Reputation and credit based incentive mechanism for data-centric message delivery in delay tolerant networks

In a Data-centric Delay Tolerant Networks (DTNs), it is essential for nodes to cooperate in message forwarding in order to enable successful delivery of a message in an opportunistic fashion with nodes having their social interests defined. In the data-centric dissemination protocol proposed here, a source annotates messages (images) with keywords, and then intermediate nodes are presented with an option of adding keyword-based annotations in order to create higher content strength messages on path toward the destination. Hence, contents like images get enriched as there is situation evolution or learned by these intermediate nodes, such as in a battlefield, or in a disaster situation. Nodes might turn selfish and not participate in relaying messages due to relative scarcity of battery and storage capacity in mobile devices. Therefore, in addition to content enrichment, an incentive mechanism is proposed in this thesis which considers factors like message quality, battery usage, level of interests, etc. for the calculation of incentives. Moreover, with the goal of preventing the nodes from turning malicious by adding inappropriate message tags in the quest of acquiring more incentive, a distributed reputation model (DRM) is developed and consolidated with the proposed incentive scheme. DRM takes into account inputs from multiple users like ratings for the relevance of annotations in the message, message quality, etc. The proposed scheme safeguards the network from congestion due to uncooperative or selfish nodes in the system. The performance evaluation shows that our approach delivers more high priority and high quality messages while reducing traffic at a slightly lower message delivery ratio compared to ChitChat --Abstract, page iv

Plausible Mobility: Inferring Movement from Contacts

We address the difficult question of inferring plausible node mobility based only on information from wireless contact traces. Working with mobility information allows richer protocol simulations, particularly in dense networks, but requires complex set-ups to measure, whereas contact information is easier to measure but only allows for simplistic simulation models. In a contact trace a lot of node movement information is irretrievably lost so the original positions and velocities are in general out of reach. We propose a fast heuristic algorithm, inspired by dynamic force-based graph drawing, capable of inferring a plausible movement from any contact trace, and evaluate it on both synthetic and real-life contact traces. Our results reveal that (i) the quality of the inferred mobility is directly linked to the precision of the measured contact trace, and (ii) the simple addition of appropriate anticipation forces between nodes leads to an accurate inferred mobility.Comment: 8 pages, 8 figures, 1 tabl

Time-Varying Graphs and Dynamic Networks

The past few years have seen intensive research efforts carried out in some apparently unrelated areas of dynamic systems -- delay-tolerant networks, opportunistic-mobility networks, social networks -- obtaining closely related insights. Indeed, the concepts discovered in these investigations can be viewed as parts of the same conceptual universe; and the formal models proposed so far to express some specific concepts are components of a larger formal description of this universe. The main contribution of this paper is to integrate the vast collection of concepts, formalisms, and results found in the literature into a unified framework, which we call TVG (for time-varying graphs). Using this framework, it is possible to express directly in the same formalism not only the concepts common to all those different areas, but also those specific to each. Based on this definitional work, employing both existing results and original observations, we present a hierarchical classification of TVGs; each class corresponds to a significant property examined in the distributed computing literature. We then examine how TVGs can be used to study the evolution of network properties, and propose different techniques, depending on whether the indicators for these properties are a-temporal (as in the majority of existing studies) or temporal. Finally, we briefly discuss the introduction of randomness in TVGs.Comment: A short version appeared in ADHOC-NOW'11. This version is to be published in Internation Journal of Parallel, Emergent and Distributed System

ReFIoV: a novel reputation framework for information-centric vehicular applications

In this article, a novel reputation framework for information-centric vehicular applications leveraging on machine learning and the artificial immune system (AIS), also known as ReFIoV, is proposed. Specifically, Bayesian learning and classification allow each node to learn as newly observed data of the behavior of other nodes become available and hence classify these nodes, meanwhile, the K-Means clustering algorithm allows to integrate recommendations from other nodes even if they behave in an unpredictable manner. AIS is used to enhance misbehavior detection. The proposed ReFIoV can be implemented in a distributed manner as each node decides with whom to interact. It provides incentives for nodes to cache and forward others’ mobile data as well as achieves robustness against false accusations and praise. The performance evaluation shows that ReFIoV outperforms state-of-the-art reputation systems for the metrics considered. That is, it presents a very low number of misbehaving nodes incorrectly classified in comparison to another reputation scheme. The proposed AIS mechanism presents a low overhead. The incorporation of recommendations enabled the framework to reduce even further detection time

10492 Abstracts Collection -- Information-Centric Networking

From December 5th to 8th 2010, the Dagstuhl Seminar 10492 on "Information-Centric Networking" was held in Schloss Dagstuhl -- Leibniz Center for Informatics. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available

A Taxonomy on Misbehaving Nodes in Delay Tolerant Networks

Delay Tolerant Networks (DTNs) are type of Intermittently Connected Networks (ICNs) featured by long delay, intermittent connectivity, asymmetric data rates and high error rates. DTNs have been primarily developed for InterPlanetary Networks (IPNs), however, have shown promising potential in challenged networks i.e. DakNet, ZebraNet, KioskNet and WiderNet. Due to unique nature of intermittent connectivity and long delay, DTNs face challenges in routing, key management, privacy, fragmentation and misbehaving nodes. Here, misbehaving nodes i.e. malicious and selfish nodes launch various attacks including flood, packet drop and fake packets attack, inevitably overuse scarce resources (e.g., buffer and bandwidth) in DTNs. The focus of this survey is on a review of misbehaving node attacks, and detection algorithms. We firstly classify various of attacks depending on the type of misbehaving nodes. Then, detection algorithms for these misbehaving nodes are categorized depending on preventive and detective based features. The panoramic view on misbehaving nodes and detection algorithms are further analyzed, evaluated mathematically through a number of performance metrics. Future directions guiding this topic are also presented