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Recent advances in nano-materials and nanotechnology have paved the way for building integrated devices with a nanometric size, named nano-nodes. These nano-nodes are composed of nano-processor, nano-memory, nano-batteries, nano-transceiver, nano-antenna and nano-sensors, which operate at nano-scale level. They are able to perform simple tasks, such as sensing, computing and actuation. The interconnection between microdevices and nanonodes/nanosensors has enabled the development of a new network standard, called Wireless Nano-Sensors Network (WNSN). This paper provides an in-depth review of WNSN, its architectures, application areas, and challenges, which need to be addressed, while identifying opportunities for their implementation in various application domains.
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Electromagnetic-Based Wireless Nano-Sensors Network:
Architectures and Applications
Ayoub Oukhatar1, Mohamed Bakhouya2, and Driss El Ouadghiri1
1 Laboratory of Informatics and Applications, Moulay Ismail University, Meknes, Morocco
2 International University of Rabat, College of Engineering and Architecture, LERMA Lab, Sala Al Jadida 11000,
Morocco
Email: ayoub.oukhatar@gmail.com, elmeloud@gmail.com, mohamed.bakhouya@uir.ac.ma
AbstractRecent advances in nano-materials and
nanotechnology have paved the way for building integrated
devices with a nanometric size, named nano-nodes. These nano-
nodes are composed of nano-processor, nano-memory, nano-
batteries, nano-transceiver, nano-antenna and nano-sensors,
which operate at nano-scale level. They are able to perform
simple tasks, such as sensing, computing and actuation. The
interconnection between microdevices and
nanonodes/nanosensors has enabled the development of a new
network standard, called Wireless Nano-Sensors Network
(WNSN). This paper provides an in-depth review of WNSN, its
architectures, application areas, and challenges, which need to
be addressed, while identifying opportunities for their
implementation in various application domains.
Index TermsNanotechnology, nanosensors, wireless nan-
sensors network, protocols and applications
I. INTRODUCTION
Recent advances in nanomaterials and nanotechnology
have enabled the development of tiny nanoscale devices,
named nanonodes or nanomachines. Composed of a nano-
battery, a memory, an antenna and an actuation unit, the
nanomachines are fully autonomous nano-nodes able to
execute simple operations while communicating at short
distances [1]. Wireless nano-nodes, which are able to
detect and interact with their environment, will bring
radical changes to everyday life applications [2]. However,
due to their tiny size, energy and physical (e.g.
computation, storage) capacities are extremely limited. As
a result, interesting applications, using wireless nano-
sensors communications network (WNSN), may require
thousands of cooperating nanonodes [3]. For instance,
nanoscale devices, which operate at the nanoscale level,
could provide very important technological solutions in
various fields, including biology, military, agriculture,
smart cities, environmental and food safety [4]-[6]. For
example, nanosensors could detect chemical compounds
at atomic level or the existence of toxic substances in the
air/water [7].
WNSN networks will increase the efficiency of nano-
devices by allowing them to perform simple sensing and
computation operations. Data sensed at nano-scale level
Manuscript received July 2, 2020; revised December 16, 2020.
Corresponding author email: ayoub.oukhatar@gmail.com
could be submitted and shared with other nodes, via hop-
by-hop routing and dissemination protocols (e.g., flooding).
Alike traditional networks, nano-routers play an important
role, by routing and communicating data from source
nodes to the nano-interface device, which acts as a bridge
between the nano world and the micro world. The
interconnection between these nanonodes can be achieved
by one of the following communication mechanisms:
electromagnetic, acoustic, nanomechanical and molecular
communication [8] [4]. In this paper, we focus on the
nano-electromagnetic communication. It is based on the
transmission and reception of radio frequency
electromagnetic waves in the Terahertz band using
nanomaterial-based antennas, in particular graphene-based
antennas, and nano transceivers [4].
The remainder of this paper is structured as follows.
We first present, in Section 2, a general overview of the
architecture of nanodevices as basic elements of WNSNs,
then we describe a typical architecture model for an EM-
based WNSN in Section 3. Opportunities, which can be
realized for WNSN applications, are highlighted in Section
4. In Section 5, we describe the main challenges of
implementing WNSN applications, in particular those
related to the deployment, data analysis, routing
technology and coexistence with other categories of
networks. Conclusions and perspectives are given in
Section 6.
II. NANO-MACHINE ARCHITECTURE
Recent progress in nanomaterial technology allows
building novel miniaturized nanomachines. Basically,
these nanomaterials can be arranged into three categories:
metal nanoparticles, quantum dots and carbon
nanomaterials. Various types of metal nanoparticles have
been extensively used in a number of nanosensor
applications, such as magnetic iron, copper, gold, zinc,
and silver [9]. The high ability of these nano particles, to
easily operating with the targeting analyte (e.g.,
pathogens, antibodies, DNA), has been exploited for use
in highly selective nanosensors [10].
Quantum Dots are nanoscale elements made out of
semiconductors materials. They are known for their
nanoscale size (1-10 nm) and have unique optical
properties from traditional fluorescent dyes. They have
very efficient wide excitation ranges and narrow and
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adjustable emissions depending on the size [11]. The high
sensitivity of fluorescent emission offered by these
Quantum Dots can be utilized to fabricate performant
selective nanosensors [12]. Recently, a novel Graphene
quantum dots based nanosensor for recognition of
Metronidazole in biological samples was introduced for a
none-fluorescent pharmaceutical compound detection
[13]. Carbon nanotubes (CNTs) and graphene are
frequently utilized for building nanosensors because of
their large surface area, great electrical conductivity, and
high thermal conductivity [14]. They have used to
ameliorate the accuracy of an electrochemical sensing
unit by extending the sensitivity of glassy carbon
electrodes [15].
The design of miniaturized nanosensors, as stated
above, will benefit from the extremely advances in
nanotechnology and nanomaterials, and has been
exploited for use in highly selective nanosensors, which
are able to examining previously inaccessible areas, such
as disease diagnosis and treatment. According to [4], a
generic nanosensor node includes an energy source
module, a communication asset, a processing remote
node with nano memory, and a module for sensing and
actuation (Fig. 1).
Fig. 1. Nanosensor architecture [4].
The sensing unit is able to detect an optical, biological
or mechanical event, with high sensitivity and selectivity,
and converting the response to an output electrical signal.
Generally, these units are developed using nanomaterial,
such as graphene GNR and nano tube carbon NTC [4]. It
is a sensing entity, which can detect new collections of
events at the nanoscale level, such as properties of
nanomaterials, the concentration of certain chemical
elements and the presence of bio-elements (e.g., virus or
bacteria). These nanosensors can be classified into three
types which will be detailed later in this section.
The discovery of new nanomaterials has greatly
accelerated the development of nano energy harvesting
systems at nanoscale with high power density, good time
to live and charge/discharge periods [16]. However, the
nano-sensor can be distributed in inaccessible areas
where it is not possible to recharge. Therefore, self-power
approaches and techniques have been introduced.
Autonomous power systems can convert one form of
energy (i.e., mechanical, thermical, vibratory or hydraulic
energies) into electrical energy. This conversion is
achieved by the nano-piezoelectric effect of zinc oxide
(Zno), which consists of converting a nanoscale
mechanical energy into electricity [17]. The authors in
[18], presented a thermo-electric energy harvesting
method, which consists of converting the heat flow into
electricity using the temperature gradient between two
dissimilar electrical conductors.
Similarly, nano-transistors based on graphene provides
ballistic electron transport that allows for the
development of faster switching devices [19], [20].
Recently, the Berkeley National Laboratory at Stanford
University in California developed the smallest transistor.
It is a molybdenum disulfide (MoS2) Nano transistor
with a 1-nm physical gate [21]. However, the nanoscale
size of the nanosensors limits the total number of
transistors in a nano processor and the complexity of
operations.
The storage unit is in charge for storing the aggregated
data coming from the sensing element. Because of the
restrictions in size of nanomachines storing, data at
nanoscale is one of the issues that still under research and
development. Recently, research has been announced that
it is possible to store data directly in the smallest possible
component. Atomic memories have now been introduced
using carbon nanotubes. Researchers have integrated
more than one million random access memory (RRAM)
cells, making it the most complex nanoelectronics system
ever created with emerging nanotechnologies [22].
Researchers have also introduced a method to magnetize
sections of nanowires. By producing a current, they can
move the magnetic sections along the wire, which
enables data to be read by a fixed nanosensor [23].
The nano-antenna entity is responsible for enabling the
communication between nano-devices. The recent
advances in nanomaterials, such as CNTs and GNRs,
have paved the way to build a Graphene based nano-
antennas and carbon nanotube antennas for
communication among nanosensor devices [24] [25]. The
reduced size nano-antenna can achieve the nano-device
size requirements and can radiate in the high operating
frequency in the terahertz band. A graphene nano-antenna
with 1 μm long can emit electromagnetic waves within
the product range 1 -10 terahertz band [4] [26].
Nano-transceivers are nanodevices able to convert a
recognition nanoscale event into a measurable physical
phenomenon, such as a change in electrical resistance. In
[27], authors demonstrate a plasmonic nano-transmitter-
receiver that allows the electrical excitation of surface
plasmon polariton (SPP) waves. These later can radiate
by a nano-antenna at Thz band frequencies. A graphene-
based plasmonic nanotransceiver for wire- less
communication in the Terahertz Band was presented
recently in [28]. The proposed system is composed by a
signal generator, which generates an electric signal that
needs to be transmitted, and a plasmonic transmitter,
which converts the signal into a modulated SPP wave.
The SPP wave is then radiated by a nano-antenna at Thz
band frequencies.
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The primary role of nano actuator units is to enable the
nano-sensor to interact with surrounding events. This
entity receives control input signal (generally in the
electrical form) and generates a change in the physical
nano-system by producing a physical feedback, such as
force, motion, heat. According to [4], there are three
types of actuators: physical, biological and chemical. The
piezoelectric nano-actuator is proposed as one of the
novel actuators, which are able to achieve nanoscale
positioning resolution. This type of actuators will provide
smart and exact actions and processes in nano-dimension
[29].
It’s worth noting that, as described above, nanosensors
are designed to respond to an actual chemical, optical, or
biological event and converts it into electrical signal.
Based on the sensing event, as depicted in Fig. 2,
nanosensors can be classified into three main categories
[4] [30]. Mechanical nanosensors are type of nano-
sensors that can measure the quantities, such as mass,
pressure, force or displacement. For example, Graphene
embedded nano-composites are utilized to monitor strain
[31]. Multi-walled carbon nano tubes are used to
supervise strain in the bridge decks. The nanosensors
might be embedded in highways or coated on bridges for
monitoring the processes that subscribe to deterioration
and cracking, and alert the appropriate authorities long
before the damage is detectable by human inspectors [32].
Unlike mechanical nanosensors, optical nanosensors
operate by using optical features of nanomaterials. They
can be applied in various domains, such as the chemical
industry, medicine, environmental security, and human
protection [33]. Recently, a method has been proposed
for early cancer detection using nano-optical devices [34].
Fig. 2. Nanosensor classification according to the sensing event.
Chemical nanosensors are built mainly to measure
quantities, such as the concentration of a certain gas, the
clear presence of specific molecules or perhaps a certain
molecular composition [35]. Their working mechanism is
based on changes in the CNT or GNR electrical
properties, when several types of molecules are absorbed
along with them. Biological nanosensors are analytical
device integrating a bio-recognition sensing element
associated with an electronic component to extract a
measurable signal and detects from a target compound.
The recognition phenomena generate a biological signal,
which is converted into a measurable quantity by the
transuding unit. The output signal can be displayed in the
form of optical (luminescence, fluorescence, surface
plasmon resonance) or electrical (capacitance, impedance,
voltammetry) or magnetical (Magnetic field, flux,
permeability) or any other format, and transmits
information about the presence of various biological
substances in the environment [36], [37].
III. WNSN ARCHITECTURE
As mentioned in the previous section, a single
nanosensor is characterized by limited sensing range,
which is reserved to its close surroundings nodes.
Therefore, WNSN nodes need to expand their coverage
areas. WNSN consists of a certain number of
interconnected nanosensors, diffused to cover larger
zones and to perform sensing and data collection from its
environment, and send it to a making decision unit via
micro-interface conventional micro-device, which can act
as an intermediate device between the WNSN and micro
world [38]. As presented in [4], a generic WNSN should
be composed by a collection of nanosensors (with fixed
position or could be mobile according to the targeted
application). These nanosensors can be diffused into a
target area for capturing different events from one
specific area. Fig. 3 illustrates the hierarchical structure
that enables WNSN to interact with microGetway. The
network architecture of WNSNs is constituted by the
following components: nano-nodes, nano-routers, and a
nano-interface.
Fig. 3. WNSN interfacing to a micro gateway.
Nano-nodes are selective nanosensors, which are able
to examine inaccessible areas by performing a simple
detection of nano component. Their constrained energy
and communication capacities make them able to
communicate only over much reduced ranges. Nano-
routers are greater than nano-nodes in size and have
larger computational resources than nanonodes. They
aggregate the data originating from nano-sensor nodes
and can issue the straightforward command to regulate
the behavior of nanonodes. Nano-micro interface
aggregates the information coming from nano-routers and
forward them to a microscale device or vice versa. They
are assumed to be hybrid devices, which can act as
gateway between the nano and the micro scale world.
They should be able to convert WNSN messages to a
traditional network system (i.e., WiFi, cellular networks...)
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and vice-versa. The micro gateway requires dual
transceivers, one to communicate with WNSN in the THz
band, while the other one is dedicated to communicate
with micro conventional network system.
The special achieved advances in the field of
nanomaterials have paved the way to the communication
between nanodevices. However, the development of a
particular microgateways device, which can connect the
nanoscale world to the microscale world, and the
management interface over standard networks, is still
constantly an open research issue. Therefore, the typical
architecture of WNSN can change according to the
targeted applications.
IV. APPLICATIONS AREAS OF WNSN
Nanosensor capabilities to sense nanoscale events
together with the development of WNSN protocols will
enable the development of new applications. Various
intelligent and smart applications in different areas are
envisioned for being developed. For instance, we can site
healthcare, agriculture, food safety and environmental
and certain cross-domain scenarios. This section
introduces some of these applications and scenarios.
A. Healthcare
A number of nanomachines could be deployed inside
the human body in order to detect the presence of
different biological elements in such environments. The
network formed by this heterogeneous set of wireless
body nano-sensor nodes is termed a wireless body-area
network (WBAN) [39]. Fig. 4 depicts the architecture for
the WNSN in healthcare applications, namely, intrabody
nanonetworks for remote healthcare. Each WBAN is
basically a composition of several wireless nano-sensor
nodes and an individual nano-interface. The nano-
interface is primarily responsible for the acquisition of
the sensed data from all nano-sensor nodes and first-level
aggregation of the data before transmitting it for further
analysis and processing to the data center.
Fig. 4. The WBAN-based medical care in a hospital environment.
The main goals of WNSN healthcare applications are
to analyze, monitor, and prevent healthy bad
circumstances as well as the presence of viruses on the
grains, cell, or the variations at DNA level [40]. Nano-
sensors can be installed in the patient’s environment to
monitor her regular activities and alert emergency units to
irregular changes in her attitude [41], [42]. WNSN health
applications can also detect the clear presence of certain
molecules, chemicals or infections and send notifications
to a control agent [43]. In addition to the above-
mentioned applications, WNSN also promote other
medical applications, such as real-time medical imaging
and video streaming, emotion detection and the obvious
presence of harmful in biological cells.
B. Food Safety and Food Packaging
WNSN enables the application of nanosensors in the
food packaging in order to monitor their quality in the
course of the various phases of the logistics process, and
to guarantee high quality of the product to consumers
[44], [45]. The intelligent packaging is a category of
container that provides particular functionality beyond
the physical barrier between the food product and the
environment [46]. Through smart packaging, WNSN can
help to provide authentication, traceability and product
location. The integration of a nanosensor into a food
container will allow for exponential growth in the field of
food security in the coming future. [47]. Another
application of the WNSN network in food safety is to
exploit nanosensors to detect molecules, gases and
oxygen by installing different nanosensors inside the
physical product to detect toxins, it enables giving a
definite and visible sign if the food is fresh or not [48].
An intelligent product is a physical product that has the
ability to communicate its condition information to a
provider’s agent, the nanosensors collect the information
from the product environment, and sent them to the
decision-making agent. The connection between the
physical product and the decision-making agent is made
using a nanointerface and a microgetway. Due to the
WNSN, the smart product will provide great
improvement in the food industry sector. The connected
nanosensors, in the form of tiny chips that are
undetectable by the human eye, are embedded in foods or
containers, allowing food to be monitored at all stages:
production, distribution and consumption [49].
C. Environment
The WNSN network will allow the use of nanosensors
in the environmental safety, through the installation of
nanosensors in high density public locations, such as
hospitals, airports, and restaurants, to trace the circulation
of viral viruses and improve the interpretation how
various types of people are affected. Wireless nanosensor
networks could also be utilized to supervise the
environment, including pollution and greenhouse gas
emissions. Similarly, the water quality sector would
benefit from the use of nanosensors to detect bacteria,
diseases and other harmful infectious agents [50]. An
electrochemical nanosensor with nanointerfaces provides
a biocompatible real-time monitoring system, these
nanosensors are the major players in detecting
environmental contaminants (especially metal ions,
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pesticides, and pathogens) in water [51]. Sensing and
detecting different contaminants in water at nanoscale
under laboratory and field conditions will offer assistance
in developing modern nanotechnologies systems that will
have better detection and sensing ability [52]. In Fig. 5,
we introduce the architecture for the WNSN used to
monitor water quality. The Smart Water nanosensors
installed in the river measure different air and water
quality parameters. Different nanonodes distributed in the
same zone send data to an Internet Gateway.
Nanointerface is used to collect the data from the
nanosensor nodes and transmit them to pollution control
authority. In this situation, the decision-making agent can
access these data in real time to know the state of the
water and correct accordingly the anomalies. This real-
time monitoring will help to provide critical data for
agronomic intelligence processes, such as optimal
planting timing, but also for water level monitoring [53].
Fig. 5. Water quality monitoring architecture.
D. Agriculture
A requirement for detecting many conditions that a
farmer desire to monitor (e.g., detection of plant viruses
or the degree of soil nutrients), has tempted to use
wireless nanosensors with nanoscale sensitivity to be
especially crucial in realizing the vision of intelligent
plants [54], [55]. A monitoring system that takes less
time and can produce results in a few hours, that is simple,
portable and accurate, allows the farmer to have all the
information about the environmental conditions of the
plants using a simple portable device. If an autonomous
nano-sensor connected to an Internet system for real-time
monitoring can be distributed around the plants to
monitor soil conditions and detection of plant viruses,
that would be an excellent solution to increase food
production, with equivalent or even higher nutritional
value, quality and safety [56]. For example, nano
encapsulated atrazine allows the utilization of lower
doses of herbicide without any loss of efficiency [57].
The combination of biotechnology and nanosensors
will allow building a device of increased sensitivity,
allowing real-time responses to attenuation in leaves due
to the effect of thickness, and presence of water contents
in leaves [58]. WNSN technology is on the verge of
generating the tools for establishing real-time plant
monitoring system, composed of chemical nanosensor
merged with plants, nano-scale interface device and
network micro gateways, as depicted in Fig. 6. Chemical
nanosensor nodes are miniaturized machines that interact
with the environment to collect a collection of chemical
compounds disseminated by plants. Nano-interfaces are
considered as control units that manage clusters of
nanosensors, for example, conducting data fusion and
planning. Finally, micro-gateways are interfaces that
stimulate the applications of wireless network distribution
networks by interconnecting data collected from the
nano-network to the external network, then to the
decision officer of the analytical laboratory [59].
Fig. 6. The architecture of a plant monitoring nanosensor network.
E. Civil Engineering
By significantly improving the performance of sensor
units and data collection systems and reducing their size,
developments in nanotechnology can also benefit
construction engineering and the built environment by
enabling the practical deployment of structural condition
monitoring systems for large civil engineering systems.
Since nanosensors can be minimized and integrated into a
composite material, this material can provide information
on its performance and environmental conditions by
monitoring structural loads, temperatures, humidity, heat
gain or loss, and air conditioning loss, can be used for the
construction of smart buildings [60]. Minimized-size
nanosensors could be integrated into highways or coated
on bridges for monitoring the processes that contribute to
degradation and cracking, and alert the making decisions
authorities a long time before the damage systems is
detectable by human inspectors [61].
Wireless nanosensors embedded into roads and
structures would allow engineers to monitor deterioration
and cracking without any additional costs of physical
intervention. Similarly, mechanical nanosensors in
bridges have been emerging as a key device for such
label-free and real-time vibration and loads
measurements, allowing researchers to assess weaknesses
and correct them long before they become apparent to
inspectors [62], [63]. Road sensor networks could gather
and provide data to transport operators to better control
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congestion and incidents and detect rapidly changing
weather conditions.
F. Accident Avoidance
A nano-sensor can be utilized to detect the drowsiness
of a driver and transfer the information to the decision
maker agent. WNSN networks increase the monitoring
range compared to conventional sensors [64], [65]. By
distributing the nanosensors into the drive’s body, we
could detect drowsiness and transfer the information to
the central control unit. Further progress can be made by
deploying and networking nano sensors between the
driver’s body and the vehicle’s engine unit to facilitate a
safely stopping of the vehicle when drivers are not
appropriate for driving [66]. The real-time driver-
alertness monitoring system is an aimed model, which
can be achieved by using the nanosensors wireless
technology. A real-time monitoring system can be built to
evaluate the driver’s situation while driving using a
network of wireless nano sensors [67]. This monitoring
system can be implemented in a flexible nano headband,
which can be designed to get an operational edition of the
proposed real-time monitoring system.
G. Wild Life Monitoring
The WNSN network can be extremely useful for
monitoring the health and condition of wildlife. The
nanoscale dimensions of the nanosensors should be in the
nanometer range and will not disrupt the life of the
animals or birds monitored. Wireless sensors are
currently used to monitor wildlife and birds [68], [69].
One of the major issues of the conventional sensors used
for the wildlife monitoring is their large size, when they
are fixed directly on the physical body of wild animals,
these living organisms feel uncomfortable, therefore they
will try break free and the system would be damaged.
Then it must be changed. The problem is that detecting
the location of this animal took a lot of time and work.
Nanosensors will help in detecting explosions and
affections by installing them along some insects such as
bees [70]. The nanosensors can be installed on the insects
delivered into surrounding environment, once the target
substance elements have been found, the nanosensor
sends data information to the nano-interface.
V. WNSN CHALLENGES
The WNSN can be applied to improve many of our
real-life applications. In order to achieve this potential,
many requirements must be incorporated into the design
of protocols. In this section, we examine the different
requirements and challenges related to the WNSN
applications deployment and management.
A. Data Collection and Processing
The detection, recognition and quantification of special
chemical and biological ingredients are essential
requirements for the development of nanosensors. In
addition, it is planned that these various collected data
will be integrated into a platform offering different
services to the end user (messaging, file transfer, terminal
emulation, monitoring,...). These services are
standardized and accessible through standard interfaces.
The application design for WNSN services needs to
satisfy real-time requirements, in the other hand, the
exceptionally short transmitting range of nanodevices
will produce an arbitrary delay, which must be taken into
account in WNSN implementation. In addition, the
heterogeneous nature of nanomachines due to their use in
different application scenarios will lead to different
representations and forms of data [71]-[74]. Therefore,
data fusion should be optimal and tolerant of delays for
applications requiring the insertion of diverse data
sources and requiring periodic monitoring by a decision-
making unit.
Data aggregation and merging are not always practical
for WNSN applications, due to real-time requirements,
and many of these applications depend on fine temporal
domain variations lost during the data aggregation
process. In the other hand, data collection procedure
presents different data-quality challenges in wireless
nanosensors network. Nanosensor can be put erroneously
on the body, or, in the event that at first is located at the
correct area, could along these times slip or become
detached. A nanodevice might intentionally corrupt data
quality to preserve battery life. These issues call for new
research in data detection quality. Finally, we require
measurements to characterize the distortions and
uncertainties related with collecting the data and the
resulting mistake because the agent decisions are based
on that data quality. The specification of such
measurements is an open research question.
B. The Addressing Process
The nanoscale level and the large number of
nanodevices in WNSN make it impractical to have
individual network addresses for each nanodevice. On the
other hand, the cluster-based addressing can be utilized
instead of nanonode-based addressing. This makes it
conceivable to identify a group of nanonodes according
to the task they execute or the phenomena they supervise.
One solution proposed in [75], which consists of tanking
in specific scenarios where is not necessary to have
information from a specific nanomachine, but, for
example, from a type of nanomachine. In particular,
different type of components may have different
addresses, but identical nanomachines can have the same
one [71]. The authors in [76] propose an 3D geographic-
based addressing to assign several neighboring nodes
located in one zone by the same address, in this case one
address refers to a territory, noted zone. The fact that all
nanonodes in one specific zone share the same geo-
address guarantees a degree of natural nanonode tipping.
Another challenge that is not debated in the design of
protocol stack for WNSN, is the flow control mechanism
and collision detection, particularly in a dense
nanomachines arrangement methodology. Despite the
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fact that a few methods from electromagnetic
communication may be adjusted for the Terahertz band, it
isn’t however clear how congestion will be addressed.
C. Routing Technology
Due to the restrictions of nanonodes (low processing
capacity, small memory unit) and high density of WNSN
nodes, the communication remains more challenging. The
routing protocol is the method of selecting
communication ways in a network and it a critical
requirement for information transmission under given
energy and complexity constraints. Basically, due to the
restricted processing capability and energy storage of
nanonodes, flood-based approaches are encouraged due
to their simplicity, in which each nanonode rebroadcasts
the packet received for the first time. Although flood-
based protocols can accomplish high robustness, the high
density of nanonodes leads to serious redundancy and
conflicts, consequently increasing energy consumption.
As a solution to this problem, threshold-based routing
schemes have been suggested to prevent certain
nanonodes from the excessive rebroadcasting by using a
certain threshold (e.g. distance, redundant message counts,
or broadcast probability) [77], [78]. All these algorithms,
schemes attempt to ameliorate the performance of
flooding schemes.
Based on the routing path, existing studies with data
forwarding techniques can be classified mainly in two
types of protocols: Multi-path protocols and single-path
protocols. In a single-path routing approach, any
nanonode received a message will retransmit the message
to a particular next-hop nanonode. This type of routing
algorithms can essentially reduce energy consumption,
but if a nanonode is located on the wrong path, it’ll result
in packet loss. Nanonodes have constrained memory
ability and cannot store neighbors’ data. Therefore, when
a nanonode gets or creates one packet, it has to select one
of the neighboring nodes for forwarding by negotiation
with them, which can increase the transmission delay.
The Multi-path protocols are flood-based, when a
nanonode receives or generates a message it exclusively
has to choose whether to rebroadcast the message or not.
Multi-path protocols ordinarily require lightweight
computing resources, but will increase energy
consumption.
TABLE I: COMPARISON OF WNSNS ROUTING PROTOCOLS
Protocols
RADAR CORONA DEROUS SLR MHTD EEMR ECR TTLF RDDA PBDA
References
[79] [80] [81] [76] [82] [83] [84] [85] [86] [87]
Deployment
2D 2D 2D 3D 2D 2D 3D 2D 2D 2D
Topology
Flat Tree Flat Flat Tree Tree Tree Flat Tree Tree
Routing Path
Multi Multi Multi Multi Single Single Single Single Single Single
Computing
Low Low Medium Medium High High Mediu
m
Low High High
Mobility
static static static static static static static static static static
Energy-aware
No No No No Yes Yes Yes Yes Yes Yes
Storage
Low Low Medium High High High Low Low Low Medium
Position
awareness
No Hop counts
to anchor
nodes
Hop counts
to radius
Hop counts
to coordi-
nate node
Distance
to nano
controller
Distance
to nano
controller
Layer No Hop counts
to
center
node
Hop
Counts to
ClusterHe
ad
In Table I, we have compared each protocol according
to several factors, including the deployment space, the
network topology, routing path, computing capacity,
nanonode mobility, position awareness, energy-aware and
storage requirements. The conception and
implementation of routing protocols are considered
imperative in WNSN. This is because of nanonetworks’
nanodevices are usually constrained in their processing
capacity, communication range and energy capacity. In
any case, several design perspectives should be taken in
to consideration. In fact, energy is the foremost critical
and restricting factor in any areas of monitoring
applications by using WNSN. In that sense, routing
protocols, which optimize the energy consumption, while
satisfying diverse constraints, are expected to have an
incredible impact on the WNSN paradigm.
D. Channel and Physical Models
At the PHY/MAC layer, WNSN applications require
the investigation of the characteristics of the
communication inside the Terahertz band, specially the
path-loss, noise-loss, bandwidth and channel capacity.
The transmission channel capacity is depending on the
nature of the obstacles located between the nano-sender
and the nano-receiver. Since the transmitting range of
nanonodes is exceptionally restricted, dense arrangement
of nanomachines are required, combined with multi-hop
communication path. This makes the choice of channel
coding a complicated operation [88]. The attainable
bandwidth depends mainly on the molecular
characteristics existed in the channel, this make the
channel models for electromagnetic radio frequency
propagation challenging to model. This enabled more
Journal of Communications Vol. 16, No. 1, January 2021
©2021 Journal of Communications 14
research to provide other communication techniques (e.g.,
ultrasonic communication) in order to perform the
internetworking of nanomachines [89]. In [90], authors
presented and evaluated a recent MAC layer protocol
based on the joint selection by the transmitter and the
receiver of the communication parameters and the
channel coding scheme that minimizes the interference.
The proposed scheme, called PHLAME, maximizes the
probability of successfully decoding of received
information. On other hand, Rikhtegar et al. developed a
recent MAC layer protocol called Energy Efficient
Wireless Nano Sensor Network MAC layer protocol
(EEWNSN-MAC). The aim of the proposed scheme is to
optimize the power consumption in wireless
nanonetworks by taking the advantages of the clustering
strategy and TDMA planning scheme to reduce the
mobility impacts and transmission collisions [91].
Similarly, another mechanism was presented in [92]. The
presented energy and spectrum-aware MAC protocol take
advantage of hierarchical architecture of WNSN, so that
all nano sensors can directly communicate with nano-
routers through single-hop. Recently, Juan Xu et al.
presented a load-aware dynamic TDMA (LAD-TDMA)
protocol. This protocol is built on a novel pulsed-based
communication scheme, called TS-OOK, and avoids
symbol collisions to achieve high energy efficiency [93].
VI. CONCLUSION AND PERSPECTIVE
Wireless nano sensors have transformed classical
approaches for solving a vast array of problems,
especially in the healthcare, smart environment,
agriculture, and food safety domains. Their flexibility and
their broad range of applications are generating more and
more interest from the research community and they have
the potential of triggering the next revolution in
information nanotechnology. In this paper, we focus on
wireless nanosensors network paradigms. The
architecture, applications fields and challenges are
summarized and elaborated. We review and present the
hierarchical architecture and applications areas of
WNSNs, and challenges including routing algorithms and
data analysis. WNSNs will serve as a more intelligent and
nanoscale monitoring model to promote the development
of IoNT. This is a valuable area of research that will
influence future academics and industry researches.
However, the special characteristics of nanoscale
machines need to be unified for the design of WNSN
architectures, and the challenges of WNSN paradigms
need to be focused on the design of efficient data
dissemination algorithms and the integration of WNSN
with current internet of things microsystems and
networks.
CONFLICT OF INTEREST
The authors declare no conflict of interest.
AUTHOR CONTRIBUTIONS
This work is done by Ayoub Oukhatar and Dr.
Mohamed Bakhouya and Dr. Driss El Ouadghiri as
follows: The first author conducted the research and
wrote the paper under the second author supervision. The
third author provided guidance. All authors have read and
agreed to the published version of the manuscript.
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reproduction in any medium, provided that the article is
properly cited, the use is non-commercial and no modifications
or adaptations are made.
Ayoub Oukhatar received engineer
degree in Telecommunications and
Networks from National School of
Applied Sciences of Tetuan in 2014,
Abdelmalek Essadi University. He is
currently a Phd student at the Department
of computer sciences at Faculty of
sciences of Meknes, Moulay Ismail
University, Morocco. His research
Journal of Communications Vol. 16, No. 1, January 2021
©2021 Journal of Communications
18
interests include wireless sensor networks, channel and network
coding.
Mohamed Bakhouya is a professor of
computer science at the International
University of Rabat. He obtained his
HDR from UHA-France in 2013 and his
PhD from UTBM-France in 2005. He
has more than ten years experiences in
participating and working in sponsored
ICT projects. He was EiC of IJARAS
journal and also serves as a guest editor of a number of
international journals, e.g., ACM Trans. on Autonomous and
Adaptive Systems, Product Development Journal, Concurrency
and Computation: Practice and Experience, FGCS, and MICRO.
He has published more than 100 papers in international journals,
books, and conferences. His research interests include various
aspects related to the design, validation, and implementation of
distributed and adaptive systems, architectures, and protocols.
Driss El Ouadghiri is a full professor at
the Science Faculty in Moulay Ismail
University, Meknes, Morocco. He is also
leader of Computer Science and
Applications Laboratory and of
Advanced Technologies and Networks
research team. He was born in
Ouarzazate, Morocco. He got his PhD in
performance evaluation in wide area networks from Moulay
Ismail University, Meknes, Morocco. His research interests
focus on evaluation performance in networks (modelling and
simulation),
Journal of Communications Vol. 16, No. 1, January 2021
©2021 Journal of Communications
19
... Similarly, nanosensor applications require nanosensors to communicate with each other. Enabling communication will facilitate nanosensors to form an ad hoc network called wireless nanosensor network (WNSN) [14]. Recently proposed graphene-based nanoantennas can efficiently radiate in the 0.1-10 terahertz (THz) range [15]. ...
... Based on the energy generation capacity of a nanosensor, the total signal energy is kept constant and equal to 500 pJ [28]. The equation for pulse generation in time domain is described by Eq. (14). ...
... The table summarizes the channel capacity of different frequency bands identified for human body and air. It can (14) be observed that W1 gives the highest spatial capacity per hertz for all the scenarios because of its huge bandwidth. Windows W3 and W4 result in a poor performance because of the high MaT All these results are in total agreement with the results of frequency band selection where it has been argued that in human body, frequency bands other than W1 cannot be used, while for short range in air, almost all the bands can be used. ...
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... The harvesting of energy from the ambient environment is a better approach to replace the conventional batteries. Several energy-harvesting strategies have been employed using pyroelectric [1], thermoelectric [2], electrostatic [3], electromagnetic [4], triboelectric [5], and piezoelectric transduction mechanisms [6]. The electric power generated by pyroelectric and thermoelectric energy harvesters is based on thermal fluctuations and available temperature gradients whereas electrostatic, electromagnetic, and triboelectric harvesters generate electric power by varying capacitance, magnetic induction, and contact electrification, respectively. ...
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