Message Ferries as Generalized Dominating Sets in Intermittently Connected Mobile Networks

Message ferrying is a technique for routing data in wireless and mobile networks in which one or more mobile nodes are tasked with storing and carrying data between sources and destinations. To achieve connectivity between all nodes, message ferries may need to relay data to each other. While useful as a routing technique for wireless mobile networks in general, message ferrying is particularly useful in intermittently connected networks where traditional MANET routing protocols are not usable. A wireless and mobile network is said to possess intrinsic message ferrying capability if a subset of the nodes can act as message ferries by virtue of their own mobility pattern, without introducing additional nodes or modifying existing node mobility. Our goal in this work is to provide a formalism by which one can characterize intrinsic message ferrying capability. We first observe that the use of message ferries is the mobile generalization of the well-known use of connected dominating set-based routing in wireless networks. We next consider the problem of identifying the set of nodes in a mobile network which can act as message ferries by virtue of their mobility pattern. To this end, we define the concept of a connected message ferry dominating set (CMFDS) in a manner that achieves data delivery within certain performance bounds. We then develop algorithms that can be used to find such a set within a mobile, wireless network. The general CMFDS algorithm is built around a core algorithm that determines whether a single node in the network can act as a ferry. We provide some illustrative examples to show the application of our algorithm to several mobility patterns

Opportunistic data collection through delegation

We consider a collection system where collectors move around gathering information generated by data producers. In such a system, data may remain uncollected when the number of collectors is insufficient to cover the whole population of producers. Motivated by the observation that node encounters are sufficient to build a connected relationship graph, we propose to take advantage of the inherent interactions among nodes and transform some producers into delegates. With such an approach, collectors only need to meet delegates that, in turn, are responsible for gathering data from a subset of standard producers. We achieve this goal through two contributions. First, we investigate several delegation strategies based on the relative importance of nodes in their social interactions (i.e., the node centrality). Second, by considering a prediction strategy that estimates the likelihood of two nodes meeting each other, we investigate how the delegation strategies perform on predicted traces. We evaluate the delegation strategies both in terms of coverage and size of the delegation existing real mobility data sets. We observe that delegation strategies that rely on localized information perform as well as the ones that consider a complete view of the topology.Nous considérons un système de collecte où les collectionneurs se déplacent et collectent les informations générées par les producteurs de données. Dans un tel système, les données peuvent ne pas être collectées lorsque le nombre de collectionneurs est insuffisant pour couvrir l'ensemble de la population des producteurs. Motivé par le fait que les rencontres de nœuds sont suffisants pour construire un graphe connecté, nous proposons de profiter des interactions inhérentes entre les nœuds et transformer certains producteurs en délégués. Avec une telle approche, les collectionneurs ont seulement besoin de rencontrer les délégués que, à leur tour, sont responsables de la collecte de données d'un sous-ensemble des producteurs. Nous atteignons cet objectif grâce à deux contributions. Tout d'abord, nous étudions plusieurs stratégies de délégation basée sur l'importance relative des nœuds dans leurs interactions sociales (par exemple, la centralité du nœud). Deuxièmement, en considérant une stratégie de prédiction qui donne les estimations de la probabilité d'une rencontre de deux nœuds, nous étudions les stratégies de délégation avec les traces prédit. Nous évaluons les stratégies de délégation à la fois en termes de couverture et de la taille du groupe de délégation en utilisant des traces de mobilité réelles. Nous n'observons que les stratégies de délégation qui se basent sur des informations localisées fournis aussi des bons résultats comparés aux résultats considérant une vue complète de la topologie

Mining and Managing Large-Scale Temporal Graphs

Large-scale temporal graphs are everywhere in our daily life. From online social networks, mobile networks, brain networks to computer systems, entities in these large complex systems communicate with each other, and their interactions evolve over time. Unlike traditional graphs, temporal graphs are dynamic: both topologies and attributes on nodes/edges may change over time. On the one hand, the dynamics have inspired new applications that rely on mining and managing temporal graphs. On the other hand, the dynamics also raise new technical challenges. First, it is difficult to discover or retrieve knowledge from complex temporal graph data. Second, because of the extra time dimension, we also face new scalability problems. To address these new challenges, we need to develop new methods that model temporal information in graphs so that we can deliver useful knowledge, new queries with temporal and structural constraints where users can obtain the desired knowledge, and new algorithms that are cost-effective for both mining and management tasks.In this dissertation, we discuss our recent works on mining and managing large-scale temporal graphs.First, we investigate two mining problems, including node ranking and link prediction problems. In these works, temporal graphs are applied to model the data generated from computer systems and online social networks. We formulate data mining tasks that extract knowledge from temporal graphs. The discovered knowledge can help domain experts identify critical alerts in system monitoring applications and recover the complete traces for information propagation in online social networks. To address computation efficiency problems, we leverage the unique properties in temporal graphs to simplify mining processes. The resulting mining algorithms scale well with large-scale temporal graphs with millions of nodes and billions of edges. By experimental studies over real-life and synthetic data, we confirm the effectiveness and efficiency of our algorithms.Second, we focus on temporal graph management problems. In these study, temporal graphs are used to model datacenter networks, mobile networks, and subscription relationships between stream queries and data sources. We formulate graph queries to retrieve knowledge that supports applications in cloud service placement, information routing in mobile networks, and query assignment in stream processing system. We investigate three types of queries, including subgraph matching, temporal reachability, and graph partitioning. By utilizing the relatively stable components in these temporal graphs, we develop flexible data management techniques to enable fast query processing and handle graph dynamics. We evaluate the soundness of the proposed techniques by both real and synthetic data. Through these study, we have learned valuable lessons. For temporal graph mining, temporal dimension may not necessarily increase computation complexity; instead, it may reduce computation complexity if temporal information can be wisely utilized. For temporal graph management, temporal graphs may include relatively stable components in real applications, which can help us develop flexible data management techniques that enable fast query processing and handle dynamic changes in temporal graphs