Communication Between Mobile Devices using Opportunistic MSNs with the Help of Community Aware Oppurtunistic Routing Algorithm

DOI : 10.17577/IJERTV3IS031720

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Communication Between Mobile Devices using Opportunistic MSNs with the Help of Community Aware Oppurtunistic Routing Algorithm

1D. Sangeetha, 2N. Karthika

1PG Student, 2Assistant Professor

1&2Department of Computer Science and Engineering,

1&2Jayaram College of Engineering and Technology, Tiruchirappalli, Tamilnadu, India.

Abstract – Mobile Social Networks (MSN) are increasing nowadays as the mobile users would like to share the knowledge with their community. Even though some of the social networks are available, users are unable to communicate only to their individual communities. The difficulty exists to communicate with the separate set of people. To improve the situation, community aware opportunistic routing algorithm has been proposed along with reverse dijkstras algorithm to efficiently reduce the delivery delay of nodes in the network. The communications among the mobile users are made to traverse in a secured manner. Since communications among the users happen in the network of community homes, computational and maintenance cost of contact information are greatly reduced. This work also proves that energy levels of mobile nodes are also increased.

Keywords Mobile Social Networks, opportunistic routing, Community Aware Opportunistic Routing


    In the modern world, the use of portable devices has increased the use of wireless communication and networking technologies. The conventional methodology such as mobile adhoc networks, intermittent and uncertain connectivity has led to the disruptive and a challenging data forwarding task. To improve the situation, new routing and data dissemination solutions in an opportunistic manner between mobile users have been proposed. The growing environment has created a great attention towards the information centric networks (ICN) in both academia and industries. ICN, an alternative approach for IP-based computer networking used for content data search and retrieval. The user is focused mainly on data and does not care about the origin and about, how it is retrieved. The data are retrieved with the help of the unique identifier.

    Public-key cryptography, a technique in the cryptographic system comprise of two separate keys. Of which, one is public and the other is private. By terms, although the keys are both different, they are linked mathematically.

    Security is not an essential consideration for public key whereas for private key, authentication of the person required to view the message is must.

    The Public-Key infrastructure (PKI) system is used to verify the binding relationship between the public-key and the user identity in public-key cryptography scheme. However, the current PKI scheme has been considered inefficient, unusable and difficult to deploy, especially for the mobile application scenario. For example, in the application scenario of vehicular network without any infrastructure, the PKI service is unusable. Mobile ICN needs a more flexible and usable mechanism to verify the binding relationship of the user identity and public-key.

    MSNs is a combination of concepts from two disciplines viz., social network and mobile communication network. Social network defines the structure of the user and the communication between their peers. Broadly, MSNs can be categorized into two types: infrastructure- based MSNs and infrastructure-less or opportunistic MSNs [2].

    Figure 1: View of Social Network

    Social networks use Infrastructure-based MSNs to acquire information from web. In all these cases, the mobile users are required to connect to the internet to communicate with their peers whereas in opportunistic MSNs, mobile devices need not connect to a centralized server for communication purposes.

    This paper mainly focuses on single-copy routing problem in MSNs. The people belonging to a community would like to communicate and share similar stuff with the people of similar communities. By considering this basic fact, a home aware community model has been proposed.

    Following this, Community Aware Opportunistic Routing (CAOR) algorithm has also been proposed and used for this purpose.

  2. ASSUMPTIONS IN NETWORK Table 1: Depiction Of Auxiliary Variable

    v, l, d, S

    v is a node, l is a location or home,

    d is a destination, and S is a set of nodes or homes.

    v, l, S, l

    The exponential distribution

    parameter of that node v and (any node in) node set S visit home l.


    the optimal relay set for the

    opportunistic routing from a message sender (i) to destination d.

    ~ Si,j

    the optimal betweens set for the

    message delivery from home i to j. When the context is clear, the subscripts are removed.


    the minimum expected delivery

    delay from i to d via relay set S, where i may be a node, or a home. Specifically, Di;d=Di;d(~Ri) when S= ~Ri.

    Bl,l (S)

    the betweenness of S, i.e., the

    expected delivery delay from home l to l only via node set S.

    In this case MSN composed of |V | nodes V = {v | v V } moving among |L| locations L={l | l L} (|L| |V |) are considered. Each mobile node visits a few locations frequently, while visiting the others rarely.

    This work also assumes that each home has a throwbox [5], which has the ability to store and transmit messages. Users of different places in the wi-fi enabled area communicate through access points. These access points can also be seen as a type of real throwboxes. This is because that each user can upload or download data from network storages through APs.

    Figure 2: Communication among peers


    The optimal opportunistic routing scheme means that each message sender delivers messages via its optimal relay set (i.e., delivers messages via the first encountered relay in this set). The key problem is to determine whether

    a relay belongs to the optimal relay set for each message sender.

    Groups can be created in two ways of physical social network and virtual social network. Users communicate with the peers within the short distance through Bluetooth. People would like to share similar stuffs belonging to their community. SOCIALNETS [3] and Haggle [4] are two well-known projects which attempt to exploit the underlying social network structure to develop effective protocols.

    The exploitation of user habits and social interactions are carried out by SOCIALNETS whereas Haggle identifies the social networks and it let users to communicate when it come close to the range. The communications occur in a secured manner, if the user thinks of it as an unwanted message, need not accept the request from the other end.

    Initially, the topologies of MSNs and Opportunistic Networks are unstable and users appear in and disappear from the network dynamically. Secondly, content producers and consumers might be completely unaware of each other, and may never be connected at the same time to the same part of the network. Thirdly, the involved protocols heavily rely on human mobility and contact opportunity, and hence, the prediction of future contact becomes a critical issue in both MSNs and Opportunistic Networks.

    A new metric named social pressure metric has been derived which motivates the friends community to share more and more information. This enables users of friends community to track the past contact detais, so that without any interruption communication can be made possible.

    Algorithm 1 Determine optimal betweenness set


    {v1,l; v1,l ; · · · ; vn,l; vn,l} (v1,l · · ·vn,l )

    Ensure: ~ Sl,l , l,l ;D l,l

    1: Initialize:

    S={v1} and Dl;l (S)= 1 v1,l+ 1 v1,l ; 2: for i=2, · · ·, n do

    3: S=S+{vi};

    4: Incrementally compute Dl,l (S) 5: if Dl,l (S) increases then

    6: Break;

    7: return ~Sl,l=S{vi} and corresponding l,l,Dl,l ;

    Algorithm 2 CAOR initialization


    G=L,W, where W ={l,l,DL,l |l, l L}

    For each community home l L do

    1: Collect v,l, v,l for each v Cl and l L{l};

    2: Use Algorithm 1 to produce ~ Sl,l and _l,l ;Dl,l ; 3: Create the virtual link

    Lambda ll:l,l ;Dl,l for each l L{l} and send the link weights to other homes;

    4: Receive the link weights from other homes; 5: Construct the contact graph G=L;W;


    Active nodes in the network can be found easily using the two metrics, Node stability and Node Influence. Stability of the node calculates about the node in the network communicate to a certain node over the particular period of time. This stableness in the network allows to identify how frequently, the communication between the two nodes occur over a period of time.

    Node influence has been calculated based on how much a node influences other nodes to join the community in the home network. High influence score is obtained based on the node joins the community which influences other nodes to be a follower of that node.


    A community is defined as the stronger ties between the members inside the group than the members who reside outside the group. The members of whatever community would like to share materials like images, study items etc to the other members within the community. This has enabled the thirst of detecting the community.

    For example, the members belong to a study level would love only to join in the same community of people rather likely to join with the oldies. Modularity methods are used to detect the community in an easier way.


This paper explains about the routing of nodes in MSNs into some overlapping home aware communities. The work simplifies the routing problem through CAOR algorithm thereby enhances the security in the communicating nodes. Energy level of the nodes has also been conserved through communication within the same network and the communities. Community detection in the MSNs has been presented through the algorithms. The future work of this paper includes implementation of community detection in a more efficient manner thereby creating routing and dissemination solutions.


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