Abstract not found

Abstract—Effective data forwarding in Delay Tolerant Networks (DTNs) is challenging, due to the low node density, unpredictable node mobility and lack of global information in such networks. Most of the current data forwarding schemes choose the nodes with the best cumulative capability of contacting others as relays to carry and forward data, but these nodes may not be the best relay choices within a short time period, due to the heterogeneity of the transient node contact patterns. In this paper, we propose a novel approach to improve the performance of data forwarding in DTNs by exploiting the transient node contact patterns. We formulate the transient node contact patterns based on experimental studies of realistic DTN traces, and propose appropriate forwarding metrics based on these patterns to improve the effectiveness of data forwarding decision. When applied to various data forwarding strategies, our proposed forwarding metrics achieve much better performance compared to existing schemes with similar forwarding cost. I.

Abstract movement models, such as Random Waypoint, do not capture reliably the properties of movement in the real life scenarios. We present and analyse a movement model for delay-tolerant network simulations that is able to produce inter-contact time and contact time distributions that follow closely the ones found in the traces from the real-world measurement experiments. We validate the movement model using the ONE simulator.

Epidemic routing has been proposed to reduce the data transmission delay in disruption tolerant wireless networks, in which data can be replicated along multiple opportunistic paths as different nodes move within each other’s communication range. With the advent of network coding, it is intuitive that data can not only be replicated, but also coded, when the transmission opportunity arises. However, will opportunistic communication with network coding perform any better than simple replications? In this paper, we present a stochastic analytical framework to study the performance of epidemic routing using network coding in opportunistic networks, as compared to the use of replication. We analytically show that network coding is superior when bandwidth and node buffers are limited, reflecting more realistic scenarios. Our analytical study is able to provide further insights towards future designs of efficient data communication protocols using network coding. As an example, we propose a priority based coding protocol, with which the destination can decode a high priority subset of the data much earlier than it can decode any data without the use of priorities. The correctness of our analytical results has also been confirmed by our extensive simulations.

In this paper we propose continuous time Markov chain models that capture the dynamics of content spreading in a disruption tolerant wireless content distribution system. We use our models to study the effect of cooperation among the mobile nodes and how limited node resources, such as battery lifetime and confined storage, affect the content distribution process. Based on our models and numerical results we deduce that limiting the number of times a node shares each content entry is a good method to conserve energy while at the same time only slightly reducing system performance. Our study also suggests that the effect of assisting nodes is greatest for content channels with few subscribers. For promoting fairness in distributing channels and giving new channels a chance to spread, assisting nodes should therefore solicit and help in spreading less popular channels.

A key challenge of message forwarding in delay tolerant networks (DTNs) is to increase delivery rate and decrease delay and cost. When information for future connectivity is not available, opportunistic routing is preferred in DTNs in which messages are forwarded opportunistic/non-deterministically to nodes with higher delivery probabilities. Many real objects have non-deterministic but cyclic motions; however, few prior research work has investigated a multi-copy opportunistic message forwarding algorithm for DTNs with cyclic mobility patterns. Cyclic MobiSpace is a generalization of DTNs with cyclic mobility patterns. In this paper, we propose an optimal opportunistic multi-copy message forwarding algorithm in Cyclic MobiSpace. Specifically, we model a Cyclic MobiSpace as a state-space graph, and apply the optimal stopping rule to derive a delivery metric for each message state using the state-space graph. We perform simulation to compare our protocol, called Multicopy Forwarding in Cyclic MobiSpace (MFC), against existing forwarding protocols, using UMassDieselNet trace. Simulation results show that, MFC delivers up to 100 % more messages than the compared forwarding protocols under the same delay and forwarding cost.

In order to better understand human and animal mobility and its potential effects on Mobile Ad-Hoc networks and Delay-Tolerant Networks, many researchers have conducted experiments which collect encounter data. Most analyses of these data have focused on isolated statistical properties such as the distribution of node inter-encounter times and the degree distribution of the connectivity graph. On the other hand, new developments in computational topology, in particular persistent homology, have made it possible to compute topological invariants from noisy data. These homological methods provide a natural way to draw conclusions about global structure based on collections of local information. We use persistent homology techniques to show that in some cases encounter traces can be used to deduce information about the topology of the physical space the experiment was conducted in, and detect certain changes in the space. We also show that one can distinguish between simulated encounter traces generated on a bounded rectangular grid from traces generated on a grid with the opposite edges wrapped (a toroidal grid). Finally, we have found that nontrivial topological features also appear in real experimental encounter traces, and we speculate on types of node behavior that could produce these results. This demonstrates the ability of persistent homology to detect topological features in encounter data that could be difficult to describe using traditional statistical and geometric methods.

thesis, including any required final revisions, as accepted by my examiners. I understand that my thesis may be made electronically available to the public.

Population protocols are a communication model for large sensor networks with resource-limited mobile agents. The agents move asynchronously and communicate via pair-wise interactions. The original fairness assumption of this model involves a high level of asynchrony and prevents an evaluation of the convergence time of a protocol (via deterministic means). The introduction of some partial synchrony in the model, under the form of cover times, is an extension that allows evaluating the time complexities. In this paper, we take advantage of this extension and study a data collection protocol used in the ZebraNet project for the wild-life tracking of zebras in a reserve in central Kenya. In ZebraNet, sensors are attached to zebras and the sensed data is collected regularly by a mobile base station crossing the area. The data collection protocol of ZebraNet has been analyzed through simulations, but to our knowledge, this is the first time, that a purely analytical study is presented. Our first result is that, in the original protocol, some data may never be delivered to the base station. We then propose two slightly modified and correct protocols and we compute their worst case time complexities. Still, in both cases, the result is far from the optimal.

Abstract—Modeling movements in office is useful for smart indoor ad hoc networks. People carrying PDA or cell phones can encounter others and in some cases are able to establish connections and transfer data between them. Currently, commonly used mobility models, such as Random Walk and Random Waypoint Model, do not capture the real movements in real life scenarios, especially in office environments where three typical patterns of heterogeneous behavior, i.e., entity movements, group movements and regular movements, often occur. In this paper we propose a novel mobility simulation framework based on behavior patterns for office environments. The base part is Simulation Time Controller, on which we model the structure of offices and define behavior patterns. In this paper we define three typical patterns of behavior to simulate the heterogeneous movements mentioned above.To simulate more real movements, people can add more patterns of behavior to this framework, which is the main motivation of our framework. We also derive theoretic results of hitting time, which determines the packet delivery delay in Delay Tolerant Networks. Simulation studies show our expressions have error always under 10%. And the staying ratio of our simulation, i.e., the ratio between the time people spend in main place and the total time, is close to the MIT real traces. I.