4,804 research outputs found
Mesoscopic structure and social aspects of human mobility
The individual movements of large numbers of people are important in many
contexts, from urban planning to disease spreading. Datasets that capture human
mobility are now available and many interesting features have been discovered,
including the ultra-slow spatial growth of individual mobility. However, the
detailed substructures and spatiotemporal flows of mobility - the sets and
sequences of visited locations - have not been well studied. We show that
individual mobility is dominated by small groups of frequently visited,
dynamically close locations, forming primary "habitats" capturing typical daily
activity, along with subsidiary habitats representing additional travel. These
habitats do not correspond to typical contexts such as home or work. The
temporal evolution of mobility within habitats, which constitutes most motion,
is universal across habitats and exhibits scaling patterns both distinct from
all previous observations and unpredicted by current models. The delay to enter
subsidiary habitats is a primary factor in the spatiotemporal growth of human
travel. Interestingly, habitats correlate with non-mobility dynamics such as
communication activity, implying that habitats may influence processes such as
information spreading and revealing new connections between human mobility and
social networks.Comment: 7 pages, 5 figures (main text); 11 pages, 9 figures, 1 table
(supporting information
Supporting Large Scale Communication Systems on Infrastructureless Networks Composed of Commodity Mobile Devices: Practicality, Scalability, and Security.
Infrastructureless Delay Tolerant Networks (DTNs) composed of
commodity mobile devices have the potential to support communication
applications resistant to blocking and censorship, as well as certain
types of surveillance. In this thesis we study the utility,
practicality, robustness, and security of these networks.
We collected two sets of wireless connectivity traces of commodity
mobile devices with different granularity and scales.
The first dataset is collected through active installation of
measurement software on volunteer users' own smartphones, involving 111 users of a DTN microblogging application that we developed. The second dataset is collected through passive observation of WiFi association
events on a university campus, involving 119,055 mobile devices.
Simulation results show consistent message delivery performances of the
two datasets. Using an epidemic flooding protocol, the large network
achieves an average delivery rate of 0.71 in 24 hours and a median delivery delay of 10.9 hours. We show that this performance is appropriate for sharing information that is not time sensitive, e.g., blogs and photos. We also show that using an energy efficient variant of the epidemic flooding protocol, even the large network can support text messages while only consuming 13.7% of a typical smartphone battery in 14 hours.
We found that the network delivery rate and delay are robust to
denial-of-service and censorship attacks. Attacks that randomly remove
90% of the network participants only reduce delivery rates by less than 10%. Even when subjected to targeted attacks, the network suffered a less than 10% decrease in delivery rate when 40% of its participants were removed.
Although structurally robust, the openness of the proposed network
introduces numerous security concerns. The Sybil attack, in
which a malicious node poses as many identities in order to gain
disproportionate influence, is especially dangerous as it breaks the assumption underlying majority voting. Many defenses based on spatial variability of wireless channels exist, and we extend them to be practical for ad hoc networks of commodity 802.11 devices without mutual trust. We present the Mason test, which uses two efficient methods for separating valid channel measurement results of behaving nodes from those falsified by malicious participants.PhDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/120779/1/liuyue_1.pd
Modeling Human Mobility Entropy as a Function of Spatial and Temporal Quantizations
The knowledge of human mobility is an integral component of several different branches of research and planning, including delay tolerant network routing, cellular network planning, disease prevention, and urban planning. The uncertainty associated with a person's movement plays a central role in movement predictability studies. The uncertainty can be quantified in a succinct manner using entropy rate, which is based on the information theoretic entropy. The entropy rate is usually calculated from past mobility traces. While the uncertainty, and therefore, the entropy rate depend on the human behavior, the entropy rate is not invariant to spatial resolution and sampling interval employed to collect mobility traces. The entropy rate of a person is a manifestation of the observable features in the person's mobility traces. Like entropy rate, these features are also dependent on spatio-temporal quantization. Different mobility studies are carried out using different spatio-temporal quantization, which can obscure the behavioral differences of the study populations. But these behavioral differences are important for population-specific planning. The goal of dissertation is to develop a theoretical model that will address this shortcoming of mobility studies by separating parameters pertaining to human behavior from the spatial and temporal parameters
D2D Data Offloading in Vehicular Environments with Optimal Delivery Time Selection
Within the framework of a Device-to-Device (D2D) data offloading system for
cellular networks, we propose a Content Delivery Management System (CDMS) in
which the instant for transmitting a content to a requesting node, through a
D2D communication, is selected to minimize the energy consumption required for
transmission. The proposed system is particularly fit to highly dynamic
scenarios, such as vehicular networks, where the network topology changes at a
rate which is comparable with the order of magnitude of the delay tolerance. We
present an analytical framework able to predict the system performance, in
terms of energy consumption, using tools from the theory of point processes,
validating it through simulations, and provide a thorough performance
evaluation of the proposed CDMS, in terms of energy consumption and spectrum
use. Our performance analysis compares the energy consumption and spectrum use
obtained with the proposed scheme with the performance of two benchmark
systems. The first one is a plain classic cellular scheme, the second is a D2D
data offloading scheme (that we proposed in previous works) in which the D2D
transmissions are performed as soon as there is a device with the required
content within the maximum D2D transmission range..
Performance Modelling and Optimisation of Multi-hop Networks
A major challenge in the design of large-scale networks is to predict and optimise the
total time and energy consumption required to deliver a packet from a source node to a
destination node. Examples of such complex networks include wireless ad hoc and sensor
networks which need to deal with the effects of node mobility, routing inaccuracies, higher
packet loss rates, limited or time-varying effective bandwidth, energy constraints, and the
computational limitations of the nodes. They also include more reliable communication
environments, such as wired networks, that are susceptible to random failures, security
threats and malicious behaviours which compromise their quality of service (QoS) guarantees.
In such networks, packets traverse a number of hops that cannot be determined
in advance and encounter non-homogeneous network conditions that have been largely
ignored in the literature. This thesis examines analytical properties of packet travel in
large networks and investigates the implications of some packet coding techniques on both
QoS and resource utilisation.
Specifically, we use a mixed jump and diffusion model to represent packet traversal
through large networks. The model accounts for network non-homogeneity regarding
routing and the loss rate that a packet experiences as it passes successive segments of a
source to destination route. A mixed analytical-numerical method is developed to compute
the average packet travel time and the energy it consumes. The model is able to capture
the effects of increased loss rate in areas remote from the source and destination, variable
rate of advancement towards destination over the route, as well as of defending against
malicious packets within a certain distance from the destination. We then consider sending
multiple coded packets that follow independent paths to the destination node so as to
mitigate the effects of losses and routing inaccuracies. We study a homogeneous medium
and obtain the time-dependent properties of the packet’s travel process, allowing us to
compare the merits and limitations of coding, both in terms of delivery times and energy
efficiency. Finally, we propose models that can assist in the analysis and optimisation
of the performance of inter-flow network coding (NC). We analyse two queueing models
for a router that carries out NC, in addition to its standard packet routing function. The
approach is extended to the study of multiple hops, which leads to an optimisation problem
that characterises the optimal time that packets should be held back in a router, waiting
for coding opportunities to arise, so that the total packet end-to-end delay is minimised
From Complex Networks to Time Series Analysis and Viceversa: Application to Metabolic Networks
In this work we present a simple and fast approach to generate network structures based on time series recurrence plots and viceversa. In addition, we discuss the application of the different analysis techniques developed in both fields, i.e. complex networks and time series analysis. Concerning the transformation from time series to networks, we propose a deterministic growth procedure which produces a new types of complex network structures that have some interesting features. This simple and fast approach is able to generate deterministic network structures based on time series recurrence plots. The generated networks contain several properties of the original time series. In this case, networks generated from chaotic attractors display interesting features from the point of view of robustness which could help in designing systems with high tolerance against errors and transfer of information. Chaotic networks based on the Lorenz attractor show that they are highly tolerant against attacks and they have a high ability for the transfer of information or on the contrary they are able to transmit infections faster. It is still necessary to investigate if such chaotic networks exist already in natural or man-made systems or, if possible, to construct such networks and test their properties. On the other hand, the transformation from networks to time series presents some problems concerning the selection of the initial time or in our case the initial node and the way in which the nodes are visited. If a network has been generated following a certain growth law it seems logical to choose the first node as the origin and then proceed following the network growth pattern. However, the situation is not so clear for example with metabolic networks, where it is difficult to select which is the first metabolite. Similar concerns would apply to other types of biological networks. In this case several alternatives could be considered, e.g. ordering using the number of connections. However, we have still to find if there are some invariant/preserved properties in the generated time series from the same network. We have found that rescaled range analysis does not preserve the fractal structure in the time series. In any case, if time series parameters would be invariant against the initial node selection, then they could be used to analyze the networks that have generated said time series. Our future work will continue along these lines.JRC.I.6-Systems toxicolog
Hybrid Routing in Delay Tolerant Networks
This work addresses the integration of today\u27s infrastructure-based networks with infrastructure-less networks. The resulting Hybrid Routing System allows for communication over both network types and can help to overcome cost, communication, and overload problems. Mobility aspect resulting from infrastructure-less networks are analyzed and analytical models developed. For development and deployment of the Hybrid Routing System an overlay-based framework is presented
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