DTN-Knca: A High Throughput Routing Based on Contact Pattern Detection in DTNs

Currently, the research community in communication networks has given special attention to the study of emerging wireless networks such as sensor networks, mesh networks, ad hoc networks, pervasive computing systems and delay/ disruption tolerant networks (DTNs). The main feature of these networks is not require the presence of a communication infrastructure and, therefore, often present decentralized operation and auto configuration. Additionally, due to the DTN s highly distributed nature, it is desirable that it be also assigned to this type of network some level of awareness of energy consumption. Thus, in this Thesis, we propose an optimization strategy for a routing algorithm that was originally proposed without regard to the issue of energy constraint in delay/disruption tolerant networks (DTNs). The routing algorithm must use some utility function based on a number of different parameters (e.g., encounter history, mobility, sociability, etc.) to discover the better relay nodes. Our proposed strategy is based on modeling of the message forwarding as Bayesian game that aims specifically to capture the dynamic nature of the message replication decisions, the energy constraint of the devices and the uncertainty about the energy of other devices. An adaptive learning framework that allows the nodes to learn the optimal strategies over time is presented. We use a system for belief update about the energy of the other DTN nodes based on the accumulated observations of the destination nodes. Simulation results show that our proposed optimization strategy is able to lead the network to remain operational for a longer period of time and, consequently, to achieve a higher final delivery ratio.

The routing efficiency in delay tolerant networks is degraded due to intermittent connection and high latency. Additionally, socially selfish nodes in social networks refuse to provide message forwarding service since there are limited resources. To solve these problems, an improved routing algorithm based on the social link awareness is proposed. In this algorithm, multiple social features of the nodes' behaviors are utilized to quantify the nodes pairs' social links. The social links of the nodes pairs are computed based on their encounter history. These social links can be used to construct the friendship communities of the nodes. The intra-community and inter-community forwarding mechanisms are implemented to raise the successful delivery ratio with low overhead and decrease the transmission delay. Simulation results show that the proposed algorithm shortens the routing delay and increases the successful delivery ratio, thereby improving the routing efficiency.

In this paper, we focus on the efficient routing of data among different areas in delay tolerant networks (DTNs). In current algorithms, packets are forwarded gradually through nodes with higher probability of visiting the destination node or area. However, the number of such nodes usually is limited, leading to insufficient throughput performance. To solve this problem, we propose an inter-landmark data routing algorithm, namely DTN-FLOW. It selects popular places that nodes visit frequently as landmarks and divides the entire DTN area into subareas represented by landmarks. Nodes transiting between landmarks relay packets among landmarks, even though they rarely visit the destinations of these packets. Specifically, the number of node transits between two landmarks is measured to represent the forwarding capacity between them, based on which routing tables are built on each landmark to guide packet routing. Each node predicts its transits based on its previous landmark visiting records using the order- k Markov predictor. When routing a packet, the landmark determines the next-hop landmark based on its routing table and forwards the packet to the node with the highest probability of transiting to the selected landmark. Thus, DTN-FLOW fully utilizes all node movements to route packets along landmark-based paths to their destinations. We analyzed two real DTN traces to support the design of DTN-FLOW. We deployed a small DTN-FLOW system on our campus for performance evaluation. We also proposed advanced extensions to improve its efficiency and stability. The real deployment and trace-driven simulation demonstrate the high efficiency of DTN-FLOW in comparison to state-of-the-art DTN routing algorithms.

Message delivery in Delay Tolerant Networks (DTNs) is challenging due to the fact that the network is intermittently connected. Mobility can be exploited to improve DTN performance. In this paper, we propose a DTN routing scheme Adaptive Spraying. Adaptive Spraying exploits mobility pattern and encounter history to predict the number of nodes with no copy a node will encounter within the expected delay. The number of nodes encountered can be viewed as the number of copies disseminated. Each node with copies dynamically chooses the number of copies by itself, instead of a fixed number determined at the source node. We present an analysis of the scheme and validate the analytical results with simulations. Simulation results show that Adaptive Spraying performs well over a variety of environmental conditions such as transmission range and traffic load.

Community has received considerable attention because of its application to many practical problems in mobile networks. However, when considering temporal information associated with community (i.e., transient community), most existing community detection methods fail due to their aggregation of the contact information into a single weighted or unweighted network. In this paper, we propose a contact-burst-based clustering method to detect transient communities by exploiting the pairwise contact processes. In this method, we formulate each pairwise contact process as regular appearance of contact bursts, during which most contacts between the pair of nodes happen. Based on such formulation, we detect transient communities by clustering the pairs of nodes with similar contact bursts together. We also propose a new data forwarding strategy for delay tolerant networks in which transient communities serve as the data forwarding unit. Evaluation results show that our strategy can achieve much higher data delivery ratio than traditional community-based strategies with comparable network overhead.

: We analyzed the impact of the physical propagation environment on the performance of mobile ad hoc network (MANET) protocols. Dynamic Source Routing (DSR) protocol is chosen as typical routing protocol for the experiment. We used the network simulator OPNET to design our airborne network (AN) environment model. Our approach utilized the experimental results that we have obtained at the Air Force Lab in our design to model a cross layer design to reduce the impact of the physical propagation parameters in AN environment. We evaluated the performance of MANET-based protocol under Air Force environment. We designed a delay tolerant network (DTN)-based probabilistic routing protocol to work in AN environment that is associated with intermittent connectivity.

In this paper, a fast algorithm for computing inbreeding coefficients in large populations in which a list of ancestors for each animal is required is modified for more quick calculation. With the current algorithm, an efficient procedure to make the list of the ancestors is used, which is based on grouping the ancestors of each animal chronologically by the use of longest ancestral path. A simulation study is carried out to compare the computational properties of the presented algorithm with those of the original and another typical algorithms. It is found that the presented algorithm becomes considerably fast relative to the original one, as the average number of generations and the number of animals increase. It is also shown that the current algorithm takes less time than another typical algorithm compared, when less than 19 generations are evaluated. The results obtained clearly indicate that the current algorithm requires much less computer time and less memory than the original and another typical algorithms, respectively.

Transcription Factor Binding Affinities and DNA Shape Readout.

Wireless integrated sensor networks, which include collecting, managing data and communication, are used more and more widely for their low cost and convenient deployment. Nowadays the research concerning each aspect of sensor networks is fairly active. Data Aggregation mechanism is one of the key problems in sensor networks. By considering the data transmission delay and overall network energy efficiency, this chapter develops a game-theoretic model of real-time reliable aggregation (RA-G) mechanism for wireless sensor networks. Based on the study of related literatures, first of all in this chapter, the research status of WSN, the system architecture, the characteristics, and the critical technologies are summarized, current typical routing algorithms of WSN are classified and introduced one by one. Taking the implicit collaborative imperative for sensors to achieve overall network objectives (accomplish real-time collection tasks effectively) subject to individual resource consumption into account, this paper proposes a game-theoretic model of reliable data aggregation architecture in wireless sensor networks, defines a multi-tier data aggregation architecture in which semantic based aggregation and average computation aggregation is performed in sensor-level and node-level aggregation respectively. All nodes that detect the same target join the same logic group. Each selected group leader uses game-theoretic model which tradeoffs between energy dissipation and data transmission delay to determine the degree of aggregation. To meet the real-time constraints and balance the energy consumption between nodes, a decision-making model based on game theory which takes delay compensation into account is proposed in the data-relaying stage. The simulation results show that the use of reliable data aggregation architecture can reduce the total transmission overhead of WSN, make the network more energy-efficient and prolong the lifetime of sensor network. On the other hand, the game-theoretic model used in group-level aggregation and data-relaying stage balance the tradeoffs between the energy dissipation and the timeliness of data transmission; therefore, also RA-G data aggregation mechanism is reliable.

The Heliospheric Imagers (HI) onboard the two spacecraft of the Solar Terrestrial Relations Observatory (STEREO) provided white‐light images of transients in the solar wind from dual perspectives from 2007 to 2014. In this paper, we develop a new method to identify and locate the transients automatically from simultaneous images from the two inner telescopes, known as HI‐1, based on a correlation analysis. Correlation coefficient (cc) maps along the Sun‐Earth line are constructed for the period from 1 January 2010 to 28 February 2011. From the maps, transients propagating along the Sun‐Earth line are identified, and a 27‐day periodic pattern is revealed, especially for small‐scale transients. Such a periodicity in the transient pattern is consistent with the rotation of the Sun's global magnetic structure and the periodic crossing of the streamer structures and slow solar wind across the Sun‐Earth line, and this substantiates the reliability of our method and the high degree of association between the small‐scale transients of the slow solar wind and the coronal streamers. Besides, it is suggested by the cc map that small‐scale transients along the Sun‐Earth line are more frequent than large‐scale transients by a factor of at least 2, and that they quickly diffused into background solar wind within about 40 Rs in terms of the signal‐to‐noise ratio of white‐light emissions. The method provides a new tool to reconstruct inhomogeneous structures in the heliosphere from multiple perspectives.

BackgroundThe lipid content of the chylomicrons is a key biomarker and risk factor of cardiovascular diseases and for the understanding of obesity. A high throughput determination of chylomicrons in human blood plasma is outlined.MethodsThe new method, which uses a combination of Nuclear Magnetic Resonance (NMR) analysis and multivariate calibration analysis (chemometrics), is based on a correlation analysis towards the established standard method (ultracentrifugation and colorimetric test kit) and enables extraordinarily fast, inexpensive, and robust prediction of triglyceride (TG) content in chylomicrons. It is the position and shape of the complex lipid methylene resonance band that determines the chylomicron TG status and this information is extracted by the multivariate regression method.ResultsThe resulting method is a relatively simple multivariate model that facilitates parsimonious and accurate prediction of chylomicron lipids from NMR spectra of blood. The chemometric model predicts the chylomicron TG content with a correlation coefficient (R) of 0.96 when plotted against density gradient ultracentrifugation data.ConclusionsThe new rapid method facilitates large scale clinical and nutritional trials with inclusion of diagnostics of chylomicron status and thus creates new opportunities for research in lifestyle diseases and obesity.

Core-based Shared Tree Multicast Routing Algorithms for LEO Satellite IP Networks

The end-point carbon content and temperature in the steelmaking process of AOD furnace are important factors affecting product quality, and the traditional algorithms have the problems of slow convergence, being easy to fall into the local optimal solution and lack a unified parameter selection criterion, which leads to the problems of slow convergence and low prediction accuracy. To solve the above problems, the research group adopts the improved arithmetic optimization algorithm and deep stochastic configuration networks (LAOA-DeepSCNs) to predict the end-point carbon content and temperature of the AOD furnace. First, correlation analysis was performed with SPSS to identify the seven factors as model inputs. Second, to verify the prediction effect of the model, the algorithm is compared with three typical algorithms: BP, RBF and SCN. The results show that LAOA-DeepSCNs have the fastest convergence speed, the highest prediction accuracy, and the strongest generalization ability. Finally, the model was applied to the actual production of a steel mill, and the results showed that the hit rate is 90.8%, 86.4%; and 92.6%, 88.1% for refining end-point carbon content and end-point temperature error within ±0.015%, ±0.01%; and ±10 °C, ±5 °C, respectively. Which can well meet the practical needs of a steel mill. It also provides theoretical guidance for the control of carbon content and temperature at the end-point of the AOD furnace.

Traditional truth inference algorithms take multiple source labels as input and infer true labels for objects. Besides source labels, object features have been introduced in inference algorithms to achieve superior performance. A typical algorithm such as learning from crowds learns a classification model with the guide of inferred true labels where true labels are inferred from source labels. However, the main shortcoming exists in current algorithms and limits their inference performance: label noise. Since source labels from real-world data are noisy, a classifier is likely to be misguided to learn an imprecise decision boundary. In this paper, we propose a deep clustering-based aggregation model (DCAM) to overcome the shortcoming. DCAM introduces clustering for object features to form fine-grained clusters, where objects in the same cluster are supposed to have similar labels. DCAM exploits a cluster label distribution to represent the labeling information of all objects in the corresponding cluster to overcome the problem of label noise. To implement the idea of clustering-based truth inference, DCAM integrates source label generation and deep clustering in a unified framework by utilizing maximum a posteriori (MAP) estimation. Therefore, the proposed model is a novel approach for truth inference with object features. Experimental results on eight real-world inference tasks show that DCAM has a significant improvement of inference accuracy over the state-of-the-art truth inference algorithms. We further discuss the effect of cluster numbers, the quality of clustering, and illustrate the learned embeddings to support the effectiveness of DCAM.

This paper proposes a new VLSI algorithm for a prime length type II generalized discrete Hartley transform (DHT) that allows a high speed VLSI implementation and an efficient incorporation of the obfuscation technique with low overheads. The proposed algorithm is highly regular and modular, presents a low computational complexity, a high throughput and it can be efficiently mapped on linear systolic arrays. Using the obfuscation method, it presents the additional advantage of the hardware security. The proposed VLSI algorithm uses some regular and modular computational structures called quasi-band correlations and can be efficiently implemented on a VLSI chip using the systolic arrays architectural paradigm.