Secured channel condition estimation algorithm to remove attacks in mobile ADHOC networks

T.Aruna et al., International Journal of Advanced Research in Innovative Discoveries in Engineering and Applications[IJARIDEA]
Vol.2, Issue 1,27 February 2017, pg. 6-10
6
© 2017, IJARIDEA All Rights Reserved
Secured channel condition estimation
algorithm to remove attacks in mobile ADHOC
networks
T.Aruna1
, M.Suresh Chinnathampy2
1
Assistant Professor, Department of ECE, Thiagarajar College of Engineering, Madurai, India
2
Assistant Professor, Department of ECE, Francis Xavier Engineering College, Tirunelveli, India
Abstract— Remote systems are powerless against assaults because of the communicate way of the
transmission medium. The hubs are frequently set in threatening or risky conditions where they are not
physically ensured. For an extensive scale remote system it is difficult to screen and shield every individual
hub independently from physical or consistent assault. Assaults may gadget diverse sorts of dangers to make
remote system framework unsteady. Security in remote systems is imperative to keep unapproved clients from
spying, discouraging and altering sensor information, and propelling disavowal of-administration (DOS)
assaults against whole system. It is extremely hard to join security systems into steering conventions after the
outline has finished. Along these lines, remote system directing conventions must be composed with security
contemplations and this is the main powerful answer for secure steering in remote systems. For this reason a
calculation is planned which examinations the channel state of the considerable number of channels and
locate the best channel for directing. This calculation is named as Secure Channel Condition Estimation
Algorithm.
Keywords— ADHOC; Ad-Hoc On-Demand Distance Vector Routing (AODV); Destination-Sequenced
Distance-Vector Routing (DSDV); MANET.
I. INTRODUCTION
The sensor hubs are haphazardly appropriated in a detecting field. We are utilizing versatile
specially appointed system (MANET). This is the foundation less system and a hub can move
autonomously. In a MANET, every hub not just functions as a host and furthermore goes
about as a switch. We can discover the correspondence run for all hubs. Each hub imparts just
inside the range. On the off chance that assume any hub out of the range, hub won't convey
those hubs or drop the bundles.
A. MANET
The term MANET (Mobile Ad hoc Network) alludes to a multi jump bundle based remote
system made out of an arrangement of versatile hubs that can convey and move in the
meantime, without utilizing any sort of settled wired framework. MANET is really self
arranging and versatile systems that can be shaped and twisted on-the-fly without the need of
any brought together organization. Something else, a remain for Mobile Ad Hoc Network A
MANET is a kind of specially appointed system that can change areas and design itself on the
fly. Since MANETS are portable, they utilize remote associations with interface with
different systems. This can be a standard Wi-Fi association, or another medium, for example,
a cell or satellite transmission.
II. SYSTEM MODEL
A. Secure Channel Condition Estimation Algorithm
Consider a system cell comprising of a base station and N clients served by the base station.
N = {1, 2,.... N} means the arrangement of all clients in the framework.

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The base station gauges channel states of every client in each vacancy utilizing L
challenges. A period interim [dt−d, dt), t ∈ Z is called schedule opening t where d is the
length of an availability.
At each schedule vacancy t, the base station utilizes our channel condition estimation to
decide a client's channel condition as a component in a set
E = {E1, E2, . . . , EL+1} with cardinality L + 1.
Every component Ei ∈ E speaks to a SINR scope of SINRi−1 ≤ SINR < SINRi, where
SINR0 = −∞and SINRL+1 =∞.
B. Usage Environment
Arrange test system 2 is utilized as the reproduction instrument in this venture. NS was
picked as the test system somewhat on account of the scope of components it gives and
halfway in light of the fact that it has an open source code that can be adjusted and expanded.
There are diverse renditions of NS and the most recent adaptation is ns-2.1b9a while ns-
2.1b10 is a work in progress
C. Organize Simulator 2.33 (Ns2)
Organize Simulator (NS2) is a discrete occasion driven test system created at UC Berkeley.
It is a piece of the VINT extend. The objective of NS2 is to bolster organizing examination
and training. It is appropriate for planning new conventions, looking at changed conventions
and activity assessments. NS2 is produced as a collective situation. It is appropriated
unreservedly and open source. A lot of organizations and individuals being developed and
look into utilization, keep up and create NS2. This expands the trust in it. Forms are
accessible for FreeBSD, Linux, Solaris, Windows and Mac OS X.
III. PROPOSED SYSTEM
In Wireless system, correspondence will be hindered by an assortment of assaults. These
assaults will debase the framework execution. Henceforth it is important to distinguish these
assaults and expel it before the transmission of information. The transmission medium is
known as a channel which is utilized to pass on the flag data. These channels are controlled
and administered by th standards and traditions called convention. Utilizing the conventions
we can direct the transmission of data. The convention that breaks down the channel data is
known as Channel mindful convention. There are channel mindful conventions accessible to
characterize the channel condition. The channel condition are the qualities of the channel, for
example, Spectral transfer speed, Symbol rate, Signal to Noise proportion, Bit mistake rate,
Latency and Delay. This channel condition data is a noteworthy contribution for outlining a
channel mindful convention. The channel condition data is basic to improve the system
execution by using the channels whose condition is adequate to exchange the information to
the collector. So for that we require precise data of the channel condition to build the
execution of the system. This channel condition data is accounted for by every client to the
chief of remote system. So that the administrator can control the system effectively. On the
off chance that this data is not exact the aggregate system execution will be influenced. So
every last client in the remote system appraise their own particular channel condition and
report it to the system supervisor by utilizing channel mindful convention. This estimation
can be modified by method for assaults by changing the data of channel condition and send it
to the system supervisor so that the aggressor can harm the system and corrupt its execution.
Consequently a guard instrument is created which gives the safe channel condition estimation
and kill the assaults.

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A. Estimation
After the base station transmits a test to a client, the client gives back the test an incentive
to the base station to demonstrate that the channel to the client is sufficient to get the relating
challenge. At the point when the base station gets the incentive from the client, the base
station watches that the esteem is indistinguishable to the one that it sent. At that point, the
base station stores the aftereffect of this check. We signify a check result for test ci at
schedule vacancy t by Fi(t).
Fi(t)=0, if challenge ci failed (1)
Fi(t)=1, if challenge ci succeeded (2)
Since our scheme uses non-ideal challenges, we need multiple sets of check results to
reduce the error in the estimated channel condition. We call the set used for estimating
channel condition a window, and we denote the size of the window as W. Intuitively, a larger
window size results in more accurate estimated channel condition but slower adaptation.
When a base station finishes collecting a window of check results Fi(t − W + 1), . . . , Fi(t), ∀i
∈{1, . . . , L} at time slot t, the base station sums the check results for each challenge ci, ∀i
∈{1, 2, . . . , L} as follows.
W−1
Si(t) = ∑Fi(t − j) ∀i ∈{1, 2, . . . , L}. (3)
j=0
Based on the values of Si(t), the base station estimates channel condition using a decision
function D. In other words, the base station decides which element in the set E = {E1,
E2, . . . , EL+1} most accurately characterizes corresponding user’s channel condition. We
denote the estimated channel condition at time slot t by Ec(t).
Ec(t) = D(S1(t), S2(t), . . . , SL(t)). (4)
We choose a threshold T ∈[0, 1]. First, we see how any of the lowest rate challenges (c1s)
are successfully received by a user; it is likely that nearly all of these challenges are received
by the user because it checks the lowest SINR range. When all c1s are successfully received,
S1(t) = W. If S1(t) ≤WT, we proceed to check S2(t). We repeat until we reach Si(t) < WT.
That is, we pick i = min j, s.t.Sj(t) < WT. The base station then estimates the channel
condition Ec(t) = Ei. For this threshold-based comparison, it is important to choose a proper
threshold T.
B. Performance Analysis
We break down the impact of parameter decisions on our channel condition estimation
calculation. In particular, we infer normal estimation mistake E[|CQI− _CQI|] as indicated by
calculation parameters, for example, window measure (W), limit (T), the span of a test and
Psref (i) of a test. CQI in the normal estimation mistake condition speaks to a real CQI-level.
_CQI speaks to an expected CQI-level.Outline of our examination is that given CQI, we
ascertain the probabilities that an expected CQI (_CQI) is resolved to be each CQI-level and
afterward, we compute normal estimation blunder. We begin by accepting that we have
capacities Ri(SINR, Psref (i)), ∀i ∈{1, 2, . . . , L} speaking to the likelihood that somewhat
of a test ci is effectively gotten given SINR. This capacity relies on upon the tweak and
coding technique utilized for developing difficulties. The likelihood Pcsi that a test ci is

4
effectively gotten, where SCi is the length in bits of test ci. Checking the quantity of effective
test gatherings from the most reduced CQI-level, our calculation decides _CQI = i when the
quantity of fruitful test gatherings for CQI-level i is not as much as WT. For CQI-level short
of what i, the quantity of effective test gatherings is more prominent than or equivalent to WT.
Hence, Pec(i, SINR), ∀i ∈{0, . . . , L − 1} is calculated as
i
Pec(i,SINR) = ∏Pt(j) * (1-Pt(i+1)) (5)
j=1
where Pt(i) = Pci (WT_)+Pci (WT_+1)+• • •+Pci (W).
For CQI-level L, we have a little bit different form.
L
Pec(L,SINR) = ∏Pt(j) (6)
j=1
With Pec(i, SINR), we can obtain the average estimation error as follows
L
E[|CQI-CQI|] = ∑|CQI –i| Pec(i,SINR) (7)
i=0
Using this analysis on average estimation error, we now want to properly set window size,
threshold, the size of a challenge and reference probability Psref (i) of a challenge so that the
average estimation error is minimized.
IV.RESULTS AND DISCUSSION
A. Implementation Environment
Network simulator 2 is used as the simulation tool in this project. NS was chosen as the
simulator partly because of the range of features it provides and partly because it has an open
source code that can be modified and extended. There are different versions of NS and the
latest version is ns-2.1b9a while ns-2.1b10 is under development.

5
Fig.1. Terminal Window
Fig.1. shows the terminal window where the source and destination nodes are assigned and
the simulation will start. Here the total numbers of nodes are set as 40. The source node
should be assigned between 0 and 6.
Fig.2. Identification of source and destination
Fig.2. shows the terminal window. In this terminal window, the total numbers of nodes are set
as 40. The source node is assigned between 0 and 6. After assigning the source node the
destination node is assigned between 25 and 39.

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Fig.3. Execution of code
Fig.3. shows the terminal window where among the set of 40 nodes, the source node is
assigned as 5 and the destination node is assigned as 33. After assigning the source and
destination nodes the simulation will start.
Fig.4. Transmission of Hello Packets
In Fig.4. all the nodes start the communication. Node 0 sends the hello packet with its public
key to its neighbor node 6. Node 1 sends the hello packet with its public key to its neighbors
0 and 6.

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Fig.5. Receiving Hello Packets
In Fig.5., the green color nodes denote that these nodes had received the hello packets from
their neighbors. The last three nodes namely 32, 33 and 34 are sending their hello packets
with its public key to its neighbors 30, 31 and 32 respectively.
Fig.6. Formation of Routing node
Fig.6. shows that source node and destination node are assigned and the route between source
and destination is formed. Here node 5 is the source node and node 33 is the destination node.
Nodes 7, 13, 24, 32 form the routing nodes.

8
Fig.7. Identification of Attacker
Fig.7. shows that node 7 which is the neighboring node of node 13 detects that node 13 is an
attacker. Node 13 send the Bogus route request to its neighboring node 7 to break the secret
key. Based on the threshold value node 7 identifies that node 13 is an attacker.
Fig.8. Alarm Message received by other nodes
Fig.8. shows that all the nodes generate the alarm message indicating the presence of the
attacker in the network. After receiving the alarm message of node 7, node 14 sends the alarm
message about the detection of attacker to its neighboring node 21. Now node 21 forwards
this alarm message to its neighbor and finally the alarm message spread over the network.
Fig.9. Packet delivery ratio after attack

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Fig.9. indicates the packet delivery ratio graph after the alarm message is sent to all the nodes
indicating the presence of an attacker node. Since all the nodes are aware of the attacker node
no packets will be forwarded through that node. Here the ratio is increased linearly and
finally it becomes a constant. This indicates that no packets are dropped.
Fig.10. Performance analysis
Fig.10. shows the overall analysis of the system performance. Initially the amount of energy
consumed is low and when the number of nodes increased the energy consumed is also high.
Finally after all the 40 nodes are formed the energy consumption becomes constant.
Fig.11. Throughput
Fig.11. shows the throughput of the system. Initially the throughput of the system is low as
the packets were forwarded through the attacker nodes. After the attacker node is identified
and the alarm message is generated, no packets will be forwarded through that node. At this
stage the throughput of the system increases linearly and finally it becomes a constant.

10
V. CONCLUSION
Remote systems are powerless against assaults because of the communicate way of the
transmission medium. The hubs are frequently set in threatening or risky conditions where
they are not physically ensured. For an extensive scale remote system it is difficult to screen
and shield every individual hub independently from physical or consistent assault. Assaults
may gadget diverse sorts of dangers to make remote system framework unsteady. Security in
remote systems is imperative to keep unapproved clients from spying, discouraging and
altering sensor information, and propelling disavowal of-administration (DOS) assaults
against whole system. It is extremely hard to join security systems into steering conventions
after the outline has finished. Along these lines, remote system directing conventions must be
composed with security contemplations and this is the main powerful answer for secure
steering in remote systems. For this reason a calculation is planned which examinations the
channel state of the considerable number of channels and locate the best channel for directing.
This calculation is named as Secure Channel Condition Estimation Algorithm.
REFERENCES
[1] Dongho Kim and Yih-Chun Hu, “A Study on False Channel Condition Reporting Attacks in Wireless
Networks.” in IEEE transactions on mobile computing, vol. 13, no. 5, May 2014.
[2] J. J. Haas and Y.-C. Hu, “Secure unified cellular ad hoc network routing,” in Proc. IEEE Globecom,
Honolulu, HI, USA, 2009.
[3] A. Sendonaris, E. Erkip, and B. Aazhang, “Increasing uplink capacity via user cooperation diversity,” in
Proc. IEEE ISIT, Cambridge, MA, USA, Aug. 1998, p. 156.
[4] H. Luo, R. Ramjee, P. Sinha, L. E. Li, and S. Lu, “Ucan: A unified cellular and ad-hoc network
architecture,” in Proc. ACM MobiCom, San Diego, CA, USA, 2003, pp. 353–367.
[5] D. S. J. De Couto, D. Aguayo, J. Bicket, and R. Morris, “A high throughput path metric for multi-hop
wireless routing,” in Proc. ACM MobiCom, San Diego, CA, USA, 2003, pp. 134–146.
[6] Aswiny.S, M.Suresh Chinnathampy ,“ NSDE Based Power Allocation and Relay Selection in Secure
Cooperative Networks”, International Journal & Magazine of Engineering, Technology, Management and
Research, Volume No: 3 (2016), Issue No: 5 (May), Page 79-82
[7] A. Jalali, R. Padovani, and R. Pankaj, “Data throughput of CDMA-HDR a high efficiency-high data rate
personal communication wireless system,” in Proc. IEEE VTC, Tokyo, Japan, 2000, pp. 1854–1858.
[8] Dr. K. Madhavi, N. Ushasree, "A Fuzzy Based Dynamic Queue Management Approach to Improve QOS
in Wireless sensor Networks.", International Journal of Advanced Research in Innovative Discoveries in
Engineering and Applications[IJARIDEA], Volume 1,Issue 1,October 2016, pp:16-21.

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