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Modeling connected-path link dominating set in MANET.

INTRODUCTION

There has been considerable interest in developing route path clustering to achieve multiple objectives for increasing packet delivery ratio and reducing overhead between different competing clustered route paths. The most current approaches aim to focus on the scheme applied for clustering and identified path for packet transfer. However, these clustering schemes do not provide a stabilized clustering approach (Haijun et al., 2012) which is essential in practice. To ensure sustained flow bandwidth, cluster based QoS algorithm (Larry et al., 2011) was evaluated using cluster heads. But, the drawback was that, an increase in the number of cluster count increased the rate of collision and therefore increased the recovery time factor.

Cooperative clustering protocol (Jong and Kyu 2011) called as Cooperative Networking (CN) protocol using Bluetooth Personal Area Network was designed with the motive of reducing the power consumption in WAN. CN protocol not only improved energy efficiency but also reduced the control overhead by evolving well distributed cluster heads. However, multi-hop clustering remained unaddressed. Packet scheduling over multiple channels was introduced in (Dan and Abdallah 2011) to significantly study the impact of channel partition size. Also assignment of packets over multiple channels using optimization framework also improved robustness. But computational complexity with respect to heterogeneous channel remained unsolved.

Unobservable Secure On-demand Routing protocol (Zhiguo et al., 2012) reduced computational complexity using ID-based encryption and group signature. The unobservable routing scheme not only provided anonymity but also included unlinkability and unobservability increasing the packet delivery ratio. The protocol remained a challenging task with delay tolerant networks. Routing protocol based on clustering (Ha and Hongyi 2010) for delay tolerant mobile networks was designed with the motive of significantly lowering the overhead and increasing the delivery ratio.

An Exponentially Moving Average Speed (EMAS) technique was used for forming cluster and selecting the gateway nodes to increase delivery ratio. With the lack of continuous communication between mobile nodes, time optimization with respect to mobile nodes becomes major challenges. The application of clustering not only reduced energy consumption but also optimized the time. Routing-aware Optimal Cluster Planning and Cluster-aware Optimal Random Relay (Tao and Marwan 2010) were used to provide a balance in power consumption. However, power consumption with respect to different types of MACs was not considered.

Decentralized QoS aware arrangement (Paul and Nian-Feng 2010), provided recovery probabilities by determining globally optimal checkpoint in mobile ad hoc network, but lack of QoS aware functionality and dynamic routing, further reduce network lifetime. Topology interference algorithms (Jian et al., 2010) were used to address dynamic routing topology so as to maximize the correctness ratio and reduce probing overhead. However, network monitoring and application design was not considered. Routing protocol and stability were proposed in (Giovanna et al., 2009) and (Sungwon et al., 2010) with the objective of increasing the optimal route in terms of path availability. However, providing optimal route may not result in route stability it does not guarantee complexity overhead.

QoS constrained Eigen trust non cooperative model (Surendran and Prakash 2014) was designed for secured routing that obtained trusted valid route and optimal route paths for addressing route failure. However, a hybrid model was not provided for real time in MANET. A real time model for improving the quality of service protocol was designed in (Iftikhar et al., 2013) with the objective of increasing the throughput and decreasing the transmission delay. But, bandwidth with respect to quality of service remained unaddressed.

On-demand Bandwidth and Stability based Unicast (OBSU) (Basarkod and Manvi 2014) was constructed with the motive of improving the packet delivery ratio and throughput minimizing the end to end delay. Route repair with respect to packet forwarding and routing was ignored. Route discovery during route repair (Mehajabeen et al., 2014) was designed using fuzzy logic and Adaptive Hello Warning Message (AHWM). But, route discovery for multicast routing protocol was not focused. Multicasting routing (Sonika and Manoj 2014) and (Rajashekhar and Sunilkumar 2011) in MANET was introduced with the objective of conserving bandwidth and network resources through QoS multicast mechanisms and information priority. However, control overhead with respect to stability remained unsolved.

Genetic algorithm in MANET (Vikas et al., 2011) using QoS routing protocol maximized the data delivery over network by generating an optimized path between the initial and goal state. An efficient routing with more reliable data transmission was ensured, but optimization of QoS routing protocol was not addressed. Agent based ant colony optimization (Ditipriya et al., 2012), elected cluster head using four different metrics namely, stability, battery power, degree of node. With the introduction of these four metrics, packet delivery ratio was increased. However coordinate location points were assumed prior to the design of networks which does not ensure data delivery with respect to load. Node communication (Ashish et al., 2011) through Cluster Head Gateway (CHG) was designed with the motive of improving throughput, reducing delay and network load. Adaptive Mobility Aware AODV (Bisengar et al., 2011) was designed to increase the ratio of packet delivery in MANET. Cluster-based routing protocol (Boukli-Hacene et al., 2014) used public key certifications for generating trust communication model to reduce the communication overhead in MANET.

The key to provide an efficient route path clustering in MANET is to design a Complete Clustering with Connected-path Link Dominating QoS routing. This can be accomplished by creating and establishing network routing using Connected-path Link Dominating set which group similar route path and thereby reducing the repetition using locally distributed nodes on path set. To acquire this objective, a framework for efficient routing is developed, and implemented in this work.

The rest of the paper is divided into five additional sections. Section 2 describes the System Model and description. Section 3 describes the system framework and the objective behind the work with the aid of neat architecture diagram. Furthermore, numerical results with simulation are illustrated in Section 4. Finally, conclusion is provided in Section 5.

System model:

In this paper, the problem of efficient clustering in mobile ad hoc network using the framework is considered along with Complete Clustering with Connected-path Link Dominating (CC-CLD) QoS routing. By considering issues related to time complexity, it is assumed that node clustering is performed that creates an effective route path and ensuring Quality of Service (QoS) to improve the performance of communication between the movable nodes in mobile ad-hoc network. Considering the packets in CC-CLD framework that utilizes routers for data transmission, the routers receive the packets and place them in a buffer. Figure 1 show the clustering method used in selecting the route path for packet forwarding to the next hop. Let CRP1, CRP2, ..., CRPn denotes the different clustered route paths in MANET that shares the stored information to several users U1, U2, U3, ..., Un

The clustering route path (i.e., CRP1, CRP2, ..., CRPn ) for data transmission is carried out initially though the proposed processing steps. By considering the intermediate nodes in CC-CLD framework, it is also assumed that specific transmission that flows in a similar manner are grouped together and router is used for packet flow. It is also assumed that the nodes which form similar type of route path are clustered together to make a hierarchical control mobile network environment. The users with 'n' count utilize the clustered group for broadcasting the information. However, in case of imperfect movement, collision may occur due to the increase in cluster count of movable nodes between sources and destination, which can be avoided by applying Complete Clustering with Connected-path Link Dominating framework.

Complete clustering with connected-path link dominating qos routing framework in manet:

In this section, a framework called Complete Clustering with Connected-path Link Dominating (CC-CLD) with QoS routing is designed to address the ad-hoc network routing overhead and minimize the recovery time. The architecture diagram of the framework, CC-CLD with QoS routing is shown in Figure 2.

As shown in Figure 2, the ad-hoc network in mobile environment competes with the movable nodes. Connected-path Link Dominating set clusters the nodes uses the locally distributed node path, by following hierarchical clustering structure. By avoiding repetitions by following same route path, collision is reduced. With the aim of increasing the broadcasting performance, a hierarchical clustering structure is used in CC-CLD framework that initializes the cluster ID for each group and also obtains the start and end node point in the group. The network performance is improved by reducing the probability of collision rate.

Complete clustering in CC-CLD framework provides effective linkage between nodes where the link between all the clusters contains all element pair of nodes which establishes the route path. The CCCLD framework finally localizes the distribution by closely relating the route path. The localized distribution in the proposed framework reduces the energy consumption factor.

Connected-path Link Dominating set:

The connected path link dominating set is a distributed route path connected in MANET. The connected-path link dominant set uses the cluster head pruning rules to remove the repetition, aiming at reducing collision factor. Let 'C' denotes the dominant connected path link set 'G' with sub graphs, G<V, E>, where V and E denotes the vertices and edges of the connected path link graph G with connected path link 'C' for route path clustering. The connected path link graph uses the Localized connected path-link for reducing the collision rate in our proposed framework. The function uses greedy procedure to increase the performance ratio in CCCLD framework and the function is given as below. Function = [add (s,i. d + In (Tikk node point}] (1)

In (1), 's', 'i', d' represents the source point intermediate and destination points. The intermediate link node point is also embedded to these three points using the function 'ln0\ Let us assume that E(C) represents the edge connected paths to establish the route for packet transfer from source to destination. Then the greedy approximation procedural function is defined as.

Greedy Approximation = m(E(C} + F(C}} (2)

From (2), the 'n' node vertex 'V(C)' and edges 'E{C)' are combined together to cluster the movable nodes with similar connected path links. Initially, the node with maximum degree of cluster connected path links is used. The CC-CLD framework uses locally distributed nodes of path set to reduce the collision factor. The following steps are involved in Greedy Approximation,

// Greedy Approximation Algorithm

1. Begin

2. Repeat

3. Let a graph 'G' with 'V' vertex and 'E' edge of the movable node path being linked

4. Apply Greedy approximation procedural function to connect 'n' intermediate nodes

5. If Current node = neighbor movable node then

6. Choose vertex to link route path for packet transfer

7. Else

8. Check with neighbor node for packet transfer

9. Until maximum repeat_count value reduced up to one

10. End

The algorithmic procedure based on the greedy approximation in CC-CLD framework is given above. The greedy approximation dominant set works with the connected path links in the form such as,

G(V, E) = [[summation].sup.n.sub.i=1[1/n]] [less than or equal to] ln [(V, E) + 1] (3)

The Greedy Approximation algorithm 'G (V, E)' achieves higher approximation with the least collision factor in MANET. From (3), the logarithmic of any number of intermediate nodes is represented as 'In.' where count of the vertices 'V' and edges E are connected to establish the route path in the proposed framework.

Complete Clustering:

Once the connected path link route path is obtained using greedy approximation, the CC-CLD framework performs the cluster head operation through complete clustering. The cluster head is formed in CC-CLD framework using the pruning rules to group similar type of route paths. Each cluster group is named with unique ID for easy broadcasting of packets through that route path. The distance between clusters 'Distance Q' equals the distance between those two movable nodes in the complete clustering with connected path links. The linkage information with start and end points are computed as,

[Cluster.sub.(s,d)] = [max.sub.s,decluster] d(s,d) = Distance (Cluster 1, 2 ... n) (4)

The cluster point with maximum number of the start '5' and end points d are computed based on the linkage information. As specified, the distance between the start's' and end d are equal to the overall distance space of that specific cluster. The cluster head is chosen with the help of formative pruning rule. The formative pruning rule takes all the movable nodes from the cluster and computes the strong linked node cluster. The strong linked node cluster with the central form is chosen as a leader (i.e., head) in the cluster.

The complete clustering with cluster and localized information about the source and destination nodes provide connected phenomenon to reduce the recovery time as shown in Figure 3. Similar structure of the route path is clustered together in CC-CLD framework. The movable nodes are closed to each other in the cluster, and as a result, the processing time gets reduced. Also, the recovery of accurate route path for packet transmission in mobile ad-hoc network is reduced. The pruning rule is adopted to select the cluster head as per the strong connection of the edges. Each cluster group contains the ID, and the start and end node information in the cluster head node for easy verification in ad-hoc network. The strongly connected path link vertex is chosen as the cluster head based on pruning rule.

Localized Distribution Procedure:

After the successful completion of cluster head operation, better clustering is achieved in MANET using the localized distributed algorithmic procedure in CC-CLD framework. The distributed movable node topology is analyzed and the link path between the connected dominating sets is monitored to group closely related group structure route path with minimal energy consumption rate. The localized procedure of clustering using the Eigen vector connected space in a circular manner is followed in the CC-CLD framework. The localized procedure of clustering is followed in such a way that the Eigen vector based centrality of source and destination node distance is equal to the sum of the centrality value of the cluster. The stepwise description of the localized distribution approximation is described as below,

Algorithmic Stepwise Description--Localized Distribution:

1. Begin

2. Cluster the movable nodes in ad-hoc network on the basis of connected path link dominating set using complete hierarchical form

3. Form cluster head on the basis of cluster ID and movable node information

4. Measure distribution of node distance using Eigen Vector based Connected Path link Centrality

5. Compute Eigen Centrality, EC as Centrality ECa 'Zn{Distanceij')

6. Select localized route path of highest matching

7. Evaluate EC

8. Use strongly connected node (i..e, cluster head) for EC computation

9. Perform message broadcasting using Eigen Connected path-link Centrality

10. End

From the above localized distribution algorithm, the distribution of movable nodes is localized and route path is established with dominant set of connected paths. The connected path link centrality is monitored with the connected dominant set to reduce the energy consumption rate. The proposed work groups closely related similar route paths in ad-hoc network using the localized information. The Eigen Connected Path link Centrality value is computed between 'n' corresponding Eigen values. The movable node network based clustering uses the connected path link resulting in high clustering efficiency rate.

Performance evaluation:

In this section we evaluate the performance of the proposed QoS routing framework with some simulation results. To evaluate the performance of the proposed Complete Clustering with Connected-path Link Dominating (CC-CLD) QoS routing framework, it was tested on NS2, and the simulation result was compared with existing Flocking Algorithm (FA) and a Particle Swarm Optimizer (PSO) (FA-PSO) (Haijun et al, 2012) framework and Cluster-based QoS Routing algorithm (C-RA) (2).

Simulation Environment:

In our simulations, nodes were initially placed randomly within a fixed size 1200 m * 1200 m square area. The nodes move with a velocity of 0-40 m/s in a square are and the motion of the nodes within the network area is described using Random Way point model for simulation. The mobile nodes use the Dynamic Source Routing (DSR) routing protocol to perform the experiment on randomly moving objects. The link layer provides the link between two nodes in a multi direction environment. In this framework, the node chooses a random direction within the network area of size 1200 m * 1200 m square area, and then it initiates the node movement toward that direction with a change in velocity within the range of 0-40 m/s.

The simulation is held for ad hoc networks of 10, 20, 30, 40, 50, 60 and 70 nodes. The simulation lasts for 100 ms for each experiment. The transport layer protocol chosen for the proposed CC-CLD framework is UDP, a 40 Constant Bit Rate (CBR) data flows each node generating 7 packets/seconds with a packet size of 512 bytes. The simulation speed is 0-40 m/s, where Omni directional antenna is used for simulation and performs single process at a time for transmitting or receiving packet. Three Cluster head nodes, three source nodes and three destination nodes and one shared storage information node are used. Table 1 shows the simulation parameters for different scenarios.

The movable nodes are clustered based on the localized information of route paths which uses the inbuilt predefined energy model for the effective clustering. Experiment is conducted on the factors such as ad-hoc network routing overhead, packet delivery ratio on using the clustering ID and recovery time.

Metrics for performance evaluation:

The present study uses three performance metrics to evaluate and compare the proposed CCCLD framework with the existing FA-PSO (Haijun et al, 2012) and C-RA (2). These metrics are ad-hoc network routing overhead, packet delivery ratio on using the clustering ID and recovery time. The following is a short description of each metric.

Ad-hoc networking routing overhead: is the amount of occurrence of routing overhead created using Connected-path Link Dominating set. The adhoc networking routing overhead is measured using the function criterion in (1). Higher the network routing overhead, lower the performance of the framework is said to be and is measured in terms of percentage (%).

Recovery time: is the amount of time taken to recover accurate route path for packet transmission and is measured in terms of milliseconds (ms).

[Recovery.sub.T] = Time ([Cluster.sub.(s,d)) (5)

From (5), the recovery time '[Recovery.sub.T]' is the time taken to recover the route path for cluster point 'Cluster ()' with maximum number of the start 's' and endpoints 'd'.

Packet delivery ratio: measure the amount of packet received to the amount of packets sent at a particular time interval and is measured in terms of percentage (%).

PDR = [Packets.sub.r]/[packets.sub.s] * 100 (6)

From (6), the packet delivery ratio 'PDR' is the ratio of the amount of packets received '[Packets.sub.r]' to the amount of packets sent '[Packets.sub.s]'.

Simulation Results and Discussions:

In all the figures, throughout the document, the blue solid line represent the proposed CC-CLD framework and the green and brown solid line represent the existing FA-PSO (Haijun et al., 2012) and C-RA (Larry et al., 2011) framework.

The performance of the ad-hoc networking routing overhead against different mobile nodes is depicted in Figure 4. All the results provided in figure 4 shows that the proposed CC-CLD framework provides significant gain (i.e., minimizes the ad-hoc networking routing overhead) over the other three curves increases with the increase in the number of mobile nodes. The results presented in Figure 4 confirms that CC-CLD framework provides significant gain in reducing the ad-hoc networking routing overhead when the source node send packet repeatedly in the same path. The better performance of ad-hoc networking routing overhead is achieved due to the fact that it provides an efficient way to identify the routing overhead using maximum-repeat count value in the CC-CLD framework with Connected-path Link Dominating set improving the ad-hoc networking routing overhead efficiency by 14.5 % compared to FA-PSO.

With the application of Connected-path Link Dominating set, the locally distributed node path are used for clustering by following a hierarchical cluster which helps in reducing the ad-hoc network routing overhead. Moreover, by applying least greedy procedure, the function criterion embeds the source, intermediate and destination node points for packet transfer between source and destination resulting in the minimized ad-hoc network routing overheard by 24.5% compared to C-RA.

The effects of recovery time against seven different clustered route paths based on the different moving speed in MANET are shown in Figure 5. The better performance that reduces the recovery time for recovering the accurate route path for packet transmission in MANET is achieved using the proposed CC-CLD framework than two state-of-the-art methods FA-PSO (Haijun et al, 2012) and C-RA (Larry et al., 2011) with the help of using unique cluster ID. It is also noticeable that the gain in performance and therefore reducing the recovery time increases with increase in the clustered route path.

The CC-CLD framework differs from the FAPSO (Haijun et al., 2012) and C-RA (Larry et al., 2011) in that we have incorporated clustering with the help of unique ID to easily extent multiple-source nodes through different route paths for multiple sources and therefore reduce the recovery time. For the most different speed rate, the CC-CLD framework achieves comparable performance to FAPSO and C-RA. The recovery time is reduced by applying link-path structure on mobile nodes using complete clustering with the help of unique ID. The application of complete clustering in CC-CLD framework initializes the cluster ID for each group that increases the broadcasting performance and reducing the recovery time by 16.5% compared to FA-PSO. By applying complete clustering and using localized information for source and destination nodes helps in obtaining the connected phenomenon where the movable nodes are closed to each other and finally reduces the recovery time by 2.5% compared to C-RA.

Figure 6 illustrate the packet delivery ratio based on different mobile nodes where the movement of speed ranges from 1 to 15 m/s. The results of 70 different mobile nodes using CC-CLD framework offer comparable values than the state-of-the-art methods. The packet delivery ratio is improved by applying the localized distribution algorithm when compared to two other methods FA-PSO (Haijun et al., 2012) and C-RA (Larry et al, 2011). The algorithm had better changes when the number of mobile nodes in the network changes rapidly that helps to easily improve the packet delivery ratio. This is achieved by applying the Eigen vector based centrality for source and destination node using the connected dominated sets.

With the application of localized distribution algorithm, the analysis of distributed movable node topology is significantly made. The CC-CLD monitors the link path between connected dominating sets for grouping the closely related group structure route path resulting in the increase in packet delivery ratio by 15.5% compared to FA-PSO. Besides, the localization procedure of clustering using the Eigen vector that is connected to in a circular manner is equal to the sum of centrality value of cluster further the packet delivery ratio by 22% compared to C-RA.

Conclusion:

In this paper, we focus on constructing an efficient route path using Connected-path Link Dominating (CC-CLD) QoS routing in MANET. We presented Localized Distribution algorithm where closely related route paths are efficiently grouped for different mobile node events. An Eigen vector based centrality in MANET is designed that reduces the recovery time. As the method uses complete clustering, cluster head uses the pruning rules to group the similar type of route paths in an efficient way thereby reducing the recovery time for recovering the accurate route path for packet transmission in MANET. As a result, the proposed localized distribution algorithm achieves comparable services reducing the energy consumption in MANET. Moreover, as the distributed route path in MANET is connected using connected path link dominant set, ad-hoc networking overhead is reduced. This improves the performance of network by applying least greedy procedure while transferring the packets using different clustered route paths and shared storage information. The shared storage information effectively assigns the users for efficient packet transfer in mobile ad hoc network. Simulation results demonstrate that the proposed CC-CLD framework provides significant gain in packet delivery ratio. The proposed Localized Distribution algorithm is also capable to reduce the ad-hoc network routing overhead between different mobile nodes.

ARTICLE INFO

Article history:

Received 12 March 2015

Accepted 28 April 2015

Available online 1 June 2015

Corresponding Author: Mrs. J.Nandhini, Research Scholar, Department of ECE, Jay Shriram Group of Institutions, Tirupur, Tamilnadu-638660, India. Tel: +919843054846 E-mail: nandhoosaran@gmail.com

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(1) Mrs. J. Nandhini, (2) Dr. D. Sharmila, (3) Dr. K.K. Savitha

(1) Research Scholar, Department of ECE, Jay Shriram Group of Institutions, Tirupur, Tamilnadu, India

(2) Prof & HOD, Department of EIE, Bannari Amman Institute of Technology, Sathyamangalam, Tamilnadu, India

(3) Associate Prof, Department of MCA, KSR College of Engineering, Tiruchengode, Tamilnadu, India

Table 1: Simulation parameters.

Parameter                              Value

Simulator                             Ns-2.31
Network Coverage area              1200m * 1200 m
Mobility model                 Random Way point model
Node movement (i.e, speed)             40 m/s
Number of nodes              10, 20, 30, 40, 50, 60, 70
Connected Path link               Multi direction
Packet rate                      7 packets/seconds
Cluster Heads                          3 CHs
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Title Annotation:mobile ad hoc networks
Author:Nandhini, J.; Sharmila, D.; Savitha, K.K.
Publication:Advances in Natural and Applied Sciences
Article Type:Report
Date:Jun 15, 2015
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