Vulnerabilities Are Highly Challenging In Manet Computer Science Essay

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In the present world the security vulnerabilities are highly challenging in MANET. To get the maximum security and minimum threat there is lots of work going on. To effectively isolate the malicious node this paper proposes a Neuro fuzzy algorithm. By using fuzzy logic we can further improve the security level by identifying the malicious node more accurately. The concept behind the paper is as in real life scenario, trust and sharing. Here in this paper we use the concept of trusting supporters, sharing the companion list and routing through data. In order to get a secure high trust level, fuzzy logic is applied for evaluating routing response and isolates the malicious node. Trusted route is evaluated in sequence of operation and data is transferred at a most trusted level. Trust values are computed to each node by setting verge values. The values of each node is checked with the verge value. If the value higher than the verge value mark it as high trusted node or else low trusted node.The fuzzy logic is implemented using aarmp routing protocol. Thus the level of trust is increased to obtain accuracy of identification. The goal of getting a robust route without any malicious node is achieved.



1.1 Introduction to Wireless Sensor Networks :

Wireless Sensor Networks (WSNs) have been widely used since they are applicable for different tasks, including surveillance, environmental monitoring, and industrial applications.When related to infrastructure-based networks, WSNs can be positioned in practically any environment,particularly those where wired connections are not possible or the terrain is inhospitable, e.g. battlefields.The wireless adhoc network is the network in which the wireless devices directly communicate with each other without central access point. The nodes are randomly positioned in which every node acts as both host and router. Mobile adhoc network (MANET) is a kind of wireless adhoc network. In that routers are moving randomly and its topology may change rapidly.

MANET comprise of erratic host. It is not necessary that all the nodes in the network should be in the corresponding communication range. Consider that two wireless hosts are not within the transmission range in ad hoc networks, other set of mobile hosts which is reside between them can lead their messages, so that complete network is formed within the mobile hosts.

The ability and realizationof the network were mainly focused in most of the traditional mobile adhoc routing protocols. Ad hoc network are wireless network with no mended infrastructure in which nodes depend on each other to get the continuous connection of the network.It provides various methods for selforganizing networks. All nodes act both as pariticpants and routers. Due to the flexibility of the node, the routing topology may be subject to constant change. Thus, ad hoc routing needs special requirements for routing protocols.

The cooperative and self-organizing environment of the Mobile Ad Hoc Networking (MANET)technology begins the network toplentiful security attacks that can actively agitate the routing protocol and exhaust communication. Recently, in order to get secure route discoveryprocess in frequently changing MANET topologies numerous amount of protocols have been proposed. Theseprotocols are mainly designed to perform route discovery process only when there is a need of packet transmission between the source and destination node.they are known as thereactive routing protocols. In many cases, proactive discovery of topology can be more convinent.e.g., in networks with low- to medium-mobility, or with high connection rates and frequent communication with a large portion of thenetwork nodes.Coming to hybrid routing protocols which are the middle ground routing protocols,which is capable of adapting their operation to achieve the best performance under differing operational conditions through locally proactive and globally reactive operation.

Due to the accelerated progress of wireless local area network technologies such as Bluetooth, ZigBee, WiFi, WiMax, to name a few, MANETs are becoming increasingly popular in different applications such as healthcare monitoring,collaborative and distributed computing, emergency preparedness and response, and military services. In the dynamic MANET environment, nodes are assumed to cooperate among each other to provide routing service and forward packets. This requirement poses a security challenge when malevolent nodes are present in the network. Indeed, the existence of such nodes may not simply disrupt the normal network operations, but cause serious message security concerns, from data availability, confidentiality, and/or integrity viewpoints. In addition, the authentication of the message sender and receiver may not be easy to track.

Ant Colony Optimization(ACO) is an evolutionary heuristic algorithm based on a graph representation that has been applied successfully to even difficult computational optimization problems. The main idea of ACO is to model the problem as the search for a minimum cost path in a graph. Artificial ants walk through this graph, looking for good paths. Each ant has a rather simple behaviour so that it will typically only find rather poor quality paths on its own. Better paths are found as the emergent result of the global cooperation among ants in the colony.


Sensors are cheap, low-power devices which have qualified resources. The size of the sensors are very small and have the effectiveness of the wireless communication is even in small distances. A motes consitently contains a power unit, a sensing unit, a processing unit, a storage unit, and a wireless transmitter / receiver .Some commercially available sensor nodes, such as Berkeley MICAz mote, include limited computational capability (8MHZ and 8-bit), with few memory (128KB programming memory). A wireless sensor network (WSN) is posssessed of large number of sensor nodes with limited power, computation, storage and communication capabilities. Wireless Sensor Networks (WSNs) have recently attracted much attention because of their wide range of application, such as military, environmental monitoring, and health care industry. Unlike wired and Mobile Ad hoc Networks, wireless sensor networks are infrastructure-less and can operate in any environment as compared to the traditional networks.

Environments, where sensor nodes are deployed, can be controlled (such as home, office, forest, etc.) or uncontrolled (such as hostile or disaster areas etc.). If the environment is

known and under control, deployment may be achieved manually to establish aninfrastructure. However, manual deployments become infeasible or even impossible as the

number of the nodes increases. Fig 1.1 shows the simple view of sensor node.

Fig.1.1: Sensor Node Architecture

If the environment is uncontrolled or the WSN is very large, deployment has to be performed by randomly scattering the sensor nodes to target area. Thus, network topology cannot be known precisely prior to deployment in large scale WSN.


Sensor networks may resides of varioustypes of sensors , which has the ability are able to supervise a wide variety of ambient conditions that includes temperature, humidity, vehicular movement, lightning condition, pressure, soil makeup, noise levels, the presence or absence of certain kinds of objects, mechanical stress levels on attached objects, and the current characteristics such as speed, direction, and size of an object. Applications are categorize into military, environment, health, home and other commercial areas.

Military applications

Wireless sensor networks can be an integral part of military command, control, communications, computing, intelligence, surveillance, reconnaissance and targeting (C4ISRT) systems. The rapid deployment, self-organization and fault tolerance characteristics of sensor networks make them a very promising sensing technique for military C4ISRT.

Environmental applications

Some environmental applications of sensor networks include tracking the movements of birds, small animals, and insects, monitoring environmental conditions that affect crops and livestock, irrigation, flood detection etc.


Unfortunately, the desirable nature of a WSN introduces many security challenges. First, no physical security is available in WSNs. Wireless sensor nodes are often deployed in an open environment such as a battlefield. Thus, attackers can capture sensor nodes to steal secret data or reprogram them to execute malicious code. Because of the cooperative, self-configuring nature of wireless sensors, other uncompromised sensors can also be affected significantly. In the worst case, the entire WSN can fall in the attacker's hand.

Most WSNs have unique traffic patterns, which are different from wired networks or other ad hoc networks. Usually, queries are disseminated from cluster heads to sensor nodes and sensor data are transmitted from nodes toward cluster heads. As a result, an adversary can observe heavier traffic near the cluster head. Also, the adversary can correlate message transmission intervals. In this way, he can figure out where the cluster head is located. Once that is determined, attacks can be concentrated on the cluster heads or the nodes closest to it for maximal impact. Thus, security is critical to the success of WSNs.


Node compromise

Compared to most computing systems, motes in a sensor network are highly susceptible to physical attack. Nodes are placed in open areas, allowing attackers to capturethem. This enables an adversary to steal cryptographic information, view and alter their programming, and damage or replace their hardware.

Compromising routing information

An adversary could easily agitate a network by communicating false routing updates. This could lead to routing loops, generation of false error conditions, an increase or decrease in path lengths, and many other attacks.

Radio jamming

A denial of service (DoS) attack is broadly defined as any event that impairs or eradicates a network's aptitude for performing its expected function.

Sinkhole attacks

Sinkholes, also called black-holes, are created when an adversary advertises a very high-quality route to the base station. This may be a true advertisement or a faked one. The result of this announcement of a high-quality path is that neighbouring nodes will choose to forward packets through the malicious or compromised node.

Sybil attacks

Another common attack is the Sybil attack in which an adversary pretends to be several different nodes. Sybil attack is relatively easy to launch in a WSN, since a node in a WSN does not usually have a unique, trusted identifier.


Wormhole attacks commonly involve two distant malicious nodes colluding to understate their distance from each other by relaying packets along an out-of-band channel available only to the attackers.


A wireless sensor network has many constraints compared to a traditional computer network. The major similarity is the multi-hop communication method.

The basic difference between Sensor Network and Ad-hoc Wireless Networks are:

More nodes are deployed in a sensor network, up to hundred or thousand nodes, than in an ad-hoc network where usually involves far fewer nodes.

Sensor nodes are more constrained in computational, energy and storage resources than ad-hoc.

Sensor nodes can be deployed in environments without the need of human intervention and can remain unattended for a long time after deployment.

Neighbouring sensor nodes often sense the same events from their environment thus forwarding the same data to the base station resulting in redundant information.

Aggregation and in-network processing often requires trust relationships between sensor nodes that are not typically assumed in ad-hoc networks.

Due to these constraints it is difficult to directly employ the existing security approaches to the area of wireless sensor networks. Therefore, to develop useful security mechanisms, it is necessary to know and understand constraints of Sensor Nodes. The important constraints are

1. Very Limited Resources

All security approaches require a certain amount of resources for the implementation, including data memory, code space, and energy to power the sensor. However, currently these resources are very limited in a tiny wireless sensor.

Limited Memory and Storage Space:

In order to build an effective security mechanism, it is necessary to limit the code size of the security algorithm. For example, one common sensor type (TelosB) has a 16-bit, 8 MHz RISC CPU with only 10K RAM, 48K program memory, and 1024K flash storage. With such a limitation, the software built for the sensor must also be quite small. The total code space of TinyOS, the de-facto standard operating system for wireless sensors, is approximately 4K, and the core scheduler occupies only 178 bytes. Therefore, the code size for the all security related code must also be small.

Power Limitation:

Energy is the biggest constraint to wireless sensor capabilities.Therefore, the battery charge taken with them to the field must be conserved to extend the life of the individual sensor node and the entire sensor network. When implementing a cryptographic function or protocol within a sensor node, the energy impact of the added security code must be considered. The extra power consumed by sensor nodes due to security is related to the processing required for security functions (e.g., encryption, decryption,verifying signatures), and the energy required to store security parameters in a secure manner (e.g., cryptographic key storage).

2. Unreliable Communication

Certainly, unreliable communication is another threat to sensor security. The security of the network relies heavily on a defined protocol, which in turn depends on communication.

Unreliable Transfer:

Normally the packet-based routing of the sensor network is connectionless and thus inherently unreliable. Packets may get damaged due to channel errors or dropped at highly congested nodes.


Even if the channel is reliable, the communication may still be unreliable. This is due to the broadcast nature of the wireless sensor network. If packets meet in the middle of transfer, conflicts will occur and the transfer itself will fail.


The multi-hop routing, network congestion and node processing can lead to greater latency in the network, thus making it difficult to achieve synchronization among sensor nodes. The synchronization issues can be critical to sensor security where the security mechanism relies on critical event reports and cryptographic key distribution.

3. Unattended Operation

Depending on the function of the particular sensor network, the sensor nodes may be left unattended for long periods of time. There are three main threats to unattended sensor nodes:

Exposure to Physical Attacks:

The sensor may be deployed in an environment open to adversaries, bad weather, and so on.

Managed Remotely:

Remote management of a sensor network makes it virtually impossible to detect physical tampering (i.e., through tamperproof seals) and physical maintenance issues (e.g., battery replacement).

No Central Management Point:

A sensor network should be a distributed network without a central management point. This will increase the vitality of the sensor network. However, if designed incorrectly, it will make the network organization difficult, inefficient, and fragile.


The wireless adhoc network is the network in which the wireless devices directly communicate with each other without central access point.The nodes are randomly positioned in which each node acts as both host and router. Mobile adhoc network (MANET) is a kind of wireless adhoc network. In that routers are moving randomly and its topology may change rapidly.

MANET comprise of erratic host. It is not necessary that all the nodes in the network should be in the corresponding communication range. Consider that two wireless hosts are not within the transmission range in ad hoc networks, other set of mobile hosts which is reside between them can lead their messages, so that complete network is formed within the mobile hosts.

The source node can send the data packets through neighbour nodes to the destination node. The packet delivery is not granted if the neighbour node is a malicious node. Many methods are proposed to find out the malicious node but they are not accurately finding out the malicious node.

This paper proposes the neurofuzzy to accurately find out the malicious node using aarmp protocol. Consider the neighbour node as a friend .Each node calculates the trust value of the friend node. If the trust value reaches the verge value then it is a trusty node otherwise it is considered to be the untrusted node. The data packets are sent through the trusty nodes.



The directions and challenges of mobile adhoc networking are discussed. Important roles to take over in the future evolution technologies is carried out. The capabilities, applications and design requirements of mobileadhoc networking is described.Themalicious nodes are detected using confidence level evaluation. All nodes keep its own and its neighbour confidence level. The confidence level is used to compute trustworthiness. Two parameters are used to distinguish malicious nodes from the normal nodes. By using this method malicious nodes can be accurately detected.An algorithm to organize gaurded routing in mobile ad hoc networks is discussed.The authentication of nodes is done through challenges and friends concept. By using challenges, the information about the misbehaving node can be gathered vigorously. The rating of each node is calculated based on the packet it sent successfully.The weighted trust evaluation scheme is used to recognize the misbehaving node by monitoring its reported data. The Forwarded node provides the expectation value for each node. The expectation value of the particular node decreases when the node sends the meaningless information. A trust assignment and updating strategy to identify and to isolate the malicious node.T_req parameter is used to find the importance of content and type of the message. The path with high trust level can be used for message forwarding.

The Message security in MANETS using a trust based multipath AOMDV combined with soft encryption. This scheme produces the minimum route selection time by deciding the message and path degree of secrecy. The trust mechanism conduces the idea of detecting malicious node by monitoring the packets. The trust value of the node changes according to the transfer time of the packets. A multipath approach to message security in adhoc networks. Here data to be transmitted is split into many packets. Using jigsaw puzzle the split packets are combined together and sent using multipath routing. The tools used are multipath routing, all or nothing transforms and properties of polynomial. An Ant colony optimization technique is used to find the solution for computer problems. By this technique the best path can be found out from the available paths. Ant concept can be implemented using fuzzy logic. It is based on the amount of truth.

The trust level is used to calculate in a quantifiable manner between the sensor nodes in the adhoc network. The trust path is determined by the degree of trust value and the establishment. The nodes are randomly positioned in the network and transmission of packets without any guidelines of centralized node . Based on the trust value cluster head is chosen so that it will not be a malicious node.The expectation criterion of each nodes about its neighbours is observed through their neighbours.The detection of unauthorized nodes, misbehaving nodes and the competence of the battery are computed. In order to prove their similarities ,there is no need of interchanging various messages.

A trust model using fuzzy logic in sensor network is discussed. Trust is an aggregation of consensus given a set of past interaction among sensors. We applied our suggested model to sensor networks in order to show how trust mechanisms are involved in communicating algorithm to choose the proper path from source to destination.

malicious and malfunctioning node detection scheme using dual-weighted trust evaluation in a hierarchical sensor network is proposed. Malicious nodes are effectively detected in the presence of natural faults and noise without sacrificing fault-free nodes. Sensor nodes comprising the networks, in practice, have limited power, memory, and computational capabilities. Such networks are vulnerable to faults and malicious attacks. Hence it is important to detect faulty or malicious nodes in the networks to make correct decisions in the monitoring applications.

A novel schemebased on weighted-trust evaluation to detect malicious nodes is proposed. The hierarchical network can reduce the communication overhead between sensor nodes by utilizing clustered topology. Through intensive simulation, we verified the correctness and efficiency of our detection.sensors are randomly deployed in thefield. They form an unattended wireless network, collect data from the field, partially aggregate them, and send them to a sink that is responsible for data fusion.

Security is hardto achieve due to the dynamic nature of nodes as well as the vulnerabilities and limitations of the wireless transmission medium. To overcome these problems a self

adaptive distributed detection system is developed. In this approach we built a system to

detect misbehaving nodes in a mobile adhoc network. Each node in the network monitored its neighboring nodes and collected one DSR protocol trace per monitored neighbor. We implemented the system in network simulator "GloMoSim". After collecting parameters for each node in network represents normal behavior of the network. In the next step we incorporate misbehavior in the system and capture behaviour of network, which yields as input to our detection system. Detection system is implemented based on fuzzy logic concept. Simulation results show that the system has good detection capabilities in finding malicious nodes in network

MANET benefits from a secure environment, although, considering the presence of malicious

nodes, As a result of open environment, dynamic topology and lake of centralized safety structure, unfortunately these kinds of networks are highly vulnerable. Initially, most routing protocols assume that, all network nodes behave in accordance with routing protocols and there are not any malicious nodes these assumptions prepare the way for attacker to network, and therefore, various hidden attacks on routing protocols of these networks can takes place.

The intrinsic nature of wireless ad hoc networks makes them vulnerable to various passive or active attacks. Thus, there is no guarantee that a routed communication path is

free of malicious nodes that will not comply with the employed protocol and attempt to interfere the network operations.We survey the problem of secure routing

in ad hoc wireless networks, and discuss the related techniques of cryptographic key distribution. However, no matter how secure the routing protocol is, it is still possible that

some nodes are compromised and become malicious. The presence of comprimised nodes, especially in nodes that are communication bottlenecks, limit the effectiveness of the

described secure routing protocols. We therefore consider the problem of intrusion detection for such nodes. The intrusion detection problem and some solutions are described

in detail for a concrete queueing model of medium access. The extensions of the solutions to address the problem in more general scenarios are also discussed.

Security mechanisms must be deployed in order to counter threats against wireless ad-hoc networks. While cryptographic mechanisms provide protection against some types of attacks from external nodes, cryptography will not protect against malicious inside nodes, which already have the required cryptographic keys. Therefore, intrusion detection mechanisms are necessary to detect these Byzantine nodes. Intrusion Detection Systemsm(IDS) may be classified based on the data collection mechanism, as well as the technique used to detect events.While the requirement of intrusion detection for both fixed wired and wireless ad-hoc networks are the same.Wireless ad-hoc networks impose additional challenges. In general, the effectiveness of solutions designed for fixed wired networks are limited for wireless ad-hoc networks.

A trust based security protocol based on a cross-layer approach which attains confidentiality and authentication of packets in both routing and link layers of MANETs. the first phase of the protocol, wedesign a trust based packet forwarding scheme for detecting andisolating the malicious nodes using the routing layer information.It uses trust values to favor packet forwarding by maintaining atrust counter for each node. A node is punished or rewarded bydecreasing or increasing the trust counter. If the trust counter value falls below a trust threshold, the corresponding intermediate node is marked as malicious. In the next phase ofthe protocol, we provide link-layer security using the CBC-Xmode of authentication and encryption.

MANET protocol that secures the discovery and the distribution of link state

information across mobile ad hoc domains. Our goal is to provide correct (i.e., factual), up-to-date, and authentic link state information, robust against Byzantine behavior and failures of individual nodes. The choice of a link state protocol provides such robustness, unlike distance vector protocols , which can be significantly more affected by a single misbehaving node.Furthermore, the availability of explicit connectivity information, present in link state protocols, has additional benefits: examples include the ability of the source to determine and route simultaneously across multiple routes , the utilization of the local topology for efficient dissemination of data or efficient propagation of control traffic . Finally, a wide range of MANET instances is targeted by our design, which avoids restrictive assumptions on the underlying network trust and membership, and does not require specialized node equipment.

Two nodes communicate securely if they are able to exchange their public keys. Security is

guaranteed because is computationally intensive to decrypt the message. Recent advantages in quantum-based simulation may weaken somewhat the security of key-based transmission.

Based on this concept and with the help of homomorphicfunctions , a threshold cryptographic scheme was proposed. The key is divided into n shares and one does not have

to receive all n pieces back in order to retrieve the information. Receiving k of the n pieces suffices to calculate the key. It is computationally impossible to calculate the key if k-1 pieces

are received. Such flexibility is needed when transmitting thekey; if the key is not received a new key must be generated.

EADC forward to sink based on the calculated threshold through next CH node having higher residual energy. EADC is a cluster-based routing protocol. EADC calculates average residual energy and waiting time of each node. If a node has not receive any header message from remaining nodes within the waiting time, then it elects as a CH. The other nodes are joined as members to CH. CH broadcasts the schedule to its cluster members. During its schedule, nodes transmit the data to CH. If the distance between the CH to BS is less than the calculated threshold, then it will have BS as next hop. Otherwise, it will forward to next CH node having higher residual energy.

Malicious node detection is to detect when a node physically measures wrong values. This may be caused by faulty sensors, converters, exceeded sensor lifetime or even manipulations.

As a consequence and in contrast to former approaches, we divide the node functionality into functional, inoperable and partly functional. We also identify different types of faults.

We define faults to be significant discrepancies of measurements in comparison to those of neighboring nodes, i.e. spatial correlation, and as unusual behavior over time, e.g. strong leaps or alternating values. Beside this temporal correlation also full-scale or out of range measurements can be detected as failures. Another classification of detection schemes can be selfdetection and cooperative detection, i.e. neighbors of a node or a central instance are to determine its functionality. Irrespective of whether such detection will be performed on

each node in the network or on a central instance, we see a variety of benefits by utilizing this knowledge. Beside the simple diagnostic information of malicious readings, we

believe, that using this information nodes can save a lot of energy and therefore prolong network lifetime. Firstly a node, being aware of its malfunction, can save energy he would

otherwise spend for sensing. Especially WSNs in the field of chemistry, equipped with catalytic or optic sensors, will profit from this approach. Secondly, nodes do not have to send

faulty measurements. This exculpates not only the node itself but mainly the whole network as the number of packet transmissions is reduced. A third advantage would be that

network management in terms of role assignment can be significantly improved, as a node, once depicted as only partly functional, can be assigned non-sensing tasks like routing or

aggregation. Consequently, a fully functional node should not be used for this work. As a result, we believe that the network will stay functional for longer time. In addition such a

functionality-based role assignment opens the door to use cheaper components, accepting some faulty sensors, for large area observations.

Sensors locations plays a major role in many sensor network applications. A number of techniques have been proposed recently to discover the locations of regular sensors based on a few special nodes called beacon nodes, which are assumed to know their locations (e.g., through GPS receivers or manual configuration. However, none of these techniques can work properly when there are malicious attacks, especially when some of the beacon nodes

are compromised. This work introduces a suite of techniques to detect and remove compromised beacon nodes that supply misleading location information to the regular

sensors, aiming at providing secure location discovery services in wireless sensor networks. These techniques start with a simple but effective method to detect malicious beacon signals. To identify malicious beacon nodes and avoid false detection, to detect replayed beacon signals there are several techniques are carried out. A method to reason about the suspiciousness of each beacon node at the base station based on the detection results collected from beacon nodes, and then revoke malicious beacon nodes accordingly. Finally, simulation analysis is carried out to evaluate the proposed techniques. The results show that our techniques are practical and effective in detecting malicious beacon nodes.




The FACES protocol accomplishes establishment of friend networks in MANETs in the same way as in real life scenarios. When people meet in a new community or a group they are strangers to one another. Tasks are completed by trusting one another unconditionally initially and with time the trust level increases with the number of successful task completions. Initially breach of trust is possible as no one has any information about the people with malicious intentions. With time, the several trust relationships are formed which leads to the formation of a community were tasks are completed efficiently. The FACES algorithm is divided into four stages, viz. ChallengeYour Neighbor, Rate Friends, Share Friends and RouteThrough Friends. The first three stages of the algorithm are periodic, while the fourth is on demand. The algorithm provides authentication of nodes by sending an initial challenge.In this work we have looked into the secure routing techniques DMR , TMR and MTMR , and have designed the proposed FACES protocol to provide better security.


The major part involved in this mechanism is that every node itself recognizes the malicious nodes accurately. The verge value is considered at each node. The choice of selecting the verge value plays a main role in finding the attacks in MANETS. When the drop rate of the packet is higher than the verge value, then the node is marked as malicious node.

The malicious nodes are finding out based on some verge values. After getting the appropriate node with certain trust values identify the nodes which are nearer to the destination node and then performs the transmission.






Wireless sensor nodes are deployed in the network. The nodes are communicated with each other in order to generate the logs of the neighbour node. By obtained log information the nodes calculate the trust values for each node. For calculating trust information fuzzy logic is applied by using aamrp routing protocol. There by the packets are sent through the trusted network in a secure manner. The performance analysis is carried out by analysing with the existing approaches.



Relay nodes

Input parameters

Direct Trust Model

Indirect Trust model

Centralized database

Overall Trust

Update Transaction log

Calculate log

Local database

Interaction data

Apply Fuzzy logic

Decision Making

Setting Threshold value

Trusted Network

Routing path

Fig 1.Source node generates the log there by calculating the overall trust for that node. Then fuzzy logic is applied. By setting the threshold value decision making is done in order to transmit the packet in a trusted network. Centralized data contain the trust value for all the nodes. Each node stores its own log value in Localized data contains the log value of the node. So the packet can be sent through a trusted network. Each node compares its trust value with verge value if it is higher then that node consider as a trusted node. Finally the packet is sent through the trusted route in a secure manner.




At first, the amount of trust in each sensor node is calculated so that sensor network can be used in a secure manner. Based on the calculated result, each sensor node come to conclusion that whether to communicate or not. The fuzzy logic [7] is implemented by using an AAMRP routing protocol. Ant colony optimization is used to find solutions for challenging problems it is helpful to find out the best paths through graphs.

Step:1 Sensor nodes are created.

Step 2:The nodes are communicating with each other to get the log of the neighbor nodes. Then the trust level is calculated.

Step 3:The trust level of the sensor nodes are calculated as follows:

Step:3.1 To calculate the trust level of sensor nodes, assume X as trustworthiness and Y as untrustworthiness.

Step:3.2 The X and Y are in the range of 0<=X<=1, 0<=Y<=1.

Step 3.3: Each sensor node has some estimation value, those values are cached in the base station.

Step 3.3: Estimation values are generated from the past actions

Step 3.4 The estimated values are sent from one association context to another.

1.Min : X = min(Xi,Xj),Min : Y = min(Yi,Yj)

2.Max: X = max(Xi,Xj),Max : Y =max(Yi,Yj)

So that the trust and untrust value can be calculated like this:

X = avg (Xi,Xj)/1-(avg(Xi,Yj)+avg(Xj,Yi))

Y = avg (Yi,Yj)/1-(avg(Xi,Yj)+avg(xj,Yi))

After calculating the max and min trust hold values compare the each node trusted value to the threshold value.

The node which has the higher value than the threshold value set as the trusted node. Then end the data packets to the destination through the trusted node.

InFig 2. The nodes are created. Node 2, node 3 and node 4 are the sender. Node 1is the wireless access point and node 0 is the sink node. In Fig 3. Nodes are communicating with each other in order to generate the log information. Thus the trust information is checked and shared. In Fig 4. Calculating the trust values and send the packets through trusted nodes in a secure manner.


Fig. 5. Number of malicious nodes isolated versus total no of nodes

The stability of the reliable routing scheme is that find out by isolating the malicious node. In the Fig 5, By using rules fuzzy recognizes higher number of malicious nodes. When a trust value of the node is lower than the verge value, it is declared as minimum trusted node and isolated from the network. So we can accurately find out the malicious node. The existing multipath routing of the node depends mainly on the trust of a node which detection of the malicious node is not accurate. So to conclude that particular node is malicious itself it takes more time. Using fuzzy, more malicious nodes are detected accurately even in large no of nodes and it takes less time to find out the path, as more conditions could be done.

Fig.6. Throughput Versus simulation time

In Fig.6, Throughput is the mean of effectual packet delivery in a channel. We can see the throughput of fuzzy is maximum, means that a larger number of data packets can be transmitted from source to destination in a given amount of time.

Fig.7. Packet dropped Versus simulation time

In the Fig.7, The packet drop is minimal in Fuzzy, means that the route which is accomadating malicious nodes can be effectively eliminated. The packet dropping rate is larger in other multipath routing protocols because the packets are routed through the misbehaving nodes. So when the number of nodes is increased , then the number of packet drop also gets increases.



In this paper, For adequate and impervious communication, the fuzzy logic technique is designed to get trust model between the nodes in the wireless sensor network. The fuzzy logic is implemented by using aarmp routing protocol. We mainly concentrate on the trust values of each node that participating in the wireless network. Each node selects the trusted node by comparing the trust value of the nodes. The packets are sent through the nodes which have high trust values and nearer to the destination. So it finds out malicious node correctly when compared to existing approaches.