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\title{Analyse the Performance of Moblie Peer to Peer Network using Ant Colony Optimization}
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             \author[1]{  R.Saraswathi}

             \affil[1]{  Anna university ,Chennai}

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\date{\small \em Received: 14 December 2012 Accepted: 2 January 2013 Published: 15 January 2013}

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\begin{abstract}
        


A mobile peer-to-peer computer network is the one in which each computer in the network can act as a client or server for the other computers in the network. The communication process among the nodes in the mobile peer to peer network requires more no of messages. Due to this large number of messages passing, propose an interconnection structure called distributed Spanning Tree (DST) and it improves the efficiency of the mobile peer to peer network. The proposed method improves the data availability and consistency across the entire network and also reduces the data latency and the required number of message passes for any specific application in the network. Further to enhance the effectiveness of the proposed system, the DST network is optimized with the Ant Colony Optimization method. It gives the optimal solution of the DST method and increased availability, enhanced consistency and scalability of the network. The simulation results shows that reduces the number of message sent for any specific application and average delay and increases the packet delivery ratio in the network.

\end{abstract}


\keywords{mobile peer to peer network, distributed spanning tree, global replica management, ACO, LRM, ORCS.}

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\let\tabcellsep& 	 	 		 
\section[{INTRODUCTION}]{INTRODUCTION}\par
a) Mobile Peer to Peer Network n a mobile P2P network, the mobile nodes are connected in mesh network within their communication range. Files can be shared directly between systems on the network without the need of a central server. The communication among the mobile nodes is to be carried in multi-hop fashion due to the design considerations such as radio power limitation and channel utilization. Any communication with external networks is performed through the AP which consumes relatively more time. In a mobile P2P network, the "peers" are computer systems which are connected to each other via the Internet.\par
A Mobile P2P network is composed of mobile hosts that are free to move around randomly, and to organize and collaborate together to share information among themselves. Files can be shared directly between systems on the network without the need of a central server. In other words, the P2P network is called a distributed structure if the participants share a part of their own resources. These shared resources are necessary to provide the service offered by the network. The participants of such a network are both resource providers and resource consumers. The P2P network has the following characteristics:\par
? All nodes are both clients and servers.\par
? Provide and consume data.\par
? Any node can initiate a connection.\par
? No centralized data source.\par
? Nodes contribute content, storage, memory, CPU.\par
? Nodes are autonomous (no administrative authority).?? ? Network is dynamic: nodes enter and leave the network "frequently". ? Nodes collaborate directly with each other.\par
? Nodes have widely varying capabilities.\par
The various benefits of P2P network has the efficient use of resources, scalability, reliability, ease of administration, Anonymity, Highly dynamic environment and Ad-hoc communication and collaboration.  
\section[{b) Distributed Spanning Tree}]{b) Distributed Spanning Tree}\par
The distributed spanning tree (DST) is an overlay structure designed to be scalable. It supports the growth from a small number of nodes to a large one. The DST is a tree without bottlenecks which automatically balances the load between its nodes. The DST breaks the common assumption that a tree is build of leaves and intermediate nodes. In a DST every nodes are equal. The nodes are put together into small cliques. Then, the cliques are put together into small cliques of higher level recursively. The cliques are represented in each node by a routing table. The memory space complexity of the routing tables is O(log(n)) for a n nodes DST.\par
The section 2 describes the related work and section 3 describes the proposed system and section 4 describes the simulation scenario and section 5 describes the conclusion of the work. In computer science and operations research, the ant colony optimization algorithm (ACO) is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs. This algorithm is a member of the ant colony algorithms family, in swarm intelligence methods, and it constitutes some metaheuristic optimizations. Initially proposed by Marco Dorigo in 1992 in his PhD thesis, the first algorithm was aiming to search for an optimal path in a graph, based on the behaviour of ants seeking a path between their colony and a source of food. The original idea has since diversified to solve a wider class of numerical problems, and as a result, several problems have emerged, drawing on various aspects of the behaviour of ants. 
\section[{II.}]{II.} 
\section[{RELATED WORK}]{RELATED WORK}\par
In \hyperref[b5]{[6]}, Takahiro Hara proposed new consistency maintenance based on local conditions such as location and time need to be investigated. It attempts to classify different consistency levels according to requirements from applications and provides protocols to realize them. In \hyperref[b7]{[8]}, Ren Xun-yil et al proposed a consistency technique based on a replica clustering coefficient to classify replica nodes into multi-levels. Replica consistency has been maintained in which the updating of the data item is performed at first-level replica nodes initially and then it is propagated to the next level of nodes in sequence. Though efficiency is proved in terms of response time and the number of message passes required. In \hyperref[b8]{[9]}, Chun-Pin et al propose a Dynamic Maintenance Service to maintain the data in gird environment. The Bandwidth Hierarchy based Replication algorithm was proposed to maintain the replica dynamically in grid environment. In \hyperref[b8]{[9]},Chao-Tung et al proposed a One-way Replica Consistency Service (ORCS) for grid environment to resolve the consistency maintenance issues and also balancing the tradeoff between the improving data Access performance and replica consistency.\par
In \hyperref[b10]{[11]} Sang-Min Park proposed a novel dynamic replication strategy; called BHR (Bandwidth Hierarchy based Replication). It tries to maximize locality of file to reduce data access time. However, grid sites may be able to hold only small portion of overall amount of data since very large quantity of data is produced in data grid and the storage space in a site is limited. Therefore, effect from this locality is limited to a certain degree. BHR strategy takes benefit from other form of locality, called network-level locality. In \hyperref[b11]{[12]}, Haiying Shen propose an Integrated file Replication and consistency Maintenance mechanism (IRM) that integrates the two techniques in a systematic and harmonized manner. It achieves high efficiency in file replication and consistency maintenance at a significantly low cost. Instead of passively accepting replicas and updates, each node determines file replication and update polling by dynamically adapting to time-varying file query and update rates, which avoids unnecessary file replications and updates. It dramatically reduces overhead and yields significant improvements on the efficiency of both file replication and consistency maintenance approaches. In \hyperref[b14]{[15]}, Xin Sun et al proposed a bidirectional linked list based replica location service to provide a global replica view for supporting the replica management to realize a replica selection strategy and optimal replication strategy on tree-based hierarchical unstructured overlay network.\par
In \hyperref[b15]{[16]}, Jun Zheng et al proposed a dynamic minimum access cost based replication strategy called MAC replication strategy. It takes into account the access frequency, the status of the network connection and average response time. It calculates an appropriate site to replicate for better shortening the response time of the data source. In \hyperref[b16]{[17]}, Wanlei et al propose the Hybrid Replica Control protocol that attempts to maximize the data availability and communication overhead. In \hyperref[b17]{[18]}, Feras et al propose a Constrained Fast Spread (CFS) method to alleviate the main problems encountered in the current replication techniques and mainly concentrating on the feasibility of replicating the requested replica on each node among the network. In \hyperref[b18]{[19]} Baskaran et al proposed a GRM in a tree structured P2P network to preserve the replica consistency through the network and reduce the traffic in the network. In \hyperref[b19]{[20]}, Sylvain Dahan et al proposed a Distributed Spanning Tree structure and it is designed to support scalable searches and traversal algorithms. The DST based searches generates less messages to send the query and avoids tree bottleneck. In \hyperref[b20]{[21]}, Sylvain Dahan et al proposed a distributed Spanning tree Structure for large scale environment. This method achieves load balancing and Fault Tolerance in the network. In \hyperref[b21]{[22]},Xin sun et al proposed the bidirectional linked list based replica location service (BLL-RLS) on tree-based hierarchical unstructured overlay networks, including the deployment of replica location service and the design of the bidirectional linked list based replica catalog. Based on the bidirectional linked list based replica catalog, replica location and selection algorithm is also proposed.  
\section[{PROPOSED SYSTEM}]{PROPOSED SYSTEM}\par
In a mobile peer to peer network, each node has to exchange information and services directly with each other without any dedicated intermittent. So it develops bottlenecks in the network due to the huge volume of messages being exchanged. This could be avoided to optimize the number of messages across the network. In this paper, a distributed spanning tree approach is proposed. The proposed system consists of the following steps:  The mobile P2P network is converted into the set of spanning trees called the Distributed Spanning Tree (DST) and the corresponding graph based algorithms are developed to optimize the number of messages across the network. The DST is an overlay structure designed to be scalable, which supports the growth of the nodes from fewer nodes to higher volumes \hyperref[b15]{[16,}\hyperref[b16]{17]}.It allows the instantaneous creation of spanning trees rooted by any node and maintains the load balancing between the nodes \hyperref[b15]{[16]}. This instantaneous creation of spanning trees improves the overall scalability of the intended network \hyperref[b17]{[18]}. So, DST structures help to automatically balance and optimize the load among the nodes.\par
The P2P network is converted into DST and each tree should have its root node, named as the Head Node (HN) and the possible Leaf Nodes (LNs). Every HN will hold the complete details regarding its LNs and vice versa. These HNs are to be generated dynamically and should hold the replica, which is to be accessed by their corresponding LNs and indeed by other HNs also. Fig \hyperref[fig_5]{2a} shows the simulation of distributed spanning tree. The DST algorithm consists of three procedures.\par
1. Initialize(): This procedure create the set of Head Nodes (HNs) in a peer network based on criteria such as user approval, traffic in a particular region, etc. This procedure creates a list on each HN to hold its LNs details. This procedure assign unique id for every HN and then it calls the procedure probe (). 
\section[{Probe():}]{Probe():}\par
This procedure creates probe message and flood this message to all the nodes connected to it. On receiving a probe messages, every node executes receive () procedure.\par
? If the probe message is received by an HN, then it will be discarded. ? If the message is received by the LN, which is not under any HN, then the LN stores the head variable as the HeadID. Then the procedure reply() and the forward() will be called. ? If the reply message is received by the LN, it will be forwarded to the HN. ? If the reply message is received by the HN, then it reads the 'HeadId' from rmsg. If the 'Headid' equals the id of the current node then it concludes that the respective head node is reached.  \par
probe message 'p' to find the optimal path between HN i and other HNs. 2. The propagate (G,x,p) ,which propagates messages through different paths is called which takes graph G, HN i as 'x' and probe message 'p' as parameters. Probe message 'p' is flooded through the possible path which increase the number of feasible path discovered between the HN i and other HN. 3. The construction (G,?,x,z) which calculates the edge value through the destination HN, is called by HN i which takes the graph G, start HN 'x' start HN 'x' specific end HN 'Z' and '?' as the parameter.\par
The '?' is the measure of cumulative edge value between 'x' and 'z'.' ? i ' value is used to decide the optimal path between the nodes. The value of 'Val' can be given asVal= (\textbf{4})\par
Where\par
? 'val' is a variable to count the value of ? on each edge from 'x' to 'z'. ? 'p' is the number of edges between HNs 'x' and 'z' in the MP2P network. ? '?' is the cumulative edge value between the node 'x' and 'z'. 4. DaemonAction(Val) is called by the end HN 'z' which takes the 'Val' as parameter and decides the optimal path between the HNs 'x' and 'z' based on the value of 't' along the path of each probe 'p'. Every probe reaches 'z' with its 'val' then 'z' decides the optimal path based on the 'val' and the component type of 't'.\par
Algorithm for Ant Colony Optimization Optimize() 1. Consider the graph that consists of vertices v and edges e such that G=(v,e). 2. Consider the HN x and z such that create a probe message p on x. 3. Call the function propagate(),construction() and daemonaction() to all HNs in the DST. 4. Consider the LN y for the particular HN x ,create a probe message p on y. 5. Call the function propagate(),construction() and daemonaction() to all the LNs. Probe() Forward the probe message p to all nodes. 1. For each non visited vertices in G, count the value of edge from node x to z and also calculate the cumulative edge value between the node x and z. Construction() 1. Initialize val(z) and trial(z). 2. For each non visited vertices in G, count the value of edge from node x to z and also calculate the cumulative edge value between the node x and z.\par
DaemonAction() 1. Compare the cumulative edge value between the node x and z. 2. Find the optimal path between the node x and z based on the cumulative edge value '?'. 
\section[{If the value '?' can be compared with the positive}]{If the value '?' can be compared with the positive}\par
Qos attribute like bandwidth and transmission speed of the node. If the node has the highest '?' value, then it chooses the optimal path between the nodes.\par
Let '? ' be a positive QoS attribute like bandwidth, transmission speed, etc., then the path with highest value of 'val' is chosen to be optimal. On the other hand, if '? ' is one of the negative QoS attributes like Hop count, congestion delay, propagation delay, etc., then the path with lowest value of 'val' is chosen to be optimal.\par
Definition 3: Let n(ACOmsgpass) be the number of message passes required for applying ACO in DST MP2P network and it can be estimated as, n(ACOmsgpass)=((L/P)×N)+((P×M)×(L/P))\par
(5)\par
Where,\par
? 'N' refers to the number of message pass between one HN and another HN. \par
Thus the total number of message passes required to perform ACO technique in DST P2P network is directly proportional to total number of peers and HNs in the network.  
\section[{Simulation Scenario}]{Simulation Scenario}\par
This section describes the experimental set-up developed for investigating the proposed method with different performance criteria. The NS2 simulator is used for comparing the performance of mobile peer to peer network. A Mobile Peer-to-Peer network is simulated with 50 mobile nodes (N1, N2,.. .N50) moving at constant speed within a bounded region. Unit propagation delay of the wireless medium has been assumed as 10 ms. The experimentation and analysis have been carried out under two different scenarios: MP2P with DST and MP2P with ACO optimized DST networks. The Table  {\ref 1} specifies the number of parameters used for the simulation of mobile peer to peer network.\par
In this phase, the performance of the Mobile P2P network is evaluated using the Ant colony optimization method. In fig 4.1a specifies the Number of nodes Vs number of packet request for that node. It shows when the number of nodes increases, the message sent for the node was also decreases by using DST method. But in optimized DST (ACO) method, the number of requested packets from the network was also decreased. In the second graph 4.1b shows the number of node Vs packet delivery ratio. By using ACO method, the packet delivery ratio for the node was increased compared to that of using DST method. In the third graph 4.1c shows number of nodes Vs average delay. By using optimized DST method, the average delay for the node was decreased when the number of node was increased.   
\section[{CONCLUSION}]{CONCLUSION}\par
By employing the DST structures in the P2P network, the consistency and replication efficiency can be achieved with the few messages compared to the traditional method. The scalability of the P2P network can be improved with the application of DST structures. The proposed model increases the data availability, reduces the bandwidth conception and number of messages in the network and also improves the fault tolerant capacity of the overall system. Further to enhance the effectiveness of the proposed system, the DST network is optimized with the Ant Colony Optimization method. It gives the optimal solution of the DST method and reduces the message sent and average delay and increases the packet delivery ratio in the network. We have to plan to achieve the cluster based replica allocation for mobile peer to peer networks and also achieve the effective service cache management in the network. \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-2.png}
\caption{\label{fig_1}?}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-3.png}
\caption{\label{fig_2}?}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{1a}\includegraphics[]{image-4.png}
\caption{\label{fig_3}Figure 1a :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{3}\includegraphics[]{image-5.png}
\caption{\label{fig_4}3 .}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{2}\includegraphics[]{image-6.png}
\caption{\label{fig_5}Figure 2 :E}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-7.png}
\caption{\label{fig_6}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{3}\includegraphics[]{image-8.png}
\caption{\label{fig_7}Figure 3 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{4}\includegraphics[]{image-9.png}
\caption{\label{fig_8}Figure 4 .}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{4}\includegraphics[]{image-10.png}
\caption{\label{fig_9}Figure 4 .}\end{figure}
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