposed a routing and
data transmission framework to optimize the use of har-
vested energy with multi-hop decision algorithm []. In this
approach, the nanorouter makes decision for a nanosensor
to transmit the sensing data using direct or multi-hop
transmission, based on the estimated distance between the
nanorouter and the nanosensor []. is framework follows
a typical Time Division Multiple Access (TDMA) scheduling
using DownLink (DL), UpLink (UL), Multi-Hop (MH),
and RandomAccess (RA). However, due to the very limited
capability of nanodevices, the devices must have simple
communication and simple medium sharing mechanism. For
this reason, our research work is based on OOK protocol
and TDMA-based framework for the ecient data collection
[,].
Furthermore, for the various combinations of transmis-
sion methods, we also study the analysis of the data trans-
mission eciency for choosing the best one adapting to the
suggested network model. Here, note that we produce an
energy dissipation model based on the path loss model of
Jornet and Akyildiz [,] and also analyze the commu-
nication from single-hop as well as multi-hop. In fact, []
revealed that the energy aware routing framework with the
combination of direct and multi-hop transmission methods
can prolong the network life time.
is paper deals with the design of a WNSN at the nano-
scale to be used for intrabody disease detection as an appli-
cation of the IoNT. e suggested model assumes hexagonal
cell-based nanosensors deployed in cylindrical shape D
hexagonal pole. Here, a hexagonal cell model represents each
cell which is the smallest living unit of organs. Note that the
proposed model assumes hexagonal cell-based nanosensors
because the nanopatch antennas are graphene-based in a
honeycomb crystal lattice.
For designing this model, at rst, we explain the network
architecture for the proposed IoNT application model and
describe the corresponding cylindrical body model using
D hexagonal pole. Second, for the supposed organ of the
human body, we present the derivation of the ideal number
of hexagonal cells and the edge length in proportion to the
needed annulus number horizontally; and then we describe
a comparative analysis of the energy eciency, in the process
of data transmission, showing that the selection of the data
transmission method plays an important role in the optimiza-
tion of energy consumption. Such a comparative analysis is
done for the various combinations of data transmission meth-
ods exploiting the hybrid, direct, and multi-hop methods.
In the following section, we explain the physical and
MAC layers concepts for our proposed WNSN model for
the supposed IoNT application. Accordingly, the nanosensor
deployment model and the energy consumption scheme are
described in Section .InSection ,wepresentthemod-
elling of the various combinations of transmission methods
and suggest their energy dissipation equations. Section
describes the conceptual design of a WNSN for intrabody
application and shows simulation results and comparative
analysis of the data transmission eciency, followed by
conclusion and future works in Section .
2. Preliminary
2.1. Physical and MAC Layer Protocol Peculiarities of
Nanosensors in the Proposed WNSN Model. e nanosensors,
equipped with graphene-based nanopatch antennas, are
envisaged to allow the implementation of nano-EM com-
munications []. EM communication waves propagating in
graphene-based antenna have a lower propagation speed than
those in metallic antenna. Nevertheless, Gbps channel capac-
ity is available by radiating EM waves in THz frequency range.
However, our suggested model for intrabody application does
not probably need such a high channel capacity. Moreover, if
we assume that each nanosensor widely dispersed in the body
has just a role to catch disease symptoms anyhow, we need
to have a simple OOK protocol to communicate between
nanorouter and each nanosensor. Here, OOK protocol
can use femtosecond pulse-based modulations in which the
transmitted pulse lies in THz, called IR-UWB, Impulse Radio
Ultra Wide Band [,]. For example, the symbol “” is sent
by using a one hundred-femtosecond-long pulse as detection
of disease symptom and the symbol “” as nondetection of
disease symptom; that is, the nanosensor remains silent. In
this process, we adopt a scheduling scheme based on TDMA
for the synchronization among nanosensors and nanorouter.
In fact, employing this scheme may lead us to need
simple MAC protocol to avoid collisions in simultaneous
transmissions. More details about the behavior of physical
and data link layer protocols are out of the scope of this paper.
International Journal of Distributed Sensor Networks
Nanolink
Nano-microinterface
Gateway
Nanosensors
Nanorouter Microlink
App. provider
Internet
F : Network architecture for the proposed IoNT application
model.
Anyway, Jornet et al. showed that time spread OOK pro-
tocol generates a very short pulse in femtosecond long [],
which has its main frequency components in the THz band.
And that is already being used in several applications such as
nanoscale spectroscopy and biological imaging [].
2.2. Network Architecture for IoNT Applications. Figure
depicts simple network architecture of WNSN to be used
for intrabody disease detection as an application of the
IoNT []. e network can be composed of nanosensors,
nanorouters, nano-microinterface and gateway regardless of
any specic application. Here nanosensor is implanted into
the organs detecting symptoms or virus, performing com-
putation with a limited memory and transmitting small data
over a short range, whereas nanorouters are relatively larger
computational resources than nanosensors. ey aggregate
data from nanomachines (nanosensors). Aer nanosensors
detect specic symptoms or virus, simple data (e.g., for
detection or for nondetection) to inform the existence
of symptoms or virus will be forwarded to the nano-
microinterface through nanorouters. Here, the gateway (i.e.,
microscale device) makes it possible to remotely control the
entire system over the Internet.
In this paper, we assume that the nanosensors are able
to detect specic symptoms or virus by means of signal
molecules [] or bacteria behaviors []. Each cell in this case
is modeled as a hexagonal shaped cell, therefore giving a D
structure for the nanosensor networks. Actually the hexa-
gonal shaped cell model is a very similar structure with the
graphene in a honeycomb crystal lattice. Here, the accu-
mulated unit layers (where the unit layer consists of unit cells)
construct a -dimensional space for the individual target
organ, for example, heart, lungs, and kidney.
3. Nanosensor Deployment and Energy
Consumption Models for the Nanonetworks
3.1. Wireless Nanosensor Network Model. e nanosensors
can be deployed arbitrarily at organs of the human body
andtheymaybemovedbybodyuid.Weassumethatthe
nanosensors are distributed in -dimensional space in the
nanonetworks according to a homogeneous spatial Poisson
process. Most of the human organs such as spleen, liver,
lung, and heart are not shaped as perpendicular structure;
rather they are closer to round shape. Geometrically, we
represent the organ specic targeting area of the human body
ascylindricalshapeDhexagonalpole,whichisclosertothe
shape of organs. Here, each hexagonal shaped cell represents
each cell which is the smallest living unit of organs. us,
in the cylindrical model, as given in Figure ,wecanputas
many nanosensors as possible and each hexagonal cell has
oneactivenanosensor.Noticethatthecustomizedmodeling
isoutofscopeinthispaper.
Let us dene the nanonetwork depth () as the height of
the D hexagonal pole. Nanosensors within each layer con-
struct a cluster, where the information sensed by each nano-
sensor is transmitted to the nano-microinterface through the
nanorouter of each cluster. Aer each nanosensor sent out
the broadcasting message in a hexagonal cell, we assume
that all the nanosensors may recognize other neighboring
nanosensors. Each hexagonal cell may have more than one
nanosensor. However, only one nanosensor that has stronger
energy than others may be selected as the active nanosensor.
e other nanosensors that were not selected as the active
nanosensor will fall into the sleep mode, referred to as sleep
nanosensors. ose sleeping nanosensors are used for the
next data transmission process so that the load of the energy
consumption can be evenly distributed in nanonetworks.
For the target organ, as shown in Figure ,theedgelength
of hexagon is derived from the network radius .Once
we choose the network radius that may be relevant to
the volume of the target organ, the relationship between the
nanonetwork width and the edge length of hexagon can
be visualized by the equations, as shown in Figure (i.e.,
is an even or odd case). Notice that the number of hexagonal
cells for the individual annulus 𝑤is 6(=1,2,...,),
where is set for 0.
Let us dene 𝑖as the nanosensor density []ofth
hexagonal cell; 𝑖 {0,1,2,3,...}.Aerwegettherela-
tionship between the number of cells in the farthest annulus
𝑊and the edge length of hexagon ,wecanmaximize
the number of each hexagonal cell by minimizing the
nanosensor density. is process allows each hexagonal cell
to have a small number of nanosensors as possible. We can
show this process by the following expression:
=arg min
𝑋3𝑊(𝑊+1)+1
𝑖=1
1
𝑖
s.t.
𝑖=0,()
where the hexagonal cells with no nanosensor (𝑖=0)were
excluded. Aer choosing , we can notice that the number
of hexagonal cells that have only one nanosensor can be
maximized. Our assumption is that only one nanosensor is
active and the others go to sleep for the next iteration.
3.2. Energy Consumption Model for Our Wireless Nanosensor
Network. Jornet and Akyildiz introduced a novel propaga-
tion model for future EM nanonetworks in the THz band.
International Journal of Distributed Sensor Networks
Relation Rand S:S = 2R/(3w + 1)2+3
Active nanosensor
Nanorouter
Sleep nanosensor
Nano-microinterface
S
S
R
reshold range (annulus t)
Layer threshold (layer l)
Event
Multi-hop transmission
Direct transmission
Multi-hop transmission (in layer)
Hybrid transmission (in layer)
Direct transmission (in layer)
Multi-hop transmission (between layers)
Direct transmission
Hybrid transmission (between layers)
(if w is even)
Gateway
Nanonetwork depth (H)
Network radius (R)
S
S
Nanonetwork width (W)
Relation Rand S:S = 2R/(3w + 1) (if wis odd)
A1Aw
Annulus A1
(between layers)
Farthest annulus AW
A1
Aw
···
···
AW
AW
F : Cylindrical shape D hexagonal pole.
THz path loss model of nanonetworks is obtained by using
the sum in dB of the spreading loss, spread,andthemolecular
absorption loss, abs,asfollows[,]:
,=spread ,+abs ,
=40
2
exp , ()
where is the wave frequency, is the total path length,
is the speed of light in the vacuum, 0is the designed center
frequency, and ()is the molecular absorption coecient.
e transmission power, (),consumedinthetransmitter
nanosensor, should make sure that a constant signal-to-noise
ratio (SNR) in the receiver nanosensor located at the distance
. is is dened as follows []:
()=SNR ,0, ()
where 0is the total molecular absorption noise power
spectral density. We assume that 0is a constant value [].
e maximum transmission capacity of the THz band, as a
function of distance ,canbecalculatedasfollows[]:
()=3dB ()log2(1+SNR),()
where 3dB is the dB bandwidth. Let us dene 𝑡(,)as
the transmitter energy dissipation to transmit -bits packet
located at the distance . Accordingly, the transmitter energy
dissipation 𝑡(,)can be expressed as follows:
𝑡(,)=()
()×=40
20SNR
log2(1+SNR)2. ()
In (), we assume that the molecular absorption does
not aect the frequency range dened by the dB band-
width, 3dB();thatis,() 0.In[], generally, in
the simple radio energy dissipation model, they introduced
that the transmitter energy consumption is depending on
the electronic energy and the amplier energy, whereas the
receiver energy consumption is only relevant to the electronic
energy []. erefore, we can dene 𝑟(,)as the receiver
energy dissipation to receive -bits packet and the value of
𝑟(,) is intrinsically less than 𝑡(,).Inourmodel,we
assume that the value of 𝑟(,) is pertaining to the value
of 𝑡(,). Accordingly, we can derive the receiver energy
dissipation () by substituting the constant unit distance 0
for the transmission distance factor as follows:
𝑟,0=×𝑡,0, ()
where is the transceiver energy dissipation ratio with the
condition; that is, <1. For the notation simplicity, we use
the receiver energy dissipation 𝑟()instead of 𝑟(,0).
4. Combination of Data Transmission Methods
and Energy Dissipation
4.1. Data Transmission Methods within Layer and between
Layers. In this section, we introduce data transmission meth-
ods to collect the data sensed from the nanosensors. Within
each layer, as shown in Figure , all the information detected
at the nanosensors (actually we assume that this information
contains the geographical location of the nanosensor that
sentthesymbol“)istransmittedtothenanorouterinits
International Journal of Distributed Sensor Networks
corresponding layer. Note that each nanorouter is located at
the center cell (annulus 0) of each layer and also served as
an active nanosensor at the same time. On the other hand,
between layers, all the collected data at the nanorouter of
each layer are sent to layer ’s nanorouter that forwards the
collected data to the nano-microinterface to communicate
with microscale devices, that is, gateway.
First, within each layer, three data transmission meth-
ods are possible horizontally, as described in Figure .In
the direct data transmission, every nanosensor sends the
sensing data directly to its corresponding nanorouter with
achieving high transmission eciency []. Meanwhile, in
the multi-hop transmission, an adjacent (immediate neigh-
bor) nanosensor one annulus nearer to the corresponding
nanorouter is randomly selected; and then each nanosensor
which detects symptoms or virus sends the data to the cho-
sen adjacent nanosensor until arriving at its corresponding
nanorouter in the way of annulus by annulus hierarchically.
On the other hand, the hybrid data transmission is achieved
by combining the multi-hop and direct transmission meth-
ods. For example, let us dene as the threshold range and
as a certain annulus number in the given layer. en,
within the threshold range, (≤), every nanosensor
sends the data directly to the nanorouter of its corresponding
laye r. However, for nan osensors loc ated outside annulus ,
that is, >, each one sends out the data to the adjacent
(immediate neighbor) active nanosensor one annulus nearer
to its nanorouter using the multi-hop transmission.
Second, between layers, three data transmission methods
arealsofeasibleasshowninFigure .Inthehybridapproach,
the collected data in each layer’s router should be forwarded
to the next immediate router using the multi-hop or direct
transmission, where the transmission method is chosen by
the threshold layer . Let us dene as a certain layer of
nanonetworks. If layer is less than or equal to the threshold
layer ; the nanorouter directly sends the collected data to
layer ’s nanorouter; otherwise, the other nanorouters trans-
mit the data to the next upper layer’s nanorouter until arriving
at the nanorouter of the threshold layer using multi-hop
transmission. On the other hand, the multi-hop transmission
is accomplished by sending the collected data from the far-
thest nanorouter of layer to the adjacent router (immediate
neighbor router) until arriving at layer ’s nanorouter in the
way of layer by layer hierarchically, as given in Figure .On
the contrary, the direct data transmission can be achieved by
sending the data collected at each layer’s nanorouter directly
to layer ’s nanorouter as explained in Figure .
4.2. Selection of Energy Ecient Data Transmission Method.
Employing the data transmission methods within layer and
between layers, explained in the previous section, we can
select the most eective combination of data transmission
methods in the way of energy eciency. For the overall
collection process, individual nanosensor creates a cluster
for each layer, as shown in Figure . Each layer can select
the nanorouter in its corresponding layer. Here, note that
wexedthelocationofeachrouteratthecentercell
(annulus 0) in each layer. Aer detecting symptoms or
virus for the specic application, all nanosensors transmit the
Designing of the best tted cylindrical model using 3D hexagonal pole
for the target organ of the body
Selection of the most appropriated nanorouter with respect to
the data transmission methods within each layer
Applying of each combination of data transmission methods within
Applying of each combination of data transmission methods
Selection of the most energy ecient combination of data
transmission methods
each layer
between layers
F : Selection of the energy ecient data transmission
method.
geographical location data to their nanorouters using data
transmission methods within layer, that is, hybrid method
(H), multi-hop method (M), or direct method (D). Aer
receiving the data from nanosensors, each router sends out
the collected data to layer ’s router using data transmission
methods between layers, that is, H, M, or D methods. Figure
summarizes the overall collection process.
Table summarizes the possible combinations of data
transmission methods. With applying one of combinations of
data transmission methods listed in Tab l e to the algorithm
given in Figure ,wecanndoutthemostenergyecient
combination. We dene this process denoted by
COMB
=min
𝐻
Router=0Horizontal
Sensor +Horizontal
Router +Ver t ic a l
Router , ()
where COMB is one of combinations of data transmission
methods minimizing the total energy dissipation of data
transmission. Router is the total energy dissipation of the
nanorouter in each layer and Sensor is the total energy
dissipation of nanosensors in each layer, respectively; and
means the depth of network. erefore, the main objective
is to nd out the most ecient combination of data trans-
mission methods which has the least total energy dissipation
Tot a l (i.e., COMB) in the given cylindrical model designed
for intrabody application.
4.3. Energy Dissipation of Each Combination of Data Trans-
mission Methods. In order to calculate Tot al (the total energy
dissipation of the nanorouter and nanosensors for each com-
bination), we need to derive Hor
mul (the multi-hop transmis-
sion range) and Hor
dir(𝑤) (the direct transmission range)
within layer horizontally, as given in the equations in
Figure (a). Respectively, between layers, we also need
Ver t
mul (the multi-hop transmission range) and Ver t
dir(ℎ) (the
direct transmission range) vertically, as derived in the equa-
tions in Figure (b).Figure shows the ranges for the multi-
hop and direct transmissions utilized for each combination.
In this paper, we assume that one sensor in each cell
transmits its location information in case of detecting a cer-
tain symptom or virus for the specic application. For all
International Journal of Distributed Sensor Networks
T : Possible combinations of data transmission methods.
Within layer Between layers
Hybrid Multi-hop Direct
Hybrid Hybrid-Hybrid (H-H) Hybrid-Multi (H-M) Hybrid-Direct (H-D)
Multi-hop Multi-Hybrid (M-H) Multi-Multi (M-M) Multi-Direct (M-D)
Direct Direct-Hybrid (D-H) Direct-Multi (D-M) Direct-Direct (D-D)
S
S
S
S
S
Cell in which nanorouter located
Network radius (R)
reshold range
Farthest annulus (W)
Cell in tier w+1
···
Cell in tier w
rHor
mul ≤S
14 rHor
dir(w) ≤S
3(w + 1)2+2
Cell in tier w
(annulus t)
(a) Within layer (horizontally)
Multi-hop transmission range:
Active nanosensor
Nanocontroller Direct transmission
Multi-hop transmission
Direct transmission range:
reshold layer
rVer t mul ≤S
14 rVer t dir(h)≤S
3(h + 1)2+2
(h l)
Layer h1
(h l)
Layer h
Layer 0
Layer l
Layer H
S
S
.
.
.
.
.
.
S
.
.
.
>
(b) Between layers (vertically)
F : Description of ranges for transmission methods.
possible combinations of data transmission methods, the
energy dissipation equations are constructed as given in
Table .
Horizontally, using () and (),Hor
mul and Hor
dir(𝑤) in
case of applying the hybrid method in each layer, the trans-
mission energy dissipation of nanosensors can be written as
Horizontal(Hy brid )
Sensor =6×𝑡,Hor
mul, if =,
Horizontal(Hy brid )
Sensor
=𝑟()×𝑊−𝑡1
𝑛=1[3(−2−1)]
+𝑡,Hor
mul
×𝑊−𝑡−1
𝑛=1−32+−22,
if <<,
Horizontal(Hy brid )
Sensor
=𝑟()×𝑊−𝑡
𝑛=1[6(−+1)]+𝑡,Hor
dir(𝑡)
×𝑊−𝑡+1
𝑛=1[6(−+1)],if =,
Horizontal(Hy brid )
Sensor =𝑡−1
𝑤=16×𝑡,Hor
dir(𝑤),
otherwise.()
In (),Horizontal(Hy brid )
Sensor total is the total energy dissipation
of nanosensors for each layer when the hybrid transmission
method is applied horizontally. Consider
Horizontal(Hy brid )
Sensor total =6×𝑡,Hor
mul+𝑟()
×𝑊−𝑡−1
𝑛=1[3(−2−1)]+𝑡,Hor
mul
×𝑊−𝑡−1
𝑛=1−32+−22+𝑟()
×𝑊−𝑡
𝑛=1[6(−+1)]+𝑡,Hor
dir(𝑡)
×𝑊−𝑡+1
𝑛=1[6(−+1)]
+𝑡−1
𝑤=16×𝑡,Hor
dir(𝑤).
()
International Journal of Distributed Sensor Networks
T : Energy dissipation of the possible combination of data transmission methods.
Methods To t a l e n e r g y d i s s ip a t io n
H-H (H,H)=Hor izontal(Hy bri d)
Sensor total +Horizontal
Router +Vert i ca l (Hybri d)
Router total +Vert i ca l
Router
M-M (M,M)=Hor izontal (Multi-hop)
Sensor total +Horizontal
Router +Vert i ca l (Multi-hop)
Router total +Vert i ca l
Router
D-D (D,D)=Hor izontal(Direct)
Sensor total +Horizontal
Router +Vert i ca l (Direct)
Router total +Vert i ca l
Router
H-D (H,D)=Hor izontal(Hy bri d)
Sensor total +Horizontal
Router +Vert i ca l (Direct)
Router total +Vert i ca l
Router
H-M (H,M)=Hor izontal(Hy bri d)
Sensor total +Horizontal
Router +Vert i ca l (Multi-hop)
Router total +Vert i ca l
Router
M-H (M,H)=Hor izontal(Mu lti-hop)
Sensor total +Horizontal
Router +Vert i ca l (Hybri d)
Router total +Vert i ca l
Router
M-D (M,D)=Hor izontal(Mu lti-hop)
Sensor total +Horizontal
Router +Vert i ca l (Direct)
Router total +Vert i ca l
Router
D-H (D,H)=Hor izontal(Direct)
Sensor total +Horizontal
Router +Vert i ca l (Hybri d)
Router total +Vert i ca l
Router
D-M (D,M)=Hor izontal(Direct)
Sensor total +Horizontal
Router +Vert i ca l (Multi-hop)
Router total +Vert i ca l
Router
On the other hand, applying () and () and Hor
mul in the
case of the multi-hop transmission method, we derive the
following equation for the transmission energy dissipation of
nanosensors (Horizont al(Multi-hop)
Sensor ) dened by
Horizontal(Multi-hop)
Sensor =6×𝑡,Hor
mul,
if =,
Horizontal(Multi-hop)
Sensor
=𝑟()×𝑊−1
𝑛=1[3(−2−1)]+𝑡,Hor
mul
×𝑊−1
𝑛=1−32+−22,
if 0 ≤<.
()
In (),Horizontal(Multi-hop)
Sensor total is the total energy dissipation
of nanosensors for each layer when the multi-hop transmis-
sion method is applied horizontally. Consider
Horizontal(Multi-hop)
Sensor total
=6×𝑡,Hor
mul+𝑟()
×𝑊−1
𝑛=1 [−3(−2−1)]+𝑡,Hor
mul
×𝑊−1
𝑛=1 −32+−2−2.
()
And then, applying () and () and Hor
dir(𝑤) for the direct
transmission method, we can dene the transmission energy
dissipation of nanosensors (Horizontal(Direct)
Sensor )writtenas
EHorizontal(Direct)
Sensor total =𝑊
𝑤=1 6×𝑡,Hor
dir(𝑤),
1≤≤. ()
On the other hand, () means the total receiving energy
dissipation for the nanorouter in the center cell of each layer
denoted by
Horizontal
Router =(+1)×𝑊
𝑤=1 6×𝑟(),
0l≤. ()
Vertically,wealsorepresenttheenergydissipationof
nanorouters in a similar way. First of all, using () and (),
Ver t
mul,andVer t
dir(ℎ) in case of applying the hybrid trans-
mission method, the equations for the transmission energy
dissipations of nanorouters (Horizont al(Hy brid )
Router )canbewritten
as
=𝑊
𝑤=1(6)+1,
Ver t i cal (hybrid)
Router =×𝑡,Ver t
mul, if =,
Ver t i cal (hybrid)
Router =𝑟()×𝐻−𝑙−1
𝑛=1 (×)
+𝑡,Ve r t
mul
×𝐻−𝑙−1
𝑛=1 [(+1)×],
if <<,
Ver t i cal (hybrid)
Router =(−)××𝑟()+(−+1)
××𝑡,Ve r t
dir(𝑙), if =,
International Journal of Distributed Sensor Networks
Ver t i cal (hybrid)
Router =𝑙−1
ℎ=1×𝑡,Ve rt
dir(ℎ),
otherwise.()
In (),Horizontal(Hy brid )
Router total refers to the total energy dissipa-
tion of nanorouters when the hybrid transmission method is
applied vertically. Consider
=𝑊
𝑤=1 (6)+1,
Ver t i cal (Hybrid)
Router total =×𝑡,Vert
mul+𝑟()
×𝐻−𝑙−1
𝑛=1 (×)
+𝑡,Ve r t
mul
×𝐻−𝑙−1
𝑛=1 [(+1)×]
+(−)××𝑟()
+(−+1)×
×𝑡,Ve r t
dir(𝑙)
+𝑙−1
ℎ=1×𝑡,Ve rt
dir(ℎ).
()
On the other hand, using () and () and Ver t
mul in the
case of the multi-hop transmission method, we derive the
transmission energy dissipation Ver t ic a l (Multi -hop )
Router of the nano-
routers given by
=𝑊
𝑤=1(6)+1,
Ver t i cal (Mult i-hop)
Router =×𝑡,Ver t
mul, if =,
Ver t i cal (Mult i-hop)
Router =𝑟()×𝐻−1
𝑛=1(×)
+𝑡,Ve r t
mul
×𝐻−1
𝑛=1[(+1)×],
if 0 ≤<.
()
In (),Ver t i ca l (Mult i-ho p)
Router total means the total energy dis-
sipation of nanorouters when the multi-hop transmission
method is adapted vertically. Consider
=𝑊
𝑤=1(6)+1,
Ver t i cal (Mult i-hop)
Router total =×𝑡,Vert
mul+𝑟()
×𝐻−1
𝑛=1 (×)
+𝑡,Ve r t
mul
×𝐻−1
𝑛=1 [(+1)×].
()
And then, applying () and () and Ve rt
dir(ℎ) to the
direct transmission method, () for the transmission energy
dissipation of the nanorouters (Vert i c al (Direct)
Sensor )canbewritten
as
=𝑊
𝑤=1(6)+1,
Ver t i cal (Direct)
Router total =𝐻
ℎ=1×𝑡,Ver
dir(ℎ),
1≤≤.
()
Meanwhile, the following equation contains the total
receiving energy dissipation for layer ’s nanorouter
which forwards the collected information to the nano-
microinterface given by
=𝑊
𝑤=1(6)+1,
Ver t i cal
Router =××𝑟().
()
Conclusively, utilizing all equations, that is, ()-() and from
() through (), we can nd the most ecient combination
of data transmission methods which has the least total energy
dissipation Tot al (i.e., COMB) in the given cylindrical model
designed. According to our simulation results, we experi-
enced that the combination of hybrid data transmissions
(within layer and between layers) (H-H) outperforms other
combinations like H-M, H-D, M-H, M-M, M-D, D-H, D-M,
and D-D.
5. Design and Analysis of Simulation Results
5.1. Design of Nanosensors Deployment Network Model. e
humanbodyismadeupofalmostorgans;theirsizeand
weight are unpredictable according to body length, body
weight, and body mass index []. For network modeling, we
assume the target organ as cylindrical shape D hexagonal
pole. Let us design a cylinder model for the supposed organ
International Journal of Distributed Sensor Networks
T : Number of hexagonal cells and edge length in proportion to the required annulus number.
Farthest annulus number      
Edge length of hexagon (mm) . . . . . .
Total number of hexagonal cells 
T : Total number of layers and maximum height of network with variable edge length .
Edge length of hexagon (mm) . . . . . .
Total number of layers      
Maximum height of network (mm) . . . . . .
of the body ( cm in diameter and cm in height). e edge
length of hexagon can be derived by applying the relation ,
as given in Figure .Table shows and the total number of
hexagonal cells (in the layer) in proportion to the annulus
number .InTa bl e ,wecannoticethatincreases as
decreases.
In addition, the total number of layers can be decided
by using .Ta b l e shows and the maximum height of
networkapproximatedtocminheight.
5.2. Calculation of the Horizontal reshold Range .For the
case of the H-H combination described in Section .,this
section explains how to calculate the horizontal threshold
range . e total horizontal transmission energy dissipation
of nanosensors is given in ().Inordertondoutfor the
target annulus number in Tab l e , we divide the total hori-
zontal transmission energy dissipation (Horizontal (Hybrid)
Sensor total)
by the total area (2) in the direct transmission range. For
example, the horizontal energy dissipation per unit area
(Hor) can be calculated as follows:
=arg min
𝑡Horizontal(Hy brid )
Sensor total
2. ()
Figure shows the energy dissipation per unit area Hor
versus threshold range .Inthesimulation,wesetthe
parameters as follows: the center frequency 0as z, the
signal-to-noise ratio (SNR) as  dB, the constant noise power
spectral density 0as dB, the packet size as  bits, and
the transceiver energy dissipation ratio as /.
As shown in Figure ,Hor has minimum value at the
threshold annulus. erefore, we can choose these threshold
ranges to calculate the energy dissipation of the proposed
hybrid data transmission method horizontally.
5.3. Calculation of the Vertical reshold Layer .is section
introduces how to calculate the vertical threshold layer ,
described in Section .. When the hybrid data transmission
method is applied between layers, () shows the total vertical
energy consumption of nanorouters. erefore, we need to
decide threshold layer in the way of minimizing the total
vertical energy dissipation. For this, the resulting relationship
is expressed as follows:
=arg min
𝑙Ver t i cal (Hybrid)
Router total(2≤<).()
Figure shows the relationship between the total number
of layers andthethresholdlayer.InFigure ,wecan
decide the threshold layer in the way of minimizing the
energy dissipation vertically.
5.4. Energy Dissipation Analysis. For the data transmission
among nanorouter and nanosensors (also among nanosen-
sors, resp.) in each layer, three dierent transmission
schemes,thatis,H,M,andDmethods,areusedtocompare
the performance of the transceiver energy consumption.
Figure shows the energy dissipation of the transceivers with
respecttothefarthestannulusnumberand the edge length
.
As shown in Figure , the data transmission methods,
H and M, outperform the method D. In the method D, the
energy consumption of the nanosensor varies with respect
to the annulus number. At the initial stage (i.e., the annulus
number =1), both methods, D and M, have the same
energy consumption. As growing the annulus number, the
energy consumption of method D is signicantly increased.
On the other hand, the energy consumption of method M has
aconstantvalue.Incaseof=2, method D consumes four
timesmoreenergythanmethodM.Otherwise,methodHhas
a good performance when the farthest annulus number is
more than a certain threshold, whereas method M is good
overall.
On the other hand, Figure compares the energy dissipa-
tion of dierent data transmission methods between layers,
thatis,methodH,methodM,andmethodD.Asgivenin
Figure , method H and method M show almost identical
results, whereas method D consumes more energy. When the
farthest annulus number is small, the energy dissipation
of method D is monotonically increased, whereas the energy
dissipation is saturated when is increased.
Figure shows the energy consumption comparison of
various combinations of the data transmission methods.
Notice that the combinations of the direct and other methods
(D-H, D-M, D-D, H-D, and M-D) destructively waste the
 International Journal of Distributed Sensor Networks
0246810
reshold range t
200
400
600
800
1000
1200
1400
ΔEHor
W = 10, S = 4.83 mm
Minimum =4
1000
2000
3000
−10000
Minimum =5
W = 20, S = 2.46 mm
0 5 10 15 20
reshold range t
0
1000
2000
3000
4000
−1000 0 5 10 15 20 25 30
reshold range t
Minimum =6
W = 30, S = 1.65 mm
0
2000
4000
6000
−2000
W = 40, S = 1.24 mm
Minimum =7
0 10203040
reshold range t
0
2000
4000
6000
8000
−2000 010 20 30 40 50
reshold range t
W = 50, S = 0.99 mm
Minimum =7
010 20 30 40 50 60
0
2000
4000
6000
8000
−2000
W = 60, S = 0.83 mm
Minimum =8
reshold range t
0
ΔEHor
ΔEHor
ΔEHor
ΔEHor
ΔEHor
F : Energy dissipation per unit area (Hor) versus threshold range ().
010 20 30
0
10
20
30
Total number of layer H
S=4.83mm,H= 31
reshold layer l
0 20 40 60
0
20
40
60
S = 2.46 mm,H= 61
reshold layer l
Total number of layer H
0 20 40 60 80
0
20
40
60
80 S = 1.65 mm,H= 91
reshold layer l
Total number of layer H
050 100
0
50
100 S = 1.24 mm, H = 121
reshold layer l
Total number of layer H
0 50 100 150
0
50
100
150
S = 0.99 mm,H= 151
reshold layer l
Total number of layer H
050 100 150
0
50
100
150 S = 0.83 mm, H = 181
reshold layer l
Total number of layer H
F : Relationship between the total number of layers and the threshold layer .
0246810
0
0.5
1
1.5
2
W = 10, S = 4.83 mm
×104
0 5 10 15 20
0
1
2
3
4
W = 20, S = 2.46 mm
×104
0 10 20 30
0
2
4
6
W = 30, S = 1.65 mm
×104
010 20 30 40
0
2
4
6
W = 40, S = 1.24 mm
EHorizontal(Hybrid)EHorizontal(Multi-hop)EHorizontal(Direct)
×104
0 10 20 30 40 50
0
2
4
6
8
W = 50, S = 0.99 mm
×104
020 40 60
0
5
10
W = 60, S = 0.83 mm
×104
F : Energy consumption analysis within layer.
International Journal of Distributed Sensor Networks 
0 102030
0
1
2
3
W = 10, S = 4.83 mm
×105
0 204060
0
0.5
1
1.5
2
W = 20, S = 2.46 mm
×106
0 20406080
0
1
2
3
4
W = 30, S = 1.65 mm
×106
0 50 100 150
0
5
10
15
W = 50, S = 0.99 mm
×106
0 50 100
0
2
4
6
8
W = 40, S = 1.24 mm
×106
0 50 100 150
0
1
2
3
4
W = 60, S = 0.83 mm
×106
EVer t ic a l(Hy brid)EVe r ti c al (Multi-hop)EVer t ic a l(Direct)
F : Energy consumption analysis between Layers.
0
0.5
1
1.5
2
2.5
W = 10, S = 4.83 mm
×105
0
0.5
1
1.5
2
2.5
W = 20, S = 2.46 mm
×105
0
0.5
1
1.5
2
2.5
W = 30, S = 1.65 mm
×105
0
0.5
1
1.5
2
2.5
E(H, H)
E(M, H)
E(D, H)
E(H, M)
E(M, M)
E(D, M)
E(H, D)
E(M, D)
E(D, D)
W = 40, S = 1.24 mm
×105
0
0.5
1
1.5
2
2.5
W = 50, S = 0.99 mm
×105
0
0.5
1
1.5
2
2.5
W = 60, S = 0.83 mm
×105
F : Energy consumption comparison of various combinations of data transmission methods.
transceiver energy compared to the combination H-H or
M-H. As shown in Figure , H-H (or M-H) combination
improves the average energy dissipation compared with D-
H, D-M, H-D, M-D, and D-D.
6. Conclusion and Future Works
In this paper, we propose a WNSN paradigm for intrabody
application. e scenario of application is that each nanosen-
sornodewouldbeplacedwithineachcellandthattheywill
communicate with their immediate neighbor cell according
to the combinations of transmission methods. Each cell is
modeled as a hexagonal shaped cell, therefore giving a D
structure for WNSN.
e contribution of this paper is twofold. First one is the
derivation of the ideal number of hexagonal cells and the
edge length in proportion to the needed annulus number
horizontally for the supposed organ of the body. e other
one is a comparative analysis of the energy eciency in the
process of data transmission, showing that H-H (or M-H)
combination improves the average energy dissipation com-
pared with D-H, D-M, H-D, M-D, and D-D combinations.
Some issues still remain to be solved in the future. First,
we need to verify the proposed conceptual model in real
 International Journal of Distributed Sensor Networks
environment to be used for intrabody application. Second, we
also need to implement the most ecient data transmission
methods and the network protocols based on OOK and
TDMA framework and to verify them in that environment.
Conflict of Interests
e authors declare that there is no conict of interests
regarding the publication of this paper.
Acknowledgment
is work was supported by the Pukyong National University
Research Abroad Fund in  (C-D--).
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... The IoMNT architecture includes nano-cameras and photodetectors, which are constructed on a nanoscale and are mostly employed in telecommunications. The development of nanomachines with communication capabilities and the integration of nanomachines with micro-and macro-devices are factors driving the expansion of the worldwide IoNT market [23,[26][27][28][29][30]. In reality, nanomachines are miniature devices that provide a variety of uses, including medicine delivery, food quality testing, and environmental monitoring [4,17]. ...
... A Body Sensor Network (B.S.N.) is one such example. A B.S.N. employs various sensors implanted inside the human body to obtain pertinent insights and diagnostically valuable data that are otherwise outside the scope of traditional diagnostic methods [4,15,16,[22][23][24][25][26]. Thanks to these nanoscale biosensors, surgeons can now access parts of the human body's internal workings that were previously out of our reach. ...
... Thanks to these nanoscale biosensors, surgeons can now access parts of the human body's internal workings that were previously out of our reach. Further nanodevices could be used to monitor and track vital signs, diagnose and treat diseases, and deliver targeted therapies to specific areas of the body [26][27][28][29][30]. For example, nanosensors could be used to detect the early stages of disease and trigger an appropriate response, such as releasing a therapeutic agent [9,17]. ...
Article
Full-text available
Throughout the course of human history, owing to innovations that shape the future of mankind, many technologies have been innovated and used towards making people’s lives easier. Such technologies have made us who we are today and are involved with every domain that is vital for human survival such as agriculture, healthcare, and transportation. The Internet of Things (IoT) is one such technology that revolutionizes almost every aspect of our lives, found early in the 21st century with the advancement of Internet and Information Communication (ICT) Technologies. As of now, the IoT is served in almost every domain, as we mentioned above, allowing the connectivity of digital objects around us to the Internet, thus allowing the remote monitoring, control, and execution of actions based on underlying conditions, making such objects smarter. Over time, the IoT has progressively evolved and paved the way towards the Internet of Nano-Things (IoNT) which is the use of nano-size miniature IoT devices. The IoNT is a relatively new technology that has lately begun to establish a name for itself, and many are not aware of it, even in academia or research. The use of the IoT always comes at a cost, owing to the connectivity to the Internet and the inherently vulnerable nature of IoT, wherein it paves the way for hackers to compromise security and privacy. This is also applicable to the IoNT, which is the advanced and miniature version of IoT, and brings disastrous consequences if such security and privacy violations were to occur as no one can notice such issues pertaining to the IoNT, due to their miniaturized nature and novelty in the field. The lack of research in the IoNT domain has motivated us to synthesize this research, highlighting architectural elements in the IoNT ecosystem and security and privacy challenges pertaining to the IoNT. In this regard, in the study, we provide a comprehensive overview of the IoNT ecosystem and security and privacy pertaining to the IoNT as a reference to future research.
... Such an arrangement would accord with the architecture for integrating intrabody networks with "off-body networks" as described in the IoNT literature (e.g. see Lee et al., 2015), include: a. nanodevices, also termed nanonodes (Cruz Alvarado & Bazán, 2019), a broad category that encompasses technology such as nanosensors (Balasubramaniam & Kangasharju, 2013;Khan et al., 2020;Lee et al., 2015), which can circulate in blood vessels ( Figure 5) and harvest energy from the bloodstream or heartbeat (Balghousoon & Mahfoudh, 2020), and/or cross the blood brain barrier to potentially read and transmit neural activity (Taylor, 2021), injectable and "single cell" nanoradios (Burke & Rutherglen, 2010;Dolev & Narayanan, 2019), nanowires (Dambri et al., 2020), nanoantennae (Akyildiz & Journet, 2010;Lee et al., 2015), magnoelectric nanorobots (Betal et al., 2018) and neural-nanorobots, comprising endoneurobots, gliabots, and synaptobots that are capable of interfacing with individual neurons and synapses to create a "human brain/cloud interface" (Martins et al., 2019), among other nanotechnologies; b. nanorouters, which "act as aggregators of information coming from nanonodes" according to IoNT literature (Cruz Alvarado & Bazán, 2019). See Balghousoon and Mahfoudh (2020) for a review of over 20 nanorouting protocols, including for intrabody applications; and, c. nanointerfaces or gateways, defined by Balghusoon and Mahfoudh (2020) as a "complex hybrid device that integrates the nano world with the external world". ...
... Such an arrangement would accord with the architecture for integrating intrabody networks with "off-body networks" as described in the IoNT literature (e.g. see Lee et al., 2015), include: a. nanodevices, also termed nanonodes (Cruz Alvarado & Bazán, 2019), a broad category that encompasses technology such as nanosensors (Balasubramaniam & Kangasharju, 2013;Khan et al., 2020;Lee et al., 2015), which can circulate in blood vessels ( Figure 5) and harvest energy from the bloodstream or heartbeat (Balghousoon & Mahfoudh, 2020), and/or cross the blood brain barrier to potentially read and transmit neural activity (Taylor, 2021), injectable and "single cell" nanoradios (Burke & Rutherglen, 2010;Dolev & Narayanan, 2019), nanowires (Dambri et al., 2020), nanoantennae (Akyildiz & Journet, 2010;Lee et al., 2015), magnoelectric nanorobots (Betal et al., 2018) and neural-nanorobots, comprising endoneurobots, gliabots, and synaptobots that are capable of interfacing with individual neurons and synapses to create a "human brain/cloud interface" (Martins et al., 2019), among other nanotechnologies; b. nanorouters, which "act as aggregators of information coming from nanonodes" according to IoNT literature (Cruz Alvarado & Bazán, 2019). See Balghousoon and Mahfoudh (2020) for a review of over 20 nanorouting protocols, including for intrabody applications; and, c. nanointerfaces or gateways, defined by Balghusoon and Mahfoudh (2020) as a "complex hybrid device that integrates the nano world with the external world". ...
... Such an arrangement would accord with the architecture for integrating intrabody networks with "off-body networks" as described in the IoNT literature (e.g. see Lee et al., 2015), include: a. nanodevices, also termed nanonodes (Cruz Alvarado & Bazán, 2019), a broad category that encompasses technology such as nanosensors (Balasubramaniam & Kangasharju, 2013;Khan et al., 2020;Lee et al., 2015), which can circulate in blood vessels ( Figure 5) and harvest energy from the bloodstream or heartbeat (Balghousoon & Mahfoudh, 2020), and/or cross the blood brain barrier to potentially read and transmit neural activity (Taylor, 2021), injectable and "single cell" nanoradios (Burke & Rutherglen, 2010;Dolev & Narayanan, 2019), nanowires (Dambri et al., 2020), nanoantennae (Akyildiz & Journet, 2010;Lee et al., 2015), magnoelectric nanorobots (Betal et al., 2018) and neural-nanorobots, comprising endoneurobots, gliabots, and synaptobots that are capable of interfacing with individual neurons and synapses to create a "human brain/cloud interface" (Martins et al., 2019), among other nanotechnologies; b. nanorouters, which "act as aggregators of information coming from nanonodes" according to IoNT literature (Cruz Alvarado & Bazán, 2019). See Balghousoon and Mahfoudh (2020) for a review of over 20 nanorouting protocols, including for intrabody applications; and, c. nanointerfaces or gateways, defined by Balghusoon and Mahfoudh (2020) as a "complex hybrid device that integrates the nano world with the external world". ...
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A survey and critical analysis of literatures in biotech, nanotech, and materials science can yield important insights on major threats facing humanity in a world divided largely by highly compartmentalized epistemic communities. Interdisciplinary research on the well-documented problems posed to human beings by the injectable mRNA platforms claiming to address COVID-19 medical complications reveal surprising, if not deeply troubling, new evidence of apparent fraud and deceit. Analysis presented here bolsters both the reported laboratory studies of blood samples from injected subjects and experimental work exploring the potential reasons for observed phenomena relating to electromagnetic properties exhibited in human bodies. The impetus for this cross-disciplinary study was current reports from a substantial proportion of injected subjects who emitted alphanumeric signals in the frequency range corresponding to Bluetooth communications networks. Discussion of these bizarre phenomena are framed by a wider historical context in nanotechnology as an emergent industry and by recent commentary emanating from noteworthy public figures concerning surveillance under the skin and the disappearance of civil and human rights.
... The advancement of graphene-based biosensors allows the application of graphene for the detection of glucose, Cyt-c (Cytochrome-c), NADH (Nicotinamide Adenine Dinucleotide Hydride), Hb (Hemoglobin), cholesterol, AA (Amino Acid), UA (Uric Acid), and DA (Diamino Acid). [4] Indeed, a number of E-Health measures were implemented during the COVID-19 pandemic, proportionally to the progress on BAN (Body Area Network) and intra-body networks, in which the BAN is a form of IoNT domain: [5] • Early Diagnosis: proposed by Doffman in 2020, with replacement of infrared thermometers with thermal camera, optical camera, GPS and LWPAN modules to identify the COVID-19 suspects. Until now, however, no one has gone so far as to implement a set of sensors from inside the body. ...
... For these reasons, however, future pandemics will be managed optimally, through predictive algorithms of contagions, just as many of the current chronic diseases will be treatable, through sending stimuli to the release or inhibition of certain elements in the body, depending on whether they are needed (or deficient), or in excess. [4] The computing power of quantum computers and the flexibility of Machine Learning will be combined with the speed and zero latency of the 5G network, to transmit the data of each individual. [5] After all, the Internet of Things (IoT) has made objects "smart" by sending data, parameters, sometimes initial processing of the same, before reaching the destination clouds. ...
Research
It is now practically established that the element graphene, a derivative of graphite and based on carbon, forming nanotubes (CNT) is present in sera, in addition to the presence of other materials derived from it, such as graphene oxide (GO). Graphene is a nanomaterial that possesses exceptional physical, thermodynamic, electronic, mechanical and magnetic properties; it can be used as a superconductor, transducer, absorber of electromagnetic waves, emitter and receiver of signals. It has also been observed that by taking a vial of Pfizer vaccine and allowing the hydrogel to dry, after 3-4 days the presence of nanocircuits can be seen under the microscope: it is the graphene that reacts to electromagnetic fields and electromagnetic microwaves, self-assembles, according to DNA-based nanopatterns to mark the order of construction and electrophoresis/teslaphoresis to trigger the process in the solution materials (hydrogel) into electronic nanocircuits, with real nanoscale components, such as nanorouter, nanoantenna, etc. , formed of graphene, which acts as a signal repeater, since it is radio modulable, i.e. able to absorb electromagnetic waves and multiply their radiation; these electronic components are organized in Graphene Quantum Dots (GQDs) and Quantum Cellular Automata (QCA), particles that enjoy the above properties of graphene, exponentially greater, thanks to the Quantum Hall effect, especially in environments such as the human body. It will thus create an intracorporal network or nanonetwork, which will detect every vital parameter, but also every slightest variation inside the body, thanks to the advanced and compressed electronics, superimposed on 3D. The collected signals would then be sent, through a gateway connected to the 5G network, on the Internet, to be stored in a huge cloud database and processed by software based on Machine Learning, exploiting the computing power of quantum computers. The ultimate goal could be to store and eventually reproduce what we call "consciousness", in perpetuity.
... This leads to a new application of the Internet of Things (IoT) for obtaining and exploiting nominal variation in temperature, pressure, and vibrations at such a low scale. This field is referred as the Internet of Nano-Things (IoNT) [1] and includes various applications such as nano-scale monitoring for health [5,6], photosynthesis in plants [7], variations in chemical processes [8] and so on. ...
... In this case, we consider a circular deployment where all nodes are equidistant from the receiver. An example of such a network is the cylindrical deployment of nanosensors for intrabody disease detection [6]. Similarly, a receiver can also be placed at the center of the catalyst tube, surrounded by nodes, in a chemical reactor [24]. ...
Article
Internet of Nano-Things (IoNT) is an expansion of the Internet of Things (IoT) with the capacity to monitor extremely fine-grained events with sensors on a scale ranging from one to a hundred nanometers. One major challenge for this type of communication paradigm is to determine the identity of the transmitting nodes and the events. From previous works, we know that different amount of energy is discharged in the environment from different events. This motivates us to propose an energy-neutral event recognition framework using pulse position modulation in which the event information is transmitted by the sensors that use the energy harvested from the event. In this framework, we use pulse position to identify transmitting nodes communicating with a single receiver. However, using this approach, we can also encode the identity of multiple receivers when a single node communicates with them without employing an addressing scheme in IoNT networks. In both cases, the energy observation of the received pulse helps in identifying the event type. The feasibility of the proposed framework is demonstrated by a large number of numerical simulations which include terahertz channels. We find that the proposed framework achieves 99% accuracy for detecting ten different event types at a distance of 30 mm.
... Nevertheless, integrating nanoscale devices with the IoTbased network states an innovative networking paradigm named the Internet of Nano Things (IoNT) [216]. IoNT is a small-scale IoT, an ideal solution for medical applications and remote environmental monitoring, and is the most ongoing nano-scale network of interconnected physical objects with nano communication [27,66,68,[218][219][220][221][222]. Furthermore, IoNT is the interconnection of nano-scale devices (shown in Figure 9), existing communication technologies (for instance, sensors networks, big data analytics, cloud, and Fog computing), and the Internet to execute various operations such as sensing, computation, processing, etc. [222]. ...
Article
Full-text available
Internet of Things is emerging as an incredible future technology to improve the existing lifestyle from the research community, industry, and the public sector. The main intention of IoT is to create an efficient, interactive, and autonomous infrastructure for a safer and healthier world. Moreover, it grows faster day-to-day with the support of many other technologies, i.e. Cloud computing, Blockchain, Wireless Body Sensor Networks, Nanotechnology, and Artificial Intelligence for smart applications, including healthcare, environment, automotive industries, transportation, agriculture, etc. Nevertheless, managing big data is one of the challenging tasks due to the increased number of devices leading to various serious issues like security, privacy, accuracy, latency, scheduling, etc. Further, specific infrastructures with remarkable techniques are required to analyze the bulk of raw data to progress the quality of life and allow timely intervention through various capabilities, i.e., data capture, unique identification, actuation, communication, data mining, etc. In past literature, numerous reviews/surveys are presented that explore the technologies mentioned above as standalone and application specific. However, this paper aims to integrate all the mentioned technologies and deliver a clear vision to future researchers (newcomers) as a kick-start article to boost up and understand the status of the existing research through a comprehensive review of the Internet of Things and its evolution in wireless telecommunications from a general perspective. The most significant challenges and issues are highlighted to research further in these evolving domains.
... A WNSN comprise of a cluster of nanosensors that are utilized to cover a wide range to carry out sensing and data collection with higher resolution as well as lower energy consumption [8]. It requires a smaller infrastructure consisting of sensor nodes having sensing range from centimetre to few kilometre and those sensors in the network could work co-ordinately for effective monitoring [9]. The architecture of a WNSN is shown in Figure 11.2. ...
Chapter
https://www.taylorfrancis.com/chapters/edit/10.1201/9781003185109-11/wireless-nanosensors-network-light-pollution-control-ajit-behera-santos-kumar-das-ramakrishna-biswal
... 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. For instance, in health monitoring systems, being substituted in the human body, nanosensors have the ability to monitor tumor markers in real time to assist highrisk populations in conducting screening of tumors, aiding in the early tumor diagnoses, and in monitoring prognoses. ...
Article
Full-text available
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).
Article
Electromagnetic Nano-networks is going to play imperative role in high-resolution agriculture. Monitoring of crop health and detection of emitted volatile organic compound are key application among the others. However, range of Nano-network, which is very much limited, can be increase with the help of relaying schemes. This paper presents novel system model for Electromagnetic Nano-networks based on Amplify-and-forward (AF) relaying for plant monitoring. Further, Packet Error Rate (PER) has been analyzed to quantify network performance. Specifically, PER of DBPSK and DQPSK modulation has been analyzed under both symmetric and asymmetric relaying schemes. For symmetric relaying, fading channel from source to relay and relay to destination both is taken as either EGK or FS distribution. Whereas, for asymmetric scheme, fading channel from source to relay and relay to destination are EGK and FS respectively. Also, for asymmetric case, FS and EGK fading channel are taken from source to relay and relay to destination respectively. Again, it is worthy to mention that in each and every case and for both the links, shaping and severity factors of both shadowing and multipath components has been included in the analysis of PER. It is observed that either increasing the value of shaping and/or severity causes a decrease in the PER and vice-versa. MATLAB-14 has been used for simulation purpose.
Article
Recent improvements in nanotechnology and Internet of Things (IoT) have led to the emergence of the concept of Internet of Nano-Things (IoNT). This concept provided ease of access and communication facilities for the nano-devices in applications of different fields operating at the molecular level such as health, industry, agriculture, robotics and military. These distributed devices interconnect over the Wireless Nano-Sensor Networks (WNSNs) in nano-scale which have limited energy and capacity. Therefore, these devices need to use their energy efficiently when communicating with each other to ensure network continuity and maintain all processes of IoNT applications. In this paper, a novel energy-efficient distributed routing algorithm, namely DEEPNT, is proposed in order to extend the lifetime of WNSNs in IoNT applications. The communication backbone of WNSNs constructed by DEEPNT by selecting cluster head nodes can be used in all types of IoNT applications in nano-scale in order to provide continuous data flow in critical areas such as healthcare running in vivo. This novel energy-efficient distributed protocol is compared with the traditional flooding based method and with an energy aware routing algorithm. According to the results, DEEPNT prolongs the WNSNs average lifetime respectively up to 10.78 and 5.95 times compared to mentioned algorithms despite the larger network sizes in nano-scale.