Chapter 3 Trust based Neighbor Identiﬁcation in WSN using ...shodhganga.inflibnet.ac.in/bitstream/10603/49296/9/09_chapter 3.pdf · Chapter 3. Trust based Neighbor Identiﬁcation

Chapter 3

Trust based Neighbor Identification in

WSN using Agents

Wireless Sensor Networks(WSNs) are vulnerable to attacks that compromises data confiden-

tiality, integrity and authentication. Secured routing in WSN should focus on identifying the

neighbors that are free from various types of attacks. It becomes a challenging task to iden-

tify the neighbors that are trustworthy since attackers make the nodes not only to pretend

as if they are trustworthy and free of any types of attacks but also create a feeling that they

are involved in avoiding any types of threats. In such a situation, traditional mechanisms

of security schemes may not be sufficient and thus we need intelligent schemes to overcome

such challenges. Software agent technology provides the promising secured routing mech-

anism where in autonomous agents are involved in identifying all types of security threats

and secured routes in WSN’s with the help of neighbor nodes that are trustworthy and the

routes may be created using such neighbors.

Fundamental components to decide upon a neighbor as trustworthy are wireless chan-

nel connecting the neighbor node and computation activity of a neighbor node. An adversary

may attack wireless channel in several forms such as jamming, radio interference, tampering,

collision, repeated requests, sybil attack, sink hole attack, black hole attack, worm hole at-

tack, hello flood attack, de-synchronization attack, reprogram attack,etc.[38]. Computation

52

Chapter 3. Trust based Neighbor Identification in WSN using Agents 53

activity of a sensor node plays significant role to perform various activities such as sensing

and interpreting different physical parameters, processing and storing the sensed data, ag-

gregating and communicating the data to neighbor nodes, etc.[39]. Attacks that affect the

computation activity of sensor nodes may be classified into two categories: (1) black out

attacks and (2) mis-behavioral attacks. A black out attack is one in which the node is not

able to perform any type of activity such as sensing the event, processing the data, commu-

nication with neighbors, etc. A node mis-behavioral attack is one in which the node exhibits

normal behavior but performs abnormal computations and this type of attack is difficult to

identify. For example, suppose a node is required to sense the current weather condition

(such as temperature, pressure, humidity, etc.) and report the same to its neighbor. In such

a situation, the mis-behaving node may correctly sense the environment; but it may alter

the position of the parameters in the data field to be communicated to the neighbor. Thus,

temperature, pressure, humidity will be read wrongly so that the network suffers in terms of

energy spent for such communication, time required to process and transmit to the neighbor.

A trusted neighbor node is one where it is free from two types of attacks listed above.

The task of securing wireless channel and computation activity of a node using tradi-

tional security mechanisms are not sufficient since such schemes do not possess intelligent

techniques. Effective deployment of security mechanism needs intelligence to identify such

security violations and should be able to take autonomous decisions intelligently. Since soft-

ware agents are suitable to take autonomous decisions and act intelligently, we use agent

technology to identify trustworthy neighbors against the two types of security violations in

WSNs.

Agents are software programs activated on an agent platform of a host. Agents use

their own knowledge base to achieve the specified goals without disturbing the activities of

the host. They have two important properties: mandatory and orthogonal, which differ-

entiates them from standard programs. Some of the mandatory properties are: autonomy,

reactive, proactive and temporally continuous. Some of the orthogonal properties are: com-

municative, mobile, learning and believable[40]. Mobile agent is an itinerant agent which

contains program, data, execution state information, migrates from one host to another host


in a heterogeneous network, and executes at a remote host until it completes a given task[41].

In this chapter, we propose a Trust based Neighbor Identication in Wireless Sensor

Networks (TNIWSN) using agents to identify trustworthy nodes in a network. The trusted

neighbor identification is necessary for routing the data through trustworthy neighbors and

avoid untrusted neighbors that are compromised by various threats. The proposed scheme

operates in following phases. (1) Defining safeguard agency that consists of one static agent

known as Safeguard Manager Agent (SMA) and one mobile agent known as Trusted Neighbor

Agent (TNA) and a knowledge base. (2) Safeguard agency identifies trustworthy neighbor

nodes using static and mobile agents by means of trust model that comprise of the probability

model and Message Authentication Code (MAC) model. The probability model identifies

trusted neighbors based upon the probabilities of trustworthiness of wireless channel and the

trustworthiness of sensor node. MAC model encrypts the message using the two keys k1 and

k2 are generated with k-ERF (Error Resilient Function) key generation process to ensure the

trustworthiness of neighbors identified by the probability model. (3) MAC’s are dynamically

computed by agents (either on sender node or on neighbor node) by generating keys with the

help of k-ERF. (4) Agents effectively identify possible security threats on wireless channel

and node. Simulation analysis shows that TNIWSN outperforms Neighbor based Malicious

Node Detection (NMND) in Wireless Sensor Networks in terms of average success ratio and

memory overhead.

3.1 Related Works

Some of the related works are as follows. Authors in [42] focus on the study of security

threats to the WSN that proposes a security solution using mobile agent technology. The

model is based on two requirements confidentiality and integrity. To maintain confidentiality

a software mobile agent based key management technique is proposed which consists of three

phases Initial key distribution phase, control phase, execution phase. For integrity the author

proposed agent based integrity maintenance model for WSNs.The owner creates dummy offer

signs with private key, encrypt with public key and then transmit. At the receiving host,


the message is decrypted using public key and private key.

A bio-inspired trust and reputation model in WSN (BTRM-WSN) is proposed in [43]

which is based on Ant Colony Systems (ACS) and aims at providing trust and reputation in

WSNs. Pheromone updating is carried out by ACS that includes measuring of the quality of

a path, how to punish or reward a server depending on dynamic behavior of pheromone. The

author in [44] proposes a method to defend against sink hole attacks using mobile agents.

A routing algorithm with multiple constraints is proposed based on mobile agents. It uses

mobile agents to collect information of all mobile sensor nodes to make every node aware of

the entire network so that a valid node will not listen the cheating information from malicious

or compromised node which leads to sink hole attack. The significant feature of the proposed

mechanism is that it does not need any encryption or decryption mechanism to detect the sink

hole attack. This mechanism does not require more energy than normal routing protocols

like AODV. The system proposes two algorithms. Agent navigation algorithm tells how

does a mobile agent gives network information to nodes and visits every node. Data routing

algorithm tells how a node uses the global network information to route data packets.

The work given in [45] presents a neighbor-based malicious node detection in WSN in

which malicious nodes are modeled as faulty nodes to lead to an incorrect decisions that

cannot be easily detected. Each sensor node makes a decision on the fault status of itself

and its neighboring nodes based on the sensor readings. Most erroneous readings due to

transient faults are corrected by filtering, while nodes with permanent faults are removed

using confidence level evaluation. Each node maintains confidence levels of itself and its

neighbors, indicating the track records in reporting past events correctly.

Security enforcement in WSN is proposed in [46] where the network is partitioned into

clusters, each having a high-end cluster head. The cluster heads are further equipped with

trusted computing technology such that they act as online trusted parties. Mutual Secure

Neighbor verification (MSN) in WSN is proposed in [47]. MSN is defined as the capability

of a node to verify the claim by another node placed within a certain physical distance from

the verifier. To mitigate the MSN problem, each node should announce its location and the

power level for transmission. Cooperative and base station verifications are used to detect


nodes that lie about their locations. The authors in [48] focus on the critical role played by

mobile agent(MA) for security and robustness of a WSN in addition to data fusion. The

design objectives JAID (Jamming Avoidance Itinerary Design) algorithm are as follows: (a)

to calculate near-optimal routes for MAs that incrementally fuse the data as they visit the

nodes and (b) in the face of jamming attacks against the WSN. It modifies the itineraries of

the MA’s to bypass the jammed area(s) while not disrupting the efficient data dissemination

from working sensors. If the number of jammed nodes is small, JAID only modifies the

pre-jamming scheduled itineraries to increase the algorithm’s promptness. Otherwise, JAID

re-constructs the agent itineraries excluding the jammed area(s). Another important feature

of JAID is the suppression of data taken from sensors when the associated successive readings

do not vary significantly. Data suppression also occurs when sensors’ readings are identical

to those of their neighboring sensors.

The authors in [49] highlights seven good reasons for using mobile agents for fault tol-

erant networks. Agents reduce the network load, enhance fault tolerant capability, provide

personal assistance, secure brokering, distributed information retrieval, support telecom-

munication networks services, work-flow applications and groupware-support for the flow

of information among coworkers, monitoring and notification-An agent can monitor a given

information source without being dependent on the system from which it originates. The au-

thors in [50] propose security goals for routing in sensor networks, show how attacks against

ad-hoc and peer-to-peer networks can be adapted into powerful attacks against sensor net-

works, introduce two classes of novel attacks against sensor networks sinkholes and hello

floods, and analyze the security of all the major sensor network routing protocols. The au-

thors describe crippling attacks against all of them and suggest countermeasures and design

considerations.

A flexible security management framework to overcome the drawbacks of early research

is proposed in [51]. The security management framework is divided into eight models. The

node is checked for particular rule if it is satisfied then it is declared as valid node other-

wise invalid. The authors in [52] propose to use the mobile agent paradigm for reducing

and aggregating data in planar sensor network architecture. The proposed architecture is


called Mobile Agent based Wireless Sensor Network(MAWSN). Agents perform the follow-

ing functions: (1) eliminating data redundancy among sensors by application context-aware

local processing at the node level, (2) eliminating spatial redundancy among closely-located

sensors by data aggregation at the task level, (3) reducing communication overhead by con-

catenating data at the combined task level. The author uses four performance metrics Energy

consumption, Average End-to-end packet delay, energy delay, packet delivery ratio.

Use of Smartcards as a tamper resistant devices to offer security to WSN is proposed

[53]. Hardware cryptographic platform includes link level communication, transport proto-

col description, application interface description and demands for power consumption. The

authors specify that smartcards are highly standardized devices that offer common commu-

nication interface and can be used with cryptographic platform in accordance standards.

Secure hardware contains secret cryptographic key, which is used only for encrypting data

and this key is stored in the card which cannot be read. The authors in [54] propose an

Agent-based Trust and Reputation Management scheme (ATRM) for wireless sensor net-

works that considers bandwidth and delay overheads. The objective of the scheme is to

manage trust and reputation locally with minimal overhead in terms of extra messages and

time delay. ATRM scheme requires that a nodes trust and reputation information be stored

respectively in the forms of t-instrument and r-certificate by the node itself. Since, nodes

cannot manage and compute their own trust and reputation, ATRM requires that every node

locally hold a mobile agent that is in charge of administrating the trust and reputation of its

hosting node. In this sense, mobile agents provide nodes a one-to-one trust and reputation

management service.

The authors in [55] propose an agent-based approach that maintains the nodes current

status. The detection of a node is possible through the ratings of each node. Ratings of

a node are known through the ratio of packet forwarded by packets received. Further, the

ratings can also be done using the E-commerce models. In E-commerce models, each node

votes the successive node depending upon the ratio of packet forwarded by packets received.

The update ratings will be done through Sporas formula or Molinas formula or with a

combination of both models. The proposed agent-based framework also uses reputation of


a node through neighboring nodes as part of trust calculation. Authors in [56] present the

IPv6 over Low power Wireless Personal Area Networks (6LoWPAN) architecture in WSN

where the routing can be automatically performed. It combines both the hierarchical Internet

protocol version 6 address structures and the secure address configuration algorithm. During

the mobility process, a mobile node does not need a care-of address, so the mobility handover

process includes neither the care-of address configuration operation nor the address-binding

operation. As a result, the mobility handover cost and delay are reduced and packet losses

caused by node failures are avoided. The key distribution scheme in WSN is proposed in

[57] where the cluster head is determined based on the probability of change in the average

path length. A security mechanism is proposed for the vital link and the ordinary link so as

to balance the energy consumption over all nodes.

3.2 Our contributions

The proposed agent based trusted neighbor identification scheme in WSN is motivated by

observing several drawbacks of existing trustworthy neighbor identification schemes that are

severely suffering from vulnerabilities of wireless channel and sensor node. In this paper, we

propose an agent based trusted neighbor identification in WSN that uses probabilities of the

channel and node along with the MAC model for their trustworthiness. Our previous work

[12] discussed trusted neighbor identification in WSN using only MAC model. The work was

not supported by probability model and also lacked detailed formulation of components of

the scheme. This paper provides an extension to the work by providing detailed functioning

of the scheme, examples and simulation based performance analysis.

Our contribution in the paper are as follows: (1) defining trust model that comprises

probability model and MAC model to find security violations of wireless channel and sensor

node, (2) employing agents to carry the encrypted message to the neighbor nodes, (2) using

agents to identify trustworthy neighbors, (3) engage agents to dynamically update trustwor-

thiness of neighbor nodes, and (4) simulation analysis in terms of average success ratio and

memory overhead and comparing our results with NMND.


3.3 Trusted Neighbor Identification

A sensor node willing to transmit the information to the sink node securely is required to

do so by finding the trusted neighbors through which the routes can be set up. It becomes

important to identify the trusted neighbors since the neighbors may be compromised by

various types of attacks. The challenge is to find trustworthy neighbors. The fundamental

requirement to identify trustworthy neighbors depends upon two components. (1) Trustwor-

thiness of a channel connecting the neighbors and (2) trustworthiness of a neighbor node

itself. These components are discussed in trust model.

3.3.1 Trust Model

Possible threats to the sensor networks from adversaries are in terms of threats to the wire-

less channel and threats to sensor nodes. The wireless channel in WSN is prone to several

attacks as there is no control over the packets transmitted. The trustworthiness of wireless

channel means a channel that provides complete security of data in terms of authenticity,

confidentiality and integrity, etc. for transmission. Wireless channel may be made more

secured if the information to be transmitted on the channel is hidden by using cryptographic

techniques. The behaviors that are used to evaluate trustworthiness of wireless channel are

ensuring secure communication and secure transmission of data over the channel. Secured

communication schemes are implemented by various means such as cryptographic techniques,

efficient key distribution and management techniques[58][59]. A secure transmission refers to

a process that involves protecting access to proprietary data as it is being transferred from

a point of origin to a point of termination[60][61]. Secure communication involves a bidi-

rectional communication with usage of processes like encryption, decryption, cryptographic

approaches, efficient key management and strategic algorithms.

One of such cryptographic mechanisms is generating Message Authentication Code

(MAC) and encrypting the resulting message thus providing authenticity, confidentiality

and integrity. Misbehavior of a neighbor node is another major concern since it is difficult

to identify whether a node is compromised. A compromised node may seem to be a normal


node but the processing of data is affected thereby the wrong computations yield unexpected

results. To handle such type of attacks, one needs intelligent techniques wherein the affected

communication and processing platforms are easily identified. We develop a trust model

that is required to tackle security violations of wireless channel and sensor nodes.

Trust model consists of the probability model and MAC model. The probability model

identifies trusted neighbors based upon the probabilities of trustworthiness of wireless channel

and the trustworthiness of sensor node. MAC model encrypts the message using the two

keys k1 and k2 generated with k-ERF to ensure the trustworthiness of neighbors identified

by the probability model.

Probability Model

The probability model to identify trustworthy neighbors is described as follows. Let a sensor

node have n number of neighbor nodes among which let [TN ] be the set of trustworthy

neighbor nodes.[TN ] contains the list of neighbor nodes which are differentiated as trusted

or untrusted by probabilities values. Our objective is to find [TN ] at a node that are

trustworthy neighbors. The two components that estimate [TN ] are the trustworthiness of

wireless channel connecting the neighbors and the trustworthiness of each neighbor node.

Let Pr(trust channel)i be the probability that the wireless channel to ith neighbor node is

trustworthy and Pr(trust node)i be the probability that the ith neighbor node is trustwor-

thy. Since Pr(trust channel)i and Pr(trust node)i are independent events, Pr(TN)i, the

probability that ith neighbor node is trustworthy defined at a node is given by equation 3.1.

Pr(TN)i = Pr(trust channel)i × Pr(trust node)i (3.1)

where 0 < Pr(TN)i ≤ 1, 0 < Pr(trust channel)i ≤ 1 and 0 < Pr(trust node)i ≤ 1.

Pr(trust channel)i and Pr(trust node)i are computed by agents as discussed in section 3.4.

A node having its neighbors list is defined as a set [TN ] as given by equation 3.2.


[TN ] = Concat[Pr(TN)i] (3.2)

for i = 1, 2, 3...n, where n is the number of neighbor nodes. Concat[Pr(TN)i] represents

concatenated set of non-negative real numbers such that 0 < [Pr(TN)i] ≤ 1. Equation

3.2 represents all the neighbor nodes of a node. The ith neighbor node is trustworthy if

Pr(TN)i ≥ θ, where θ (0 < θ ≤ 1) is a threshold that decides the neighbor node as trust-

worthy. Finally, [TN ] is updated with binary values with the following rule: If Pr(TN)i ≥ θ,

then Pr(TN)i = 1, else Pr(TN)i = 0. Since each binary value in [TN ] represents the status

of a neighbor node, all trustworthy neighbor nodes will have its corresponding bit in [TN ]

as 1. The threshold θ is application dependent and it is set by the administrator.

For example, a node X computes trustworthy neighbors list in [TN ] by entering prob-

ability values corresponding to its neighbor nodes [1, 2, 3, 4, 5, 6, 7] as shown in Figure 3.1.

X

1

2

3

4

5

7

Z

WY

6

0.23

0.01

0.56

0.78

0.26

0.34

0.98

Figure 3.1: Trusted Neighbor Node Selection

Corresponding to 7 neighbors, the probability values in [TN ] of node X are given in

equation 3.3.

[TN ] = [0.98, 0.23, 0.01, 0.56, 0.78, 0.26, 0.34] (3.3)

Suppose, the threshold θ = 0.25, then the trusted neighbors list is updated with binary


numbers as shown in equation 3.4.

[TN ] = [1, 0, 0, 1, 1, 1, 1] (3.4)

Thus, among 7 neighbor nodes, only 5 nodes are trustworthy (i.e., node 1, node 4, node

5, node 6 and node 7) and node 2, node 3 are not trustworthy. Further, MAC model ensures

the trustworthiness among trusted neighbors identified by probability model. That means,

among 5 trustworthy neighbors found by probability model, some of them may proved to be

untrusted using MAC model.

MAC Model

The cryptographic systems are designed to perform complicated encryption and the creation

of message authentication becomes challenging in spite of various attacks from adversaries.

Many protocols are designed based on the assumption that the hosts posses a secret random

string known as key and it is conveniently taken for granted that the entire key is kept secret

from an adversary. There might be a possibility that an adversary may detect a part or

entire key which is called as key exposure problem and it has significant practical interests.

The keys required for obtaining MAC and encrypted message are generated by Exposure

Resilient Function (ERF)[62].

The reason for using ERF for the key generation is that it provides highly randomness

in the generated key such that if any part of the key is known to the adversary, it is not

possible to recover the entire key. We introduce the mechanism of MAC generation using

ERF and we describe how the scheme is implemented to identify the trusted neighbors in

order to maintain confidentiality, authentication and integrity.

In specific, we use an adaptive k-ERF to generate a random key. k-ERF is defined as

a function f for a random input seed s. Suppose (P (A(s)) = 1) represents the probability

that an adversary reads all the s bits out of R bits such that k bits cannot be read by the

adversary is given in Equation 3.5.


|Pr[A(s) = 1]− Pr[A(R) = 1]| ≤ ǫ (3.5)

where k = R− s and ǫ→ 0.

The process of generating MAC using k-ERF is shown in Figure 3.2.

Message

+

Message+MAC

k1

+Transmitted message

k2

k−ERF k−ERF

Figure 3.2: MAC generation to identify trusted neighbors

The MAC encrypted message is sent to the neighbors that are found to be trustworthy

by probability model. Re-computation of MAC on neighbor nodes ensures the trustworthi-

ness of the wireless channel and the sensor node thereby endorsing the trusted neighbors

identified by the probability model.

3.4 Trusted Neighbor Identification using Agents

Software agents are used to identify the trusted neighbors with the help of trust model

discussed in section 3.3.1. Trusted Neighbor Identification using Agents in WSN comprises

of Safeguard Agency (SA) and a Knowledge Base (KB) as shown in Figure 3.3. Routing

Agency (RA) shown in dotted box in the figure helps in establishing secured routes through

trusted nodes identified by SA. The components of RA, its functioning and secured routing

is discussed in chapter 4.

We use Safeguard Agency for the purpose where in agents take decisions autonomously

by establishing secured communication with neighbors.

SA comprises of agents and a KB. A static agent known as Safeguard Manager Agent

(SMA) triggers mobile agent known as Trusted Neighbor Agent (TNA). SMA and TNA


KB

SMA

Safeguard

TNA

TNA

Agent dispatches

Agent Arrives

Routing

Agency(SA)

Agency(RA)

Figure 3.3: Secured Routing Agency

identify trusted neighbors that are free from various types of attacks. RA comprises of a

static agent known as Route Manager Agent (RMA) and mobile agents known as Route

Construction Agent (RCA) and Route Sustenance Agent (RSA). RMA and RCA construct

secured routes from a source node to a sink node with the help of only trusted neighbors that

are identified by safeguard agency. Once the secured routes are constructed, it is equally

important to sustain the routes for the complete duration of data transfer. RMA and RSA

perform the task of route sustainability against security violations. KB consists of various

information used by the agents to identify trusted neighbors.

Trusted neighbors are identified in every node proactively, i.e., trusted neighbors are

updated regularly so that the RA constructs the routes only with trusted neighbors thereby

avoiding possible security violations. Trusted neighbor based routing operates in two phases:

(1) Identifying trusted neighbors using SA and (2) constructing secured routes using RA.

In this paper, we propose the scheme for trusted neighbor identification using SA and

the scheme of routing with trusted neighbors using RA will be our future work.


Agent Structure

The packet structure of an agent and its attributes to perform the given task of neighbor

node identification is given in Table 3.1.

Table 3.1: Agent packet structure and its attributesAgent class Agent header Agent functions Agent dataStatic Src. & Destn. Static & mobile InformationMobile Addresses, lifetime, agent functions needed to perform

Clone number, etc. agent functions

Agent class: There are two types of agents- static agents and mobile agents. Static

agents perform various tasks at the node such as monitoring the performance of the node,

creating mobile nodes and deploy them for specific application, take autonomous decisions,

etc. Whereas mobile agents perform the task assigned by the static agent by visiting other

nodes and taking autonomous decisions at the visited node, returning the refined information

to the static agent. Agent header: This field contains source address, visiting node address,

lifetime of an agent, clone number, etc.

Agent functions: Specific functions of agents include MAC computation, carrying keys

to the visiting nodes, destroying itself either at the expiry of its lifetime or the visited node

is found to be untrustworthy, etc. ⁀Agent data: It is the information needed to perform agent

functions.

The details of agents and their functions are discussed in the following section.

3.4.1 Identification of Trusted Neighbors using Safeguard Agency

In this section, we discuss the procedure to identify the trusted neighbors by SA. SA identifies

the trusted neighbors against channel and node vulnerabilities. The structure of SA on a

node willing to identify its trusted neighbors is shown in Figure 3.4 which contains agents

SMA, TNA, k-ERF generator and a Knowledge Base (KB). Suppose the SA in node 2


wishes to identify its trusted neighbors, the SMA triggers the mobile agent TNA to visit its

neighbors and identify whether the neighbor is a trusted one.

1

2

3

4

5

67

8

9

1211

10SMA

TNA

k−ERFGenerator

Safeguard Agency

SMA: Safeguard Manager AgentTNA: Trusted Neighbor Agent

Sensor Ntework

KB

Figure 3.4: Safeguard agency for trusted neighbor identification

The MAC computation and encrypting the message is given in Algorithm 2. The

secured message M to be communicated to the neighbor is comprised of information such

as source address (SRC .ADDR.), destination address (DESTN .ADDR.), Time Stamp(TS ),

nonce (NONCE ) and agent code (AGENTCODE ). The message is broken into blocks Mi,

where i = 1, 2, ...n. For each block Mi, MAC is computed with the key k1 generated using

the k-ERF model given in Equation 3.5 and the final encrypted message ENC is obtained

by encrypting the MAC appended message with k2. SMA generates the number of TNA’s

equivalent to the number of neighbors presently existing and each TNA carries the message

ENC to the respective neighbors.

Knowledge Base

The knowledge base maintained by each node is organized in two parts: one part is node

knowledge base (node KB) and the other part is neighbor node’s knowledge base (neighbor


Algorithm 2 Encryption of a Message by SMA

1: Begin2: Initialize input data as M ; M = {M1, M2, M3, ..., Mn};3: Mi = {SRC.ADDR., DEST.ADDR., TS, NONCE, AGENTCODE}; for i = 1, 2, ...n;4: Generate 2 keys k1 and k2 using k-ERF;5: C0 ← 0;6: C[k]=array of length k;7: for i = 0 to n− 1 do8: Compute MAC with following steps;9: Ci+1 = E(k1, Mi+1 ⊕ Ci);

10: Ci ← Ci+1;11: C[k] = Ci+1;Ci+1 are stored in an array C[k];12: end for13: C = C[1]⊕ C[2]⊕ C[3]⊕ ....⊕ C[n];14: Final encrypted message ENC = E(k2, C);15: End

node KB). KB in SA helps to identify trusted neighbors using trust model. The components

of node KB are: node address, neighbor node address, TS, k1, k2, ENC, [TN ], θ and nonce.

The components of neighbor node KB are: Pr(trust channel) and Pr(trust node). Tables

3.2 and 3.3 depict the organization of node KB and neighbor node KB, respectively.

Table 3.2: Node KBNode address 172.121.253.4TS 2012-11-14 T2:30 UTCk1, k2 128 bitsENC Encrypted Message[TN] [1, 0, 0, 1, 1, 1, 1]θ 0.22NONCE 02

Table 3.3: Neighbor node KB172.121.253.6 Pr(trust channel) 0.63

Pr(trust node) 0.54

The specific purpose of KB components are listed below.

• Node address: It is the address of a node willing to identify its trustworthy neighbors.


• TS: It is the maximum amount of time required to perform trusted neighbor identi-

fication mechanism. If TNA brings the information about a neighbor within a time

stamp, then the information is accepted (based on this information, the node may be

proved to be trustworthy or untrustworthy); else the node is rejected assuming the

node as not trustworthy.

• k1 and k2: Random keys generated by k-ERF generator for encrypting the message

using MAC.

• ENC: Encrypted message.

• [TN ]: List of all the neighbor nodes (in binary) that depicts whether the nodes are

trustworthy or untrustworthy.

• θ: Probability threshold, used to differentiate trusted and untrusted neighbors.

• Nonce: Used to identify the session uniquely.

A node also maintains the data related to its neighbors in Neighbor node KB. For

example, the components corresponding to a neighbor 172.121.253.6, the node 172.121.253.4

maintains the following data.

• Pr(trust channel): It is the probability of a channel to the neighbor node being trust-

worthy.

• Pr(trust node: It is the probability of a neighbor node being trustworthy.

Similarly, the node 172.121.253.4 maintains the data related to all its neighbors.

Functioning of Agency

Safeguard Agency (comprising of SMA and TNA) is responsible for identifying trusted

neighbors. The sequence of operations performed by SMA and TNA are discussed in this

section.


• Safeguard Manager Agent (SMA): It is a static agent in SA that gets activated whenever

a node wishes to identify its trusted neighbors. The functions of SMA are as follows. (1)

Creates TNA and clones number of TNA’s equal to the number of neighbor nodes. (2)

TNA’s visit neighbor nodes and compute Pr(trust channel) based on bit errors, (3)

On visited neighbor node, TNA gets Pr(trust node) computed by visited node SMA,

as SMA has the knowledge of security level of that node. (4) SMA of originator node

computes Pr(TN) based on the Pr(trust channel) and Pr(trust node) brought by

TNA from neighbor node and calculates the set of trusted neighbor nodes [TN ] with

a given threshold θ. (5) SMA of originator node generates k1 and k2 using k-ERF

generator. (6) Computes MAC using k1 and encrypts the sum of message Mi and

MAC using k2 and generates encrypted message ENC as shown in Figure 3.2. (7)

Sender SMA generates a NONCEn1. (8) Each TNA carries the copy of ENC and

NONCEn1 to the SMA of visited neighbor node that are listed in [TN ]. (9) The

SMA of neighbor node decrypts ENC using k2 to obtain (Message + MAC) and

further computes MAC using k1 for the message (note that k1 and k2 are the same

keys brought by TNA to visited neighbor SMA). (10) Compare MAC computed by

visited node SMA with the MAC appended to the message. (11) If the time required

to perform steps 9 and 10 is greater than TS, then go to step (a), else go to step (b):

(a) stop processing and kill TNA, (b) if both the MAC’s are matched then visited

node SMA generates ack packet (that includes NONCEn2) by encrypting with k2

and hands over encrypted ack to TNA. (12) If MAC computed by visited node SMA

does not match with the MAC appended to the message, it kills the TNA and do not

send any message to originator node SMA.

• Trusted Neighbor Agent (TNA): It is a mobile agent generated by SMA. The functions

of TNA are as follows. (1) Visits the neighbor node and computes Pr(trust channel)

based on bit errors. (2) Returns with Pr(trust channel) of the channel and Pr(trust node)

of visited node. (3) Carry ENC and NONCEn1 computed by SMA to a neighbor

node along with the generated keys k1 and k2 and the same is handed over to the

SMA in the visited node. (4) Returns the ack and NONCEn2 generated by SMA of


the visited neighbor to SMA of originator node. (5) If visited neighbor node SMA

is not successful in computing correct MAC, TNA is destroyed by the visited node

SMA.

The purpose of finding trusted neighbors using the probability model is that it identifies se-

curity violations of the channel and the sensor node thereby it provides a means of first level

security in finding trusted neighbors. The purpose of computing the MAC and encrypting

the message for a given data at both the nodes (sender node and its neighbor node) and

comparing them is to achieve authentication, confidentiality and integrity. These security

features that have been incorporated in agent based system are as follows. (1) Authenti-

cation: Since the secret keys (k1 and k2) are known only to the sender and its neighbor

nodes, if the MAC brought by TNA from originator node matches with the MAC com-

puted at its neighbor, then neighbor is assured that message is not altered. This is because

an adversary can only alter the message but not the MAC as the keys are not known to

the adversary. Thus, it is not possible for the adversary to alter the message. Thus, au-

thentication is achieved. (2) Confidentiality: The message concatenated with MAC at the

sender is encrypted using secret key k2 and the encrypted message is carried by TNA. The

neighbor node decrypts the encrypted message brought by TNA with the same key k2. The

result of decryption is to obtain the message concatenated with MAC. At both sender and

its neighbor node, same key k2 is used and as the key k2 remains secret throughout the

transmission, the message leakage is not possible thus providing the confidentiality of data.

(3) Integrity: Since the agent based scheme uses proper sequence numbers to keep track of

transmitted messages and the adversary cannot alter the sequence numbers, the integrity of

the data is achieved.

Sometimes, the identification of trustworthy nodes in WSN using agents may be a

problem to handle security threats for resource constrained sensor motes. However, we

deploy agents to perform trustworthy neighbor node identification and monitoring security

violations. If the node is resource constrained, the agent dies there itself and such node

is eliminated from the network. Hence, the resource constrained sensor motes may also be

involved in the process.


3.5 Simulation Model

Agent based trusted neighbor selection scheme is simulated in various network scenarios to

assess the performance and effectiveness of the approach. Event driven simulation is used

in which the execution of various functions takes place at discrete events in a chronological

sequence. Simulation environment for the proposed work consists of four models: (1) Net-

work model, (2) Trust model, (3) Propagation model and (4) Traffic model. The models are

discussed below.

• Network model: A sensor network is generated in an area of l× b square meters. It

consists of N number of nodes that are assumed to be connected to a base station at

the boundary of a network.

• Trust model: Trust model consists of Pr(TN) computed by agents which is used

to identify first level trusted neighbors based on the threshold θ and MAC model to

confirm the trustworthy neighbors.

• Propagation model: Free space propagation model is used with propagation constant

β. Transmission range of a node is r for a one-hop distance.

• Traffic model: Constant bit rate model is used to transmit fixed size packets, Trpkts.

Coverage area around each node has a bandwidth, BWsingle−hop, shared among its

neighbors.

3.5.1 Simulation procedure

The proposed scheme is simulated using the following simulation inputs. l = 1000 mtrs., b

= 1000 mtrs., N =[50 to 300], Pr(TN)= 0 and 1, θ=[0 to 0.2], r = 350 mtrs., Trpkts =

multiples of 1000, BWsingle−hop = 500 Kbps.

Simulation procedure involves following steps.

1. Generate sensor network environment: The nodes are randomly deployed in a fixed


area and the topology changes for every instant defined by simulation inputs. Within

certain interval, the performance evaluation is carried out.

2. Agents visit the neighbor node and bring channel and node probability for their trust-

worthiness using probability model.

3. Agents ensure trustworthiness of channel and node using MAC model.

4. Compute performance parameters of the system: Performance parameters are assessed

and plotted with different variables.

The following performance parameters are assessed.

• Average Success Ratio (ASR): It is the average of Success Ratios (SR). SR for single

node in the network is defined as a ratio of number of trustworthy neighbor nodes

identified by agent based scheme to the actual number of trustworthy neighbor nodes.

SR at a node is given by the equation 3.6.

Success Ratio(SR) =Number of trustworthy neighbor nodes identified

Actual number of trustworthy neighbor nodes(3.6)

ASR is defined for certain number of randomly selected nodes in a network as given in

equation 3.7.

Average Success Ratio(ASR) =

∑Kk SRk

K(3.7)

where K is the number of randomly selected nodes among all the nodes in a network

for which trusted neighbors are identified.

• Memory Overhead: It is defined as the total memory required (in bytes) to store Node

KB, Neighbor Node KB and agent codes for identifying trusted neighbors.

• Communication Overhead: It is defined as the total number of control packets that are

necessary to identify trustworthy neighbors.


• Energy Consumption: It is the average energy in joules consumed for transmission and

reception of packets for all the nodes in a network to identify trustworthy neighbors.

• Agent Overhead: It is defined as the additional number of control packets required to

define the agency and their activities that are necessary to implement trusted neighbor

selection in WSN.

3.6 Results

The simulation is carried out on Pentium IV machine using ’C’ language. The analysis of

performance parameters are given in this section.

3.6.1 Analysis of Average Success Ratio (ASR)

θθθθ 10

20

30

40

50

60

70

80

90

100

50 100 150 200 250 300

Ave

rag

e S

ucc

ess

Ra

tio (

%)

No of Nodes

Average Success Ratio Vs. No of Nodes

TNIWSN, =0.1TNIWSN, = 0.2

NMND, = 0.2NMND, = 0.1

Figure 3.5: Average Success Ratio Vs Number of nodes

ASR is assessed through simulation to find the effectiveness of the scheme with the

varying number of nodes in a network as shown in Figure 3.5. We find that there is an

increase in ASR with increase in the number of nodes for two threshold values θ =0.1 and

0.2. This is due to the fact that there is a possibility that the number of neighbor nodes for

a selected node increases with increase in total number of nodes in a network. Among the


10

20

30

40

50

60

70

80

90

100

50 100 150 200 250 300

Ave

rag

e S

ucc

ess

Ra

tio (

%)

No of Nodes

Average Success Ratio Vs. No of Nodes

TNIWSN, K = 8TNIWSN, K = 6

NMND, K = 8NMND, K = 6

Figure 3.6: Average Success Ratio Vs Number of nodes

existing neighbor nodes, the number of trusted neighbors identified by agents also increases.

The number of trustworthy neighbors identified with agents increases since the agents use two

stage mechanism to identify such neighbors; first stage being trusted neighbor identification

by agents using probability model and the second stage being the use of MAC model to

ensure the trustworthiness of neighbor nodes. The number of nodes at which agents apply

trust model also increases with increase in number of nodes. We also observe that ASR is

higher for lower probability threshold value (θ) and it is less for higher θ. This illustrates

that agents are effective in capturing the trustworthy neighbors by using trust model and

autonomously taking decisions immediately to identify trusted channel and trusted nodes.

The behavior of ASR with varying number of nodes for different values of K is shown

in Figure 3.6. As in earlier case, here also ASR increases with increase in the number of

nodes. ASR is more for higher value of K is observed since agents may identify more number

of trustworthy neighbors as K increases. This is because, a neighbor node may be identified

as untrustworthy by one selected node, whereas due to increase in K, the same neighbor

node may be proved to trustworthy by another node. Thus, agents are effective in identifying

trustworthy nodes. For the obvious reasons, ASR increases with the increase in total number

of nodes in a network.

Figure 3.7 shows the increase in ASR with increasing K and ASR is more for higher


10

20

30

40

50

60

70

80

90

100

5 6 7 8 9 10

Ave

rag

e S

ucc

ess

Ra

tio (

%)

K

Average Success Ratio Vs. K

TNIWSN, Nodes=200TNIWSN, Nodes=100

NMND, Nodes=200NMND, Nodes=100

Figure 3.7: Average Success Ratio Vs Number of selected nodes(K)

number of nodes. This is because there is a possibility of more number of neighbors for any

given node and among them, the chance of having higher number of trustworthy neighbors

is more.

In all the cases ASR in TNIWSN is better then NMND because NMND hardly detects

malicious nodes behaving normally and detects only malicious nodes with some intelligence

which might behave differently from normal nodes.

3.6.2 Analysis of Memory Overhead

Additional memory required to store Node KB, Neighbor Node KB and agent codes is shown

in Figure 3.8 with the number of nodes(K) at which trusted node selection scheme is applied

for 150 and 250 node topology. As the selected number of nodes(K) increases where the

proposed scheme is applied, there is an increase in neighbor nodes and hence the memory

required to store such neighbor information in node’s database increases. However, rate of

increase in memory overhead is more after K=8 because there may be multiple KB entries

in database of selected nodes for a given neighbor node and hence such duplicate database

entry increases memory overhead.

In TNIWSN, the memory overhead is due to the memory required to store various


1000

1500

2000

2500

3000

5 6 7 8 9 10

Me

mo

ry o

verh

ea

d (

No

of

byt

es)

K

Memory overhead Vs. K;

TNIWSN, No of Nodes= 150 TNIWSN, No of Nodes= 250

NMND, No of Nodes= 150NMND, No of Nodes= 250

Figure 3.8: Memory overhead vs. Number of selected nodes(K)

databases and agents on each trustworthy nodes thereby eliminating storage requirement of

untrusted nodes. Whereas, in NMND, since the scheme is event driven there may be many

fault events triggered due to faulty nodes and malicious nodes; intentionally or unintention-

ally. Under all such events, NMND invokes malicious node identification and overheads for

such detection increases.

Figure 3.9 shows memory overhead with simulation time. The oscillatory nature of

memory storage depicts the type of node distribution and the effective number of neighbor

nodes for which the database is maintained at selected nodes. However, we observe that the

memory overhead is higher for more number of nodes in a given topology.

3.6.3 Analysis of Communication Overhead

Communication overhead to identify trusted neighbors is shown in Figure 3.10 for both

TNIWSN and NMND with increase in number of nodes for (K = 6, 8).

Communication overhead is observed to be higher for higher value of K and it is

increasing with increase in number of nodes. This is because, as the density of neighbor

nodes increases with increase in total number of nodes in a network, various control packets

increase corresponding to the density of nodes. More number of control packets are needed


500

1000

1500

2000

2500

3000

500 1000 1500 2000 2500 3000

Me

mo

ry o

verh

ea

d (

No

of

byt

es)

Simulation time (in seconds)

Memory overhead Vs. Simulation time

TNIWSN, No of Nodes= 150TNIWSN, No of Nodes= 250

NMND, No of Nodes= 150NMND, No of Nodes= 250

Figure 3.9: Memory overhead vs. simulation time

in NMND compared to TNIWSN since the sensor nodes send alarm signals every time to

its neighbors whenever it detects unusal pattern in event driven mode. In periodic mode,

each sensor node periodically sends a report to its neighbors, regardless of the occurrence

of an event. In both cases of event driven and periodic modes, the communication overhead

increases. Whereas in TNIWSN, the communication overhead is kept at a minmum value

since mobile agents avoid the nodes if they identify vulnerability in the message.

3.6.4 Analysis of Energy Consumption

Energy consumption in joules with increasing number of nodes is given in Figure 3.11 for

TNIWSN and NMND. We see that the energy consumption is less for received packets

(Rxpkts) than transmitted packets (Txpkts). Since the agents in TNIWSN do not return

back if the visited node is found to be compromised (all such agents die there itself) thereby

it reduces the power consumption required to bring the compromised node information back

to the original node. However, NMND needs proactive and reactive alarm messages to be

exchanged between the nodes to identify vulnerability patterns that lead to higher traffic

and corresponding energy consumption as compared to TNIWSN.


200

300

400

500

600

700

800

900

1000

1100

50 100 150 200 250 300

Co

mm

un

ica

tion

ove

rhe

ad

(p

ack

ets

)

No of Nodes

Communication overhead Vs. No of Nodes

TNIWSN, K = 6TNIWSN, K = 8

NMND, K = 6NMND, K = 8

Figure 3.10: Communication overhead vs. No of nodes

3.6.5 Analysis of agent overhead

The additional number of control packets necessary to implement the agent based scheme

with number of nodes is shown in Figure 3.12.

We see that agent overhead is increased with increase in number of neighbor nodes

since additional number of agents are required to identify trusted neighbors. The increase in

agent overhead is remaining constant after 200 nodes since agents take autonomous decisions

to identify trusted neighbors by visiting one node to another and if a visited node is found

to be untrustworthy, agents kill themselves. With the increase in number of nodes, more

agents will be killed thereby agent overhead almost remains constant. Significance of the

agents is observed for large scale networks where there is a negligible agent overhead with

increase in number of nodes in a network.

3.6.6 Benefits of Using Agents

Agent based trusted neighbor selection offers flexibility, scalability, efficiency, adaptability

and maintainability. We explain below how they are achieved by using the proposed scheme.


0

0.05

0.1

0.15

0.2

0.25

0.3

100 150 200 250 300 350 400 450 500

En

erg

y co

nsu

mp

tion

(J)

No of Nodes

Energy consumption Vs. No of Nodes

TNIWSN, RxpktsTNIWSN, Trpkts

NMND, RxpktsNMND, Trpkts

Figure 3.11: Energy consumption vs. No of nodes

Flexibility: Agents are flexible to implement trusted node identification in WSN. For ex-

ample, TNA’s generated by SMA clone themselves to visit neighbor nodes and identify

trustworthiness of visited nodes. Scalability: The scheme may scale to larger networks since

agents function in a distributed fashion thereby it provides similar security level as that of

smaller networks. Efficiency: Network efficiency is improved since TNA and SMA agents

take autonomous decisions in order to identify trustworthy neighbors. Adaptability: SMA

and TNA adapt themselves to dynamic behavior of the network nodes and correctly elimi-

nate untrustworthy neighbor nodes. Maintainability: The components of old agents may be

inherited in the components of new agents and thus the network maintainability improves

with varying conditions. Encapsulation of a protocol: A mobile agent can be coded to per-

form aggregated tasks such as identification of trustworthy neighbor nodes. Thus, TNA’s

encapsulate the protocols that are customized based on functionality.

Whereas reputation based schemes of trusted neighbor identification operate over a

dynamic cost function or multiple cost functions that do not possess flexibility, scalability,

adaptability and encapsulation of protocols.


Agent Overhead 100

110

120

130

140

150

160

170

180

190

50 100 150 200 250 300

No

of

Co

ntr

ol P

ack

ets

(b

yte

s)

No of Nodes

No of Control Packets(agents)Vs. No of Nodes;

Figure 3.12: Agent Overhead

3.7 Summary

In this chapter, we proposed an idea of identifying trustworthy neighbors in WSN using

agents through Safeguard Agency. Agents effectively perform the function of finding trusted

neighbors using probability based trust model and MAC model ensuring higher security.

Two phases are involved in identifying trusted neighbors: in the first phase, agents visit all

the neighbors and bring probability of all the neighbors using trust model and in the second

phase, agents ensure the trusted neighbors using MAC model. Simulation analysis shows

that there is an improvement in average success ratio of finding trustworthy neighbors with

little overheads due to the usage of agents. Memory overhead is essential since it requires

memory storage to store various node related and neighbor node related information to

identify trusted neighbors. The results of TNIWSN outperform compared to the results of

NMND.

In the next chapter, we design a routing scheme using trusted neighbors identified in

this chapter to route the information to a base station or sink node.

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