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An Efficient Trust Based Cross Layer Protocol for

Wireless Sensor Networks

Nithin Kumar Goud1

Master of Technology in Information Security and Cyber Forensics

SRM University, Kattankulathur, Chennai, India,

E-mail: n [email protected]

J. Godwin Ponsam

2

Assistant Professor(S.G), I.T. Department

SRM University, Kattankulathur, Chennai, India,

E-mail: [email protected]

Abstract—The flexibility of the wireless sensor network communication is increased by the cross

layering concept. It also decreases the modularity through the inter layer exchange of parameters which

improves the performance, reliability, and efficiency of wireless sensor network communication. The inter

layer data exchange is employed to enable the choice of nodes that are capable of efficient communication.

However, the nodes used for data forwarding is vulnerable to security attacks because of the cross

layering concept. In this paper, we propose a cross layering architecture using trust to provide defense

against those security attacks by employing a fuzzy logic trust calculation mechanism, that is used to

calculate the reputation in the nodes to ensure secure forwarding and reliable delivery of data. This trust

based cross layer architecture is used to design a protocol known as cross layer protocol using fuzzy logic

trust, that uses multiple parameters which are extracted through the inter layer information exchange so

as to mitigate the effects of security threats in wireless sensor network. The proposed cross layer protocol

using fuzzy logic trust is compared with the present protocols. The conducted experiments evaluated the

security performance of the protocols and the results show that the proposed protocol outperforms the

present protocols in terms of packet delivery.

Key words: Wireless sensor networks; fuzzy logic trust calculation; cross layer architecture; efficient

protocol, security attacks.

I. INTRODUCTION

Cross layering is a method of designing a standard protocol which has a reference layered architecture

that is altered according to a specific layered architecture which incorporates developing new interfaces among

layers of the Open Systems Interconnect (OSI) model, constructing the boundaries of the layers, gathering the

informat ion of ways any other layer is designed and creating a protocol using that information at a particular

intersection of layers, extracting required parameters from all the layers. When the protocols are designed by

collaborating layers then the layered architecture is violated. Protocols designed in that manner will have few

conditions on the processing of informat ion at the other layers. The OSI model is a layered architecture

consisting of seven different layers which has the general tasks of networking div ided into different layers and

their priority of services which are provided by utilizing the parameters from each layer. When the protocols are

designed at every distinct layer, the respective services are also implemented. This kind of architecture does not

allow communication direct ly among the non adjoining layers because of the procedural calls and procedural

responses. Instead, the reference architecture is violated and protocols are designed through permitting

communicat ion directly among the protocols and sharing variables at the non adjoining layers. If the layered

1 To whom any correspondence should be addressed.

2 To whom any correspondence should be addressed.

International Journal of Pure and Applied MathematicsVolume 118 No. 20 2018, 343-357ISSN: 1314-3395 (on-line version)url: http://www.ijpam.euSpecial Issue ijpam.eu

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architecture is violated and parameters are transferred among the adjoining layers then it is known as the cross

layer design with respect to the reference architecture.

TABLE I: PARAMETERS INVOLVED IN CROSS-LAYER DESIGN

Layer Information Parameters

Physical Layer

Sensor node location, Communication

pattern, Radio signal range, Signal to

noise ratio.

Signal strength, Distortion and equalization

parameters, Dynamic range

MAC/Link Layer MAC packet delay, Link bandwidth,

Link quality

Waveform, Transmission power, Operation

bandwidth, Carrier channel frequency,

Dig ital modulat ion, Signal processing gain,

Duty cycle, Type of pulse shaping filter,

Cyclic prefix and FFT size for OFDM

Network Layer Multiple routing, Routing affin ity,

Routing lifet ime,

Clustering parameters, Packet routing

algorithm, Packet routing metrices,

Network scheduling algorithm

Trans port Layer Timeout clock, Congestion window,

Packet losses rate

Packet congestion control, Packet rate

control

Application

Layer

Network map, Topology control

algorithm, Server location

simplex, half-duplex, full-duplex

communicat ion mode, Encryption,

Decryption, Source coding

Mainly the proposals of cross layering design intend to resolve only a few specific issues created by wireless

sensor nodes. The conventional TCP sender mistaking a wireless error to be a demonstration of community congestion is an instance of this type of issue. The primary characteristics of the wireless medium is altered

opportunistically by the conventional types of cross layer designs with the aid of using the variations in network channel from d iminishing on the top layers of the OSI model. Therefore the overall purpose of designing the cross

layer protocol is to work according to the modifications done in the wireless network conditions. The wireless medium creates the new modular ties and it is employed by the cross layer design of the wireless sensor network

and it can also be accommodated in the process of further layering in the OSI model. The cross layer design is typically classified into the following four types in the context of cognitive networks

Creation of the new interface between the layers.

Collaborating of adjoining layers.

Coupling of cross layer designs without embedding new interface.

Vertical calibration among the existing layers.

A. Creation of the new interface between the layers

The creation of new interfaces between the layers of the network model is required in a cross layer design

for parameters sharing among the layers during the execution. The implementation of a new interface between

the layers, generally not found in any layered architecture, vio lates the architecture. There are different

approaches of creating new interface based upon the flow of informat ion between the new interfaces:

Unidirectional upward flow of informat ion from lower layer to higher layer.

Unidirectional downward flow of informat ion from higher layer to lower layer.

Bi-directional flow of information iteratively between the higher and lower layers.

B. Collaborating of adjoining layers

When a super layer i.e. the combination of more than one adjoining layers are collectively used for the

implementation of the cross layer design and the services obtained from this super layer is the collaboration of

the services of the layers constituting the super layer which does not require creation of interface in the

International Journal of Pure and Applied Mathematics Special Issue

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remain ing stack. The interfaces which exists in the original network architecture can be interfaced with the

remain ing stack and collaboratively into the super layer. Theoretically it is possible to design a cross layer that

creates a super layer in the network model but practically it is not been implemented yet. The collaboration

among the PHY layer and the MAC layer to create a super layer has a tendency to reduce the boundary among

those adjoining layers by cross layer designing.

C. Coupling of cross layer designs without embedding new interface

This is the designing of cross layer in which two or more layers are collaborated for data sharing without

developing any new interface at the time of execution. The architectural cost is that the feasibility is very low to

update first layer without doing relevant modifications to second layer because no new interfaces are created.

The capability of multipacket reception is more by the PHY layer and due to this the design of MAC layer is

considered for the uplink of a wireless sensor networks. Which means that the physical layer is able to receive

multip le packets in a given time. This unique functionality of receiving mult iple packets at the physical layer

will substantially modifies the function of the MAC layer and redesigning will be done. The design of smart

antennas at the PHY layer coupled with MAC layer of wireless sensor networks can also be considered.

D. Vertical calibration among the existing layers

The final class in which the parameters that span throughout layers are adjusted is called as the vertical

calibrat ion among the existing layers of the network model. The overall performance of the network observed at

the extent of the cross layer application is a function of all the parameters at the different layers under it. The

joint tuning improvises to attain higher overall performance than the individual settings of the parameters present

at the different layers. The vertical calibrat ion is the setting of parameters of the different layers during the time

of designing with the enhancement of few metric in a static procedure. The setting of parameters is done only

once in static vertical calibration. The designing can also be done dynamically during execution time by

emulating a cross layer oriented protocol that functions according to the network conditions such as varying

network traffic, etc. Dynamic vertical calibration continuously updates the informat ion retrieved from the

parameters ext racted from various layers of the network model.

Fig. 1. Cross layer communication.


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The definition of the trust is the belief or expectation or guarantee of integrity, sincerity and competency

of the entities that possess on each other while performing a transaction. Trust in the area of network security is

to safeguard and control interactions among the wireless sensor nodes. This trust is acquired from the events that

occurred during the previous transactions which are obtained as feedback and this permits the nodes to

participate in future transactions. Trust is derived from the concept of control theory and is collaborated with

subjectivity and uncertainty. Trust measures the degree of truthiness of one node to another node and it is

denoted by the variable that has the numeric representation of the trust relationships in a prescribed range

determining the participat ing nodes in a wireless sensor networks .

E. Trust characteristics

The trust of the sensor nodes has the following attributes in the wireless sensor networks:

Reflexive : It is the self trust of the nodes which are participating in the trust estimation process.

Subjective: It is the trust estimation depending upon the observers and records of past behaviour of the

sensor nodes.

Context sensitive: It is the trust estimat ion with a specific importance of the sensor nodes in a wireless

sensor network.

Dynamic: It is a changing trust value over time and space for non static wireless sensor networks .

Asymmetric: It is the trust estimation which is mutually independent between the two nodes which are

participating in the trust estimation process . In this case first node trusts the second node but the second

node may or may not trust the first node.

Incomplete transitive: It is the trust link between two or more sensor nodes and depending on the limit of

the trust relationships between chain of the participating nodes. In this case first node trusts the second

node, the second node trust the third node but the first node may or may not trust the third node.

Fig. 2. Trust Estimat ion Processes.


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F. Fuzzy logic trust model

In most of the scenarios, trust is a indistinct relationship between two or more entities so it is incorrect to

interpret it as the chance of probability which ranges from 0 to 1. On the other hand, fuzzy logic is a type of logic

which has multiple values that is approximate rather than specific and it is incorporated from the fuzzy based set

theory which is implemented on reasoning. Uncertainty is the most intrinsic character in the wireless sensor

networks based on trust, due to the fact the rules which must be enforced and the proof that must be supported

might be interpreted as fuzzy. The assessment of indecision is basically done by the subject of logic trust model

where only a few uncertainties are assumed as a value of probability and thus cannot be described using the

model of p robability. There are many fuzzy log ic based trust models that have been presented which deals with

indecision in management of trust by creating a sequence of fuzzy rules . The fuzzy logic based trust models are

mainly used for recognising the patterns and taking appropriate decisions in the control systems . Fuzzy logic

method does not solve the model system mathematically and uses a sequence of rules which have the „if and

then‟ conditions in order to resolve the control problem. The steps followed in fuzzy ru les are the following:

Defining the fuzzy sets and it's rules.

Initialising the value of the input variable to the process of the fuzzy logic system and the degree is

calculated to which the input variable fo llows the rules of fuzzy logic.

The output data is determined by applying the fuzzy ru les and conclude it based on the degree of

match.

The result is evaluated and relevant feedback is given to moderate the fuzzy logic rules.

II. LITERATURE SURVEY

Idris Abubakar Umar in [1] proposed an improvised version of the XLM framework known as the T-

XLM whose security features are not strong. They also proposed TruFix which is a protocol based on the T-

XLM framework and the security features was compared with other security protocols. The DWSIGF secure

protocol modifies the routing method with the adjacent nodes and achieves security and the FuGeF secure

protocol, the security was achieved by using the fuzzy logic scheme for selecting the nodes for data forwarding.

I.F.Akyildiz and M.C.Vuran in [2] proposed an initiative determination concept which combines many

networking and communicat ion functionalities and implement them into a single protocol. The cross-layer

procotcol (XLP) p rovides the functionalities of congestion control, medium access and routing. The initiative

determination determines the trust and performs the data forwarding depending upon the trust values , contention

on the receiving node, congestion control in the local network, and the duty cycle operation which is distributed

in order to achieve efficiency and reliability in the process of communication in the wireless sensor networks.

H.Shen and H.Zhang in [3] proposed a routing protocol which has the properties of power aware as well

as geographic routing namely energy efficient beaconless geographic routing protocol EBGR. It does routing

from sensor to sink in dynamic wireless sensor networks which does not have any state, loop free and energy

conserving. The protocols such as GPER, BLR and EBGR has greater performance on energy consumption and

hop counts for packet delivery from sensor to sink with min imal loss of packets using greedy forwarding in

various energy versus time interval of RIS and sleeping probability of theoretical analysis in wireless sensor

network simulations.

S.Palazzo, G.Morabito, A.Leonardi and L.Galluccio in [4] proposed new forward ing paradigms, such as

geographic forwarding for the wireless sensor networks having low battery, storage and processing capabilities

of wireless sensor nodes which make communications simple and efficient in wireless sensor networks without

the requirement of exchanging any signaling for getting the location informat ion and thus enabling the nodes to

use varying level of transmission power. Thus the MACRO protocol was proposed which was based on the

MAC layer and the routing layer to perform packet forward ing using the informat ion of own position and the

destination position.

T.He, J.A.Stankovic, L.Fang, and A.D.Wood in [5] proposed a configurable secure routing protocols i.e.

Secure Implicit Geographic Forwarding SIGF-0,1 and 2 known as resource bound security solutions (RBSS)

using a contention based approach. The normal protocol semantics is modified to achieve security by altering it

the with a fixed period of collect ion window and the number of responses send by the participating nodes in the

form of routing parameters of MAC and routing layer. SIGF-0 is stateless and provides security using

probability, SIGF-1 uses the record of events and trust is calculated to protect against attacks and SIGF-2

provides security by using the state which is shared by the adjacent nodes.


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M.Mahdavi, Z.M.Ismail, KJumari and M.Hanapi in [6] proposed a Dynamic Window Secured Implicit

Geographic Forwarding (DWSIGF) uses random collection window period to advance the security performance

of SIGF-0 by creating extemporaneity in the protocol semantics . During the blackhole and sybil attacks the

DWSIGF provides protection and outperforms the SIGF-0 protocol. When the collection window requires

dynamic t ime to collect Clear to Send (CTS) message in data control packet, it uses DWSIGF to select the

forwarding node.

Z.A.Zulkarnain, Z.M.Hanapi, I.A.Umar and A.Sali in [7] proposed Fuzzy-based Geographic Forward ing

(FuGeF) which improves the security performance and maintains the QoS performance of DWSIGF by using a

fuzzy logic system by extracting physical, MAC and routing layer parameters for analysis in the wireless sensor

networks. The FuGeF protocol utilizes parameters such as progressive distance, remain ing energy, connectivity

cost and the fuzzy logic system for selecting nodes. The packet delivery rate of FuGeF is more than the

DWSIGF-P and DWSIGF-R in a network with chances of attack without altering the performance of the

wireless sensor networks.

John S.Baras, Nassir Benammar, Svetlana and Radosavac in [8] proposed an attack detection scheme for

wireless sensor networks based on modelling of MAC layer protocols using Extended Finite State Machines

(EFSM) and analysed the denial of service attacks in wireless sensor networks which propagates from MAC to

routing layer and breaks the connectivity of important routes using several traffic patterns that an intelligent

attacker can generate.

Dr.P.Manimegalai and M.Balamurugan in [9] proposed a Fuzzy logic based Secure Intrusion Detection

System (FSIDS) for detecting the intrusion and avoiding it by providing data integrity and authentication. The

routing is performed on the grouped nodes and recognition is done which depends on the trust evaluated on the

adjacent sensor nodes in the wireless sensor network. The trust of the sensor nodes is calculated using the

certification revocation record, packet identification and receiv ing capability with clock based intrusion detection

system.

M.K.Ghose, Kalpana and Sharma in [10] proposed an united all-inclusive security framework using

Intelligent Security Agent (ISA) to improvise the level of security in transacting entities and cross layer

interactions at different layers that will p rovide security services in wireless sensor network.. Th is framework has

many components like link layer communication protocol, trust framework, intrusion detection and prevention

system and key management scheme.

Dr S.Palan iswami and Rajaram.A in [11] proposed a security protocol based on trust which achieves

authentication and confidentiality of data packets in the link layer and routing layer of wireless sensor networks.

The packet forward ing scheme is based on trust and it is used for detecting and detaching the suspicious sensor

nodes by using the informat ion gathered from routing layer. Also, security is provide using the CBC-X mode of

authenticating and encrypting the data packets at the link layer.

A.Govardhan Kumar, V.Bapuji and R.Naveen in [12] proposed a protocol namely DAS which is not

centralized for neutralizing the sybil attacks by limiting the quantity and size of the sybil groups using the assets

of the users i.e. the segment of the network which is trustworthy and the malicious nodes for attacking but

comparatively lesser edges of attack.

Tanveer A.Zia, Chee Onn Chow, Idrees Sarhan and I.Gawdan in [13] presented a novel idea of solutions

related to security in wireless sensor networks using the concept of Cross Layer Based Security Framework

(CLBCSF). The CLBCSF framework forms an efficient framework model with energy saving, distance aware

and dynamic clustering routing protocol which is secure, works on the basis of exchanging the parameters

present at the cross layer with the parameters present at all the layers of the protocol heap, for hierarchical

clustering in the wireless sensor networks.

M.K.Ghose and Sharma Kalpana in [14] proposed a security framework CLIFFs using ISA, that has an

adaptive security solution for every application scenario and saves energy to a large extent in wireless sensor

networks. CLIFFs can be enhanced using keying techniques and energy efficient routing protocols.

Ma Bin in [15] proposed a cross layer trust model in wireless sensor network using the cloud theory

where each node assists each other by sharing control and data packets from one node to another.

Samir I.Shaheen, Hosam A, Rahhal and Ihab A.Ali in [16] proposed trust based cross layer model for the

wireless sensor network which is static and it is based on the concept of cross layering in wireless sensor

networks. The performance is evaluated when the network has many sensors and when the number of malicious

nodes increase in the network, and the proposed model performed well even if the malicious nodes were high in

the wireless sensor networks.

M Jevtović and Veličković Z in [17] proposed Markov Process Decision (MDP) an optimization

algorithm that reduces long time average throughput of a single user in the wireless sensor network. MDP is


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used to resolve the min imization problem using the cost functions that provides network parameters optimizat ion

of mult imedia QoS applicat ions.

Yan Sun, Husheng Li, Zhu Han, Wenkai, and Wang in [18] researched on security issues in wireless

sensor networks focusing on security breaches and its defense in each layer of wireless sensor networks. A trust

based cross layer defense framework is proposed that detects abnormality in every layer of the wireless sensor

network and cross layer trust fusion reducing the damage caused by attackers.

Ozur B.Akan, Akyildiz, Ian F and Mehmet C.Vuran in [19] proposed a cross layer protocol which is

better than the existing conventional layered protocol arch itecture that are presently being used in wireless sensor

networks. The functionalities and the informat ion of conventional communication layers are combined to create

a unique protocol. This unique protocol performs distributed duty cycle operation, local congestion control and

received based contention using initiative determination in order to obtain efficient and reliable communicat ion

in wireless sensor networks.

Ping Li, Yu, Keqiu Li, Yanli, and Wanlei Zhou in [20] presented a review of all the trust methods of

wireless sensor nodes as the trust issue is becoming a v ital factor in security schemes in wireless sensor networks

and the existing methodologies of trust is utilized to implement attack avoidance schemes . These are further

categorized into various types of attacks and avoidance methods related to trust schemes in wireless sensor

networks.

III. PROPOSED SYSTEM

In this paper, a cross layer architecture using trust is proposed which utilizes trust to determine whether

the node is elig ible for routing process or not. The cross layer protocol using fuzzy logic trust is also proposed

which is designed using the trust based cross layer arch itecture. This protocol uses the fuzzy logic systems for

estimating trust between the sensor nodes participating in data forward ing in a wireless sensor network. This

estimation has an ability to utilize multip le inputs from participating sensor nodes, take appropriate decisions as

well as overcome the uncertainties in order to produce the most legitimate output such that trust are not assumed

as the probabilit ies instead as a human decision resulting in an entity being trusted depending upon factors such

as high, more or less.

Fig. 3. Proposed System Design


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A. Cross Layer Architecture Using Trust

In this proposed cross layer architecture using trust, the information is pulled from the various layers of the sensor nodes group which intends to ensure the secure communication between the sensor nodes in the wireless

sensor networks. The proposed cross layer architecture using trust concept R is formulated in equation 1, which is the assessment of initiation (AI) in association with acquiring the trust (T) between the sensor nodes. The complete

procedure of assessment of init iation (AI) is shown in equation 2.

R = AI ⨂ T (1)

Good, if

AI (2)

Fair, if

Unsuited, if Otherwise

AI is expressed apart from the binary form i.e. it has a fuzzy value. Additionally, Signal to Noise Ratio (SNR) is denoted by ξRTS which is the determination of the Ready to Send broadcast from received Signal to Noise Ratio

ξRTS, the rate at which the packets are being relayed by a node is denoted by λrelay and it is determined by the time required to receive the packets by the Ready to Send broadcast, βop is the occupancy period of the buffer memory

and ϵrem is the energy remained in a node of the wireless sensor network.

Trusted, if

T (3)

Uncertain, if

Untrusted, if Otherwise

The trust of the sensor nodes is represented by T whose value updates the trust which is framed in

equation 3. The initial values of T is subsequently iterated when the data packets are routed and is presented in

equation 4. Furthermore, the ratio of the successful packet delivery is represented by ∂r, the fairness ratio is

represented by fr which selects the best route and the duration of data transfer is measured by . The threshold

values which are associated with the arrays of the parameters are represented by the dissimilarities in both

equation 2 and 3. These constraints ensures the ability of the cross layering framework using the trust values to

neutralize the effect of security attacks without compromising the reliab ility of communication that defines the

assessment of init iation using trust.

The reputation of the node are marked as trusted if (T ≥ TTh

), uncertain (if Tmin

≤ T < TTh

) and distrusted if it falls below threshold. This trust calculation uses the fuzzy logic trust estimation process in order to obtain the trust

value. The trust value of each participating node is analogous when it is directly observed and the trust value is

estimated by using the proposed framework. The fo llowing equation calculates the trust value ( ) of node a with


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respect to node b within the range of radio signal as function (f) of the network data packets rate (α) and network

data packet volume (µ) :

Which is the Bayesian function of the data packets dropped in the wireless and the packets falsely introduces in

the wireless sensor networks.

α is denoted as the packets dropped by node a and b which has the fo llowing conditions

Packets sent by node A to node B and that are discarded by node B

Total packets discarded by node B which may be due to congestion or not

Node A‟s calculation of node B‟s priority vs all other packets of remaining nodes

Delay in forward ing the packets by node B

µ is denoted as the packets which are sent by a malicious node and has the following conditions

Packets misrouted by node A

Packets misleadingly inserted by node A.

The constraints both emulate the functions similar to the parameters of traffic and volume which are required

to generate trust based values that allows the operations of the layers of sensor nodes .

B. Cross Layer Protocol Using Fuzzy Logic Trust

The information is provided for the proposed cross layer protocol using fuzzy logic trust in this phase by using

a modified IEEE 802.11 wireless channel and fuzzy logic calculation system to generate a feedback which enables the secure routing in the wireless sensor networks . It is implemented both in the network acquiring phase which is

the assessment of initiation of the eligible sensor nodes and the data forwarding phase which is the trust between

the eligible sensor nodes. This determination is done to initiate secure selection of sensor nodes for forwarding and routing data packets to their destination.

a. The Network Acquiring Phase

The network acquiring phase is initiated when a node A broadcasts the Request to Send (RTS) signal

which includes the location of node A‟s and the destination node within the radio range. When the broadcast is

received, the participating node set up a Clear to Send (CTS) response to node A. Trust values for all the

participating nodes are recalculated and updated as the number of interactions increases which is calculated by the

fuzzy logic method to denote the suitability of the participating sensor nodes that is good, fair or unsuited. If the

node has the highest trust value the it is given priority according to the fuzzy logic and the functions which is set

to rebuild the boundary conditions.

b. The Data Forwarding Phase

When the suitable node is selected for forwarding the data, the data forwarding phase is initiated. Data

is progressed from the source node to the forwarding node and finally to the destination node. Parameters such as success ratio of forwarding (Sr), which is the reliability in delivery of data, transfer duration (τ) which is the

period elapsed for sending a data packet in relation to number of data packets moved within that duration and fairness ratio (fr) which decides and selects the node for the relay in next hop between the neighbor nodes. If a

node was selected in the forwarding process previously and found to be participating again is penalized by decreasing its fr and τ values which is done to make sure that the node is isolated for further rounds in order to

avoid malicious activity.


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Fig. 4. Flow Diagram.


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C. Proposed Protocol:

Source ID Destina tion ID Fuzzy Trust Value Next Hop Data Flags

4 4 2 4 16 2

Fig. 5. Proposed protocol format

The proposed protocol is shown above which has the source node ID of 4 bytes, destination node ID of 4

bytes, the fuzzy trust value of 2 bytes which is the trust of the participating node, the next hop of 4 bytes which is

the next forward ing node, data of 16 bytes which is the payload and flags of 2 bytes for priority etc.

The proposed protocol is having the following properties:

• Lightweight and flexible enough to provide security.

• Cooperative for providing mult ipath routing.

• Trustworthy by providing fuzzy trust mechanism for selecting nodes.

• Energy efficient to extend the network lifetime .

• Compatible with the existing wireless sensor networks.

• Scalable according to the rapidly growing size of wireless sensor network.

The proposed protocol uses the trust values to provide data packet forwarding and maintain a trust value for

each node involved in data forwarding. The trust value for every node is either increased or decreased by punishing or rewarding the node. The participating node identifies the data packets by adding its trust value and

forwards the data packet towards the destination node. If the trust value is below the trust threshold then the corresponding and the intermediate node is penalized and marked as malicious and unsafe for data forwarding.

IV. RESULTS

The simulation results shows the effectiveness of the cross layer protocol using fuzzy logic trust. The

Network Simulator 2 (NS2) is used to perform the simulation of the proposed protocol. The graphical

representation of the following is depicted in the below p ictures :

Packet delivery rat io

Network delay

Energy consumption

Packet loss rate

The packet delivery ratio is the rate at which the packets is being send from node A and delivered at

destination node. The network delay is the time consumed in delivering the packet. The energy consumption is

the amount of energy utilized to perform the transmission of the packets. The packet loss ratio is the rate at

which the packets has been dropped. The simulation results depicts the existing and the proposed systems in the

graphs.


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Fig. 6. Packet delivery ratio Fig. 7. Network delay

Fig. 8. Energy consumption Fig. 9. Packet loss ratio


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V. CONCLUSION

In this paper, we have proposed a trust architecture based on cross layering concept, which is an advanced

model of the cross layering framework that previously lacks in persisting security in the wireless sensor

networks. The cross layer protocol using fuzzy logic trust is designed using the trust based cross layer

framework and it is compared with other secure protocols. This proposed protocol includes node selection

mechanis m, fuzzy logic t rust estimat ion and forced fairness mechanisms to provide secure routing in the wireless

sensor networks. The simulation evaluated the effectiveness of these introduced mechanisms within the cross

layer protocol using fuzzy logic trust. The proposed protocol outperforms the existing protocols in terms of

security performance which is shown in the results . Hence it enables to achieves an collaboration between

security and other QoS metrics in the wireless sensor networks . Future implementation could be preventing

different kinds of attacks and evaluation of various parameters to determine the role the existing parameters play

in analyzing various forms of attack.

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Nithin Kumar Goud is a final year M tech student specializing in Information Security and Cyber Forensics in SRM University. His area of interest is in information and wireless security.

J.Godwin Ponsam is an Assistant Professor (SG) in SRM University. He received his M.E in Computer Science and Engineering from Karunya University, 2006 and B.E in Computer Science and Engineering from M.S. University, 2004. His research interest is in Manet Security.

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