Agreement in epidemic information dissemination

Consensus is one of the fundamental problems in multi-agent systems and distributed computing, in which agents or processing nodes are required to reach global agreement on some data value, decision, action, or synchronisation. In the absence of centralised coordination, achieving global consensus is challenging especially in dynamic and large-scale distributed systems with faulty processes. This paper presents a fully decentralised phase transition protocol to achieve global consensus on the convergence of an underlying information dissemination process. The proposed approach is based on Epidemic protocols, which are a randomised communication and computation paradigm and provide excellent scalability and fault-tolerant properties. The experimental analysis is based on simulations of a large-scale information dissemination process and the results show that global agreement can be achieved without deterministic and global communication patterns, such as those based on centralised coordination

Optimal epidemic information dissemination in uncertain dynamic environment

Optimization of stochastic epidemic information dissemination plays a significant role in enhancing the reliability of epidemic networks. This letter proposes a multi-stage decision making optimization model for stochastic epidemic information dissemination based on dynamic programming, in which uncertainties in a dynamic environment are taken into account. We model the inherent bimodal dynamics of general epidemic mechanisms as a Markov chain, and a state transition equation is proposed based on this Markov chain. We further derive optimal policies and a theoretical closed-form expression for the maximal expected number of successfully delivered messages. The properties of the derived model are theoretically analyzed. Simulation results show an improvement in reliability, in terms of accumulative number of successfully delivered messages, of epidemic information dissemination in stochastic situations

Hybrid Dissemination: Adding Determinism to Probabilistic Multicasting in Large-Scale P2P Systems

Abstract. Epidemic protocols have demonstrated remarkable scalability and robustness in disseminating information on internet-scale, dynamic P2P systems. However, popular instances of such protocols suffer from a number of significant drawbacks, such as increased message overhead in push-based systems, or low dissemination speed in pull-based ones. In this paper we study push-based epidemic dissemination algorithms, in terms of hit ratio, communication overhead, dissemination speed, and resilience to failures and node churn. We devise a hybrid push-based dissemination algorithm, combining probabilistic with deterministic properties, which limits message overhead to an order of magnitude lower than that of the purely probabilistic dissemination model, while retaining strong probabilistic guarantees for complete dissemination of messages. Our extensive experimentation shows that our proposed algorithm outperforms that model both in static and dynamic network scenarios, as well as in the face of large-scale catastrophic failures. Moreover, the proposed algorithm distributes the dissemination load uniformly on all participating nodes. Keywords: Epidemic/Gossip protocols, Information Dissemination, Peer-to-Peer

A comparison of epidemic algorithms in wireless sensor networks

Cataloged from PDF version of article.We consider the problem of reliable data dissemination in the context of wireless sensor networks. For some application scenarios, reliable data dissemination to all nodes is necessary for propagating code updates, queries, and other sensitive information in wireless sensor networks. Epidemic algorithms are a natural approach for reliable distribution of information in such ad hoc, decentralized, and dynamic environments. In this paper we show the applicability of epidemic algorithms in the context of wireless sensor environments, and provide a comparative performance analysis of the three variants of epidemic algorithms in terms of message delivery rate, average message latency, and messaging overhead on the network. © 2006 Elsevier B.V. All rights reserved

Large Scale Model for Information Dissemination with Device to Device Communication using Call Details Records

In a network of devices in close proximity such as Device to Device ($D2D$) communication, we study the dissemination of public safety information at country scale level. In order to provide a realistic model for the information dissemination, we extract a spatial distribution of the population of Ivory Coast from census data and determine migration pattern from the Call Detail Records ($CDR$) obtained during the Data for Development ($D4D$) challenge. We later apply epidemic model towards the information dissemination process based on the spatial properties of the user mobility extracted from the provided $CDR$. We then propose enhancements by adding latent states to the epidemic model in order to model more realistic user dynamics. Finally, we study dynamics of the evolution of the information spreading through the population.Comment: Accepted in Computer Communications journa

Epcast: Controlled Dissemination in Human-based Wireless Networks by means of Epidemic Spreading Models

Epidemics-inspired techniques have received huge attention in recent years from the distributed systems and networking communities. These algorithms and protocols rely on probabilistic message replication and redundancy to ensure reliable communication. Moreover, they have been successfully exploited to support group communication in distributed systems, broadcasting, multicasting and information dissemination in fixed and mobile networks. However, in most of the existing work, the probability of infection is determined heuristically, without relying on any analytical model. This often leads to unnecessarily high transmission overheads. In this paper we show that models of epidemic spreading in complex networks can be applied to the problem of tuning and controlling the dissemination of information in wireless ad hoc networks composed of devices carried by individuals, i.e., human-based networks. The novelty of our idea resides in the evaluation and exploitation of the structure of the underlying human network for the automatic tuning of the dissemination process in order to improve the protocol performance. We evaluate the results using synthetic mobility models and real human contacts traces

Using Data to Guide and Evaluate Responses to the Opioid Crisis: Rhode Island\u27s Drug Overdose Dashboard

The rapidly evolving nature of the nation’s overdose epidemic necessitates the dissemination of timely information to inform effective public health responses. Unfortunately, many overdose surveillance systems suffer from delays in reporting and other logistical challenges. In this webinar, Dr. Brandon Marshall from the Brown University School of Public Health will discuss Rhode Island’s drug overdose information and surveillance “dashboard”, PreventOverdoseRI.org. Participants will learn how the timely analysis and public dissemination of data being used to guide and evaluate policy and public health response to the overdose crisis in Rhode Island. Learning Objectives: Participant will: Learn about national and state surveillance systems to track the overdose epidemic Learn about strategies to improve the collection, analysis, and dissemination of overdose surveillance data Discuss best practices for the communication of overdose-related public health informatio