124,316 research outputs found
Mobility modeling and management for next generation wireless networks
Mobility modeling and management in wireless networks are the set of tasks performed in order to model motion patterns, predict trajectories, get information on mobiles\u27 whereabouts and to make use of this information in handoff, routing, location management, resource allocation and other functions.
In the literature, the speed of mobile is often and misleadingly referred to as the level of mobility, such as high or low mobility. This dissertation presents an information theoretic approach to mobility modeling and management, in which mobility is considered as a measure of uncertainty in mobile\u27s trajectory, that is, the mobility is low if the trajectory of a mobile is highly predictable even if the mobile is moving with high speed. On the other hand, the mobility is high if the trajectory of the mobile is highly erratic. Based on this mobility modeling concept, we classify mobiles into predictable and non-predictable mobility classes and optimize network operations for each mobility class. The dynamic mobility classification technique is applied to various mobility related issues of the next generation wireless networks such as location management, location-based services, and energy efficient routing in multihop cellular networks
Breaking the habit: measuring and predicting departures from routine in individual human mobility
Researchers studying daily life mobility patterns have recently shown that humans are typically highly predictable in their movements. However, no existing work has examined the boundaries of this predictability, where human behaviour transitions temporarily from routine patterns to highly unpredictable states. To address this shortcoming, we tackle two interrelated challenges. First, we develop a novel information-theoretic metric, called instantaneous entropy, to analyse an individual’s mobility patterns and identify temporary departures from routine. Second, to predict such departures in the future, we propose the first Bayesian framework that explicitly models breaks from routine, showing that it outperforms current state-of-the-art predictor
Coupling Human Mobility and Social Ties
Studies using massive, passively data collected from communication
technologies have revealed many ubiquitous aspects of social networks, helping
us understand and model social media, information diffusion, and organizational
dynamics. More recently, these data have come tagged with geographic
information, enabling studies of human mobility patterns and the science of
cities. We combine these two pursuits and uncover reproducible mobility
patterns amongst social contacts. First, we introduce measures of mobility
similarity and predictability and measure them for populations of users in
three large urban areas. We find individuals' visitations patterns are far more
similar to and predictable by social contacts than strangers and that these
measures are positively correlated with tie strength. Unsupervised clustering
of hourly variations in mobility similarity identifies three categories of
social ties and suggests geography is an important feature to contextualize
social relationships. We find that the composition of a user's ego network in
terms of the type of contacts they keep is correlated with mobility behavior.
Finally, we extend a popular mobility model to include movement choices based
on social contacts and compare it's ability to reproduce empirical measurements
with two additional models of mobility
Throughput Maximization for Mobile Relaying Systems
This paper studies a novel mobile relaying technique, where relays of high
mobility are employed to assist the communications from source to destination.
By exploiting the predictable channel variations introduced by relay mobility,
we study the throughput maximization problem in a mobile relaying system via
dynamic rate and power allocations at the source and relay. An optimization
problem is formulated for a finite time horizon, subject to an
information-causality constraint, which results from the data buffering
employed at the relay. It is found that the optimal power allocations across
the different time slots follow a "stair-case" water filling (WF) structure,
with non-increasing and non-decreasing water levels at the source and relay,
respectively. For the special case where the relay moves unidirectionally from
source to destination, the optimal power allocations reduce to the conventional
WF with constant water levels. Numerical results show that with appropriate
trajectory design, mobile relaying is able to achieve tremendous throughput
gain over the conventional static relaying.Comment: submitted for possible conference publicatio
Patterns of Individual Shopping Behavior
Much of economic theory is built on observations of aggregate, rather than
individual, behavior. Here, we present novel findings on human shopping
patterns at the resolution of a single purchase. Our results suggest that much
of our seemingly elective activity is actually driven by simple routines. While
the interleaving of shopping events creates randomness at the small scale, on
the whole consumer behavior is largely predictable. We also examine
income-dependent differences in how people shop, and find that wealthy
individuals are more likely to bundle shopping trips. These results validate
previous work on mobility from cell phone data, while describing the
unpredictability of behavior at higher resolution.Comment: 4 pages, 5 figure
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