Mobile Conductance in Sparse Networks and Mobility-Connectivity Tradeoff

In this paper, our recently proposed mobile-conductance based analytical framework is extended to the sparse settings, thus offering a unified tool for analyzing information spreading in mobile networks. A penalty factor is identified for information spreading in sparse networks as compared to the connected scenario, which is then intuitively interpreted and verified by simulations. With the analytical results obtained, the mobility-connectivity tradeoff is quantitatively analyzed to determine how much mobility may be exploited to make up for network connectivity deficiency.Comment: Accepted to ISIT 201

Inondation dans les réseaux dynamiques

International audienceCette note résume nos travaux sur l'inondation dans les réseaux dynamiques. Ces derniers sont définis à partir d'un processus Markovien de paramètres $p$ et $q$ générant des séquences de graphes $(G_0,G_1,G_2,\ldots)$ sur un même ensemble $[n]$ de sommets, et tels que $G_t$ est obtenu à partir de $G_{t-1}$ comme suit~: si $e\notin E(G_{t-1})$ alors $e\in E(G_{t})$ avec probabilité $p$, et si $e\in E(G_{t-1})$ alors $e\notin E(G_{t})$ avec probabilité $q$. Clementi et al. (PODC 2008) ont analysé différent processus de diffusion de l'information dans de tels réseaux, et ont en particulier établi un ensemble de bornes sur les performances de l'inondation. L'inondation consiste en un protocole élémentaire où chaque n{\oe}ud apprenant une information à un temps $t$ la retransmet à tous ses voisins à toutes les étapes suivantes. Evidemment, en dépit de ses avantages en terme de simplicité et de robustesse, le protocole d'inondation souffre d'une utilisation abusive des ressources en bande passante. Dans cette note, nous montrons que l'inondation dans les réseaux dynamiques peut être mis en {\oe}uvre de façon à limiter le nombre de retransmissions d'une même information, tout en préservant les performances en termes du temps mis par une information pour atteindre tous les n{\oe}uds du réseau. La principale difficulté de notre étude réside dans les dépendances temporelles entre les connexions du réseaux à différentes étapes de temps

Fuzzy tuned gossip algorithms in mobile ad hoc networks

“This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder." “Copyright IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.” DOI: 10.1109/MED.2009.5164552Mobile ad hoc networks (MANET) are modeled as agents that form communities without infrastructure, for a random period of time and with usually cooperative behavior. The nodes of MANET often carry information to disseminate. The dynamics of information delivery, mostly referred as average consensus, is a common problem in these networks. The gossip protocols are designed to implement this task. The standard algorithms that are used in these protocols exploit the network describing matrix, aka Laplacian, and exchange information to all node neighbors. In static networks the problem can be considered as an output feedback problem but in the case of MANET the problem is getting complicated due to the continuous change of network topology. In this paper the fuzzy reasoning approach is proposed to tune and leverage the gossip protocol. Illustrative simulations are included to demonstrate the application of the method and to present comparative results in various cases

Epidemic Spreading with External Agents

We study epidemic spreading processes in large networks, when the spread is assisted by a small number of external agents: infection sources with bounded spreading power, but whose movement is unrestricted vis-\`a-vis the underlying network topology. For networks which are `spatially constrained', we show that the spread of infection can be significantly speeded up even by a few such external agents infecting randomly. Moreover, for general networks, we derive upper-bounds on the order of the spreading time achieved by certain simple (random/greedy) external-spreading policies. Conversely, for certain common classes of networks such as line graphs, grids and random geometric graphs, we also derive lower bounds on the order of the spreading time over all (potentially network-state aware and adversarial) external-spreading policies; these adversarial lower bounds match (up to logarithmic factors) the spreading time achieved by an external agent with a random spreading policy. This demonstrates that random, state-oblivious infection-spreading by an external agent is in fact order-wise optimal for spreading in such spatially constrained networks

Gossip Algorithms for Distributed Signal Processing

Gossip algorithms are attractive for in-network processing in sensor networks because they do not require any specialized routing, there is no bottleneck or single point of failure, and they are robust to unreliable wireless network conditions. Recently, there has been a surge of activity in the computer science, control, signal processing, and information theory communities, developing faster and more robust gossip algorithms and deriving theoretical performance guarantees. This article presents an overview of recent work in the area. We describe convergence rate results, which are related to the number of transmitted messages and thus the amount of energy consumed in the network for gossiping. We discuss issues related to gossiping over wireless links, including the effects of quantization and noise, and we illustrate the use of gossip algorithms for canonical signal processing tasks including distributed estimation, source localization, and compression.Comment: Submitted to Proceedings of the IEEE, 29 page