PROTECT: Proximity-based Trust-advisor using Encounters for Mobile Societies

Many interactions between network users rely on trust, which is becoming particularly important given the security breaches in the Internet today. These problems are further exacerbated by the dynamics in wireless mobile networks. In this paper we address the issue of trust advisory and establishment in mobile networks, with application to ad hoc networks, including DTNs. We utilize encounters in mobile societies in novel ways, noticing that mobility provides opportunities to build proximity, location and similarity based trust. Four new trust advisor filters are introduced - including encounter frequency, duration, behavior vectors and behavior matrices - and evaluated over an extensive set of real-world traces collected from a major university. Two sets of statistical analyses are performed; the first examines the underlying encounter relationships in mobile societies, and the second evaluates DTN routing in mobile peer-to-peer networks using trust and selfishness models. We find that for the analyzed trace, trust filters are stable in terms of growth with time (3 filters have close to 90% overlap of users over a period of 9 weeks) and the results produced by different filters are noticeably different. In our analysis for trust and selfishness model, our trust filters largely undo the effect of selfishness on the unreachability in a network. Thus improving the connectivity in a network with selfish nodes. We hope that our initial promising results open the door for further research on proximity-based trust

Practical Bloom filter based epidemic forwarding and congestion control in DTNs: A comparative analysis

International audienceEpidemic forwarding has been proposed as a forwarding technique to achieve opportunistic communication in delay tolerant networks (DTNs). Even if this technique is well known and widely referred, one has to address several practical problems before using it. Unfortunately, while the literature on DTNs is full of new techniques, very little has been done in comparing them. In particular, while Bloom filters have been proposed to exchange information about the buffer content prior to sending information in order to avoid redundant retransmissions, up to our knowledge no real evaluation has been provided to study the tradeoffs that exist for using Bloom filters in practice. A second practical issue in DTNs is buffer management (resulting from finite buffers) and congestion control (resulting from greedy sources). This has also been the topic of several papers that had already uncovered the difficulty to acquire accurate information mandatory to regulate the data transmission rates and buffer space. In this paper, we fill this gap. We have been implementing a simulation of different proposed congestion control schemes for epidemic forwarding in ns-3 environment. We use this simulation to compare different proposed schemes and to uncover issues that remain in each one of them. Based on this analysis, we proposed some strategies for Bloom filter management based on windowing and describe implementation tradeoffs. Afterwards, we propose a back-pressure rate control as a well as an aging based buffer managing solution to deal with congestion control. By simulating our proposed mechanisms in ns-3 both with random-waypoint mobility and realistic mobility traces coming from San-Francisco taxicabs, we show that the proposed mechanisms alleviate the challenges of using epidemic forwarding in DTN

Prediction-based Routing with Packet Scheduling under Temporal Constraint in Delay Tolerant Networks

is a challenging problem due to the intermittent connectivity between the nodes. Researchers have proposed many routing protocols that adapt to the temporary connections of DTNs. One classification of routing protocols makes use of historical information to predict future contact patterns for any pair of nodes. However, most existing protocols focus on the probability of a path from the source to the destination without considering the information in a packet which includes the source, destination, size, TTL (Time-To-Live) and limited resources such as available buffer size and bandwidth. In this paper, we propose a new prediction-based routing algorithm that takes into account packet information under the conditions of limited transmission opportunities. The goal of this protocol is to increase the overall delivery ratio through scheduling packets at each node. Meanwhile, this protocol may sacrifice some messages ’ delivery delay time to some extent. Extensive simulation results with real traces show that our protocol with packet scheduling has better performance than the pure probabilistic routing algorithms in term of delivery ratio. Our protocol’s performance advantage is more obvious for nodes with higher packet intensity and shorter TTL in packets. I