Abstract:
To adapt to the highly dynamic changing and improve the routing performance in delay tolerant networks (DTNs),this paper proposed a density-aware routing (DAR) scheme for DTNs based on the spatial node distribution and degree centrality. DAR introduced node distribution of mobility model. With the node density of current location, DAR dynamically chose the number of message copies disseminated. Relay selection and copies division were based on degree centrality. Simulation results show that DAR achieves high delivery ratio and low delivery delay with low resource consumption.

Abstract:
We extend the concept of eigenvector centrality to multiplex networks, and introduce several alternative parameters that quantify the importance of nodes in a multi-layered networked system, including the definition of vectorial-type centralities. In addition, we rigorously show that, under reasonable conditions, such centrality measures exist and are unique. Computer experiments and simulations demonstrate that the proposed measures provide substantially different results when applied to the same multiplex structure, and highlight the non-trivial relationships between the different measures of centrality introduced.

Abstract:
In the study of static networks, numerous "centrality" measures have been developed to quantify the importances of nodes in networks, and one can express many of these measures in terms of the leading eigenvector of a matrix. With the increasing availability of network data that changes in time, it is important to extend eigenvector-based centrality measures to time-dependent networks. In this paper, we introduce a principled generalization that is valid for any eigenvector-based centrality measure in terms of matrices of size $NT\times NT$, where the components of the dominant eigenvector of such a matrix describes the centralities of $N$ nodes during $T$ time layers. Our approach relies on coupling centrality values between neighboring time layers with a inter-layer edge, whose weight controls the extent to which centrality trajectories change over time. By studying the limit of strong coupling between layers, we derive expressions for "time-averaged centralities," which are given by the zeroth-order terms of a singular perturbation expansion. We also study first-order terms to obtain "first-order-mover scores," which concisely describe the magnitude of nodes' centrality changes over time. Importantly, we compute these quantities by solving linear algebraic equations of dimension $N$. This is much more computationally efficient than computing the dominant eigenvector of the full $NT \times NT$ matrix. As examples, we apply our method to three empirical temporal networks: the United States Ph.D. exchange in mathematics, costarring relationships among top-billed actors during the Golden Age of Hollywood, and citations of decisions from the Supreme Court of the United States.

Abstract:
This paper addresses the problem of routing in delay tolerant networks (DTNs). Delay tolerant networks are wireless networks where disconnections occur frequently due to mobility of nodes, failures of energy, the low density of nodes, or when the network extends over long distances. In these cases, traditional routing protocols that have been developed for mobile ad hoc networks prove to be ineffective to the extent of transmitting messages between nodes. To resolve this problem and improve the performance of routing in delay tolerant networks we propose a new routing protocol called Spray and Dynamic; this approach represents an improvement of the spray and wait protocol by combining it with the two protocols: MaxProp and the model of “transfer by delegation” (Custody Transfer). To implement our approach Spray and Dynamic, we have developed a DTN simulator according to DTN network architecture.

Abstract:
Graph Isomorphism is one of the classical problems of graph theory for which no deterministic polynomial-time algorithm is currently known, but has been neither proven to be NP-complete. Several heuristic algorithms have been proposed to determine whether or not two graphs are isomorphic (i.e., structurally the same). In this research, we propose to use the sequence (either the non-decreasing or nonincreasing order) of eigenvector centrality (EVC) values of the vertices of two graphs as a precursor step to decide whether or not to further conduct tests for graph isomorphism. The eigenvector centrality of a vertex in a graph is a measure of the degree of the vertex as well as the degrees of its neighbors. We hypothesize that if the non-increasing (or non-decreasing) order of listings of the EVC values of the vertices of two test graphs are not the same, then the two graphs are not isomorphic. If two test graphs have an identical non-increasing order of the EVC sequence, then they are declared to be potentially isomorphic and confirmed through additional heuristics. We test our hypothesis on random graphs (generated according to the Erdos-Renyi model) and we observe the hypothesis to be indeed true: graph pairs that have the same sequence of non-increasing order of EVC values have been confirmed to be isomorphic using the well-known Nauty software.

Abstract:
Functional magnetic resonance data acquired in a task-absent condition (“resting state”) require new data analysis techniques that do not depend on an activation model. In this work, we introduce an alternative assumption- and parameter-free method based on a particular form of node centrality called eigenvector centrality. Eigenvector centrality attributes a value to each voxel in the brain such that a voxel receives a large value if it is strongly correlated with many other nodes that are themselves central within the network. Google's PageRank algorithm is a variant of eigenvector centrality. Thus far, other centrality measures - in particular “betweenness centrality” - have been applied to fMRI data using a pre-selected set of nodes consisting of several hundred elements. Eigenvector centrality is computationally much more efficient than betweenness centrality and does not require thresholding of similarity values so that it can be applied to thousands of voxels in a region of interest covering the entire cerebrum which would have been infeasible using betweenness centrality. Eigenvector centrality can be used on a variety of different similarity metrics. Here, we present applications based on linear correlations and on spectral coherences between fMRI times series. This latter approach allows us to draw conclusions of connectivity patterns in different spectral bands. We apply this method to fMRI data in task-absent conditions where subjects were in states of hunger or satiety. We show that eigenvector centrality is modulated by the state that the subjects were in. Our analyses demonstrate that eigenvector centrality is a computationally efficient tool for capturing intrinsic neural architecture on a voxel-wise level.

Abstract:
Delay Tolerant Networks (DTNs) enable data transfer when mobile nodes are onlyintermittently connected. DTN routing usually follows store-carry-and-forward mechanism. Therefore,the willingness of nodes to relay messages for other nodes plays a significant role in the routing process.Moreover, since the resources in mobile devices are generally limited, carriers of mobile devices maybe unwilling to relay messages for other nodes in order to conserve their scarce resources. Whenconsidering routing in DTNs, such selfish nodes have to be considered. Existing routing algorithmsdetects routing misbehavior can be caused by selfish nodes that are unwilling to spend resources suchas power and buffer on forwarding packets of others, or caused by malicious nodes that drop packets tolaunch attacks. To mitigate routing misbehavior by limiting the number of packets forwarded to themisbehaving nodes. Many challenges considers of selfishness of users who prefer to relay data forothers with strong social ties. Such social selfishness of users is a new constraint in network protocoldesign. Proposed work carries Social Selfishness Aware Routing (SSAR) algorithm to allow userselfishness and provide better routing performance in an efficient way. SSAR considers both users’willingness to forward and their contact opportunity, resulting in a better forwarding strategy.

Abstract:
Performance of data forwarding in Delay Tolerant Networks (DTNs) benefits considerably if one can make use of human mobility in terms of social structures. However, it is difficult and time-consuming to calculate the centrality and similarity of nodes by using solutions for traditional social networks, this is mainly because of the transient node contact and the intermittently connected environment. In this work, we are interested in the following question: Can we explore some other stable social attributes to quantify the centrality and similarity of nodes? Taking GPS traces of human walks from the real world, we find that there exist two known phenomena. One is public hotspot, the other is personal hotspot. Motivated by this observation, we present Hoten (hotspot and entropy), a novel routing metric to improve routing performance in DTNs. First, we use the relative entropy between the public hotspots and the personal hotspots to compute the centrality of nodes. Then we utilize the inverse symmetrized entropy of the personal hotspots between two nodes to compute the similarity between them. Third, we exploit the entropy of personal hotspots of a node to estimate its personality. Besides, we propose a method to ascertain the optimized size of hotspot. Finally, we compare our routing strategy with other state-of-the-art routing schemes through extensive trace-driven simulations, the results show that Hoten largely outperforms other solutions, especially in terms of combined overhead/packet delivery ratio and the average number of hops per message.

Abstract:
In this paper, we address the problem of efficient routing in delay tolerant network. We propose a new routing protocol dubbed as ORION. In ORION, only a single copy of a data packet is kept in the network and transmitted, contact by contact, towards the destination. The aim of the ORION routing protocol is twofold: on one hand, it enhances the delivery ratio in networks where an end-to-end path does not necessarily exist, and on the other hand, it minimizes the routing delay and the network overhead to achieve better performance. In ORION, nodes are aware of their neighborhood by the mean of actual and statistical estimation of new contacts. ORION makes use of autoregressive moving average (ARMA) stochastic processes for best contact prediction and geographical coordinates for optimal greedy data packet forwarding. Simulation results have demonstrated that ORION outperforms other existing DTN routing protocols such as PRoPHET in terms of end-to-end delay, packet delivery ratio, hop count and first packet arrival.

Abstract:
We try to formulate the delay-tolerant networking routing problem, where messages are to be moved end-to-end across a connectivity graph that is time-varying but whose dynamics may be known in advance. The problem has the added constraints of finite buffers at each node and the general property that no contemporaneous end-to-end path may ever exist. This situation limits the applicability of traditional routing approaches that tend to treat outages as failures and seek to find an existing end-to-end path. We propose a framework for evaluating routing algorithms in such environments. We then develop two algorithms and use simulations to compare their performance with respect to the amount of knowledge they require about network topology. We find that with additional knowledge, far less than complete global knowledge; efficient algorithms can be constructed for routing in such environments.