Gossiping is the process of information diiusion in which each node of a network holds a block that must be communicated to all the other nodes in the network. We consider the problem of gossiping in communication networks under the restriction that communicating nodes can exchange up to a xed number p of blocks during each call. We study the minimum number of calls necessary to perform gossipi...

Most existing communications technologies are either not scalable at all, or scale only under carefully controlled conditions. This threatens an emerging generation of mission-critical but very large computing systems, which will need communication support for such purposes as system management and control, policy administration, data dissemination, and to initiate adaptation in demanding envir...

In this paper we propose a new decentralized algorithm to solve the consensus on the average problem on arbitrary strongly connected digraphs through a gossip algorithm based on broadcasts. We directly extend previous results by not requiring that the digraph is balanced. Our algorithm is an improvement respect to known gossip algorithms based on broadcasts in that the average of the initial st...

The problems of gossiping and broadcasting in one-way communication mode are investigated. Optimal algorithms for gossip problem are known only for the complete graphs, paths, some simple trees, and cycles. In this paper some lower bounds on gossiping in graphs with bridges or with edge disjoint cycles are proved. A direct consequence of these lower bounds are optimal gossip algorithms for some...

Evolutionary theories concerning the origins of human intelligence suggest that cultural transmission might be biased toward social over non-social information. This was tested by passing social and non-social information along multiple chains of participants. Experiment 1 found that gossip, defined as information about intense third-party social relationships, was transmitted with significantl...

We consider a set of learning agents in a collaborative peer-to-peer network, where each agent learns a personalized model according to its own learning objective. The question addressed in this paper is: how can agents improve upon their locally trained model by communicating with other agents that have similar objectives? We introduce and analyze two asynchronous gossip algorithms running in ...

Gossip-based aggregation is an emerging paradigm to perform distributed computations and measurements in a large-scale setting. In this paper we explore the possibility of using gossip-based aggregation to estimate churn in arbitrarily large networks. To this end, we introduce a new model to compute local estimates and formally prove how aggregated values closely match the real churn with high ...

Effective context-aware broadcasting of information to the areas of interest (AoI) is a challenging problem in vehicular ad-hoc networks. It is usually assumed that the information about these AoI are a priori known, either by a centralized source of information or by the entire set of vehicles. In this paper, we propose a self-adaptive broadcast scheme based on distributed reinforcement learni...

This paper discusses an interpretive case study on feelings and emotions in an information systems (IS) project focusing on the subsequent emotional practices adopted by the IS project members. We analyzed the data from interviews with fourteen IS project members. The analysis revealed the connection between specific feelings (dissatisfaction, fear, irritation, blame, frustration, feeling of fa...

To balance production and consumption on the power grid, efforts are mostly made on the production side. Demand response allows to harness electricity consumption to overcome the constraints brought by the democratisation of renewable energy sources and self-production. In this context, the main problem is to coordinate agents, representing the network’s nodes, in order for them to be able to a...