[0001] The present disclosure relates to the wireless sensor network. In particular, the
present disclosure provides a system and method for assessing vulnerability of wireless sensor
network.
BACKGROUND
[0002] The background description includes information that may be useful in
understanding the present invention. It is not an admission that any of the information provided
herein is prior art or relevant to the presently claimed invention, or that any publication
specifically or implicitly referenced is prior art.
[0003] Wireless Sensor Networks, or, WSNs are the self-configured and infrastructureless wireless networks, which can be utilized for monitoring physical or environmental
conditions, such as temperature, sound, vibration, pressure, motion or pollutants. Sensor nodes of
a WSN work cooperatively to pass their data through the network to a specific location, or a
sink, or a base station that acts like an interface between users and the network. The data can be
analyzed or observed at the sink or base station. Typically a wireless sensor network contains
hundreds of thousands of sensor nodes. The sensor nodes may communicate among themselves
using radio signals. Each of the sensor nodes of the WSN is incorporated with transceivers,
sensing and computing modules, and power supply and management units.
[0004] Wireless Sensor Networks (WSN) are used in many industrial applications and
they are a key ingredient in the growing Internet of Things (IoT). WSNs are widely used in
agricultural sensors related systems, alarm systems, arena lightning, asset tracking, energy
management systems, traffic monitoring, and the like.
[0005] But, the WSNs are prone to cyber-attacks such as spoofing attack, passive attack
such as eavesdropping, and active attack such as packet delayed, packet dropped, and packet
tampering, which leads to malfunctioning of the whole system.
3
[0006] There is, therefore, a need in the art to provide an effective and accurate system to
overcome the above mentioned problems, and provide a means for hassle-free data flow, and
providing/ ensuring an attack-proof network/ system.
OBJECTS OF THE PRESENT DISCLOSURE
[0007] Some of the objects of the present disclosure, which at least one embodiment
herein satisfies are as listed herein below.
[0008] It is an object of the present disclosure to provide a system and method for
assessing traffic flow among one or more nodes of a network.
[0009] It is another object of the present disclosure to provide a system and method for
determining vulnerability status of a network, or, a part of the network.
[0010] It is an object of the present disclosure to provide a system and method for
enabling emulation of one or more nodes of a network.
[0011] It is another object of the present disclosure to provide and method for sending a
warning, in case a network, or, a part of the network is determined to be vulnerable, to a
government authority, or a corresponding person.
[0012] It is another object of the present disclosure to provide an accurate, fast, efficient,
cost effective and simple network vulnerability assessment system.
[0013] These and other objects of the present invention will become readily apparent
from the following detailed description taken in conjunction with the accompanying drawings.
SUMMARY
[0014] The present disclosure relates to the wireless sensor network. In particular, the
present disclosure provides a system and method for assessing vulnerability of wireless sensor
network.
[0015] An aspect of the present disclosure pertains to amethod for assessing vulnerability
of a wireless sensor network, said method comprising: maintaining, at one or more processors of
a processing unit, a predetermined transmission information associated with a first set of data
packets flowing across one or more nodes selected from a plurality of nodes of the network;
extracting, at the one or more processors, a second set of data packets from the first set of data
packets, where the second set of data packets corresponds to a data packet transmission
4
information across the one or more nodes among the plurality of nodes; comparing, at the one or
more processors, the extracted second set of data packets with the maintained predetermined
transmission information of the first set of data packets to determine a set of traffic parameters,
the set of traffic parameters being an aggregation of the transmission information associated with
the second set of data packets, associated with the received second set of data packets; and in
response to the determined set of traffic parameters, determining, at the one or more processors,
a corresponding vulnerability status of the network.
[0016] In an aspect, wherein the transmission information may comprise any or a
combination of rate of dataflow, number of data packets transmitted, number of data packets
dropped, number of data packets received at the one or more nodes, throughput, delay, and
packet delivery ratio.
[0017] In an aspect, in the event of the determined set of traffic parameters matching
with a predetermined security threshold, the vulnerability status of the network may be
determined to be non-vulnerable.
[0018] In an aspect, in the event of the determined set of traffic parameters being beyond
the predetermined security threshold, the vulnerability status of the network may be determined
to be vulnerable.
[0019] In an aspect, the method may comprise a step of updating, at the one or more
processors, based on training-and-testing data packets during the assessment, the maintained
predetermined transmission information.
[0020] In an aspect, the method may comprise a step of emulating, at the one or more
processors, at least one of the one or more nodes, and wherein emulation of the at least one of the
one or more nodes may be performed by extracting nodal attributes of the corresponding nodes.
[0021] In an aspect, the nodal attributes may be any or a combination of identity (ID),
internet protocol, and location of the one or more nodes, and distance and channels between the
one or more nodes.
[0022] In an aspect, the method may comprise a step of representing, at a display unit,
through any or a combination of Augmented Reality (AR) view, Virtual Reality (VR) view, two
dimensional (2D) view, and three dimensional (3D) view, the at least one emulated node.
[0023] Another aspect of the present disclosure pertains to anetwork vulnerability
assessment system comprising: one or more processors, and a memory coupled to the one or
5
more processors comprising computer readable program code embodied in the memory that is
executable by the one or more processors to: maintain, at the network, a predetermined
transmission information associated with flow of set of data packets among a plurality of nodes
of the network; extract, from one or more nodes among the plurality of nodes, a second set of
data packets from a first set of data packets across one or more nodes selected from the plurality
of nodes of the network, where the second set of data packets corresponds to a data packet
transmission information across the one or more nodes; compare the extracted second set of data
packets with the maintained predetermined transmission information associated with the flow of
set of data packets to determine a set of traffic parameters, the set of traffic parameters being an
aggregation of the transmission information associated with the second set of data packets,
associated with the received second set of data packets; and in response to the determined set of
traffic parameters, determine, at the one or more processors, a corresponding vulnerability status
of the network.
[0024] In an aspect, the one or more nodes may be any or a combination of sink node,
source node, and cluster head node.
BRIEF DESCRIPTION OF THE DRAWINGS
[0025] The accompanying drawings are included to provide a further understanding of
the present disclosure, and are incorporated in and constitute a part of this specification. The
drawings illustrate exemplary embodiments of the present disclosure and, together with the
description, serve to explain the principles of the present disclosure.
[0026] The diagrams are for illustration only, which thus is not a limitation of the present
disclosure, and wherein:
[0027] FIG.1 illustrates exemplary network architecture of the proposed system to
illustrate its overall working in accordance with an embodiment of the present disclosure.
[0028] FIG. 2 illustrates exemplary engines of a processing unit in accordance with an
exemplary embodiment of the present disclosure.
[0029] FIGs. 3A-3D illustrate exemplary representations of the proposed system in
accordance with an embodiment of the present invention.
[0030] FIG. 4 illustrates a method to elaborate working of the proposed system in
accordance with an exemplary embodiment of the present disclosure.
6
[0031] FIG. 5 illustrates an exemplary computer system in which or with which
embodiments of the present invention can be utilized in accordance with embodiments of the
present disclosure.
DETAILED DESCRIPTION
[0032] In the following description, numerous specific details are set forth in order to
provide a thorough understanding of embodiments of the present invention. It will be apparent to
one skilled in the art that embodiments of the present invention may be practiced without some
of these specific details.
[0033] Embodiments of the present invention may be provided as a computer program
product, which may include a machine-readable storage medium tangibly embodying thereon
instructions, which may be used to program a computer (or other electronic devices) to perform a
process. The machine-readable medium may include, but is not limited to, fixed (hard) drives,
magnetic tape, floppy diskettes, optical disks, compact disc read-only memories (CD-ROMs),
and magneto-optical disks, semiconductor memories, such as ROMs, PROMs, random access
memories (RAMs), programmable read-only memories (PROMs), erasable PROMs (EPROMs),
electrically erasable PROMs (EEPROMs), flash memory, magnetic or optical cards, or other
type of media/machine-readable medium suitable for storing electronic instructions (e.g.,
computer programming code, such as software or firmware).
[0034] Various methods described herein may be practiced by combining one or more
machine-readable storage media containing the code according to the present invention with
appropriate standard computer hardware to execute the code contained therein. An apparatus for
practicing various embodiments of the present invention may involve one or more computers (or
one or more processors within a single computer) and storage systems containing or having
network access to computer program(s) coded in accordance with various methods described
herein, and the method steps of the invention could be accomplished by engine s, routines,
subroutines, or subparts of a computer program product.
[0035] If the specification states a component or feature “may”, “can”, “could”, or
“might” be included or have a characteristic, that particular component or feature is not required
to be included or have the characteristic.
7
[0036] As used in the description herein and throughout the claims that follow, the
meaning of “a,” “an,” and “the” includes plural reference unless the context clearly dictates
otherwise. Also, as used in the description herein, the meaning of “in” includes “in” and “on”
unless the context clearly dictates otherwise.
[0037] The recitation of ranges of values herein is merely intended to serve as a
shorthand method of referring individually to each separate value falling within the range. Unless
otherwise indicated herein, each individual value is incorporated into the specification as if it
were individually recited herein. All methods described herein can be performed in any suitable
order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of
any and all examples, or exemplary language (e.g. “such as”) provided with respect to certain
embodiments herein is intended merely to better illuminate the invention and does not pose a
limitation on the scope of the invention otherwise claimed. No language in the specification
should be construed as indicating any non-claimed element essential to the practice of the
invention.
[0038] Groupings of alternative elements or embodiments of the invention disclosed
herein are not to be construed as limitations. Each group member can be referred to and claimed
individually or in any combination with other members of the group or other elements found
herein. One or more members of a group can be included in, or deleted from, a group for reasons
of convenience and/or patentability. When any such inclusion or deletion occurs, the
specification is herein deemed to contain the group as modified thus fulfilling the written
description of all groups used in the appended claims.
[0039] Exemplary embodiments will now be described more fully hereinafter with
reference to the accompanying drawings, in which exemplary embodiments are shown. This
invention may, however, be embodied in many different forms and should not be construed as
limited to the embodiments set forth herein. These embodiments are provided so that this
disclosure will be thorough and complete and will fully convey the scope of the invention to
those of ordinary skill in the art. Moreover, all statements herein reciting embodiments of the
invention, as well as specific examples thereof, are intended to encompass both structural and
functional equivalents thereof. Additionally, it is intended that such equivalents include both
currently known equivalents as well as equivalents developed in the future (i.e., any elements
developed that perform the same function, regardless of structure).
8
[0040] The present disclosure relates to the wireless sensor network. In particular, the
present disclosure provides a system and method for assessing vulnerability of wireless sensor
network.
[0041] According to an aspect the present disclosure pertains to amethod for assessing
vulnerability of a wireless sensor network, said method including: maintaining, at one or more
processors of a processing unit, a predetermined transmission information associated with a first
set of data packets flowing across one or more nodes selected from a plurality of nodes of the
network; extracting, at the one or more processors, a second set of data packets from the first set
of data packets, where the second set of data packets corresponds to a data packet transmission
information across the one or more nodes among the plurality of nodes; comparing, at the one or
more processors, the extracted second set of data packets with the maintained predetermined
transmission information of the first set of data packets to determine a set of traffic parameters,
the set of traffic parameters being an aggregation of the transmission information associated with
the second set of data packets, associated with the received second set of data packets; and in
response to the determined set of traffic parameters, determining, at the one or more processors,
a corresponding vulnerability status of the network.
[0042] In an embodiment, the transmission information can include any or a combination
of rate of dataflow, number of data packets transmitted, number of data packets dropped, number
of data packets received at the one or more nodes, throughput, delay, and packet delivery ratio.
[0043] In an embodiment, in the event of the determined set of traffic parameters
matching with a predetermined security threshold, the vulnerability status of the network can be
determined to be non-vulnerable.
[0044] In an embodiment, in the event of the determined set of traffic parameters being
beyond the predetermined security threshold, the vulnerability status of the network can be
determined to be vulnerable.
[0045] In an embodiment, the method can include a step of updating, at the one or more
processors, based on training-and-testing data packets during the assessment, the maintained
predetermined transmission information.
[0046] In an embodiment, the method can include a step of emulating, at the one or more
processors, at least one of the one or more nodes, and wherein emulation of the at least one of the
one or more nodes can be performed by extracting nodal attributes of the corresponding nodes.
9
[0047] In an embodiment, the nodal attributes can be any or a combination of identity
(ID), internet protocol, and location of the one or more nodes, and distance and channels between
the one or more nodes.
[0048] In an embodiment, the method can include a step of representing, at a display
unit, through any or a combination of Augmented Reality (AR) view, Virtual Reality (VR) view,
two dimensional (2D) view, and three dimensional (3D) view, the at least one emulated node.
[0049] According to another aspect the present disclosure pertains to anetwork
vulnerability assessment system can be including: one or more processors, and a memory
coupled to the one or more processors can be including computer readable program code
embodied in the memory that is executable by the one or more processors to: maintain, at the
network, a predetermined transmission information associated with flow of set of data packets
among a plurality of nodes of the network; extract, from one or more nodes among the plurality
of nodes, a second set of data packets from a first set of data packets across one or more nodes
selected from the plurality of nodes of the network, where the second set of data packets
corresponds to a data packet transmission information across the one or more nodes; compare the
extracted second set of data packets with the maintained predetermined transmission information
associated with the flow of set of data packets to determine a set of traffic parameters, the set of
traffic parameters being an aggregation of the transmission information associated with the
second set of data packets, associated with the received second set of data packets; and in
response to the determined set of traffic parameters, determine, at the one or more processors, a
corresponding vulnerability status of the network.
[0050] In an embodiment, the one or more nodes can be any or a combination of sink
node, source node, and cluster head.
[0051] FIG.1 illustrates exemplary network architecture of the proposed system to
illustrate its overall working in accordance with an embodiment of the present disclosure.
[0052] According to an embodiment of the present disclosure a network vulnerability
assessment system 100 (also referred to as the system 100, hereinafter) can detect traffic and
correspondingly assess vulnerability of a Wireless Sensor Network (also, referred to as WSN,
herein). As illustrated, in an embodiment, the system 100 can include an processing unit 102
that can be communicatively coupled with one or more sensor nodes 106-1, 106-2,.., 106-N
(also, individually referred to as node 106, and collectively referred to as a plurality of nodes
10
106, or nodes 106, hereinafter) through a network 104. In an embodiment, the nodes 106 can be
any or a combination of sink node, source node, cluster head node, and the likes. In another
embodiment, each of the nodes 106 can be linked with other nodes 106, through a wireless
communication module, or the network 104, which can be coupled to a server 108. In an
embodiment, the system 100 can be implemented using any or a combination of hardware
components and software components such as a cloud, a server, a computing system, a
computing device, a network device and the like. Further, in an embodiment, the processing unit
102 can interact with the nodes 106 through a website or an application that can reside in the
nodes 106. In an implementation, the system 100 can be accessed by website or application that
can be configured with any operating system, including but not limited to, AndroidTM, iOSTM
,
and the like. Examples of the nodes 106can include, but are not limited to, a computing device, a
smart phone, a portable computer, a personal digital assistant, a handheld device and the like.
[0053] Further, the network 104 can be a wireless network, a wired network or a
combination thereof that can be implemented as one of the different types of networks, such as
Intranet, Local Area Network (LAN), Wide Area Network (WAN), Internet, and the like.
Further, the network 104 can either be a dedicated network or a shared network. The shared
network can represent an association of the different types of networks that can use variety of
protocols, for example, Hypertext Transfer Protocol (HTTP), Transmission Control
Protocol/Internet Protocol (TCP/IP), Wireless Application Protocol (WAP), and the like.
[0054] In an embodiment, the system 100 can enable maintaining, at the network, a
predetermined transmission information associated with flow of set of data packets among a
plurality of nodes of the network. In an embodiment, configuration of the plurality of nodes,
constituting the network, can be based on various distribution techniques, such as, but not limited
to, uniformdistribution, normaldistribution, exponentialdistribution, binomialdistribution, and
poissondistribution.
[0055] In an embodiment, the processing unit 102 can detect and process a first set of
data packets flowing across one or more nodes 106, where the one or more nodes 106 can be
selected from the plurality of nodes 106 constituting the network. In another embodiment, the
processing unit 102 can extract, through the one or more nodes 106, a second set of data packets
from the first set of data packets flowing across the one or more nodes 106, where the second set
of data packets can correspond to a data packet transmission information across the one or more
11
nodes 106. In an embodiment, the transmission information can include any or a combination of
rate of dataflow, number of data packets transmitted, number of data packets dropped, number of
data packets received at the one or more sensor nodes, throughput, delay, and packet delivery
ratio.
[0056] In an embodiment, a comparison, can be performed at the processing unit 102, of
the extracted second set of data packets with the maintained predetermined transmission
information associated with the flow of set of data packet, where a set of traffic parameters
associated with the extracted second set of data packet scan be determined based on the
comparison performed. In an embodiment, in response to the determined set of traffic
parameters, a corresponding vulnerability status of the network, or a part of the network, can also
be determined.
[0057] In an embodiment, the vulnerability status of the network can be determined to be
non-vulnerable, when the determined set of traffic parameters match with a predetermined
security threshold. In another embodiment, the vulnerability status of the network can be
determined to be vulnerable, when the determined set of traffic parameters are beyond the
predetermined security threshold.
[0058] According to various embodiments of the present disclosure, the system 100 can
be updated,based on training-and-testing data packets during the assessment, and,
correspondingly updation of the maintained predetermined transmission information of the first
set of data packets can take place. In an embodiment, any or a combination of the predetermined
transmission information, and the pre-determined security threshold, can be configured
manually, or, can be updated based on the updation of the system 100, or, can be estimated
throughanalytics approaches using Artificial Intelligence/Deep Learning/ Machine Learning
techniques such as neural network, convolutional neural network, Keras, Tensor Flow, and the
like that can be based on programming languages such as PHP, Python, HTML, Django, Angular
JS, etc.
[0059] In an embodiment, at least one of the plurality of nodes 106 can be emulated at
the processing unit 102, where the emulation of the at least one of the plurality of nodes 106 can
be performed by extracting nodal attributes of the at least one of the plurality of nodes 106. The
nodal attributes of the nodes 106 can be including any or a combination of identity (ID), internet
12
protocol, and location of the one or more sensor nodes, distance and channels between the nodes
106, and the likes.
[0060] In an embodiment, the system 100 can include a display unit 110. The display unit
110 can be operatively coupled to the processing unit 102 to process and display one or more
executions being performed, and instructions being obtained at the processing unit 102, where
the display unit 110 can be any or a combination of a computer screen, a laptop screen, screen of
a smart phone, a handheld device, and the likes.
[0061] In an embodiment, the display unit 110 can be configured to represent the at least
one emulated node 106 through any or a combination of Augmented Reality (AR) view, Virtual
Reality (VR) view, two dimensional (2D) view, and three dimensional (3D) view, and the
likes.In another embodiment, the display unit 110 can be configured to represent, through/
towards the at least one emulated node 106, the flow of the first set of data packets, the flow of
the second set of data packets, corresponding transmission information, vulnerability status of
the network and the likes.
[0062] In an illustrative implementation, transmission information associated with flow
of first set of data packets between a first node 106-1 and a second node 106-2 can differ from
transmission information associated with flow of first set of data packets between the second
node 106-2 and a third node 106-3. Moreover, in another illustrative implementation,
transmission information associated with flow of first set of data packets between the first node
106-1 and the second node 106-2 can vary with varying time-periods in a day. For example, a
first transmission information associated with flow of first set of data packets between the first
node 106-1 and the second node 106-2 during working hours, let’s say from 9 am to 5 pm can
differ from a second transmission information associated with flow of first data packets between
the first node 106-1 and the second node 106-2 during non-working hours, let’s say from 6 pm to
8 am. In yet another illustrative embodiment, the predetermined transmission information
associated with flow of set of data packets among the plurality of nodes 106, which is maintained
at the network, can be configured accordingly, and, transmission information associated with
flow of first set of data packets between the first node 106-1 and the second node 106-2, or the
second node 106-2 and the third node 106-3 can be compared, respectively, withthe
predetermined transmission information. In an embodiment, thepredetermined security threshold
can also be configured, manually, or, automatically, through testing-and-training procedure, in a
13
similar manner to comply with variations in the transmission information associated with the
flow of data packets in the network, throughout a day, and also, during various days.
[0063] In an illustrative embodiment, a first set of traffic parameters can be determined
for the first node 106-1 and a second set of traffic parameters can be determined for the second
node 106-2 based on any or a combination of the first transmission information, the second
transmission information, and the likes. In another illustrative embodiment, the determined first
set of traffic parameters and the determined first set of traffic parameters can be compared with
the predetermined security threshold. In an embodiment, the vulnerability status of the network,
or a part of the network, can be determined to be non-vulnerable, when the determined set of
traffic parameters match with the predetermined security threshold. In another embodiment, the
vulnerability status of the network, or a part of the network, can be determined to be vulnerable,
when the determined set of traffic parameters are beyond the predetermined security threshold.
[0064] In an illustrative implementation, when the network, or a part of the network is
found to be vulnerable, the system 100 is configured to identify problem and generate
corresponding data packets, which can be processed to be displayed at the display unit 110, in
coherence with the emulated view of the corresponding nodes 106, to represent the identified
problem. For example, in case, the first set of data packets are being transmitted from the first
node 106-1 to the third node 106-3, where the first set of data packets are passing through the
second node 106-2, and, if a delay is detected in the flow of the first set of data packets from the
second node 106-2 to the third node 106-3, then, normal transmission of the first set of data
packets can be represented at the display unit 110, whereas, a delay in the transmission of whole,
or, partial of the first set of data packets, as the case may be, can be seen, at the display unit 110,
between the second node 106-2 and the third node 106-3.
[0065] In an embodiment, the processing unit 102 can be configured to send a warning,
in case a network, or, a part of the network is determined to be vulnerable, to a government
authority, or a corresponding person.
[0066] FIG. 2 illustrates exemplary engines of a processing unit in accordance with an
exemplary embodiment of the present disclosure.
[0067] As illustrated, the processing unit 102 can include one or more processor(s) 202.
The one or more processor(s) 202 can be implemented as one or more microprocessors,
microcomputers, microcontrollers, digital signal processors, central processing units, logic
14
circuitries, and/or any devices that manipulate data based on operational instructions. Among
other capabilities, the one or more processor(s) 202 are configured to fetch and execute
computer-readable instructions stored in a memory 204 of the processing unit 102. The memory
204 can store one or more computer-readable instructions or routines, which may be fetched and
executed to create or share the data units over a network service. The memory 204 can include
any non-transitory storage device including, for example, volatile memory such as RAM, or nonvolatile memory such as EPROM, flash memory, and the like.
[0068] In an embodiment, the processing unit 102 can also include an interface(s) 206.
The interface(s) 206 may include a variety of interfaces, for example, interfaces for data input
and output devices, referred to as I/O devices, storage devices, and the like. The interface(s) 206
may facilitate communication of the processing unit 102 with various nodes and devices coupled
to the processing unit 102. The interface(s) 206 may also provide a communication pathway for
one or more components of the processing unit 102. Examples of such components include, but
are not limited to, processing engine(s) 208 and data 210.
[0069] In an embodiment, the processing engine(s) 208 can be implemented as a
combination of hardware and programming (for example, programmable instructions) to
implement one or more functionalities of the processing engine(s) 208. In examples described
herein, such combinations of hardware and programming may be implemented in several
different ways. For example, the programming for the processing engine(s) 208 may be
processor executable instructions stored on a non-transitory machine-readable storage medium
and the hardware for the processing engine(s) 208 may include a processing resource (for
example, one or more processors), to execute such instructions. In the present examples, the
machine-readable storage medium may store instructions that, when executed by the processing
resource, implement the processing engine(s) 208. In such examples, the processing unit 102 can
include the machine-readable storage medium storing the instructions and the processing
resource to execute the instructions, or the machine-readable storage medium may be separate
but accessible to processing unit 102 and the processing resource. In other examples, the
processing engine(s) 208 may be implemented by electronic circuitry. The data 210 can include
data that is either stored or generated as a result of functionalities implemented by any of the
components of the processing engine(s) 208.
15
[0070] In an embodiment, the processing engine(s) 208 can include an extraction engine
212, an assessment engine 214, an emulation engine 216, a configurationengine218, and other
engine(s) 220. The other engine(s) 220 can implement functionalities that supplement
applications or functions performed by the processing unit 102 or the processing engine(s) 208.
[0071] In an embodiment, the extraction engine 212 of the processing unit 102 can
facilitate extraction of a second set of data packets from a first set of data packets, where the first
set of data packets can be flowing across the one or more nodes 106, and where the second set of
data packets can correspond to a data packet transmission information across the one or more
nodes 106 through which the first set of data packets can be flowing. In an embodiment, the
transmission information can include any or a combination of rate of dataflow, number of data
packets transmitted, number of data packets dropped, number of data packets received at the one
or more sensor nodes, throughput, delay, and packet delivery ratio.
[0072] In another embodiment, the extraction engine 212 can enable extraction of nodal
attributes of at least one of the plurality of nodes 106. The nodal attributes of the nodes 106 can
be including any or a combination of identity (ID), internet protocol, and location of the one or
more sensor nodes, distance and channels between the nodes 106, and the likes. In an illustrative
embodiment, the extracted nodal attributes of at least one of the plurality of nodes 106 can be
utilized for emulation of the at least one of the plurality of nodes 106.
[0073] In an embodiment, the assessment engine 214 of the processing unit 102 can
facilitate determination of a set of traffic parameters associated with the extracted second set of
data packets. In an embodiment, a comparison, of the extracted second set of data packets with
the maintained predetermined transmission information associated with the flow of set of data
packet, can be performed, which, further, can aid indetermining theset of traffic parameters. In
an illustrative implementation, the set of traffic parameters can be evaluated by aggregating the
transmission information associated with the second set of data packets.
[0074] In another embodiment, the assessment engine 214 can facilitate determination of
vulnerability status of the network, or a part of the network, corresponding to the determined set
of traffic parameters. In an illustrative embodiment, the vulnerability status of the network can be
determined to be non-vulnerable, when the determined set of traffic parameters match with a
predetermined security threshold. In another embodiment, the vulnerability status of the network
16
can be determined to be vulnerable, when the determined set of traffic parameters are beyond the
predetermined security threshold.
[0075] In an embodiment, the assessment engine 214 can facilitate assessment of the
network, or a part of the network, through watch dog mechanism. The watch dog mechanism can
be utilized for detection of the nodes 106 that are misbehaving, which can be done by
maintaining a buffer that can include data packets that are recently sent by the network, or a part
of the network. When the data packets are forwarded by the fist node 106-1, the watchdog
mechanism ensures that the second node 106-2, which is next to the fist node 106-1, in a channel
associated with the flow of the data packets, also forwards the data packets. The watchdog
mechanism can do so by listening all the nodes 106 associated with the channel. If the second
node 106-2 does not forward the data packet, then, the second node 106-2 can be termed as
misbehaving. In other words, in the mechanism, every data packet that is overheard by the
watchdog can be compared with the data packet in the buffer. In case, the data packet that is
overheard by the watchdog matches with the data packet in the buffer match, it can confirm that
the data packet has been successfully delivered, and the data packet in the buffer can be
removed. If the data packet remains in the buffer beyond a timeout period, then a failure counter
for any or a combination of the first node 106-1, and the second node 106-2, responsible for
forwarding the packet is incremented. If failure counter exceeds a predetermined security
threshold then the node is termed as misbehaving/malicious and the network is informed
accordingly by generating corresponding data packets, which can be sent by a node 106 that
detects the misbehaving node 106 and/ or related issue.
[0076] In an illustrative implementation, in case, the first set of data packets are being
transmitted from the first node 106-1 to the third node 106-3, where the first set of data packets
are passing through the second node 106-2, and, if some of the first set of data packets are being
dropped during the flow of the first set of data packets from the first node 106-1 to the second
node 106-2, which results in a first set of traffic parameters, which is beyond the predetermined
security threshold, in such case, the assessment engine 214 can determine the part of the
network, between the first node 106-1 and the second node 106-2, to be vulnerable, and generate
corresponding data packets.
[0077] In another illustrative implementation, the assessment engine 214 can determine,
in case the network, or a part of the network is found to be vulnerable, an extent of vulnerability,
17
based on the extent of deviation of the determined set of traffic parameters from the
predetermined security threshold.
[0078] In an embodiment, the emulation engine 216 of the processing unit 102 can
enable emulation of theat least one of the plurality of nodes 106. The at least one of the plurality
of nodes 106 can be emulated based on the extracted nodal attributes of the at least one of the
plurality of nodes 106. In an embodiment, the emulated view,of the at least one of the plurality of
nodes 106, can be displayed at a display unit 110, where the display unit 110 can be any or a
combination of a computer screen, a laptop screen, screen of a smart phone, a handheld device,
and the likes. In an embodiment, the display unit 110 can be configured to represent the at least
one emulated node 106, based on an emulation technique, in any or a combination of Augmented
Reality (AR) view, Virtual Reality (VR) view, two dimensional (2D) view, and three
dimensional (3D) view, and the likes.In another embodiment, the display unit 110 can be
configured to represent, through/ towards the at least one emulated node 106, the flow of the first
data packets, the flow of the second data packets, corresponding transmission information,
vulnerability status of the network and the likes.
[0079] In an illustrative implementation, for emulating, theat least one of the plurality of
nodes 106, the extracted nodal attributes of the at least one of the plurality of nodes 106 can be
compared with an emulation dataset. The emulation of the at least one of the plurality of nodes
106 can be performed based on the comparison of the extracted nodal attributes with the
emulation dataset. The emulation dataset can include nodal attributes associated with multiple
models of nodes. The emulation of the at least one of the plurality of nodes 106 can be done
based on matching of nodal attributes of the at least one of the plurality of nodes 106 with nodal
attributes of at least one model of the multiple models of nodes associated with the emulation
dataset. In an embodiment, the emulation dataset can be associated with the data 210 of the
processing unit 102. In another embodiment, the emulation dataset can be acquired from a third
source. In an embodiment, in case the extracted nodal attributes of the at least one of the plurality
of nodes 106 do not match with the emulated dataset, the nodal attributes of the at least one of
the plurality of nodes 106 is required to be extracted again. Also, in case of, negative comparison
of the extracted nodal attributes of the at least one of the plurality of nodes 106 with the emulated
dataset can symbolize that the emulation dataset do not include nodal attributes of the at least one
of the plurality of nodes 106, and the emulation dataset is needed to be updated.
18
[0080] In an illustrative implementation, when the part of the network, between the first
node 106-1 and the second node 106-2, is determined to be vulnerable. The corresponding data
packets are, then, processed to display at the display unit 110, in coherence with the emulated
view of the first node 106-1 and the second node 106-2, to represent the corresponding drop in
some of the first set of data packets, while transmission of the first set of data packets from the
first node 106-1 to the second node 106-2.
[0081] In an embodiment, the configuration engine 218 of the processing unit 102 can
facilitate configuration of any or a combination of the predetermined transmission information
associated with flow of set of data packets among the plurality of nodes 106, the predetermined
security threshold, the emulation dataset, and the likes. In an illustrative embodiment, the
configuration can be done manually, by assessing the processing unit 102, through an
interrogation process. In another illustrative embodiment, the configuration can be performed
automatically, through testing-and-training techniques. In the testing-and-training techniques,the
system 100 can be updated based on training-and-testing data packets during the assessment,
and, correspondingly updation of the maintained predetermined transmission information of the
first set of data packets can be performed. In an embodiment, the configuration can be performed
automatically based on the updation of the system 100, or, can be estimated throughanalytics
approaches using Artificial Intelligence/Deep Learning/ Machine Learning techniques such as
neural network, convolutional neural network, Keras, TensorFlow, and the likes.
[0082] FIGs. 3A-3D illustrate exemplary representations of the proposed system in
accordance with an embodiment of the present invention.
[0083] As illustrated, in an embodiment, in FIG. 3A, the system 100 can be distinguished
into two parts based on functions of the system 100, where a first part 350 of the system 100 can
perform traffic analysis function, and a second part 360 of the system 100 can perform
vulnerability assessment, and where, both of the first part 350 of the system 100 and the second
part 360 of the system 100 can function simultaneously as well as coherently.
[0084] In an embodiment, the first part 350 of the system 100, assigned for traffic
analysis in a network, or a part of the network, can perform deployment analysis at block 302. At
the block 302, deployment/ configuration of nodes 106 of the network, or a part of the network,
can be analysed. In an illustrative embodiment, the nodes 106 of the network, or a part of the
19
network, can be configured based on various distribution techniques, such as, but not limited to,
uniform, normal, exponential, binomial, and poisson distribution.
[0085] In an embodiment, at block 304 network emulation can be performed. In an
embodiment, the first part 350 can be configured to emulate the network, or a part of the
network, corresponding to the deployment of the nodes 106 that is being analysed at the block
302. In an illustrative implementation, the network can be emulated through NS 3.5 network
simulator. In an illustrative implementation, the network can be emulated through various
techniques, such as, but not limited to, area, traffic type, and radio propagation model.
[0086] In an embodiment, at block 304 traffic assessment can be done. In an
embodiment, the first part 350 can be configured to monitor the traffic in the network, or a part
of the network, which can be assessed and aided through monitoring of the traffic at a block 308,
maintaining activity database at a block 320 and ascertaining data flow optimization at a block
310.In an illustrative embodiment, at the block 308, traffic flowing through the network, or a part
of the network, can be passively scanned, and, simultaneously, the log of the traffic and related
activities can be maintained and updated at the block 320. At the block 310, data flow
optimization can be ascertained, by monitoring the data flow rate using machine learning
techniques.
[0087] In an embodiment, process of traffic analysis at the first part 350 of the system
100 can result in a decrease in the risk of data packet drop by evaluating the optimum rate at
which the data packet is to be traversed.
[0088] In an embodiment, the second part 360 of the system 100 can perform statistical
evaluation at a block 330, vulnerability assessment at a block 332. The process of the statistical
evaluation at the block 330, and the vulnerability assessment at the block 332 can be aided
through maintaining a potential vulnerability log at a block 336, and updating the system 100
through machine learning at a block 334. In an illustrative embodiment, the maintenance and
updating of the potential vulnerability log at the block 336 can be based on the block 320 related
to the maintaining of activity database. In an illustrative embodiment, updating the system 100
through machine learning at the block 334 can play a vital role in data flow prediction and
optimization.
[0089] In an illustrative implementation, the vulnerability assessment, at the block 332,
of the network, or a part of the network, can be performed through watchdog mechanism for
20
analysing behaviour of the network, or a part of the network. In an embodiment, the watch dog
mechanism is a mechanism utilized for detection of the nodes 106 that are misbehaving, which
can be done by maintaining a buffer that can include data packets that are recently sent by the
network, or a part of the network. When the data packets are forwarded by the fist node 106-1,
the watchdog mechanism ensures that the second node 106-2, which is next to the fist node 106-
1, in a channel associated with the flow of the data packets, also forwards the data packets. The
watchdog mechanism can do so by listening all the nodes 106 associated with the channel. If the
second node 106-2 does not forward the data packet, then, the second node 106-2 can be termed
as misbehaving. In other words, in the mechanism, every data packet that is overheard by the
watchdog can be compared with the data packet in the buffer. In case, the data packet that is
overheard by the watchdog matches with the data packet in the buffer match, it can confirm that
the data packet has been successfully delivered, and the data packet in the buffer can be
removed. If the data packet remains in the buffer beyond a timeout period, then a failure counter
for any or a combination of the first node 106-1, and the second node 106-2, responsible for
forwarding the packet is incremented. If failure counter exceeds a predetermined security
threshold then the node is termed as misbehaving/malicious and the network is informed
accordingly by generating corresponding data packets, which can be sent by a node 106 that
detects the misbehaving node 106 and/ or related issue.
[0090] As illustrated, in an embodiment, in FIGs. 3B-3D, the nodes 106 associated with
the system 100 can include any or a combination of sink node 106, source node 106, and cluster
head node 106. The system 100 can be configured to detect a spoofing attack, such as, man-inthe-middle (also, referred to as MITM, herein) attack. In the MITM attack, an attacker, which
can be a pseudo node, or a misbehaving/ or malicious node, which can be present, or attached,
between any or a combination of the sink node 106, the source node 106, and the cluster head
node 106. In an embodiment, FIG. 3B illustrates normal flow of data packets through a network
which includes a first node 106-1, a second node 106-2, a third node 106-4, a cluster head node
106-4, and a sink node 106-5.
[0091] In another embodiment, FIGs. 3C-3D illustrate, in case of the MITM attack, flow
of data packets in the network when an attacker is present at one or more positions in the
network. In an illustrative embodiment, as illustrated in FIG. 3C, during the MITM attack, the
attacker can be present at a first position between the second node 106-2, and the cluster head
21
node 106-4. In another illustrative embodiment, as illustrated in FIG. 3D, the MITM attack, the
attacker can be present at a second position between the cluster head node 106-4 and the sink
node 106-5. In an embodiment, the attacker can be present at both, the first position and the
second position, simultaneously. In an embodiment, the system 100 can detect the MITM attack
at any or a combination of the first position and the second position, through the traffic analysis
that is being performed at the first part 350, and coherently, assessing the vulnerability of the
network at the second part 360.
[0092] FIG. 4 illustrates a method to elaborate working of the proposed system in
accordance with an exemplary embodiment of the present disclosure.
[0093] As illustrated in FIG. 4, in an embodiment, amethod for assessing vulnerability of
a wireless sensor network is disclosed. In an embodiment, the proposed method can include a
step 402 ofmaintaining, at one or more processors of a processing unit 102, a predetermined
transmission information, which can be associated with a first set of data packets flowing across
one or more nodes 106 selected from a plurality of nodes 106 of the network.
[0094] In an embodiment, the proposed method can include a step 404 of extracting, at
the one or more processors, a second set of data packets from the first set of data packets whose
predetermined transmission information is maintained at step 402, where the second set of data
packets corresponds to a data packet transmission information across the one or more nodes
among the plurality of nodes 106. In an embodiment, the transmission information can be
including any or a combination of rate of dataflow, number of data packets transmitted, number
of data packets dropped, number of data packets received at the one or more nodes, throughput,
delay, packet delivery ratio, and the likes.
[0095] In an embodiment, the proposed method can include a step 406 of comparing, at
the one or more processors, the second set of data packets that is extracted in step 404 with the
maintained predetermined transmission information of the first set of data packets to determine a
set of traffic parameters, the set of traffic parameters being an aggregation of the transmission
information associated with the second set of data packets, associated with the received second
set of data packets.
[0096] In an embodiment, the proposed method can include a step 408 of determining, at
the one or more processors, in response to the set of traffic parameters determined in step 406, a
corresponding vulnerability status of the network. In an illustrative embodiment, in the event of
22
the determined set of traffic parameters matching with a predetermined security threshold, the
vulnerability status of the network can be determined to be non-vulnerable. In another illustrative
embodiment, in the event of the determined set of traffic parameters being beyond the
predetermined security threshold, the vulnerability status of the network can be determined to be
vulnerable.
[0097] In an embodiment, the proposed method can include a step of updating, at the one
or more processors, based on training-and-testing data packets during the assessment, the
maintained predetermined transmission information. In an embodiment, the proposed method
can enable fore-casting of the vulnerability status through the updation and the training-andtesting data packets.
[0098] In an embodiment, the proposed method can include a step of emulating, at the
one or more processors, at least one of the nodes 106, where emulation of the at least one of the
one or more nodes can be performed by extracting nodal attributes of the corresponding nodes
106.The nodal attributes can include any or a combination of identity (ID), internet protocol, and
location of the one or more nodes, and distance and channels between the one or more nodes,
and the likes.
[0099] In an embodiment, the proposed method can include a step of representing, at a
display unit 110, through any or a combination of Augmented Reality (AR) view, Virtual Reality
(VR) view, two dimensional (2D) view, and three dimensional (3D) view, the at least one
emulated node 106.
[00100] FIG. 5 illustrates an exemplary computer system in which or with which
embodiments of the present invention can be utilized in accordance with embodiments of the
present disclosure.
[00101] As shown in FIG. 5, computer system includes an external storage device 510, a
bus 520, a main memory 530, a read only memory 540, a mass storage device 550,
communication port 560, and a processor 570. A person skilled in the art will appreciate that
computer system may include more than one processor and communication ports. Examples of
processor 570 include, but are not limited to, an Intel® Itanium® or Itanium 2 processor(s), or
AMD® Opteron® or Athlon MP® processor(s), Motorola® lines of processors, FortiSOC™
system on a chip processors or other future processors. Processor 570 may include various
engines associated with embodiments of the present invention. Communication port 560 can be
23
any of an RS-232 port for use with a modem based dialup connection, a 10/100 Ethernet port, a
Gigabit or 10 Gigabit port using copper or fiber, a serial port, a parallel port, or other existing or
future ports. Communication port 560 may be chosen depending on a network, such a Local
Area Network (LAN), Wide Area Network (WAN), or any network to which computer system
connects.
[00102] In an embodiment, the memory 530 can be Random Access Memory (RAM), or
any other dynamic storage device commonly known in the art. Read only memory 540 can be
any static storage device(s) e.g., but not limited to, a Programmable Read Only Memory
(PROM) chips for storing static information e.g., start-up or BIOS instructions for processor 570.
Mass storage 550 may be any current or future mass storage solution, which can be used to store
information and/or instructions. Exemplary mass storage solutions include, but are not limited to,
Parallel Advanced Technology Attachment (PATA) or Serial Advanced Technology Attachment
(SATA) hard disk drives or solid-state drives (internal or external, e.g., having Universal Serial
Bus (USB) and/or Firewire interfaces), e.g. those available from Seagate (e.g., the Seagate
Barracuda 7102 family) or Hitachi (e.g., the Hitachi Deskstar 7K1000), one or more optical
discs, Redundant Array of Independent Disks (RAID) storage, e.g. an array of disks (e.g., SATA
arrays), available from various vendors including Dot Hill Systems Corp., LaCie, Nexsan
Technologies, Inc. and Enhance Technology, Inc.
[00103] In an embodiment, the bus 520 communicatively couples processor(s) 570 with
the other memory, storage and communication blocks. Bus 520 can be, e.g. a Peripheral
Component Interconnect (PCI) / PCI Extended (PCI-X) bus, Small Computer System Interface
(SCSI), USB or the like, for connecting expansion cards, drives and other subsystems as well as
other buses, such a front side bus (FSB), which connects processor 570 to software system.
[00104] In another embodiment, operator and administrative interfaces, e.g. a display,
keyboard, and a cursor control device, may also be coupled to bus 520 to support direct operator
interaction with computer system. Other operator and administrative interfaces can be provided
through network connections connected through communication port 560. External storage
device 510 can be any kind of external hard-drives, floppy drives, IOMEGA® Zip Drives,
Compact Disc - Read Only Memory (CD-ROM), Compact Disc - Re-Writable (CD-RW), Digital
Video Disk - Read Only Memory (DVD-ROM). Components described above are meant only to
24
exemplify various possibilities. In no way should the aforementioned exemplary computer
system limit the scope of the present disclosure.
[00105] Thus, it will be appreciated by those of ordinary skill in the art that the diagrams,
schematics, illustrations, and the like represent conceptual views or processes illustrating
systems and methods embodying this invention. The functions of the various elements shown in
the figures may be provided through the use of dedicated hardware as well as hardware capable
of executing associated software. Similarly, any switches shown in the figures are conceptual
only. Their function may be carried out through the operation of program logic, through
dedicated logic, through the interaction of program control and dedicated logic, or even
manually, the particular technique being selectable by the entity implementing this invention.
Those of ordinary skill in the art further understand that the exemplary hardware, software,
processes, methods, and/or operating systems described herein are for illustrative purposes and,
thus, are not intended to be limited to any particular named.
[00106] While embodiments of the present invention have been illustrated and described,
it will be clear that the invention is not limited to these embodiments only. Numerous
modifications, changes, variations, substitutions, and equivalents will be apparent to those skilled
in the art, without departing from the spirit and scope of the invention, as described in the claim.
[00107] In the foregoing description, numerous details are set forth. It will be apparent,
however, to one of ordinary skill in the art having the benefit of this disclosure, that the present
invention may be practiced without these specific details. In some instances, well-known
structures and devices are shown in block diagram form, rather than in detail, to avoid obscuring
the present invention.
[00108] As used herein, and unless the context dictates otherwise, the term "coupled to" is
intended to include both direct coupling (in which two elements that are coupled to each other
contact each other)and indirect coupling (in which at least one additional element is located
between the two elements). Therefore, the terms "coupled to" and "coupled with" are used
synonymously. Within the context of this document terms "coupled to" and "coupled with" are
also used euphemistically to mean “communicatively coupled with” over a network, where two
or more devices are able to exchange data with each other over the network, possibly via one or
more intermediary device.
25
[00109] It should be apparent to those skilled in the art that many more modifications
besides those already described are possible without departing from the inventive concepts
herein. The inventive subject matter, therefore, is not to be restricted except in the spirit of the
appended claims. Moreover, in interpreting both the specification and the claims, all terms
should be interpreted in the broadest possible manner consistent with the context. In particular,
the terms “comprises” and “comprising” should be interpreted as referring to elements,
components, or steps in a non-exclusive manner, indicating that the referenced elements,
components, or steps may be present, or utilized, or combined with other elements, components,
or steps that are not expressly referenced. Where the specification claims refers to at least one of
something selected from the group consisting of A, B, C …. N, the text should be interpreted as
requiring only one element from the group, not A plus N, or B plus N, etc.
[00110] While the foregoing describes various embodiments of the invention, other and
further embodiments of the invention may be devised without departing from the basic scope
thereof. The scope of the invention is determined by the claims that follow. The invention is not
limited to the described embodiments, versions or examples, which are included to enable a
person having ordinary skill in the art to make and use the invention when combined with
information and knowledge available to the person having ordinary skill in the art.
ADVANTAGES OF THE PRESENT DISCLOSURE
[00111] The present disclosure provides system and method for assessing traffic flow
among one or more nodes of a network.
[00112] The present disclosure provides system and method for determining vulnerability
status of a network, or, a part of the network.
[00113] The present disclosure provides system and method for enabling emulation of one
or more nodes of a network.
[00114] The present disclosure provides system and method for sending a warning, in case
a network, or, a part of the network is determined to be vulnerable, to a government authority, or
a corresponding person.
26
[00115] The present disclosure provides an accurate, fast, efficient, cost effective and
simple network vulnerability assessment system.
FOR CHITKARA INNOVATION INCUBATOR FOUNDATION
Tarun Khurana

We Claim:

1. A method for assessing vulnerability of a wireless sensor network, said method
comprising:
maintaining, at one or more processors of a processing unit, a predetermined
transmission information associated with a first set of data packets flowing across one or
more nodes selected from a plurality of nodes of the network;
extracting, at the one or more processors, a second set of data packets from the
first set of data packets, where the second set of data packets corresponds to a data packet
transmission information across the one or more nodes among the plurality of nodes;
comparing, at the one or more processors, the extracted second set of data packets
with the maintained predetermined transmission information of the first set of data
packets to determine a set of traffic parameters, the set of traffic parameters being an
aggregation of the transmission information associated with the second set of data
packets, associated with the received second set of data packets; and
in response to the determined set of traffic parameters, determining, at the one or more
processors, a corresponding vulnerability status of the network.
2. The method as claimed in claim 1, wherein the transmission information comprises any
or a combination of rate of dataflow, number of data packets transmitted, number of data
packets dropped, number of data packets received at the one or more nodes, throughput,
delay, and packet delivery ratio.
3. The method as claimed in claim 1, wherein, in the event of the determined set of traffic
parameters matching with a predetermined security threshold, the vulnerability status of
the network is determined to be non-vulnerable.
4. The method as claimed in claim 1, wherein, in the event of the determined set of traffic
parameters being beyond the predetermined security threshold, the vulnerability status of
the network is determined to be vulnerable.
5. The method as claimed in claim 1, wherein the method comprises a step of updating, at
the one or more processors, based on training-and-testing data packets during the
assessment, the maintained predetermined transmission information.
28
6. The method as claimed in claim 1, wherein the method comprises a step of emulating, at
the one or more processors, at least one of the one or more nodes, and
wherein emulation of the at least one of the one or more nodes can be performed by
extracting nodal attributes of the corresponding nodes.
7. The method as claimed in claim 6, wherein the nodal attributes are any or a combination
of identity (ID), internet protocol, and location of the one or more nodes, and distance
and channels between the one or more nodes.
8. The method as claimed in claim 6, wherein the method comprises a step of representing,
at a display unit, through any or a combination of Augmented Reality (AR) view, Virtual
Reality (VR) view, two dimensional (2D) view, and three dimensional (3D) view, the at
least one emulated node.
9. A network vulnerability assessment system comprising:
one or more processors, and a memory coupled to the one or more processors
comprising computer readable program code embodied in the memory that is executable
by the one or more processors to:
maintain, at the network, a predetermined transmission information
associated with flow of set of data packets among a plurality of nodes of the
network;
extract, from one or more nodes among the plurality of nodes, a second set
of data packets from a first set of data packets across one or more nodes selected
from the plurality of nodes of the network, where the second set of data packets
corresponds to a data packet transmission information across the one or more
nodes;
compare the extracted second set of data packets with the maintained
predetermined transmission information associated with the flow of set of data
packets to determine a set of traffic parameters, the set of traffic parameters being
an aggregation of the transmission information associated with the second set of
data packets, associated with the received second set of data packets; and
in response to the determined set of traffic parameters, determine, at the one or
more processors, a corresponding vulnerability status of the network.
29
10. The system as claimed in claim 9, wherein the one or more nodes are any or a
combination of sink node, source node, and cluster head node.