Infrastructure of MANETs  MANETS are without a fixed infrastructure  Network Graphs in MANETS are rarely or ever connected  MANET routing protocols such as AODV,DSR,DSDV and LAR make the assumption that network graph is fully connected.

Workarounds?  Exploit the mobility of the network to transfer messages.  Mobility assisted routing that assist the store-and-forward model.  BUT NODES DO NOT HAVE AN EQUAL PROBABILITY OF ENCOUNTERING EACH OTHER.

Problems  Not all nodes are equally likely to encounter each other.  Nodes need to assess the probability of encountering the destination node. Social Networking to the rescue…..

Social Networking  Social Networking comes from the observation that individuals are linked by a small chain of acquaintances.  Milgram’s 1967 experiment and 6 degrees of separation.  Hsu and Helmy’s research promotes the idea that Social Network analysis techniques can be applied to disconnected MANETs.

Social Networking in MANETs  In Millgram’s experiment, the carriers of messages were not randomly selected. The participant sent the letter to the person who might be perceived as the best carrier.  Intelligent selection of good carriers based on the local information needs to be explored.  This paper proposes the use of social network analysis techniques in order to exploit the underlying social structure in order to provide information flow from source to destination in a DDTM.

Related Work  Data Mule Project  Message ferrying project  Time dependent network graphs  Epidemic Routing  PRoPHET Routing  Usage of KALMAN filters in selectively forwarding messages.

SimBetTS Routing Metric  SimBetTS Routing metric as proposed in this paper is comprised of both a node’s centrality and its social similarity.  SimBetTS Routing improves upon encounter-based strategies where direct or indirect encounters between source and destination may not be available.

Social Networks For Information Flow  Social network analysis is the study of relationships between entities and on the patterns and implications of these relationships.  Graphs may be used to represent the relational structure of social networks in a natural manner.  Each of the nodes may be represented by a vertex of a graph.  Relationships between nodes may be represented as edges of the graph.

Centrality in Graph Theory It is the quantification of the relative importance of a vertex within the graph.  Central node has a stronger capability of connecting other network members.  Three widely used centrality measures are Freeman’s degree, closeness, and betweenness measures

Centrality Measures  Freeman’s degree: - It is the number of direct ties that involve a given node. - Segregates the ‘popular’ from the ‘unpopular’ nodes. - Degree centrality (for a node pi) is calculated as follows:

Centrality Measures….  Closeness - Measures the reciprocal of the mean geodesic distance, which is the shortest path between the node and all other reachable nodes. - Closeness centrality can be measured by:

Centrality Measures….  Betweenness - This centrality measures the extent to which a node lies on the geodesic paths linking other nodes. - It can be regarded as how much a node can facilitate communication to other nodes in the network. - It can be calculated as follows:

Ego Networks  Freeman’s centrality metrics become difficult to evaluate in a network with large node populations.  This is the reason for the proposal of ego networks..  Ego networks can be defined as a network consisting of a single actor (ego) together with the actors they are connected to (alters) and all the links among those alters.  Ego network analysis can be performed locally by individual nodes without complete knowledge of the entire network.

Ego Networks vs. Sociocentric Networks  Degree centrality can easily be measured for an ego network where it is a simple count of the number of contacts.  Closeness centrality is uninformative in an ego network, since by definition an ego network only considers nodes directly related to the ego node.  Betweenness centrality in ego networks has shown to be quite a good measure when compared to that of the sociocentric measure.

Betweenness Centrality in Ego- Networks In the figure below, the rankings of the betweenness are identical for Sociocentric and Egocentric.

Drawbacks of routing based on Centrality  Central nodes suffer serious traffic congestion.  Centrality does not take into account the time varying nature of the links and the availibility of the link in the network

Strong Ties for Information Flow  Tie strength is a quantifiable property that characterizes the link between two nodes.  Brown and Reingen found that strong ties were more likely to be activated for information flow when compared to weak ties.  Tie strength factors in the time varying nature of the links.

Tie-Strength Indicators  Frequency  Intimacy/Closeness  Long Period of time (Longetivity)  Reciprocity  Recency  Multiple Social Contexts  Mutual-Confiding Trust

Usage of Tie-Strength Indicators  Tie strength indicators can be used for information flow to determine which contact has the strongest social relationship to a destination.  Messages can be forwarded through links possessing the strongest relationship, link representing a strong relationship will be more likely to be activated for information flow.

Utility of Weak ties  Granovetter emphasized that weak ties lead to information dissemination between groups.  Weak ties can act as ‘bridges’  Need is there to find routing based on a combination of strong ties and identified bridges.

Tie Predictors  Tie strength evaluates already existing connections whereas predictors use information from the past to predict likely future connections.  A network is said to show “clustering” if the probability of two nodes being connected by a link is higher when the nodes in question have a common neighbor.

Common Neighbor Metrics Here ‘score’ is the future collaboration between nodes x and y. N(x) and N(y) are neighbors of x and y respectively. P(x,y) weighs the ‘rarer’ neighbors more heavily than common neighbors.

Usage of Tie-Predictors  Tie predictors can be used in order to predict the evolution of the graph and evaluate the probability of future links occurring.  Tie predictors may be used not only to reinforce already existing contacts but to anticipate contacts that may evolve over time.

Routing based on Social Metrics  In this paper it is proposed that the combination of centrality, tie strengths, and tie predictors are highly useful in routing based on local information when the underlying network exhibits a social structure.  The combined metric is known as SimBetTS utility.

Sim-Bet-TS utility  Sim(Similarity)Bet(Betweenness)TS(Tie-Strength)  When two nodes meet, they exchange a list of encountered nodes.  this list is used to locally calculate the betweenness utility, the similarity utility, and the tie strength utility.  Each node then examines the messages it is carrying and computes the SimBetTS utility of each message destination.  Messages are then exchanged where the message is forwarded to the node holding the highest SimBetTS utility for the message destination node.

Betweenness in SimBetTS The betweenness centrality is calculated by computing the number of nodes that are indirectly connected through the ego node. Betweenness in SimBetTS relies on the Adjacency Matrix representation of the ego-nodes and its neighbors.

Similarity in SimBetTS  The adjacency matrix allows for the calculation of similarity for nodes that have been met directly, but during the exchange of the node’s contact list, information can be obtained with regard to nodes that have yet to be encountered. In the above similarity calculation Nn and Ne are the set of contacts held by node n and e, respectively.

Tie-Strength in SimBetTS  Frequency  Intimacy/Closeness  Recency The tie strength of node n for an encountered node m, where T is the set of social indicators, is given by:

Node-Utility in SimBetTS  The aim is to select the node that provides the maximum utility for carrying the message. This is achieved using a pairwise comparison matrix on the normalized relative weights of the attributes  The attributes here are the ‘Similarity’, ‘Betweenness’ and ‘Tie-Strength’

Node-Utility in SimBetTS Here U is {TSUtil,SimUtil,BetUtil}

Replication in SimBetTS  Replication may be used in order to increase the probability of message delivery.  Messages are assigned a replication value R. When two nodes meet, if the replication value R > 1 then a message copy is made. The value of R is divided between the two copies of the message. The Replication values are dependant upon the SimBetTS utility of the node

Performance of SimBetTS  SimBetTS Routing achieves the highest delivery performance by combining the three metrics. The load distribution shows that routing based on the combined metrics reduces congestion on highly central nodes.

Performance of SimBetTS

SimBetTS Routing Protocol Behavior  Similarity+Tie-Strength: The sending node has a nonzero tie strength value for the destination node and has a nonzero similarity to the destination node

SimBetTS Routing Protocol Behavior  Similarity: The sending node has never encountered the destination node resulting in a zero tie strength.

SimBetTS Routing Protocol Behavior  None: The sending node has never encountered the destination node and has no common neighbors, hence a zero similarity value.

Conclusions from the Routing Protocol Behavior  The message is forwarded to nodes with a high betweenness and social similarity, until a node with a high tie strength for the destination node is found.  In all cases, the tie strength utility for the final hop is the highest contributing utility value.  In all cases, the betweenness utility value is much reduced in its influence of the forwarding decision as the message is routed closer to the destination

SimBetTS vs. Epidemic/PRoPHET  Single-copy and multicopy SimBetTS Routing show higher message delivery when compared to that of PRoPHET.  Multicopy SimBetTS Routing achieves delivery performance similar to that of Epidemic Routing with short path lengths and low end-to-end delay.  The use of replication comes at the cost of an increased number of forwards and an increase in control data. However, when compared to that of Epidemic Routing, these metrics are still relatively low in terms of overhead

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