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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 eectively 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 eciency 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 eciency, 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 oces. 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 specic 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
diculties 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 signicant
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 ecient 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 ecient data collection
[,].
Furthermore, for the various combinations of transmis-
sion methods, we also study the analysis of the data trans-
mission eciency 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 eciency, 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 eciency, 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 specic 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). Aer nanosensors
detect specic 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 specic 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
andtheymaybemovedbybodyuid.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 specic targeting area of the human body
ascylindricalshapeDhexagonalpole,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 dene 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. Aer 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 dene 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. Aer 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 ,
=40
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 coecient.
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 dened 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 dene 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(,)=()
()×=40
20SNR
log2(1+SNR)2. ()
In (), we assume that the molecular absorption does
not aect the frequency range dened 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 amplier energy, whereas the
receiver energy consumption is only relevant to the electronic
energy []. erefore, we can dene 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 eciency []. 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 dene 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 dene 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 Ecient Data Transmission Method.
Employing the data transmission methods within layer and
between layers, explained in the previous section, we can
select the most eective combination of data transmission
methods in the way of energy eciency. 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
wexedthelocationofeachrouteratthecentercell
(annulus 0) in each layer. Aer detecting symptoms or
virus for the specic 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 ecient combination of data
transmission methods
each layer
between layers
F : Selection of the energy ecient 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). Aer
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 ,wecanndoutthemostenergyecient
combination. We dene this process denoted by
COMB
=min
𝐻
Router=0Horizontal
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 ecient 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 specic 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−𝑡−1
𝑛=1[−3(−2−1)]
+𝑡,Hor
mul
×𝑊−𝑡−1
𝑛=1−32+−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=1−32+−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 ) dened 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=1−32+−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 −32+−2−2.
()
And then, applying () and () and Hor
dir(𝑤) for the direct
transmission method, we can dene 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(),
0≤l≤. ()
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 ecient 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
humanbodyismadeupofalmostorgans;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
networkapproximatedtocminheight.
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 ().Inordertondoutfor 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
𝑙Ver 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 dierent 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 signicantly 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 dierent 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 eciency 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 ecient 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 conict 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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