A SPLIT HALF RELIABILITY COEFFICIENT BASED MATHEMATICAL MODEL FOR MITIGATING SELFISH NODES IN MANETs J.Sengathir Research Scholar, Dept of CSE PEC, Pondicherry
[email protected]
Abstract— The data dissemination among the wireless nodes in an ad hoc environment mainly depends on the cooperation maintained between them. This co-operation is essential for establishing both forward and reverse route as well as relaying the packets. Due to the limited availability of resources in an ad hoc scenario, some of the nodes may tend to drop the packets coming from their neighbor nodes while forwarding their own packets. This type of node’s behavior is known as ―selfish behavior‖. In this paper, we devise and propose a mathematical model that could detect the selfish nodes based on the split half reliability co-efficient. This split half reliability is a two level consistency check mechanism, which is based on the aggregate number of packets entering and leaving a node at a time instant. The effective performance of the devised mathematical model is studied through ns–2 simulation with the help of parameters namely packet delivery ratio, control overhead, total overhead and throughput by varying the number of selfish nodes. The results of the experimental study depicts that this proposed model could detect the selfish nodes rapidly when compared to any other existing models available in the literature. Keywords— Reliability Coefficient; split half reliability correlation; Selfish node.
I.
INTRODUCTION
Providing co-operation among the mobile nodes in a MANET is a critical issue that is not explored by most of the researchers in the past decade [1]. This is due to the lack of central infrastructure and dynamic nature of the ad hoc network. If the nodes in an ad hoc environment deny to cooperate, then the network performance degrades [2]. Hence, there is need for devising reliability coefficients based algorithm for detecting and preventing the selfish nodes or the non-cooperating nodes. It is proven that reliability coefficients can test the statistical data more accurately when compared to the other reliability consistency check mechanisms [13]. Basically, there are three types of methods for reliability based consistent checking of the nodes in a network. They are Cornbach alpha, Kuder-Richardson Coefficient and Regression coefficient.
R.Manoharan Associate Professor, Dept of CSE ACET, Pondicherry
[email protected]
In this work we employ a split half based reliability coefficient for testing the consistency of nodes in MANET. Here, the protocol used for our study is the tree based and reactive protocol called.AODV.This protocol supports control packets like RREQs, RREPs, and RRERs for establishing both the forward route as well as reverse route. The remaining part of the paper is organized as follows. In section II, We present some of the existing works available in the literature with various other possible coefficients that could be useful for checking node consistency and the extract of the survey. The elaborate explanation of the proposed mathematical model is depicted in section III.The algorithm of the proposed mathematical model when deployed in the routing of the protocol, AODV is presented in section IV. An illustration of the devised model is portrayed in section V. The experimental analysis and the Simulations results are presented in section VI. Section VII concludes the paper. II.
EXISTING WORK
A lot of intensive research has been carried out on the formulation of reputation algorithms for detecting and mitigating selfish nodes in an ad hoc environment. The proposed solutions can be categorized as first hand reputation mechanism and second hand reputation mechanism. List of previously available solutions in the existing literature are discussed below. Alessandro Mei and Juliana Stefan,[3] proposed their reputation mechanism based on the assumption that the two protocols used for their study are strategy proven and not even a single node has the interest to deviate. They introduced a technique called force faithful behavior, which has the capability of increasing the network performance by reducing the number of duplicate thus saving the storage requirements. Stephan Eidenbenz et al,[4] anticipated a distributed algorithm, which was devised based on the four important properties. They are as follows: first the nodes should be rational in routing, second the nodes must be truthful, third, the packet has to be relayed on the most energy efficient path and last but not the least the complexity of the message
transmitted must be low. They also proposed a VCG payment scheme combined with the game theoretic technique to achieve the reliability of the node in the entire network. Tamer Rafael et al.[5] proposed a reputation evaluation mechanism ,which is deployed in each and every node of an ad hoc network. In this mechanism the nodes maintains a reputation index and a reputation table. Here the reputation index of an node is monotonically increasing value based on the successful delivery of packets to its neighbors whereas the reputation table in turn stores the updated reputation index at each and every time instant. They also proposed three heuristic reputation functions namely Hops away from source, double decrement/single increment ratio and random early probation. Ze Li and Haiying Shen,[6] addressed a reputation mechanism that makes use of reputation values for detecting selfish nodes. This detection is based on a reputation threshold that distinguishes the nodes as trustworthy and untrustworthy. it also emphasized on a virtual cash mechanism to control the packet servicing activity of a node. The algorithm proposed in this work is based on the game theory in order to investigate on the cooperation of the nodes in the ad hoc network. it was also devised as an integrated approach for dealing with selfish nodes. Feng Li et al,[7] proposed a game theory based analysis model in order to increase the interaction among the wireless mobile nodes in MANETs. They modeled the scenario as dynamic Bayesian signaling game ,which discriminates the difference in the behavior when the normal nodes updates their strategy based on the malicious node whereas the malicious nodes always has an eye on determining strategy that could help to escape from the network. this mechanism also possess the concept of sequential rationality and random property. Shukor et al,[8] addressed a mechanism that enhances the co-operation among the nodes without exhausting their stringent resources. They proposed a friendship mechanism that could minimize the false positives raised during the detection of the selfish nodes. This was accomplished through two methodologies namely direct and indirect friendship. Their work mainly targeted on the six degree of separation that could arise between the nodes in the network and how to cope with this kind of separation. They used a voting strategy that could discriminate between the selfish and the normal node in an ad hoc network. Hazer Inaltekin and Stephen B.Wicker [9] addressed the various issues that prevent the co-ordination between the nodes in an ad hoc scenario. They devised a game theoretic solution developed based on Lévesque measure that assigns a probability value to all the participating nodes in the network. They also analyzed the behavior of the network based on Nash
Equilibrium function, which is manipulated based on the cost of failed transmission A. Similar mechanisms for computing Reliability coefficient for checking the consistency of a node A number of methods are available in the literature that could perform a consistency check in the behavior of a node that are participating in the communication of the network. Short explanations of some of the coefficients available for predicting the reliability are depicted below. 1) Cornbach Alpha Coefficient[10]: This kind of reliable consistency check coefficients are mainly suited in a scenario, where the behavior of the node are monitored based on binary outcomes or large scale data. This coefficient provides the accurate reliability of the nodes rather than proving under or over estimation on the reliability of the nodes. 2) Kuder-Richardson Coefficient [11]: This kind of reliable consistency check coefficients are mainly suited in a scenario, where the behavior of the node are monitored based on dichotomous outcomes. This coefficient could provide high degree of accuracy when compared to Cornbach Alpha Coefficient 3) Regression coefficient [12]: In this method of coefficient computation, the degree of dissimilarity between the incoming number of packets and the outgoing number of packets are considered. higher the regression value than there is a high possibility for a node to be selfish. B. Extract of the Literarture Survey The solutions available in the existing literature for detecting and preventing selfish nodes lack in the following issues. They are:
A Second hand Repudiation mechanism that makes use of two levels of detection that is based upon the manipulation of reliability coefficient has not been proposed.
A mechanism which can rapidly predict a node‟s selfishness not only based on the present scenario but also on the past history has not been implemented.
A Methodology which could predict the selfish nodes based on the correctional factor obtained based on the number of incoming packets to the number of outgoing packets has not been explored.
These are the motivational factors for devising a two degree level detection mechanism that helps in detecting and preventing the selfish nodes based on the computation of correlated Reliability coefficient.
III.
SPLIT HALF RELIABILITY COEFFICIENT BASED MATHEMATICAL MODEL
In this section, we present a split half reliability coefficient based mathematical model devised for detecting and mitigating selfish nodes in an ad hoc scenarios. In proposed model, the detection of selfish nodes is achieved through a parameter called split half reliability coefficient is computed with aid of Karl Pearson and the reevaluation of the co-efficient is computed through spearman brown formula. Suppose if „INP‟ be the number of incoming packets for each and every node from its neighbor in „k‟ sessions. Then, the sum of all incoming packets for a node in „k‟ session is given by (1) SINP(k)=INP(1)+INP(2)+…+INP(K)
(1)
(7) The half split reliability coefficient is given by (8)
(8) The computed split half coefficient predicts the two categories of a node. If the RC is positive then the node is a normal node and if the RC is negative then the node is a selfish node. Again, the computed RC value can be revaluated by Spearman – Brown formula given by (9)
Now, the average number of incoming packets in „k‟ scenarios is manipulated through (2) (2) Hence, the sum of squares of deviation between the actual incoming packets to the average number of incoming packets is given by (3)
(3) Where (4) Likewise, let „ONP‟ be the number of outgoing packets for each and every node to its neighbors in „k‟ sessions then sum of all outgoing packets for a node in „k‟ sessions is given by (4) Then, the average number of outgoing packets from a node is computed through (5)
(5) Now, the sum of squares of derivation between the actual outgoing packets to the average number of outgoing packets is given by (6)
(6) Where, Then the product of deviation in the number of incoming and outgoing packets in „k‟ sessions for each and every node is given by (7)
(9) The obtained CRC detects that the node is selfish with high reliability coefficient. This detection mechanism is a second hand reputation based strategy since the values of Rc and CRc are computed through the neighbors nodes of each and every node in the ad hoc environment. This is a distributed approach because each and every node in the network poses the capability of detecting selfishness. Once a particular node is detected as selfish, then the network can be reorganized in order to mitigate the straight selfish nodes. This mechanism has the following benefits. a) The detection mechanism detects the selfish nodes rapidly not only based on the number of incoming and outgoing packet of a node in the present session but also based on the past history of packet relay. b) This mechanism is a two level detection methodology where the reliability of a node is computed through Karl Pearson method and reevaluated by spearman – brown formula. c) The mechanism is distributed in each and every node in the entire network. IV.
DEPLOYMENT OF DEVISED MATHEMATICAL MODEL FOR AODV ROUTING.
ALGORITHM: Split Half Reliability coefficient dependant model for detecting selfish behavior in an ad hoc scenario. Notations: SRN: Source Node DSN: Destination node RC: Split Reliability coefficient. CRC: Connected Split half Reliability coefficients
1.
SRN broadcast RREQ through all feasible paths.
2.
DSN acknowledge through RREP using Reverse route.
3.
SRN relays packets to DSN through established optimal path
4.
The neighbor node of each and every node determines the total number of incoming and outgoing packets.
5.
Again, the neighbors determine the sum of squares of the deviation in the incoming and outgoing packets with the aggregate number of incoming and outgoing packets.
6.
Then, the neighbor computers RC with the aid of step (4) and (5)
7.
If (RC < 0) then
8.
The node is a selfish node
9.
Call Recuperate ()
10.
Else
neighbor nodes with the help of a test called REL-COEFFTEST (Reliability Coefficient test) to detect the selfish behavior of nodes. This test is carried out by determining the total number of incoming and outgoing packets for some sessions called „k‟. For instance consider, a group of nodes in MANET based on AODV protocol as shown in the figure 1.
11. Normal Node 12. The neighbor reevaluates the node genuineness using second level corrected reliability coefficient CRC. 13.
If (CRC>40)
14. Then, The neighbor confirms reliability of the node. 14
Else
15.
Call Recuperate ()
In an AODV Protocol, the source node broadcasts the RREQs to all possible paths available through forward routes. The Destination nodes upon receiving the RREQs acknowledges by sending an RREP through the reverse route established. Now, When the data packet is relayed through the nodes of a ad hoc network, Each and every nodes are monitored by their neighbours.Every neighbours of a node initially computes the reliability coefficient RC based on the number of incoming as well as outgoing packets for some sessions „k‟. This is computed based on the Karl –Pearson Correlation Coefficient (reliability coefficient). This coefficient is obtained is retested to greater accuracy with the aid of Spearman-Brown formula. This retest value is called as the corrected reliability coefficient- CRC. When the CRC value is greater than 40% then it is likely to be a normal node and when the CRC is less that 40%, the node is a selfish node. V.
Figure 1 Group of Mobile nodes in an AODV Protocol
Suppose if we consider a node „C‟ as a monitored node by its neighbor „A‟ and „G‟ based on the number of incoming and outgoing packets from that node. Let us consider the node „C‟ receives 1000, 980,960,940 and 960 packets in five sessions and transmits 920,900,900,880 and 920 respectively. Then the reliability coefficient (RC)
Now, the corrected reliability coefficient CRC. is manipulated by
ILLUSTRATION OF PROPOSED WORK.
In this section, we depict a split half coefficients based mathematical model for detecting and mitigating selfish nodes with a reactive protocol called AODV. This is accomplished through the computation of two interdependent reliability coefficients called RC and CRC. In this devised mathematical model, each and every node in the ad hoc scenario is monitored through their
When indicated in percentage it is 82.49 % of reliability. Hence it is deduced to be a normal node. VI.
SIMULATION RESULTS AND DISCUSSION
The extensive experimental simulations of our work is carried out through the network simulator tool ns-2.26.our ad hoc scenario consists of 50 mobile nodes deployed in a terrain
size of 1000X1000 square meters. The results are determined based on the aggregate of 10 simulation rounds. The maximum number of packets used for carrying out our study is 1000, which is quite enough to continue the session up to the end of the simulation time. The refresh interval used is 1 sec. Here the maximum size of the packet transmitted from the source is 512 bytes and the wireless channel capacity is 1 Mbps. A. Metrics Used The experimental analysis of the devised split half coefficient based mathematical model is carried out based on the following metrics. 1) Packet delivery ratio: It may be defined as the ratio of the maximum number of packets received by the destination nodes to the actual number of packets generated from the source nodes. 2) Control overhead: It is the maximum number of bytes of packets that are used for establishing communication between the source nodes and the destination nodes. 3) Total overhead: It is the ratio between the aggregate sum of both the control packet and the data packet to the number of data packets that is delivered to the destination. 4) Throughput: It is defined as the sum of all the packets that reaches the destination node from the source node within a interval of time. The simulation table used for our experimental study is illustrated with the help of table I. TABLE I.
Figure 2 Performance analysis for SHRCM based on number of Selfish nodes and Packet delivery ratio.
This mathematical model shows a phenomenal increase of 23 % in Packet Delivery Ratio. 2) Control Overhead: The AODV protocol performance in terms of control overhead increases when the number of selfish nodes existing in the scenario increases. Since, the performance of the protocol with respect to the control overhead is directly proportional to the existence of the selfish nodes, the need for the deployment of SHRCM arises. From the figure 3 it is obvious that the deployment of SHRCM in the AODV protocol shows a steady decrease in the control overhead.
SIMULATION PARAMETERS
General Parameter Number of Nodes 50 Type of Protocol AODV Simulation Time 1000 MAC Layer 802.11 Range 200 meters Simulation Area 1000x1000 sq.meter Traffic Model Parameter Traffic model Constant bit rate Packet size 512 bytes Interval 1 sec Type of antenna Antenna/ Omni Antenna Type of Propogation Two Ray Ground Type of interface queue Queue/Drop tail/Priority Queue
B. Performance Evaluation for Split Half Reliability coefficient Model (SHRCM): 1) Packet delivery ratio: The AODV protocol performance decreases when the number of selfish nodes existing in the scenario increases. Since, the performance of the protocol is inversely proportional to the existence of the selfish nodes, the need for the deployment of SHRCM arises. From the figure 2 it is obvious that the deployment of SHRCM in the AODV protocol shows a steady increase in performance in terms of Packet Delivery Ratio.
Figure 3 Performance analysis for SHRCM based on number of Selfish nodes and Control Overhead
This mathematical model shows a phenomenal decrease of 22 % in the total overhead 3) Total Overhead: The AODV protocol performance in terms of total overhead increases when the number of selfish nodes existing in the scenario increases. Since, the performance of the protocol with respect to the total overhead is directly proportional to the existence of the selfish nodes, the need for the deployment of SHRCM arises. From the figure 4 it is obvious that, deployment of SHRCM in the AODV protocol shows a steady decrease in the total overhead.
omnipresent in each and every node of the network. The results of this proposed second hand reputation mechanism makes it obvious that it provides better performance in terms of Packet Delivery Ratio, Total overhead, Control overhead and Throughput. In the near future, new reputation mechanism for detecting the selfish nodes based on Alpha coefficient, Cohen‟s kappa or Kuder-Richardson formula can be explored.
[1]
[2] Figure 4 Performance analysis for SHRCM based on number of Selfish nodes and Total overhead [3]
This mathematical model shows a phenomenal decrease of 33 % in the total overhead. [4]
4) Throughput: The throughput of the AODV protocol decreases when the number of selfish nodes existing in the scenario increases. Since, the throughput of the protocol is inversely proportional to the existence of the selfish nodes, the need for the deployment of SHRCM arises. From the figure 5 it is obvious that the deployment of SHRCM in the AODV protocol shows a steady increase in the throughput.
[5]
[6]
[7]
[8]
[9]
[10]
Figure 5 Performance analysis for SHRCM based on number of Selfish nodes and Throughput.
This mathematical model shows a phenomenal increase of 19 % in Throughput. VI.
CONCLUSION
In this paper, we have elaborately discussed the split half reliability coefficient based mathematical model, which is
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