Research Article Angle and Context Free Grammar Based ...downloads.hindawi.com/journals/tswj/2016/3596345.pdfResearch Article Angle and Context Free Grammar Based Precarious Node Detection

Research ArticleAngle and Context Free Grammar Based Precarious NodeDetection and Secure Data Transmission in MANETs

Anitha Veerasamy1 Srinivasa Rao Madane2 K Sivakumar3 and Audithan Sivaraman4

1Department of Information Technology Anna University Chennai Tamil Nadu 600025 India2Department of Computer Science and Engineering Adhiparasakthi College of Engineering Vellore Tamil Nadu 603319 India3Department of Computer Science College of Computer Science King Khalid University Abha 62529 Saudi Arabia4Department of Electronics and Communication Engineering PRIST University Thanjavur Tamil Nadu 613403 India

Correspondence should be addressed to Anitha Veerasamy anithavannaunivgmailcom

Received 5 August 2015 Accepted 12 November 2015

Academic Editor Juan M Corchado

Copyright copy 2016 Anitha Veerasamy et al This is an open access article distributed under the Creative Commons AttributionLicense which permits unrestricted use distribution and reproduction in any medium provided the original work is properlycited

Growing attractiveness of Mobile Ad Hoc Networks (MANETs) its features and usage has led to the launching of threats andattacks to bring negative consequences in the society The typical features of MANETs especially with dynamic topology and openwireless medium may leave MANETs vulnerable Trust management using uncertain reasoning scheme has previously attemptedto solve this problem However it produces additional overhead while securing the network Hence a Location and Trust-basedsecure communication scheme (LampTS) is proposed to overcome this limitation Since the design securing requires more thantwo data algorithms the cost of the system goes up Another mechanism proposed in this paper Angle and Context Free Grammar(ACFG) based precarious node elimination and secure communication inMANETs intends to secure data transmission and detectprecarious nodes in aMANET at a comparatively lower costThe Elliptic Curve function is used to isolate amalicious node therebyincorporating secure data transfer Simulation results show that the dynamic estimation of themetrics improves throughput by 26in LampTS when compared to the TMUR ACFG achieves 33 and 51 throughput increase when compared to LampTS and TMURmechanisms respectively

1 Introduction

Mobile Ad Hoc Networks (MANETs) embrace various com-putational nodes that can communicate with one anotherwithin a specifiedwireless rangeThemost favoured feature ofMANETs is their capability to allow communication duringnode mobility However the shared wireless medium ofMANET facilitates inactive and adversarial eavesdroppingon data communications where adversaries can start variousoverwhelming attack on the network Many protocols havebeen designed for protecting the wireless communicationbut do not grant significance in privacy protection and leavemobile nodes to be noticeable by wireless analysis Securedata transmission in MANET is thus a very challenging taskAn example MANET is shown in Figure 1

To overcome this problem two strategies are proposedand evaluated in this paper Location and Trust-based secure

communication scheme (LampTS) and Angle and ContextFree Grammar (ACFG) based precarious node detection andsecure communication in MANETS

Firstly a Location and Trust-based secure communica-tion scheme assigns algorithms for data integrity based onhow far the nodes are located from one another The nexthop is selected based on the trust A trust value is calculatedbased on the previous network operations for effective nexthop selection which makes this scheme work efficientlyeven under high mobility conditions A design limitationidentified in this scheme has motivated us in the design ofACFG scheme for MANETs

In ACFG scheme the next hop is selected based on anodersquos angle and a nodersquos left most and right most deriva-tions There are three levels of assessment first the nodersquoslocation is assessed using the angle method followed bythe CFG to detect which node among the neighbors has

Hindawi Publishing Corporatione Scientific World JournalVolume 2016 Article ID 3596345 12 pageshttpdxdoiorg10115520163596345

2 The Scientific World Journal

Source

Transmission rangeDestination

Figure 1 Mobile Ad Hoc Networks

faked the location the last confirmation using an ellipticcryptography function is achieved to publish that a nodeis malicious In this scheme there is no requirement forextraordinary nodes for the localization process or for otherspecial purposesHence it provides better performancewhencompared to LampTSmechanismTheorganization of the paperis thus related works following Introduction the proposedmethods and simulation analysis

2 Related Work

The works related to the mechanism proposed here arebroadly classified based on security location and Angle andContext Free Grammar (CFG) used as estimation parameterswhile routing

21 Security Many protocols have improved security usingdifferent aspects in the literature The ad hoc networksare classified into three types open managed-open andmanaged-hostile dissimilar in the safety requirementSPAAR is one protocol that aims to provide security ina managed-hostile environment which is described as aMANET created using military nodes in a battle situation[1] Secure Routing Protocol (SRP) [2] on the other handneeds a security association across end nodes assuring thatthey can differentiate and drop reply messages giving anyfake information or even stop receiving the same This isachieved by employing and using a shared secret to themain routing protocol for example AODV The trust basedrouting protocols [3 4] are assigned trusted values and thedata are routed only through trusted nodes

Selection of security scheme for every data packet andmanagement of the same is a tedious and expensive pro-cess [5] In the Authenticated Anonymous Secure Routing(AASR) [6] the RREQ packets are legitimated using groupsignatures and to preserve vigorous attacks from unveilingthe node distinctiveness The key-encrypted onion routingcontaining a route secret verification message prevents thenodes from avoiding misinterpretation of a genuine inten-tion Provision of high anonymity and security is considered

an advantage However there is enormous packet delayduring data transmission The sensorsrsquo decision reports areused to discover themalicious nodes and estimate their attackbehaviour The detection procedure is analyzed using theentropy-defined trust model [7]

22 Location Localization verification technique [8]depends on the received signal strength A node confirmsthe truthfulness of the other node by predicting its nextgeographical localizations and checks the similarity tothe actual localizations found A node confirms thetruthfulness of the other node by predicting its nextgeographical localizations and checks the similarity tothe actual localizations found in Efficient Mobility BasedLocalization (EMBL) [9] During the localization methodeach node predicts its future mobility pattern according toits past known location information However there is pooraccuracy in predicting the real and estimated node positionsand distances from a reference node Ordinary nodes amonganchor nodes and unknown nodes build a shortest path bygreedy approach [10] This shortest path is approximately astraight line between unknown nodes and reference nodesThe disadvantage of this algorithm is the poor accuracywhen there are too many routers

In Modified Parametric Location Identification (MPLI)[11] location is identified separately using the 119909 and 119910

coordinates angle of arrival time distance and circularregion quadrants It also provides timely updates of positionsto make the routing more robust and position aware toavoid data losses and connection termination due tomobilityMultihop Localization Algorithm (MLA) [12] has five stepsas follows (1) achieving neighbor distances (2) calculatingreference node distances (unidentified nodes can estimate thetotal distances to nodes with the data obtained from step 1and they decide the shortest paths to anchor) (3) choosingthe reference nodes (4) acquiring angles and (5) fitting theshortest path to straight line

23 Angle Decoupled Maximum Likelihood (DEML) angleestimator determines the angle of arrival [13] The DEML

The Scientific World Journal 3

estimator is no longer asymptotically statistically proficientAngular Routing Protocol (ARP) [14] based on position thatuses an improved geographic forwarding to route packets tothe destination The geographic forwarding fails at a timeused by the angle-based forwarding method It does notrequire establishing routes The indefinite node estimates itsangle to each of the three reference nodes based on theseangles and the positions of the reference nodes (that forma triangle) and computes its own position using simpletrigonometrical relationships [15] The triangular zone [16]is used to reduce the route searching space This mechanismavoids huge routing traffic and collision

The angle is found based on slope of line Slope valuesare found in all neighboring nodes [17] The angles betweenunidentified node and several fixed nodes are used in theAOA (angle of arrival) [18] to evaluate the position whichis a little costly to perform Orthomorphic Analyst 119896-NearestNeighbor method [19] detects the intrusion activity based onthe traffic intensity at inner boundary instance within thecommunication MANETs Angle based distance is measuredbetween the node points for easy detection of traffic creatingnodes It measures how far each pair of mobile nodes is andevaluates correct angle of positionwithin the inner boundary

24 CFG One-time authentication information [20] dis-cussed structured and unstructured techniques for generat-ing strings and analyses of the difficulty of guessing strings insuch a language This authentication information is used forthe generation of one-time passwordsThis one-time authen-tication information is still inclined to operate in the middleattacks Probabilistic Context Free Grammars (PCFGs) [21]recognized events from raw sensor network measurementsIt derived a brief probabilistic Context Free Grammar fromthe known examining data A metric depending on Bayesianformula for maximizing grammar a posteriori probabilitygiven the training data is used here Advantages of theproperties are the chunk and merge operations

Translate natural language sentence into database queryNLDBI (Natural Language Interface to a Database) structure[22] including its probabilistic Context Free Grammar it isused to construct the parse tree and this algorithm calculatesthe probabilities CFG has a formalization capability indescribing most sentence structures and so well formed thatefficient sentence parser

Trust management system enhances the protection inMANETs [18] the trust model has trust from direct andindirect observations In direct observation the trust valueis derived using Bayesian inference it is a type of uncertainreasoning where the full probability model can be definedIn indirect observation the trust value is derived usingthe Dempster-Shafer theory it is another type of uncertainreasoning where the proposition of interest can be derived

This method separates data packets and control packetsand mitigates factors that cause packet losses After exami-nation of the trust value there is a possibility for the insideattackers to conquer the other nodes In such a situation theLocation and Trust-based secure communication scheme isused to provide security in MANETs

3 Location and Trust-Based SecureCommunication Scheme (LampTS)

The proposed scheme provides a technique to manage anduse different security schemes in a single MANET simulta-neously It uses the security algorithms available as securityproviders based on how far the nodes are located fromthe corresponding destination nodes The security schemesare known in prior to the nodes operating in the MANETEach node chooses its own security algorithm based onhow far the destination is located and sends the data tothe destination This is performed with an understandingthat the farthest nodes or the nodes that require multihoptransmission require higher security than nodes that arecloser to each other and are capable of direct transmission

31 Working of the Scheme The actual working of the schemecan be divided into the following processes Data CollectionLampTS Management Selection of Security Algorithm andData Transmission The architectural working of the LampTSin MANETs is described in Figure 2

311 Data Collection Each node in the MANET sends aroute request RREQ message along with the location infor-mation in order to transmit data to its destination In returnit receives a route reply RREP message that also containsthe location information of the destination The informationabout themost recent transmissions for that node is collectedfrom every node as well

312 LampTS Management An arbitrary manager called theLampTS manager processes the information collected Basedon the location information of the source and destinationthe distance between them is calculated dynamically andcomparedwith threshold values th

1 th2 and th

3(in this case)

and it can go up to th119894 The trust values 119879(119899) are also assigned

based on the most recent transmissions

313 Selection of Security Algorithm A number of securityalgorithms (ie RSA + MD5 HMAC + MD5 and HMAC +SHA-1) are stored in a database and based on the comparisonof the LampTS manager a particular transmission is assignedAlgorithm 119894 For example if 119889 value is less than th

1then

Algorithm 1 is selected if it is less than th2 then Algorithm

2 is selected and so on up to Algorithm 119894 It is significant tomention that Algorithm 1 has lower security standard thanAlgorithm 2

314 Data Transmission Thedata transmission is carried outbased on the trust values 119879(119899) of the nodes in the networkthrough shortest path distance If the source and destinationarewithin the range of each other then direct communicationis performedThe threshold values are stable yet the mobilityof the nodes alters the type of security algorithm used whenthe 119889 value switches from one threshold value to anotherThis makes the transmission security switch from Algorithm1 to Algorithm 2 This is a well-informed process so thedestination alone knows which algorithm is used and when

Figure 3 shows the Trusted Route obtained from thehistory of usage of a node for previous network operations A


LampTS managercalculates distance

Source

DestinationData transmission

Compare with threshold

Security

Algorithm 1

Algorithm 2

Algorithm 3

d lt th1

d gt th2

th1 lt d lt th2

Figure 2 Working of the LampTS scheme

node that is newly introduced into the MANETs is assignedan optimum history value 119867 by authenticated persons Fora normal node as the network operations are performed inthe system the value of 119879(119899) is incremented relatively bythe nodes that communicate to it If the quality of service(QoS) of the node is diminishing then the value of 119879(119899) isdecremented Based on the value of 119879(119899) at the instant 119894 therouting to the destination along with the selected securityscheme is performed Distrusted nodes are not preferred forthe routing operations this provides security during routing

315 Result Notification In this phase the security is pro-vided based on the destination location from the source Thetrust value 119879 is estimated from history of usage of previousnetwork communication process It works well under highmobility

32 Limitation of LampTS The forwarding node is selectedbased on the history of previous transmission There existsa drawback in LampTS that has been identified in this sectionThe cost of this system is considerably high

4 Angle and Context Free Grammar(ACFG) Based Precarious Node Detectionand Secure Data Transmission

Trust management using uncertain reasoning method hashigher delay and packet loss Even after the evaluation ofthe trust value there is a possibility for the insider attackers

Trusted route

3 4

52

Source Destination

61

T(4) = 5T(3) = 3

T(2) = 9 T(5) = 6

Figure 3 Trust route formation

to ruin the nodes Therefore there is an absolute need tointroduce a novel method to improve the security Pertain-ing to this concern an Angle and CFG based precariousnode detection and secure data transmission mechanism isproposed in MANETs In this scheme the precarious nodedetection uses Angle and CFG method (Levels 1 and 2) andsecure data transmission after publishing malicious nodesusing Elliptic Curve Cryptography (Level-3)

41 Forwarding Node Selection If a source node (119878) likes tosend a packet to the destination (D) the source selects a nexthop (119873) from its neighbor table The next hop selects theclosest node to the destination among the neighboring nodesof the source node The next hop node is selected based onthe minimum spanning tree shortest path algorithm


A

B

S

N

E

C

D

120579

120579120579

120579

120579 120579

Figure 4 Next hop neighbor angle

42 Neighbor Angle Computation The source selects theneighbor node 119873 and collects their surrounding neighbornodes and then computes the angles of the neighbor nodes

Figure 4 shows that the node 119878 is a source119873 is a next hopnode and 119860 119861 119862 119863 and 119864 are node 119873rsquos neighbors Sourcecollects the angles that node 119873 makes with 119873rsquos neighbors(shown as 120579 in Figure 4) Note that 120579 is an arbitrary value andvaries from one node to another unlike in Figure 4

Figure 5 shows the nodes 119878 and 119860 belonging to 119873rsquosneighbor list In triangle the distance of 119889

119873119878 119889119878119860 and 119889

119873119860

is obtained from the RSS values of the acknowledgementreceived from node 119878 and node 119860 The angle ang119878119873119860 isrepresented as 120579 The value of 120579 is computationally obtainedby the equation given below in (2)

The distance is measured by

119889 = radic(119909 minus 1199091)2

+ (119910 minus 1199101)2

(1)

where 1199091and 1199092are the coordinates of the two nodes between

distances The angle 120579 is calculated based on

120579 = arccos[1198872+ 1198882minus 1198862

2119887119888

] (2)

where 119886 rarr 119889119878119860 119887 rarr 119889

119873119878 119888 rarr 119889

119873119860

Similarly the algorithm computes every neighbor angleof the node 119873 The source stores every neighbor node anglein a table Source 119878 next hop node119873 common neighbor of 119878and119873 = node119860 form a triangleThe three interior angles arediscovered and added up Figure 6 shows that the 120579

1 1205792 and

1205793are three interior anglesMathematically the addition of all

three interior angles is 180∘ where ang119878119873119860 = 1205791 ang119860119878119873 = 120579

2

and ang119873119860119878 = 1205793

ang119878119873119860 + ang119860119878119873 + ang119873119860119878 = 180∘

1205791+ 1205792+ 1205793= 180

∘

(3)

A

S

N

120579

dNS

dSA

dNA

Figure 5 Individual neighbor angle

A

S

N

1205792

1205791

1205793

Figure 6 Interior angles

In Figure 5 the angle computed from the RSS for ang119878119873119860 = 120579

replaces the interior angle ang119878119873119860 = 1205791 Now according to the

property of a triangle

120579 + 1205792+ 1205793= 180

∘ (4)

If the sum of the angles in the expression (4) adds up to make180 degrees then the nodes that make up the triangle areproved to be legitimate nodes Hence the source selects thenext hop node only if the node is legitimate according to thismethod Otherwise one among the three nodes forming thetriangle is said to be faulty and Level 1 test fails here Thesource needs to identify which node is a precarious node andfor that a novel Context Free Grammar verification methodis proposed in the following section

43 CFG Based Node Verification TheCFG based node veri-fication (Level 2 test) is only performed when the angle baseddetection mechanism detects a faulty node Generally allnodes of the network have a mapping variable The Leftmostand Rightmost derivations are obtained based on this map-ping variable The source 119878 computes the leftmost derivationand the next node 119873 computes the rightmost derivationFinally the source checks whether these derivations are equal


(1) Input Source 119878 Destination119863 a new node119873 and Common Near node 119860(2) Output Legitimate Forwarder node(3) Begin procedure(4) While source not reach119863 do(5) Collect119873 neighbor node(6) Foreach neighbor node do(7) Neighbor angle 120579(8) 119878119873 and 119860 to form a triangle then(9) compute the interior angles of 119878119873 and 119860(10) 120579

1+ 1205792+ 1205793= 180

∘

(11) Replaced corresponding interior angle by the neighbor angle 120579(12) 120579 + 120579

2+ 1205793= 180

∘

(13) If 120579 == 1205791then

(14) Select node is a119873 goto (4)(15) Else Let LM be the Leftmost Derivation(16) Let RM be the Rightmost Derivation(17) To compute LM and DM of 119878 and119873(18) If LM == RM then(19) Choose119873 is a Forward node goto (4)(20) Else To compute LM and RM of 119878 and 119860(21) If LM == RM then(22) Choose 119860 is a Forward node goto (4)(23) Else(24) Set Flarr Level3 ECC Check()(25) If 119865 = 0 then(26) Source select next near node119873 goto (1)(27) Else To continue routing to same node and goto (4)(28) End procedure

Algorithm 1

or not If it is equal then node is legitimate otherwise thenode is precarious

431 Leftmost and Rightmost Derivation A leftmost deriva-tion chooses the leftmost nonterminal to expand and theright most derivation chooses the rightmost nonterminals toexpand Every node is assigned a mapping function in theproposed system and the leftmost and rightmost derivationsare obtained based on this mapping function

In Figure 7 119878 is a source 119873 is a next hop and 119860 is acommon node Let node 119886 be a mapping function of 119860 let 119899be a mapping function of119873 and let 119904 be a mapping functionof 119878 these mapping functions will be applied to the CFGThe source checks nodes 119860 and 119873 which node of left mostand right most derivation is equal so that it can be selectedas the next forwarding node For example the leftmost andrightmost derivation of nodes 119878 and119873 is given below

Consider Figure 7 the derivation is obtained amongnodes 119878119860 and119873Themapping functions are 119904 119886 and 119899Theleftmost and rightmost derivation between 119878 and 119873 is givenbelow

Given the grammar 119866 = (119878 119904 119899119877 119878)

Consider the grammar 119866 with production119877 = 119878 997888rarr 119904119878119878119899 119878 997888rarr 119886 119878 997888rarr 119899 119878 997888rarr 119904 (5)

The leftmost derivation of source and next hop is given below

119878

LM997904997904997904rArr 119904119878119878119899

LM997904997904997904rArr 119904119904119878119899

LM997904997904997904rArr 119904119904119899119899 (6)

n

as

A

S

N

Figure 7 Mapping function

Rightmost derivation of source and next hop is the following

119878

RM997904997904997904rArr 119904119878119878119899

RM997904997904997904rArr 119904119878119899119899

RM997904997904997904rArr 119904119904119899119899 (7)

The leftmost derivation is obtained based on the map-ping function and stores the string value in a table Nextnodecommon node is derived by the rightmost derivationIf the source checks leftmost and right most derivations areequals that node is legitimate otherwise it is a precariousnode The algorithm used to design the ACFG mechanismis given in Algorithm 1 that corresponds to the various stepsillustrated in the flowchart of Figure 8


No

Yes

Yes No

Yes

No

Source chooses next hop N based onshortest distance

Collect next hop neighbor node and find

Source next hop and common node to form a triangle

Compute interior angle and adding theangle values

Interior angle is replaced corresponding toneighbor angle

Legitimate node

Stop

Has source data reachedthe destination

Start

Yes

Select the nextnearest node

No

Send

LM = RM

Precarious node ECC-check Is RKEY = FKEY

Check N A node and LMand RM derivation

A or N is aprecarious node

data to A

L3 broadcast A isldquomaliciousrdquo

among angle 120579

Is sum = 180∘

Figure 8 Flowchart of ACFG scheme

44 Secure Data Transmission Using Reinforcement Thereinforcement action of this scheme is to publish that anode is malicious to all other nodes so that the node canbe excluded from the communication process This comesunder Level 3 (L-3) action of the proposed mechanism Toincorporate this there is need to confirm that a node istotally illegitimate before broadcasting its label ldquomaliciousrdquoto all nodes Therefore the node that fails the CFG testis further examined using the elliptical curve cryptographytechnique using the Weierstrass elliptic function Considerthe coordinate points of the source and the next node 119873 tobe 119868 and 119869 respectively there is another point119870which formsa straight line as illustrated in Figure 9

TheWeierstrass elliptic function is defined

1199102= 1199093+ 119901119909 + 119902

119868 + 119869 = 119870 where 119868 = 119869 forall119868 119869 isin 119864

(8)

where (119909119868 119910119868) and (119909

119869 119910119869) and (119909

119870 119910119870) are the coordinates

of the 119868 119869 and119870 points forming an elliptic curve Thereforethe coordinates (119909

119870 119910119870) can be obtained from the following

expressions

119909119870= 1205732minus 119909119868minus 119909119869

119910119870= 120573 (119909

119868minus 119909119870) minus 119910119868

(9)

where 120573 = (119910119869minus 119910119868)(119909119869minus 119909119868)

The commutative property of this function states that

119868 + 119869 = 119869 + 119870 (10)

The working of this Level 3 test for publishing the node asmalicious is given by Algorithm 2 The algorithm shows thatthe node estimates and sends L3REQ to the next node 119873


(1) Level3 ECC Check()(2)

(3) 119878 sends L3REQ to the next node119873(4) Source 119878 estimates 119865KEY larr 119868 + 119869(5) 119873 replies with estimated 119877KEY larr 119869 + 119870(6) If (119877KEY = 119865KEY)(7) Remove119873 from neighbor list(8) Broadcast119873 is ldquomaliciousrdquo to all nodes(9) Return 1(10) end if(11) Else (12) Set false alarm high(13) Return 0(14) end else(15) end

Algorithm 2

K

J

I

minusK

Figure 9 Elliptic Curve representing source and next node coordi-nates 119868 and 119869

Meanwhile the 119865KEY is estimated using (11) at the source endThe next node replies to the L3REQ with 119877KEY using (12)

119865KEY = 119868 + 119869 (11)

119877KEY = 119869 + 119870 (12)

The source node 119878 compares its 119865KEY with the 119877KEY toconclude that the next node 119873 is a malicious node When119865KEY is not same as 119877KEY then the node is published asmalicious to all other nodes and the next nearest node isconsidered for communication Algorithm 2 is a part of themain Algorithm 1

5 Performance Metrics

Five metrics are assessed in the simulation analysis of thenetwork They are Packet Delivery Ratio Packet Loss RatioThroughput Delay and Detection Rate

51 Packet Delivery Rate Packet Delivery Rate (PDR) is theratio of the total number of packets successfully delivered tothe total packets sent It is obtained from (13) below where 119899represents the total number of nodes in the networks

PDR =

sum119899

0119875119896119905119904119863119890119897119894V119890119903119890119889

Time (13)

Here 119875119896119905119904119863119890119897119894V119890119903119890119889 is the number of packets received bythe destination and119875119896119905119904119878119890119899119905 is the number of packets sent bythe source

52 Packet Loss Rate Packet Loss Rate (PLR) is the ratio ofthe packets lost to the total packets sent estimated by

PLR =

sum119899

0119875119896119905119904119871119900119904119905

Time0 le PLR le infin (14)

53 Throughput Throughput is defined as the rate at data issuccessfully transmitted for every packet sent evaluated by

Throughput =119899

sum

0

(

119875119886119888119896119890119905119904119877119890119888119890119894V119890119889 lowast 8Delay in ms

) kbps

0 le Throughput le infin

(15)

54 Delay Delay is defined as the time difference betweenthe current packets received and the previous packet receivedevaluate by (16) below where 119899 is the number of nodes

Delay =sum119899

0119875119896119905119877119890119888V119889 Time minus 119875119896119905119878119890119899119889 Time

119899

0 le PDR le infin

(16)

55 Detection Ratio In this paper we observe the detectionratio and false detection ratio of AODV and ACFG routingprotocols The detection ratio and false detection ratio aredefined as (17) follows

DR =

119863pn

119879pn0 le DR le 1

FDR =119872pn

119879nn0 le FDR le 1

(17)

where119863pn is the number of precarious node detected by oneor more normal nodes 119879pn is the total number of precariousnodes119872pn is the number of normal nodes misidentified asthe precarious node by one or more normal nodes and 119879nn isthe total number of normal node

6 Experimental Results and Discussion

TheNetwork Simulator-2 is used to study the performance ofour precarious node detection and secure data transmissionin MANETs We apply the IEEE 80211MAC with channeldata rate 10Mbps Remaining parameters are available inTable 1


Table 1 Simulation parameters

Parameter ValueSimulation area 1000 times 1000mNumber of nodes 30Channel WirelessPhyChannel data rate 10MbpsRadio propagation model TwoRayGroundAntenna type Omni AntennaTraffic models CBRTCPCBR interval 10msCommunication model UDPMobility model Random way pointSimulation time 100

LampTS

0

2

4

6

8

10

12

Num

ber o

f sw

aps a

lgor

ithm

2 4 6 8 100Speed of motion (ms)

Figure 10 Effect of mobility in LampTS scheme

61MobilityAnalysis Mobility inMANETs is a hindrance forthe implementation of many security schemes since it affectstheQoS of the systemThe security of LampTS scheme increasesas the mobility increases Figure 10 indicates mobility versusthe number of swaps between the algorithms used Greaterthe number of swaps greater the security of the systemThereis a tradeoff between QoS and security in this scheme

62 Comparison of TMUR LampTS andACFGwithCBRTrafficModels In order to validate the efficiency of the ACFG wecompare it with TMUR and LampTS protocolThe performanceand metrics described in Section 5 are used here

Figures 11 and 12 show the Packet Delivery Rate andPacket Loss Rate of the TMUR LampTS and ACFG mech-anisms respectively These two metrics are proportionalto each other and indicate the successful communicationamong the mobile nodes in any MANET Therefore it canbe observed from the graphs that LampTS performs better thanTMURandACFGperforms better than both themechanismsdue to secure communication and the three-level checksperformed over the nodes in the MANET

Figure 13 shows the throughput obtained for the ofTMUR LampTS and ACFG mechanisms It can be observedthat the maximum throughput obtained for ACFG is greaterthan the LampTS mechanism which is in turn greater thanthe TMUR Similarly the delay observed in a MANET

0

500

1000

1500

2000

2500

0 10 20 30 40 50 60 70 80 90 100

TMURLampTS

ACFG

Simulation time (ms)

Pack

et D

elive

ry R

ate (

bps)

Figure 11 Packet Delivery Rate of TMUR LampTS and ACFG

TMURLampTS

ACFG

0

200

400

600

800

1000

1200

1400

1600N

umbe

r of p

acke

ts dr

oppe

d

10 20 30 40 50 60 70 80 90 1000Simulation time (ms)

Figure 12 Packet Loss Rate of TMUR LampTS and ACFG

0 10 20 30 40 50 60 70 80 90 100

TMURLampTS

ACFGSimulation time (ms)

0

50000

100000

150000

200000

250000

Thro

ughp

ut (b

ps)

Figure 13 Throughput of TMUR LampTS and ACFG


0 10 20 30 40 50 60 70 80 90 100

TMURLampTS

ACFG


0

3

6

9

12

15

18

Del

ay (m

s)

Figure 14 Delay of TMUR LampTS and ACFG

TMURLTSUC

ACFG

Det

ectio

n ra

tio

0

02

04

06

08

1

12

2 3 4 5 61Number of experiments

Figure 15 Detection ratio of TMUR LampTS and ACFG

operated using the three protocols is also plotted in Figure 14The ACFG mechanism shows the least delay comparedto the LampTS that is also lower than the TMUR protocolThe malicious nodes present in the network obstruct thecommunication in the network and therefore the ACFGmechanism both avoids and detects the best performanceamong the three protocols

The detection ratio of TMUR LampTS and ACFG mech-anisms is plotted in Figure 15 After modeling a 20 ofthe nodes as attack nodes the three methods are testedwhether they are able to efficiently identify and detect themalicious nodes It can be observed from the figure that thedetection rate of ACFG is greater than both LampTS and ACFGmechanisms Also the false positive ratio is plotted for thethree comparing mechanisms in Figure 16 to observe that theACFG mechanism has the lowest false positive ratio

63 Comparison ofThroughput in CBR and TCPModels Theoperation of the ACFG mechanism using both Constant BitRate (CBR) and Variable Bit Rate (VBR) traffic models arevalidated UDP does not contain acknowledge packet (ACK)

1 2 3 4 5 6

TMURLampTS

ACFG

Number of experiments

0

01

02

03

04

05

06

07

False

pos

itive

ratio

Figure 16 False positive ratio of TMUR LampTS and ACFG

ACFG-TCPACFG-CBR

0

500

1000

1500

2000

2500

3000

3500Th

roug

hput

(kby

tes)


Figure 17 Throughput of ACFG with CBR and TCP models

that permits the nonstop packet stream as opposed to usingTCP that acknowledges a set of packets calculated by usingthe TCP window size and Round Trip Time (RTT)

Figure 17 shows the throughput of ACFG with both CBRand TCPmodels ACFG works better with CBR trafficmodelwhen compared with the TCP models Figure 18 shows thethroughput of the LampTS for CBR and TCP trafficmodelsTheCBR generates slightly longer throughput than TCPTheCBRtraffic model is better when compared to the TCP

64 Comparison of Throughput against Node Mobility InFigure 19 it can be observed that the TMUR suffers morefrom the speed of motion compared with the ACFG andLampTS The security in TMUR does not vary under mobilitybecause the mobility is increased when security algorithm isincreased Hence the throughput does not change based onthe mobility However ACFG obtains better throughput ratecompared to TMUR


LampTS-TCPLampTS-CBR


0

50

100

150

200

250

300

350

400

Thro

ughp

ut (k

byte

s)

Figure 18 Throughput of LampTS with CBR and TCP models

0

50

100

150

200

250

300

350

400

450

0 4 8 12

TMURLampTS

ACFG

Speed of motion (ms)

Thro

ughp

ut (k

bps)

Figure 19 Throughput against node mobility

65 Comparison of Throughput against Number of NodesAccording to analysis performed the scalability of the pro-posed mechanisms is achieved by obtaining the throughputvarying the number of nodes from 25 to 150 within an area of1000 times 1000m simulation field From Figure 20 it has beenobserved that ACFGperforms better than LampTSwith averagethroughput crossing 3000 kbps

A summary of the results shown in Figures 10ndash20 istabulated in Table 2 The various parameters measured forTMUR LampTS and ACFG are tabulated to analyze the overallimprovements

The simulation results show that the dynamic estimationof the metrics improves throughput by 26 in LampTS whencompared to the TMUR ACFG achieves 33 and 51throughput increase when compared to LampTS and TMURmechanisms respectively

Table 2 Comparison of results

Parameters TMUR LampTS ACFGPacket Delivery Rate (bps) 1015 1376 2083Packet Loss Rate (packets) 1465 454 118Throughput (bps) 101599 137604 208319Delay (ms) 155 104 86Average detection ratio 049 069 095Average false positive ratio 050 030 004

0500

10001500200025003000350040004500

25 50 100 150

LampTSACFG

Number of nodes

Thro

ughp

ut (k

bps)

Figure 20 Throughput against number of nodes

7 Conclusions

In this paper we have proposed two new mechanismsfor incorporating secure communication in MANETs Thispaper contains two strategies Location and Trust-basedsecure routing (LampTS) as well as Angle and CFG basedprecarious node detection (ACFG) with secure data trans-mission in MANETs LampTS method uses various cryptogra-phy algorithms based on distance and includes trust basedrouting ACFGmethod isolates the precarious node based onthe Angle and Context Free Grammar and secures data trans-mission using the SHA-1 algorithm The simulation resultsevaluate that both the ACFG and LampTS mechanisms offerimproved throughput and reduced delay more so the ACFGIn future works we intend to investigate the precarious nodedetection in Cognitive Networks

Conflict of Interests

The authors of this paper have no conflict of interests

References

[1] B Dahill B Levine E Royer and C Shields ldquoA secure routingprotocol for ad hoc networksrdquo Tech Rep 01-37 University ofMassachusetts 2000

[2] P Papadimitratos and Z J Haas ldquoSecure routing for mobilead hoc networksrdquo in Proceedings of the SCS CommunicationNetworks and Distributed Systems Modeling and SimulationConference (CNDS rsquo02) San Antonio Tex USA January 2002


[3] Y Zheng Z Peng and V Teemupekka Trust Evaluation BasedSecurity Solution in Ad Hoc Networks Nokia Research Centre2006

[4] K C Joshi and D Pant ldquoSecurity of mobile Ad-hoc networkswith five layer security architecturerdquo Institute of TechnicalTeachers 2010

[5] M Saleh and L Dong ldquoReal-time scheduling with securityenhancement for packet switched networksrdquo IEEE Transactionson Network and Service Management vol 10 no 3 pp 271ndash2852013

[6] W Liu and M Yu ldquoAASR authenticated anonymous securerouting forMANETs in adversarial environmentsrdquo IEEE Trans-actions on Vehicular Technology vol 63 no 9 pp 4585ndash45932014

[7] M Abdelhakim L E Lightfoot J Ren and T Li ldquoDis-tributed detection in mobile access wireless sensor networksunder byzantine attacksrdquo IEEE Transactions on Parallel andDistributed Systems vol 25 no 4 pp 950ndash959 2014

[8] M S Bouassida G Guette M Shawky and B DucourthialldquoSybil nodes detection based on received signal strength varia-tions within VANETrdquo International Journal of Network Securityvol 9 no 1 pp 22ndash33 2009

[9] P T V Bhuvaneswari S Karthikeyan B Jeeva and M APrasath ldquoAn efficient mobility based localization in underwa-ter sensor networksrdquo in Proceedings of the 4th InternationalConference on Computational Intelligence and CommunicationNetworks pp 90ndash94 IEEE Mathura India November 2012

[10] W Heiko and J Schiller ldquoDistance-based distributed multihoplocalization in mobile wireless sensor networksrdquo in 8th GIITGKuVS Fachgesprach Drahtlose Sensornetze Hamburg GermanyAugust 2009 httppagemifu-berlindeekewill09FGSNpdf

[11] A Karma and J Choudhary ldquoMPLI a novel modified paramet-ric location identification for AODV in MANETrdquo InternationalJournal of ComputerApplications vol 100 no 4 pp 48ndash53 2014

[12] Z ZhuW Guan L Liu S Li S Kong and Y Yan ldquoAmulti-hoplocalization algorithm in underwater wireless sensor networksrdquoin Proceedings of the 6th International Conference on WirelessCommunications and Signal Processing (WCSP rsquo14) pp 1ndash6IEEE Hefei China October 2014

[13] J Li B Halder P Stoica and M Viberg ldquoComputationallyefficient angle estimation for signals with known waveformsrdquoIEEE Transactions on Signal Processing vol 43 no 9 pp 2154ndash2163 1995

[14] C Venkata and M Singhal ldquoAngular routing protocol formobile ad-hoc networksrdquo in Proceedings of the 25th IEEEInternational Conference on Distributed Computing SystemsWorkshops (ICDCSW rsquo05) Columbus Ohio USA June 2005

[15] A Boukerche H A B F Oliveira E F Nakamura and A A FLoureiro ldquoLocalization systems for wireless sensor networksrdquoIEEE Wireless Communications vol 14 no 6 pp 6ndash12 2007

[16] T-F Shih and H-C Yen ldquoLocation-aware routing protocolwith dynamic adaptation of request zone for mobile ad hocnetworksrdquoWireless Networks vol 14 no 3 pp 321ndash333 2008

[17] P K Suri M K Soni and P Tomar ldquoFramework for locationbased power aware routing in MANETrdquo IJCSI InternationalJournal of Computer Science Issues vol 8 no 3 2011

[18] L Cheng C Wu Y Zhang H Wu M Li and C Maple ldquoAsurvey of localization in wireless sensor networkrdquo InternationalJournal of Distributed Sensor Networks vol 2012 Article ID962523 12 pages 2012

[19] R M Chamundeeswari and P Sumathi ldquoEfficient detectionof intrusion using inner and outer boundary models withtransductive learning concept in mobile Adhoc networkrdquo Inter-national Journal of Scientific amp Engineering Research vol 5 no6 2014

[20] A Singh D Dagon and A L M Dos Santos ldquoAuthentica-tion protocols making use of context free grammar guessingstringsrdquo Tech Rep GIT-CERCS-04-24 Georgia Institute ofTechnology Atlanta Ga USA 2004

[21] S C Geyik and B K Szymanski ldquoEvent recognition in sensornetworks by means of grammatical inferencerdquo in Proceedings ofthe 28th IEEE International Conference on Computer Commu-nications (INFOCOM rsquo09) pp 900ndash908 IEEE Rio de JaneiroBrazil April 2009

[22] S Jadhav and U L Kulkarni ldquoNatural language databaseinterface with probabilistic context free grammarrdquo IJRIT Inter-national Journal of Research in Information Technology vol 2no 6 pp 119ndash126 2014

International Journal of

AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

RoboticsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014


Active and Passive Electronic Components

Control Scienceand Engineering

Journal of



RotatingMachinery


Hindawi Publishing Corporation httpwwwhindawicom

Journal ofEngineeringVolume 2014

Submit your manuscripts athttpwwwhindawicom

VLSI Design



Shock and Vibration


Civil EngineeringAdvances in

Acoustics and VibrationAdvances in



Electrical and Computer Engineering

Journal of

Advances inOptoElectronics


Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

SensorsJournal of


Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014


Chemical EngineeringInternational Journal of Antennas and

Propagation




Navigation and Observation



DistributedSensor Networks



Source

Transmission rangeDestination

Figure 1 Mobile Ad Hoc Networks

faked the location the last confirmation using an ellipticcryptography function is achieved to publish that a nodeis malicious In this scheme there is no requirement forextraordinary nodes for the localization process or for otherspecial purposesHence it provides better performancewhencompared to LampTSmechanismTheorganization of the paperis thus related works following Introduction the proposedmethods and simulation analysis

2 Related Work

The works related to the mechanism proposed here arebroadly classified based on security location and Angle andContext Free Grammar (CFG) used as estimation parameterswhile routing

21 Security Many protocols have improved security usingdifferent aspects in the literature The ad hoc networksare classified into three types open managed-open andmanaged-hostile dissimilar in the safety requirementSPAAR is one protocol that aims to provide security ina managed-hostile environment which is described as aMANET created using military nodes in a battle situation[1] Secure Routing Protocol (SRP) [2] on the other handneeds a security association across end nodes assuring thatthey can differentiate and drop reply messages giving anyfake information or even stop receiving the same This isachieved by employing and using a shared secret to themain routing protocol for example AODV The trust basedrouting protocols [3 4] are assigned trusted values and thedata are routed only through trusted nodes

Selection of security scheme for every data packet andmanagement of the same is a tedious and expensive pro-cess [5] In the Authenticated Anonymous Secure Routing(AASR) [6] the RREQ packets are legitimated using groupsignatures and to preserve vigorous attacks from unveilingthe node distinctiveness The key-encrypted onion routingcontaining a route secret verification message prevents thenodes from avoiding misinterpretation of a genuine inten-tion Provision of high anonymity and security is considered

an advantage However there is enormous packet delayduring data transmission The sensorsrsquo decision reports areused to discover themalicious nodes and estimate their attackbehaviour The detection procedure is analyzed using theentropy-defined trust model [7]

22 Location Localization verification technique [8]depends on the received signal strength A node confirmsthe truthfulness of the other node by predicting its nextgeographical localizations and checks the similarity tothe actual localizations found A node confirms thetruthfulness of the other node by predicting its nextgeographical localizations and checks the similarity tothe actual localizations found in Efficient Mobility BasedLocalization (EMBL) [9] During the localization methodeach node predicts its future mobility pattern according toits past known location information However there is pooraccuracy in predicting the real and estimated node positionsand distances from a reference node Ordinary nodes amonganchor nodes and unknown nodes build a shortest path bygreedy approach [10] This shortest path is approximately astraight line between unknown nodes and reference nodesThe disadvantage of this algorithm is the poor accuracywhen there are too many routers

In Modified Parametric Location Identification (MPLI)[11] location is identified separately using the 119909 and 119910

coordinates angle of arrival time distance and circularregion quadrants It also provides timely updates of positionsto make the routing more robust and position aware toavoid data losses and connection termination due tomobilityMultihop Localization Algorithm (MLA) [12] has five stepsas follows (1) achieving neighbor distances (2) calculatingreference node distances (unidentified nodes can estimate thetotal distances to nodes with the data obtained from step 1and they decide the shortest paths to anchor) (3) choosingthe reference nodes (4) acquiring angles and (5) fitting theshortest path to straight line

23 Angle Decoupled Maximum Likelihood (DEML) angleestimator determines the angle of arrival [13] The DEML













1 th2 and th

3(in this case)




1then







Source



Security

Algorithm 1

Algorithm 2

Algorithm 3

d lt th1

d gt th2

th1 lt d lt th2







Trusted route

3 4

52

Source Destination

61

T(4) = 5T(3) = 3

T(2) = 9 T(5) = 6





A

B

S

N

E

C

D

120579

120579120579

120579

120579 120579





119873119878 119889119878119860 and 119889

119873119860



119889 = radic(119909 minus 1199091)2

+ (119910 minus 1199101)2

(1)



120579 = arccos[1198872+ 1198882minus 1198862

2119887119888

] (2)

where 119886 rarr 119889119878119860 119887 rarr 119889

119873119878 119888 rarr 119889

119873119860


1 1205792 and



2

and ang119873119860119878 = 1205793

ang119878119873119860 + ang119860119878119873 + ang119873119860119878 = 180∘

1205791+ 1205792+ 1205793= 180

∘

(3)

A

S

N

120579

dNS

dSA

dNA


A

S

N

1205792

1205791

1205793





120579 + 1205792+ 1205793= 180

∘ (4)





1+ 1205792+ 1205793= 180

∘


2+ 1205793= 180

∘

(13) If 120579 == 1205791then


Algorithm 1








119878

LM997904997904997904rArr 119904119878119878119899

LM997904997904997904rArr 119904119904119878119899

LM997904997904997904rArr 119904119904119899119899 (6)

n

as

A

S

N



119878

RM997904997904997904rArr 119904119878119878119899

RM997904997904997904rArr 119904119878119899119899

RM997904997904997904rArr 119904119904119899119899 (7)



No

Yes

Yes No

Yes

No






Legitimate node

Stop


Start

Yes


No

Send

LM = RM




data to A


among angle 120579

Is sum = 180∘




1199102= 1199093+ 119901119909 + 119902


(8)

where (119909119868 119910119868) and (119909

119869 119910119869) and (119909




expressions

119909119870= 1205732minus 119909119868minus 119909119869

119910119870= 120573 (119909

119868minus 119909119870) minus 119910119868

(9)



119868 + 119869 = 119869 + 119870 (10)





Algorithm 2

K

J

I

minusK



119865KEY = 119868 + 119869 (11)

119877KEY = 119869 + 119870 (12)





PDR =

sum119899

0119875119896119905119904119863119890119897119894V119890119903119890119889

Time (13)



PLR =

sum119899

0119875119896119905119904119871119900119904119905



Throughput =119899

sum

0

(


) kbps


(15)


Delay =sum119899

0119875119896119905119877119890119888V119889 Time minus 119875119896119905119878119890119899119889 Time

119899

0 le PDR le infin

(16)


DR =

119863pn


FDR =119872pn


(17)







LampTS

0

2

4

6

8

10

12

Num

ber o

f sw

aps a

lgor

ithm







0

500

1000

1500

2000

2500

0 10 20 30 40 50 60 70 80 90 100

TMURLampTS

ACFG


Pack

et D

elive

ry R

ate (

bps)


TMURLampTS

ACFG

0

200

400

600

800

1000

1200

1400

1600N

umbe

r of p

acke

ts dr

oppe

d



0 10 20 30 40 50 60 70 80 90 100

TMURLampTS


0

50000

100000

150000

200000

250000

Thro

ughp

ut (b

ps)



0 10 20 30 40 50 60 70 80 90 100

TMURLampTS

ACFG


0

3

6

9

12

15

18

Del

ay (m

s)


TMURLTSUC

ACFG

Det

ectio

n ra

tio

0

02

04

06

08

1

12






1 2 3 4 5 6

TMURLampTS

ACFG


0

01

02

03

04

05

06

07

False

pos

itive

ratio


ACFG-TCPACFG-CBR

0

500

1000

1500

2000

2500

3000

3500Th

roug

hput

(kby

tes)









0

50

100

150

200

250

300

350

400

Thro

ughp

ut (k

byte

s)


0

50

100

150

200

250

300

350

400

450

0 4 8 12

TMURLampTS

ACFG


Thro

ughp

ut (k

bps)







0500

10001500200025003000350040004500

25 50 100 150

LampTSACFG

Number of nodes

Thro

ughp

ut (k

bps)


7 Conclusions




References


























RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation





















1 th2 and th

3(in this case)




1then







Source



Security

Algorithm 1

Algorithm 2

Algorithm 3

d lt th1

d gt th2

th1 lt d lt th2







Trusted route

3 4

52

Source Destination

61

T(4) = 5T(3) = 3

T(2) = 9 T(5) = 6





A

B

S

N

E

C

D

120579

120579120579

120579

120579 120579





119873119878 119889119878119860 and 119889

119873119860



119889 = radic(119909 minus 1199091)2

+ (119910 minus 1199101)2

(1)



120579 = arccos[1198872+ 1198882minus 1198862

2119887119888

] (2)

where 119886 rarr 119889119878119860 119887 rarr 119889

119873119878 119888 rarr 119889

119873119860


1 1205792 and



2

and ang119873119860119878 = 1205793

ang119878119873119860 + ang119860119878119873 + ang119873119860119878 = 180∘

1205791+ 1205792+ 1205793= 180

∘

(3)

A

S

N

120579

dNS

dSA

dNA


A

S

N

1205792

1205791

1205793





120579 + 1205792+ 1205793= 180

∘ (4)





1+ 1205792+ 1205793= 180

∘


2+ 1205793= 180

∘

(13) If 120579 == 1205791then


Algorithm 1








119878

LM997904997904997904rArr 119904119878119878119899

LM997904997904997904rArr 119904119904119878119899

LM997904997904997904rArr 119904119904119899119899 (6)

n

as

A

S

N



119878

RM997904997904997904rArr 119904119878119878119899

RM997904997904997904rArr 119904119878119899119899

RM997904997904997904rArr 119904119904119899119899 (7)



No

Yes

Yes No

Yes

No






Legitimate node

Stop


Start

Yes


No

Send

LM = RM




data to A


among angle 120579

Is sum = 180∘




1199102= 1199093+ 119901119909 + 119902


(8)

where (119909119868 119910119868) and (119909

119869 119910119869) and (119909




expressions

119909119870= 1205732minus 119909119868minus 119909119869

119910119870= 120573 (119909

119868minus 119909119870) minus 119910119868

(9)



119868 + 119869 = 119869 + 119870 (10)





Algorithm 2

K

J

I

minusK



119865KEY = 119868 + 119869 (11)

119877KEY = 119869 + 119870 (12)





PDR =

sum119899

0119875119896119905119904119863119890119897119894V119890119903119890119889

Time (13)



PLR =

sum119899

0119875119896119905119904119871119900119904119905



Throughput =119899

sum

0

(


) kbps


(15)


Delay =sum119899

0119875119896119905119877119890119888V119889 Time minus 119875119896119905119878119890119899119889 Time

119899

0 le PDR le infin

(16)


DR =

119863pn


FDR =119872pn


(17)







LampTS

0

2

4

6

8

10

12

Num

ber o

f sw

aps a

lgor

ithm







0

500

1000

1500

2000

2500

0 10 20 30 40 50 60 70 80 90 100

TMURLampTS

ACFG


Pack

et D

elive

ry R

ate (

bps)


TMURLampTS

ACFG

0

200

400

600

800

1000

1200

1400

1600N

umbe

r of p

acke

ts dr

oppe

d



0 10 20 30 40 50 60 70 80 90 100

TMURLampTS


0

50000

100000

150000

200000

250000

Thro

ughp

ut (b

ps)



0 10 20 30 40 50 60 70 80 90 100

TMURLampTS

ACFG


0

3

6

9

12

15

18

Del

ay (m

s)


TMURLTSUC

ACFG

Det

ectio

n ra

tio

0

02

04

06

08

1

12






1 2 3 4 5 6

TMURLampTS

ACFG


0

01

02

03

04

05

06

07

False

pos

itive

ratio


ACFG-TCPACFG-CBR

0

500

1000

1500

2000

2500

3000

3500Th

roug

hput

(kby

tes)









0

50

100

150

200

250

300

350

400

Thro

ughp

ut (k

byte

s)


0

50

100

150

200

250

300

350

400

450

0 4 8 12

TMURLampTS

ACFG


Thro

ughp

ut (k

bps)







0500

10001500200025003000350040004500

25 50 100 150

LampTSACFG

Number of nodes

Thro

ughp

ut (k

bps)


7 Conclusions




References


























RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation











Source



Security

Algorithm 1

Algorithm 2

Algorithm 3

d lt th1

d gt th2

th1 lt d lt th2







Trusted route

3 4

52

Source Destination

61

T(4) = 5T(3) = 3

T(2) = 9 T(5) = 6





A

B

S

N

E

C

D

120579

120579120579

120579

120579 120579





119873119878 119889119878119860 and 119889

119873119860



119889 = radic(119909 minus 1199091)2

+ (119910 minus 1199101)2

(1)



120579 = arccos[1198872+ 1198882minus 1198862

2119887119888

] (2)

where 119886 rarr 119889119878119860 119887 rarr 119889

119873119878 119888 rarr 119889

119873119860


1 1205792 and



2

and ang119873119860119878 = 1205793

ang119878119873119860 + ang119860119878119873 + ang119873119860119878 = 180∘

1205791+ 1205792+ 1205793= 180

∘

(3)

A

S

N

120579

dNS

dSA

dNA


A

S

N

1205792

1205791

1205793





120579 + 1205792+ 1205793= 180

∘ (4)





1+ 1205792+ 1205793= 180

∘


2+ 1205793= 180

∘

(13) If 120579 == 1205791then


Algorithm 1








119878

LM997904997904997904rArr 119904119878119878119899

LM997904997904997904rArr 119904119904119878119899

LM997904997904997904rArr 119904119904119899119899 (6)

n

as

A

S

N



119878

RM997904997904997904rArr 119904119878119878119899

RM997904997904997904rArr 119904119878119899119899

RM997904997904997904rArr 119904119904119899119899 (7)



No

Yes

Yes No

Yes

No






Legitimate node

Stop


Start

Yes


No

Send

LM = RM




data to A


among angle 120579

Is sum = 180∘




1199102= 1199093+ 119901119909 + 119902


(8)

where (119909119868 119910119868) and (119909

119869 119910119869) and (119909




expressions

119909119870= 1205732minus 119909119868minus 119909119869

119910119870= 120573 (119909

119868minus 119909119870) minus 119910119868

(9)



119868 + 119869 = 119869 + 119870 (10)





Algorithm 2

K

J

I

minusK



119865KEY = 119868 + 119869 (11)

119877KEY = 119869 + 119870 (12)





PDR =

sum119899

0119875119896119905119904119863119890119897119894V119890119903119890119889

Time (13)



PLR =

sum119899

0119875119896119905119904119871119900119904119905



Throughput =119899

sum

0

(


) kbps


(15)


Delay =sum119899

0119875119896119905119877119890119888V119889 Time minus 119875119896119905119878119890119899119889 Time

119899

0 le PDR le infin

(16)


DR =

119863pn


FDR =119872pn


(17)







LampTS

0

2

4

6

8

10

12

Num

ber o

f sw

aps a

lgor

ithm







0

500

1000

1500

2000

2500

0 10 20 30 40 50 60 70 80 90 100

TMURLampTS

ACFG


Pack

et D

elive

ry R

ate (

bps)


TMURLampTS

ACFG

0

200

400

600

800

1000

1200

1400

1600N

umbe

r of p

acke

ts dr

oppe

d



0 10 20 30 40 50 60 70 80 90 100

TMURLampTS


0

50000

100000

150000

200000

250000

Thro

ughp

ut (b

ps)



0 10 20 30 40 50 60 70 80 90 100

TMURLampTS

ACFG


0

3

6

9

12

15

18

Del

ay (m

s)


TMURLTSUC

ACFG

Det

ectio

n ra

tio

0

02

04

06

08

1

12






1 2 3 4 5 6

TMURLampTS

ACFG


0

01

02

03

04

05

06

07

False

pos

itive

ratio


ACFG-TCPACFG-CBR

0

500

1000

1500

2000

2500

3000

3500Th

roug

hput

(kby

tes)









0

50

100

150

200

250

300

350

400

Thro

ughp

ut (k

byte

s)


0

50

100

150

200

250

300

350

400

450

0 4 8 12

TMURLampTS

ACFG


Thro

ughp

ut (k

bps)







0500

10001500200025003000350040004500

25 50 100 150

LampTSACFG

Number of nodes

Thro

ughp

ut (k

bps)


7 Conclusions




References


























RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










A

B

S

N

E

C

D

120579

120579120579

120579

120579 120579





119873119878 119889119878119860 and 119889

119873119860



119889 = radic(119909 minus 1199091)2

+ (119910 minus 1199101)2

(1)



120579 = arccos[1198872+ 1198882minus 1198862

2119887119888

] (2)

where 119886 rarr 119889119878119860 119887 rarr 119889

119873119878 119888 rarr 119889

119873119860


1 1205792 and



2

and ang119873119860119878 = 1205793

ang119878119873119860 + ang119860119878119873 + ang119873119860119878 = 180∘

1205791+ 1205792+ 1205793= 180

∘

(3)

A

S

N

120579

dNS

dSA

dNA


A

S

N

1205792

1205791

1205793





120579 + 1205792+ 1205793= 180

∘ (4)





1+ 1205792+ 1205793= 180

∘


2+ 1205793= 180

∘

(13) If 120579 == 1205791then


Algorithm 1








119878

LM997904997904997904rArr 119904119878119878119899

LM997904997904997904rArr 119904119904119878119899

LM997904997904997904rArr 119904119904119899119899 (6)

n

as

A

S

N



119878

RM997904997904997904rArr 119904119878119878119899

RM997904997904997904rArr 119904119878119899119899

RM997904997904997904rArr 119904119904119899119899 (7)



No

Yes

Yes No

Yes

No






Legitimate node

Stop


Start

Yes


No

Send

LM = RM




data to A


among angle 120579

Is sum = 180∘




1199102= 1199093+ 119901119909 + 119902


(8)

where (119909119868 119910119868) and (119909

119869 119910119869) and (119909




expressions

119909119870= 1205732minus 119909119868minus 119909119869

119910119870= 120573 (119909

119868minus 119909119870) minus 119910119868

(9)



119868 + 119869 = 119869 + 119870 (10)





Algorithm 2

K

J

I

minusK



119865KEY = 119868 + 119869 (11)

119877KEY = 119869 + 119870 (12)





PDR =

sum119899

0119875119896119905119904119863119890119897119894V119890119903119890119889

Time (13)



PLR =

sum119899

0119875119896119905119904119871119900119904119905



Throughput =119899

sum

0

(


) kbps


(15)


Delay =sum119899

0119875119896119905119877119890119888V119889 Time minus 119875119896119905119878119890119899119889 Time

119899

0 le PDR le infin

(16)


DR =

119863pn


FDR =119872pn


(17)







LampTS

0

2

4

6

8

10

12

Num

ber o

f sw

aps a

lgor

ithm







0

500

1000

1500

2000

2500

0 10 20 30 40 50 60 70 80 90 100

TMURLampTS

ACFG


Pack

et D

elive

ry R

ate (

bps)


TMURLampTS

ACFG

0

200

400

600

800

1000

1200

1400

1600N

umbe

r of p

acke

ts dr

oppe

d



0 10 20 30 40 50 60 70 80 90 100

TMURLampTS


0

50000

100000

150000

200000

250000

Thro

ughp

ut (b

ps)



0 10 20 30 40 50 60 70 80 90 100

TMURLampTS

ACFG


0

3

6

9

12

15

18

Del

ay (m

s)


TMURLTSUC

ACFG

Det

ectio

n ra

tio

0

02

04

06

08

1

12






1 2 3 4 5 6

TMURLampTS

ACFG


0

01

02

03

04

05

06

07

False

pos

itive

ratio


ACFG-TCPACFG-CBR

0

500

1000

1500

2000

2500

3000

3500Th

roug

hput

(kby

tes)









0

50

100

150

200

250

300

350

400

Thro

ughp

ut (k

byte

s)


0

50

100

150

200

250

300

350

400

450

0 4 8 12

TMURLampTS

ACFG


Thro

ughp

ut (k

bps)







0500

10001500200025003000350040004500

25 50 100 150

LampTSACFG

Number of nodes

Thro

ughp

ut (k

bps)


7 Conclusions




References


























RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation











1+ 1205792+ 1205793= 180

∘


2+ 1205793= 180

∘

(13) If 120579 == 1205791then


Algorithm 1








119878

LM997904997904997904rArr 119904119878119878119899

LM997904997904997904rArr 119904119904119878119899

LM997904997904997904rArr 119904119904119899119899 (6)

n

as

A

S

N



119878

RM997904997904997904rArr 119904119878119878119899

RM997904997904997904rArr 119904119878119899119899

RM997904997904997904rArr 119904119904119899119899 (7)



No

Yes

Yes No

Yes

No






Legitimate node

Stop


Start

Yes


No

Send

LM = RM




data to A


among angle 120579

Is sum = 180∘




1199102= 1199093+ 119901119909 + 119902


(8)

where (119909119868 119910119868) and (119909

119869 119910119869) and (119909




expressions

119909119870= 1205732minus 119909119868minus 119909119869

119910119870= 120573 (119909

119868minus 119909119870) minus 119910119868

(9)



119868 + 119869 = 119869 + 119870 (10)





Algorithm 2

K

J

I

minusK



119865KEY = 119868 + 119869 (11)

119877KEY = 119869 + 119870 (12)





PDR =

sum119899

0119875119896119905119904119863119890119897119894V119890119903119890119889

Time (13)



PLR =

sum119899

0119875119896119905119904119871119900119904119905



Throughput =119899

sum

0

(


) kbps


(15)


Delay =sum119899

0119875119896119905119877119890119888V119889 Time minus 119875119896119905119878119890119899119889 Time

119899

0 le PDR le infin

(16)


DR =

119863pn


FDR =119872pn


(17)







LampTS

0

2

4

6

8

10

12

Num

ber o

f sw

aps a

lgor

ithm







0

500

1000

1500

2000

2500

0 10 20 30 40 50 60 70 80 90 100

TMURLampTS

ACFG


Pack

et D

elive

ry R

ate (

bps)


TMURLampTS

ACFG

0

200

400

600

800

1000

1200

1400

1600N

umbe

r of p

acke

ts dr

oppe

d



0 10 20 30 40 50 60 70 80 90 100

TMURLampTS


0

50000

100000

150000

200000

250000

Thro

ughp

ut (b

ps)



0 10 20 30 40 50 60 70 80 90 100

TMURLampTS

ACFG


0

3

6

9

12

15

18

Del

ay (m

s)


TMURLTSUC

ACFG

Det

ectio

n ra

tio

0

02

04

06

08

1

12






1 2 3 4 5 6

TMURLampTS

ACFG


0

01

02

03

04

05

06

07

False

pos

itive

ratio


ACFG-TCPACFG-CBR

0

500

1000

1500

2000

2500

3000

3500Th

roug

hput

(kby

tes)









0

50

100

150

200

250

300

350

400

Thro

ughp

ut (k

byte

s)


0

50

100

150

200

250

300

350

400

450

0 4 8 12

TMURLampTS

ACFG


Thro

ughp

ut (k

bps)







0500

10001500200025003000350040004500

25 50 100 150

LampTSACFG

Number of nodes

Thro

ughp

ut (k

bps)


7 Conclusions




References


























RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










No

Yes

Yes No

Yes

No






Legitimate node

Stop


Start

Yes


No

Send

LM = RM




data to A


among angle 120579

Is sum = 180∘




1199102= 1199093+ 119901119909 + 119902


(8)

where (119909119868 119910119868) and (119909

119869 119910119869) and (119909




expressions

119909119870= 1205732minus 119909119868minus 119909119869

119910119870= 120573 (119909

119868minus 119909119870) minus 119910119868

(9)



119868 + 119869 = 119869 + 119870 (10)





Algorithm 2

K

J

I

minusK



119865KEY = 119868 + 119869 (11)

119877KEY = 119869 + 119870 (12)





PDR =

sum119899

0119875119896119905119904119863119890119897119894V119890119903119890119889

Time (13)



PLR =

sum119899

0119875119896119905119904119871119900119904119905



Throughput =119899

sum

0

(


) kbps


(15)


Delay =sum119899

0119875119896119905119877119890119888V119889 Time minus 119875119896119905119878119890119899119889 Time

119899

0 le PDR le infin

(16)


DR =

119863pn


FDR =119872pn


(17)







LampTS

0

2

4

6

8

10

12

Num

ber o

f sw

aps a

lgor

ithm







0

500

1000

1500

2000

2500

0 10 20 30 40 50 60 70 80 90 100

TMURLampTS

ACFG


Pack

et D

elive

ry R

ate (

bps)


TMURLampTS

ACFG

0

200

400

600

800

1000

1200

1400

1600N

umbe

r of p

acke

ts dr

oppe

d



0 10 20 30 40 50 60 70 80 90 100

TMURLampTS


0

50000

100000

150000

200000

250000

Thro

ughp

ut (b

ps)



0 10 20 30 40 50 60 70 80 90 100

TMURLampTS

ACFG


0

3

6

9

12

15

18

Del

ay (m

s)


TMURLTSUC

ACFG

Det

ectio

n ra

tio

0

02

04

06

08

1

12






1 2 3 4 5 6

TMURLampTS

ACFG


0

01

02

03

04

05

06

07

False

pos

itive

ratio


ACFG-TCPACFG-CBR

0

500

1000

1500

2000

2500

3000

3500Th

roug

hput

(kby

tes)









0

50

100

150

200

250

300

350

400

Thro

ughp

ut (k

byte

s)


0

50

100

150

200

250

300

350

400

450

0 4 8 12

TMURLampTS

ACFG


Thro

ughp

ut (k

bps)







0500

10001500200025003000350040004500

25 50 100 150

LampTSACFG

Number of nodes

Thro

ughp

ut (k

bps)


7 Conclusions




References


























RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation












Algorithm 2

K

J

I

minusK



119865KEY = 119868 + 119869 (11)

119877KEY = 119869 + 119870 (12)





PDR =

sum119899

0119875119896119905119904119863119890119897119894V119890119903119890119889

Time (13)



PLR =

sum119899

0119875119896119905119904119871119900119904119905



Throughput =119899

sum

0

(


) kbps


(15)


Delay =sum119899

0119875119896119905119877119890119888V119889 Time minus 119875119896119905119878119890119899119889 Time

119899

0 le PDR le infin

(16)


DR =

119863pn


FDR =119872pn


(17)







LampTS

0

2

4

6

8

10

12

Num

ber o

f sw

aps a

lgor

ithm







0

500

1000

1500

2000

2500

0 10 20 30 40 50 60 70 80 90 100

TMURLampTS

ACFG


Pack

et D

elive

ry R

ate (

bps)


TMURLampTS

ACFG

0

200

400

600

800

1000

1200

1400

1600N

umbe

r of p

acke

ts dr

oppe

d



0 10 20 30 40 50 60 70 80 90 100

TMURLampTS


0

50000

100000

150000

200000

250000

Thro

ughp

ut (b

ps)



0 10 20 30 40 50 60 70 80 90 100

TMURLampTS

ACFG


0

3

6

9

12

15

18

Del

ay (m

s)


TMURLTSUC

ACFG

Det

ectio

n ra

tio

0

02

04

06

08

1

12






1 2 3 4 5 6

TMURLampTS

ACFG


0

01

02

03

04

05

06

07

False

pos

itive

ratio


ACFG-TCPACFG-CBR

0

500

1000

1500

2000

2500

3000

3500Th

roug

hput

(kby

tes)









0

50

100

150

200

250

300

350

400

Thro

ughp

ut (k

byte

s)


0

50

100

150

200

250

300

350

400

450

0 4 8 12

TMURLampTS

ACFG


Thro

ughp

ut (k

bps)







0500

10001500200025003000350040004500

25 50 100 150

LampTSACFG

Number of nodes

Thro

ughp

ut (k

bps)


7 Conclusions




References


























RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation












LampTS

0

2

4

6

8

10

12

Num

ber o

f sw

aps a

lgor

ithm







0

500

1000

1500

2000

2500

0 10 20 30 40 50 60 70 80 90 100

TMURLampTS

ACFG


Pack

et D

elive

ry R

ate (

bps)


TMURLampTS

ACFG

0

200

400

600

800

1000

1200

1400

1600N

umbe

r of p

acke

ts dr

oppe

d



0 10 20 30 40 50 60 70 80 90 100

TMURLampTS


0

50000

100000

150000

200000

250000

Thro

ughp

ut (b

ps)



0 10 20 30 40 50 60 70 80 90 100

TMURLampTS

ACFG


0

3

6

9

12

15

18

Del

ay (m

s)


TMURLTSUC

ACFG

Det

ectio

n ra

tio

0

02

04

06

08

1

12






1 2 3 4 5 6

TMURLampTS

ACFG


0

01

02

03

04

05

06

07

False

pos

itive

ratio


ACFG-TCPACFG-CBR

0

500

1000

1500

2000

2500

3000

3500Th

roug

hput

(kby

tes)









0

50

100

150

200

250

300

350

400

Thro

ughp

ut (k

byte

s)


0

50

100

150

200

250

300

350

400

450

0 4 8 12

TMURLampTS

ACFG


Thro

ughp

ut (k

bps)







0500

10001500200025003000350040004500

25 50 100 150

LampTSACFG

Number of nodes

Thro

ughp

ut (k

bps)


7 Conclusions




References


























RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










0 10 20 30 40 50 60 70 80 90 100

TMURLampTS

ACFG


0

3

6

9

12

15

18

Del

ay (m

s)


TMURLTSUC

ACFG

Det

ectio

n ra

tio

0

02

04

06

08

1

12






1 2 3 4 5 6

TMURLampTS

ACFG


0

01

02

03

04

05

06

07

False

pos

itive

ratio


ACFG-TCPACFG-CBR

0

500

1000

1500

2000

2500

3000

3500Th

roug

hput

(kby

tes)









0

50

100

150

200

250

300

350

400

Thro

ughp

ut (k

byte

s)


0

50

100

150

200

250

300

350

400

450

0 4 8 12

TMURLampTS

ACFG


Thro

ughp

ut (k

bps)







0500

10001500200025003000350040004500

25 50 100 150

LampTSACFG

Number of nodes

Thro

ughp

ut (k

bps)


7 Conclusions




References


























RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation












0

50

100

150

200

250

300

350

400

Thro

ughp

ut (k

byte

s)


0

50

100

150

200

250

300

350

400

450

0 4 8 12

TMURLampTS

ACFG


Thro

ughp

ut (k

bps)







0500

10001500200025003000350040004500

25 50 100 150

LampTSACFG

Number of nodes

Thro

ughp

ut (k

bps)


7 Conclusions




References


























RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation
































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation











RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation









Documents

Research Article Angle and Context Free Grammar Based ...downloads.hindawi.com/journals/tswj/2016/3596345.pdfResearch Article Angle and Context Free Grammar Based Precarious Node Detection