Information Dissemination Algorithm Research Articles

An empirical study of the impact of biological information dissemination in social media on public science literacy

Abstract In this paper, we first establish a locally converged bioinformatics dataset based on gradient sampling and design an optimal data mining control model to improve the accuracy of bioinformatics big data feature mining. The performance of the Compressive Tracking algorithm and Online Bosting algorithm is compared with the mining error as a test index. At the same time, we propose a social media information dissemination algorithm applicable to large-scale social network datasets, taking the degree value of each node as the node’s full influence and comparing and analyzing the dissemination influence of BP-IM, RAND and MC-CELF algorithms. Finally, taking public health big data as the research object, the least squares regression method was used to analyze the influence of the amount of public attention to bioinformatics scientific knowledge on their scientific literacy in different media. The results showed that there was a significant positive correlation between scientific literacy and willingness to engage in science participation behavior on social media when the amount of public attention to scientific information was β =0225, p <0.01. When more people are interested in scientific knowledge of bioinformatics on social media, their scientific literacy will improve.

Design of control algorithms for false information dissemination in smart city social media based on symmetric triangle interval

Design of control algorithms for false information dissemination in smart city social media based on symmetric triangle interval

A Novel Collective User Web Behavior Simulation Method

A collective user web behavior simulation is an import means for generating a large-scale user network behavior in a network testbed or cyber range. Existing studies almost focus on individual web behavior analysis and prediction, which cannot simulate human dynamics that widely exist in large-scale users’ behaviors. To address these issues, we propose a novel collective user web behavior simulation method, in which an algorithm for constructing a connected virtual social network is proposed, and then a collective user web behavior simulation algorithm is designed on the virtual social network. In the simulation method, a new epidemic information dissemination algorithm based on the SIR model is proposed to drive the user web behavior with Breadth—First Search algorithm on the connected virtual social network. We specially build an experiment environment with 12 servers by using Docker container technology and then perform a wide range of experiments with different user scales to evaluate the method. The experimental results demonstrate that not only the degrees of the social network but also the time intervals of the collective users’ web behavior can be well fitted to a power-law distribution and show that our simulation method can well simulate a collective user web behavior.

CaBIUs: Description of the Enhanced Wireless Campus Testbed of the Ionian University

Technological evolution and in particular the development of the Internet of Things (IoT) has paved the way for material prosperity and a better standard of living. A critical factor in the effectiveness of emerging IoT applications, which heavily rely on sensor information flow, is the development of a functional and efficient Wireless Sensor Network. Additionally, the levels of automation are conducive to usability and time efficiency by reducing the need for human intervention, as well as increasing the rate at which experiments can be carried out. In current work, an already installed infrastructure on the Ionian University campus is considered and enhanced, with the goal of elevating accessibility and user-friendliness, by designing a web platform. The presented platform enables the remote development, execution and monitoring of simple but necessary network-based algorithms using a custom language, without requiring code to be uploaded to remote nodes. As a proof of concept, three information dissemination algorithms are implemented and provided as example templates for users, promoting simultaneously ease of use.

An Effective MEC Sustained Charging Data Transmission Algorithm in VANET-Based Smart Grids

Available charging information exchanges between mobile electric vehicles (EVs) and charging stations are significantly critical in spatio-temporal coordinated charging services introduced by smart girds. In this paper, we propose an efficient information transmission strategy for intelligent charging navigations of enormous moving EVs in large-scale urban environments. Specifically, we firstly design a heterogeneous VANET-based (vehicular ad hoc network) communication framework by means of mobile edge computing concept. In addition, based on the established multi-objective communication optimization problem, we propose an effective charging information dissemination algorithm between mobile edge computing servers and moving EVs. Moreover, in order to further increase charging information delivery efficiency and reduce redundant overheads, an improved local relaying scheme for charging information is designed on the basis of the formulated waiting time model. Finally, a series of simulation experiments are implemented to demonstrate the excellence and feasibility of our charging information transmission strategy.

Slow Links, Fast Links, and the Cost of Gossip

Consider the classical problem of information dissemination: one (or more) nodes in a network have some information that they want to distribute to the remainder of the network. In this paper, we study the cost of information dissemination in networks where edges have latencies, i.e., sending a message from one node to another takes some amount of time. We first generalize the idea of conductance to weighted graphs by defining $\phi_*$ to be the "critical conductance" and $\ell_*$ to be the "critical latency". One goal of this paper is to argue that $\phi_*$ characterizes the connectivity of a weighted graph with latencies in much the same way that conductance characterizes the connectivity of unweighted graphs. We give near tight lower and upper bounds on the problem of information dissemination, up to polylogarithmic factors. Specifically, we show that in a graph with (weighted) diameter $D$ (with latencies as weights) and maximum degree $\Delta$, any information dissemination algorithm requires at least $\Omega(\min(D+\Delta, \ell_*/\phi_*))$ time % in the worst case. We show several variants of the lower bound (e.g., for graphs with small diameter, graphs with small max-degree, etc.) by reduction to a simple combinatorial game. We then give nearly matching algorithms, showing that information dissemination can be solved in $O(\min((D+\Delta)\log^3{n}, (\ell_*/\phi_*)\log n)$ time. This is achieved by combining two cases. We show that the classical push-pull algorithm is (near) optimal when the diameter or the maximum degree is large. For the case where the diameter and the maximum degree are small, we give an alternative strategy in which we first discover the latencies and then use an algorithm for known latencies based on a weighted spanner construction. (Our algorithms are within polylogarithmic factors of being tight both for known and unknown latencies.)

The (T, L)-Path Model and Algorithms for Information Dissemination in Dynamic Networks

A dynamic network is the abstraction of distributed systems with frequent network topology changes. With such dynamic network models, fundamental distributed computing problems can be formally studied with rigorous correctness. Although quite a number of models have been proposed and studied for dynamic networks, the existing models are usually defined from the point of view of connectivity properties. In this paper, instead, we examine the dynamicity of network topology according to the procedure of changes, i.e., how the topology or links change. Following such an approach, we propose the notion of the “instant path” and define two dynamic network models based on the instant path. Based on these two models, we design distributed algorithms for the problem of information dissemination respectively, one of the fundamental distributing computing problems. The correctness of our algorithms is formally proved and their performance in time cost and communication cost is analyzed. Compared with existing connectivity based dynamic network models and algorithms, our procedure based ones are definitely easier to be instantiated in the practical design and deployment of dynamic networks.

On the Performance of Multi-Message Algebraic Gossip Algorithms in Dynamic Random Geometric Graphs

In this letter, we investigate the performance of algebraic gossip algorithms for information dissemination in mobile ad hoc networks (MANETs). In the gossip algorithms, in each time slot, each node communicates with a neighboring node, which is randomly selected by an Exchange algorithm, to exchange their information. The goal of the algorithm is to efficiently disseminate all the messages to all the nodes as fast as possible. We analyze the convergence time of network coding (NC) based gossip algorithm by taking into account the node mobility in MANETs, and derived a lower bound on the convergence time. It showed that the convergence time of the NC gossip algorithm with node mobility can be reduced by the order of $O(n^{1/2}/\log ^{3/2}n)$ over that without considering node mobility. But it is only $O(\log n)$ quicker than the gossip algorithm with node mobility but no NC.

Cluster-based efficient information dissemination in dynamic networks

Dynamic network is an abstraction of networks with frequent topology changes arising from node mobility or other reasons. In this article, we first propose a dynamic network model, named ( T, L)-HiNet, to extend existing dynamic network models with clusters. ( T, L)-HiNet includes several properties defining the stability of cluster hierarchy in a dynamic network, including cluster head set, cluster member set and the connections among them. Based on ( T, L)-HiNet, we design several hierarchical information dissemination algorithms for different scenarios of dynamics. Furthermore, we extend the ( T, L)-HiNet model and corresponding algorithms in two directions, that is, stability of cluster head set and cluster size. The correctness of our algorithms is proved rigorously, and their performance is evaluated via both numerical analysis and simulations. The results show that compared with the algorithm recently proposed by Kuhn et al., our design can significantly reduce communication cost and also time cost.

Non-localized and localized data storage in large-scale communicating materials: Probabilistic and hop-counter approaches

The rapid development of Internet of Things has triggered the multiplication of communication nodes based on Radio-Frequency Identification (RFID) and Wireless Sensor Networks (WSNs) in various domains such as building, city, industry, and transport. These communication nodes are attached to a thing or directly included in the material of the thing to form a communicating material. In communicating material, one of the desired objectives is to merge the logical data with its physical material, thus simplifying the monitoring of its life cycle, the maintenance operations, and the recycling process. In this context, the initial form of the communicating material can evolve during its lifecycle. It can be split, aggregated with other materials, or partially damaged. However, the entire information in the material should always be accessible after each change. Thus, the objective of this research is to develop specific algorithms for efficient dissemination of information in the material in order to limit information losses. Two dissemination algorithms hop-counter-based and probabilistic-based are proposed for storing data by using WSNs, and non-localized and localized storage is considered. Non-localized storage ensures that information can be retrieved from each piece of the material by using a uniform data replication process. Localized storage ensures that the information is stored in a limited region of the material. Castalia/OMNeT++ simulator is used to compare the performance of the proposed algorithms with other similar protocols such as DEEP, Supple, and RaWMS.

Energy evaluation of AID protocol in Mobile Ad Hoc Networks

Mobile Ad Hoc Networks (MANETs) are communication networks formed on the fly by radio-equipped mobile nodes without relying on any fixed infrastructure. Flooding is the simplest technique for information dissemination in ad hoc based networks, in which nodes disseminate a received message to all their neighbors. This algorithm leads to the broadcast storm problem that severely affects the energy consumption due to redundant submissions. To regulate redundant submissions, which can cause more collisions and requires more energy, recently there have been developed numerous broadcasting techniques. These techniques have been mainly proposed to solve the storm problem by preventing certain nodes from rebroadcasting received messages and/or by differentiating the timing of rebroadcasts. In this paper, we have evaluated and compared an adaptive information dissemination (AID) algorithm with other MANETs broadcasting protocols with respect to the energy efficiency. In AID, each node can dynamically adjust the values of its local parameters using information from neighboring nodes without requiring any additional effort, such as distance measurements or exact location-determination of nodes. Simulation results are reported and show that adaptive broadcasting schemes are most efficient with respect to save broadcast, energy consumption, and reachability.

Bounded information dissemination in multi-channel wireless networks

More and more wireless networks and devices now operate on multiple channels, which poses the question: How to use multiple channels to speed up communication? In this paper, we answer this question for the case of wireless ad-hoc networks where information dissemination is a primitive operation. Specifically, we propose a randomized distributed algorithm for information dissemination that is very near the optimal. The general information dissemination problem is to deliver $$k$$k information packets, stored initially in $$k$$k different nodes (the packet holders), to all the nodes in the network, and the objective is to minimize the time needed. With an eye toward the reality, we assume a model where the packet holders are determined by an adversary, and neither the number $$k$$k nor the identities of packet holders are known to the nodes in advance. Not knowing the value of $$k$$k sets this problem apart from broadcasting and all-to-all communication (gossiping). We study the information dissemination problem in single-hop networks with bounded-size messages. We present a randomized algorithm which can disseminate all packets in $$O(k(\frac{1}{\mathcal {F}}+\frac{1}{\mathcal {P}})+\log ^2n)$$O(k(1F+1P)+log2n) rounds with high probability, where $$\mathcal {F}$$F is the number of available channels and $$\mathcal {P}$$P is the bound on the number of packets a message can carry. Compared with the lower bound $$\varOmega (k(\frac{1}{\mathcal {F}}+\frac{1}{\mathcal {P}}))$$Ω(k(1F+1P)), the given algorithm is very close to the asymptotical optimal except for an additive factor. Our result provides the first solid evidence that multiple channels can indeed substantially speed up information dissemination, which also breaks the $$\varOmega (k)$$Ω(k) lower bound that holds for single-channel networks (even if $$\mathcal {P}$$P is infinity).

A decentralized approach for information dissemination in Vehicular Ad hoc Networks

Substantial research efforts on Ad hoc networks have been devoted recently to Vehicular Ad hoc NETworks (VANETs) to target Vehicle to Vehicle (V2V) and Vehicle to Roadside unit (V2R) communications in order to increase driver/vehicle safety, transport efficiency and driver comfort. VANETs are special subclass of Mobile Ad hoc NETworks (MANETs) for inter-vehicle communication and have relatively more dynamic nature compared to MANETs due to the rapid network topology changes. The development and implementation of efficient and scalable algorithms for information dissemination in VANETs is a major issue which has taken enormous attention in the last years. In this paper, an efficient distributed information dissemination approach is proposed, inspired by Ant-colony communication principles, such as scalability and adaptability that are useful for developing a decentralized architecture in highly dynamic networks. The main objective is to provide each vehicle with relevant information about its surrounding to allow drivers to be aware of undesirable events and road conditions. A “relevance” value into emergency messages is defined as an analog to pheromone throwing in Ant colony, to take an appropriate action. Simulations are conducted using NS2 network simulator and relevant metrics are evaluated under different node speeds and densities to show the effectiveness of the proposed approach.

GoDisco++: A gossip algorithm for information dissemination in multi-dimensional community networks

In this paper we propose GoDisco++, a gossip based approach for information dissemination in online social community networks. GoDiscoo++ uses local information available to nodes—that is information associated with a node and its neighbors. The algorithm exploits multiple relations which may exist between nodes, and applies social principles and behavior inspired decentralized mechanisms for targeted dissemination. The dissemination process works with the dual aims of (i) maximizing the spread among relevant nodes (high recall) and (ii) minimizing spamming among non-relevant nodes (high precision). Such a designed dissemination scheme can have interesting applications like probabilistic publish/subscribe, decentralized recommendation and contextual advertisement systems, to name a few. We validate the proposed approach with simulation experiments performed using real and synthetic datasets.

Tighter Worst-Case Bounds on Algebraic Gossip

Gossip and in particular network coded algebraic gossip have recently attracted attention as a fast, bandwidth-efficient, reliable and distributed way to broadcast or multicast multiple messages. While the algorithms are simple, involved queuing approaches are used to study their performance. The most recent result in this direction shows that uniform algebraic gossip disseminates k messages in O(Δ(D + k + log n)) rounds where D is the diameter, n the size of the network and Δ the maximum degree. In this paper we give a simpler, short and self-contained proof for this worst-case guarantee. Our approach also allows to reduce the quadratic Δ D term to min{3n, Δ D}. We furthermore show that a simple round robin routing scheme also achieves min{3n, Δ D} + Δ k rounds, eliminating both randomization and coding. Lastly, we combine a recent non-uniform gossip algorithm with a simple routing scheme to get a O(D + k + log^{O(1)}) gossip information dissemination algorithm. This is order optimal as long as D and k are not both polylogarithmically small.

Trust-Aware Information Dissemination in Social Network

Information dissemination security is a crucial factor for social network interaction. In this paper, we present a trust-aware information dissemination algorithm for social network in order to make the nodes transmitting more trustable information. Firstly, we propose information trust computation from two aspects: entity trust of information and source trust. Two above aspects can be integrated into whole trust of information. Likewise, we propose the trust relationship comutation method among nodes. Further, we present a multiple paths trust computation method (MPTC) for information dissmination in social network. Then, we address the trust-aware information dissemination algorithm (TIDA) based on above trust computaiton. Finally, our experiments show that TIDA is a feasible and effective solution for social network information dissemination.

Optimized cluster-based filtering algorithm for graph metadata

With the increasing amount of information on the Web and the proliferation of RSS offerings, efficient graph-based metadata filtering algorithm for large scale information dissemination is very important today. Matching graph-based documents is expensive due to the expressiveness of the language. The centralized architecture does not work well for the large scale information dissemination service. To address these problems, in this paper we develop a cluster-based publish/subscribe system for filtering graph-based RSS documents. Essentially, we develop two indexing algorithms to enable workload distribution and cluster-based filtering. Furthermore, we proposed an optimized graph matching algorithm which speeds up the constraint evaluation for subscriptions. The experimental results show that we can support one million subscriptions on a compute cluster with 5–20 nodes and the throughput scales linearly with the number of cluster nodes.

An adaptive epidemic information dissemination model for wireless sensor networks

We propose an adaptive bio-inspired information dissemination model that exploits the specific characteristics of the sampled/generated data stream (DS) in a wireless sensor network. Our model extends the basic epidemic algorithm by adapting key operational parameters (i.e., the forwarding probability and validity period) of the data dissemination process. The main idea is that the forwarding probability is tuned according to the variability of the involved DS. Our findings from the introduction of this adaptive epidemic are quite promising. Our scheme supersedes conventional probabilistic information dissemination algorithms in terms of efficiency and reliability.

An adaptive approach for information dissemination in Vehicular Ad hoc Networks

Vehicular Ad hoc Networks (VANETs) appeared as a subclass of MANETs for inter-vehicle communication. However, VANETs have a relatively more dynamic nature as compared to MANETs concerning the network topology. The design and implementation of an efficient and scalable algorithm for information dissemination in VANETs constitutes a major issue that should be tackled. Indeed, in this dynamic environment, an increasing number of redundant broadcast messages will increase resource utilization, which would indirectly affect the network performance. In the past few years, several statistical-based broadcasting schemes, such as counter-based protocol, for information dissemination have been proposed in the context of MANETs. These schemes are based on various threshold parameters to help nodes to decide whether to rebroadcast or discard received messages. However, in dynamic networks, such as in VANETs, it is difficult even impossible to determine a priori these threshold values. Dynamically changing these values, to minimize the number of redundantly received messages while maintaining good latency and reachability, is a complex issue in the absence of centralized controllers or constant threshold parameters. This paper presents a decentralized and adaptive approach for information dissemination (AID) in VANETs. Simulations are conducted and results are presented to show that adaptive approaches have a better performance over statistical-based approaches.

Distributed computing column 41 computing over dynamic networks

This column focuses on the rather recent topic of dynamic communication networks, which are modeled as evolving graphs. Admittedly, the idea that a network can be dynamic is hardly new— networking research has been considering network topology changes and churn for decades. But by and large, past research considered such changes to be exceptions, and focused on adapting to them and re-stabilizing. Only in recent years, we begin to see works that treat dynamic changes as the norm. Such work on constantlyevolving networks may prove important in capturing many real world scenarios, in particular, ones that arise in wireless networks with mobile devices. In today’s column, Fabian Kuhn and Rotem Oshman survey recent work on dynamic networks. They consider adversarial as well as random models of graph evolution. In this context, they explain how the classical graph notions of diameter and cover time generalize to dynamic ones. These notions have important implications on information dissemination in the network. Fabian and Rotem then present an example algorithm for counting and information dissemination on adversarially evolving graphs. As research on dynamic networks is still in its infancy, many questions remain open; the column concludes with a discussion of some promising directions for future research. Many thanks to Fabian and Rotem for this survey!