2,768 research outputs found
Measuring Temporal Lags in Delay-Tolerant Networks
Abstract-Delay-tolerant networks (DTNs) are characterized by a possible absence of end-to-end communication routes at any instant. In most cases, however, a form of connectivity can be established over time and space. This particularity leads to consider the relevance of a given route not only in terms of hops (topological length), but also in terms of time (temporal length). The problem of measuring temporal distances between individuals in a social network was recently addressed, based on a posteriori analysis of interaction traces. This paper focuses on the distributed version of this problem, asking whether every node in a network can know precisely and in real time how out-ofdate it is with respect to every other. Answering affirmatively is simple when contacts between the nodes are punctual, using the temporal adaptation of vector clocks provided i
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
Shortest, Fastest, and Foremost Broadcast in Dynamic Networks
Highly dynamic networks rarely offer end-to-end connectivity at a given time.
Yet, connectivity in these networks can be established over time and space,
based on temporal analogues of multi-hop paths (also called {\em journeys}).
Attempting to optimize the selection of the journeys in these networks
naturally leads to the study of three cases: shortest (minimum hop), fastest
(minimum duration), and foremost (earliest arrival) journeys. Efficient
centralized algorithms exists to compute all cases, when the full knowledge of
the network evolution is given.
In this paper, we study the {\em distributed} counterparts of these problems,
i.e. shortest, fastest, and foremost broadcast with termination detection
(TDB), with minimal knowledge on the topology.
We show that the feasibility of each of these problems requires distinct
features on the evolution, through identifying three classes of dynamic graphs
wherein the problems become gradually feasible: graphs in which the
re-appearance of edges is {\em recurrent} (class R), {\em bounded-recurrent}
(B), or {\em periodic} (P), together with specific knowledge that are
respectively (the number of nodes), (a bound on the recurrence
time), and (the period). In these classes it is not required that all pairs
of nodes get in contact -- only that the overall {\em footprint} of the graph
is connected over time.
Our results, together with the strict inclusion between , , and ,
implies a feasibility order among the three variants of the problem, i.e.
TDB[foremost] requires weaker assumptions on the topology dynamics than
TDB[shortest], which itself requires less than TDB[fastest]. Reversely, these
differences in feasibility imply that the computational powers of ,
, and also form a strict hierarchy
Anonymizing continuous queries with delay-tolerant mix-zones over road networks
This paper presents a delay-tolerant mix-zone framework for protecting the location privacy of mobile users against continuous query correlation attacks. First, we describe and analyze the continuous query correlation attacks (CQ-attacks) that perform query correlation based inference to break the anonymity of road network-aware mix-zones. We formally study the privacy strengths of the mix-zone anonymization under the CQ-attack model and argue that spatial cloaking or temporal cloaking over road network mix-zones is ineffective and susceptible to attacks that carry out inference by combining query correlation with timing correlation (CQ-timing attack) and transition correlation (CQ-transition attack) information. Next, we introduce three types of delay-tolerant road network mix-zones (i.e.; temporal, spatial and spatio-temporal) that are free from CQ-timing and CQ-transition attacks and in contrast to conventional mix-zones, perform a combination of both location mixing and identity mixing of spatially and temporally perturbed user locations to achieve stronger anonymity under the CQ-attack model. We show that by combining temporal and spatial delay-tolerant mix-zones, we can obtain the strongest anonymity for continuous queries while making acceptable tradeoff between anonymous query processing cost and temporal delay incurred in anonymous query processing. We evaluate the proposed techniques through extensive experiments conducted on realistic traces produced by GTMobiSim on different scales of geographic maps. Our experiments show that the proposed techniques offer high level of anonymity and attack resilience to continuous queries. © 2013 Springer Science+Business Media New York
Surge pricing on a service platform under spatial spillovers: evidence from Uber
Ride-sharing platforms employ surge pricing to match anticipated capacity spillover with
demand. We develop an optimization model to characterize the relationship between surge
price and spillover. We test predicted relationships using a spatial panel model on a dataset
from Ubers operation. Results reveal that Ubers pricing accounts for both capacity and price
spillover. There is a debate in the management community on the ecacy of labor welfare
mechanisms associated with shared capacity. We conduct counterfactual analysis to provide
guidance in regards to the debate, for managing congestion, while accounting for consumer
and labor welfare through this online platform.First author draf
Random walks on temporal networks
Many natural and artificial networks evolve in time. Nodes and connections
appear and disappear at various timescales, and their dynamics has profound
consequences for any processes in which they are involved. The first empirical
analysis of the temporal patterns characterizing dynamic networks are still
recent, so that many questions remain open. Here, we study how random walks, as
paradigm of dynamical processes, unfold on temporally evolving networks. To
this aim, we use empirical dynamical networks of contacts between individuals,
and characterize the fundamental quantities that impact any general process
taking place upon them. Furthermore, we introduce different randomizing
strategies that allow us to single out the role of the different properties of
the empirical networks. We show that the random walk exploration is slower on
temporal networks than it is on the aggregate projected network, even when the
time is properly rescaled. In particular, we point out that a fundamental role
is played by the temporal correlations between consecutive contacts present in
the data. Finally, we address the consequences of the intrinsically limited
duration of many real world dynamical networks. Considering the fundamental
prototypical role of the random walk process, we believe that these results
could help to shed light on the behavior of more complex dynamics on temporally
evolving networks.Comment: 14 pages, 13 figure
Role of the cerebellum in adaptation to delayed action effects
Actions are typically associated with sensory consequences. For example, knocking at a door results in predictable sounds. These self-initiated sensory stimuli are known to elicit smaller cortical responses compared to passively presented stimuli, e.g., early auditory evoked magnetic fields known as M100 and M200 components are attenuated. Current models implicate the cerebellum in the prediction of the sensory consequences of our actions. However, causal evidence is largely missing. In this study, we introduced a constant delay (of 100 ms) between actions and action-associated sounds, and we recorded magnetoencephalography (MEG) data as participants adapted to the delay. We found an increase in the attenuation of the M100 component over time for self-generated sounds, which indicates cortical adaptation to the introduced delay. In contrast, no change in M200 attenuation was found. Interestingly, disrupting cerebellar activity via transcranial magnetic stimulation (TMS) abolished the adaptation of M100 attenuation, while the M200 attenuation reverses to an M200 enhancement. Our results provide causal evidence for the involvement of the cerebellum in adapting to delayed action effects, and thus in the prediction of the sensory consequences of our actions
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