1,076 research outputs found
STEPS - an approach for human mobility modeling
In this paper we introduce Spatio-TEmporal Parametric Stepping (STEPS) - a simple parametric mobility model which can cover a large spectrum of human mobility patterns. STEPS makes abstraction of spatio-temporal preferences in human mobility by using a power law to rule the nodes movement. Nodes in STEPS have preferential attachment to favorite locations where they spend most of their time. Via simulations, we show that STEPS is able, not only to express the peer to peer properties such as inter-ontact/contact time and to reflect accurately realistic routing performance, but also to express the structural properties of the underlying interaction graph such as small-world phenomenon. Moreover, STEPS is easy to implement, exible to configure and also theoretically tractable
On the Dynamics of Human Proximity for Data Diffusion in Ad-Hoc Networks
We report on a data-driven investigation aimed at understanding the dynamics
of message spreading in a real-world dynamical network of human proximity. We
use data collected by means of a proximity-sensing network of wearable sensors
that we deployed at three different social gatherings, simultaneously involving
several hundred individuals. We simulate a message spreading process over the
recorded proximity network, focusing on both the topological and the temporal
properties. We show that by using an appropriate technique to deal with the
temporal heterogeneity of proximity events, a universal statistical pattern
emerges for the delivery times of messages, robust across all the data sets.
Our results are useful to set constraints for generic processes of data
dissemination, as well as to validate established models of human mobility and
proximity that are frequently used to simulate realistic behaviors.Comment: A. Panisson et al., On the dynamics of human proximity for data
diffusion in ad-hoc networks, Ad Hoc Netw. (2011
Plausible Mobility: Inferring Movement from Contacts
We address the difficult question of inferring plausible node mobility based
only on information from wireless contact traces. Working with mobility
information allows richer protocol simulations, particularly in dense networks,
but requires complex set-ups to measure, whereas contact information is easier
to measure but only allows for simplistic simulation models. In a contact trace
a lot of node movement information is irretrievably lost so the original
positions and velocities are in general out of reach. We propose a fast
heuristic algorithm, inspired by dynamic force-based graph drawing, capable of
inferring a plausible movement from any contact trace, and evaluate it on both
synthetic and real-life contact traces. Our results reveal that (i) the quality
of the inferred mobility is directly linked to the precision of the measured
contact trace, and (ii) the simple addition of appropriate anticipation forces
between nodes leads to an accurate inferred mobility.Comment: 8 pages, 8 figures, 1 tabl
Trajectory design for autonomous underwater vehicles based on ocean model predictions for feature tracking
Trajectory design for Autonomous Underwater Vehicles (AUVs) is of great importance to the oceanographic research community. Intelligent planning is required to maneuver a vehicle to high-valued locations for data collection. We consider the use of ocean model predictions to determine the locations to be visited by an AUV, which then provides near-real time, in situ measurements back to the model to increase the skill of future predictions. The motion planning problem of steering the vehicle between the computed waypoints is not considered here. Our focus is on the algorithm to determine relevant points of interest for a chosen oceanographic feature. This represents a first approach to an end to end autonomous prediction and tasking system for aquatic, mobile sensor networks. We design a sampling plan and present experimental results with AUV retasking in the Southern California Bight (SCB) off the coast of Los Angeles
The impact of mobility models on the performance of mobile Ad Hoc network routing protocol
A mobility model represents nodes distribution and movement over the network. Several research works have shown that a selection of mobility model can affect the outcome of routing performance simulation in Mobile Ad Hoc Networks. Thus, a routing protocol may only be effective in a particular mobility model or scenario but performs inferiorly in another. As a result, analyses of routing protocol performance are often based on inadequate information leading to inaccurate argument and conclusion. In this paper, three different mobility models have been selected, where each of them is highly distinctive in terms of nodes movement behavior. In addition, a new measurement technique called probability of route connectivity is introduced. The technique is used to quantify the success rate of route established by a routing protocol. Extensive simulation runs are done and results are compared between each mobility model
Improved Bounds on Information Dissemination by Manhattan Random Waypoint Model
With the popularity of portable wireless devices it is important to model and
predict how information or contagions spread by natural human mobility -- for
understanding the spreading of deadly infectious diseases and for improving
delay tolerant communication schemes. Formally, we model this problem by
considering moving agents, where each agent initially carries a
\emph{distinct} bit of information. When two agents are at the same location or
in close proximity to one another, they share all their information with each
other. We would like to know the time it takes until all bits of information
reach all agents, called the \textit{flood time}, and how it depends on the way
agents move, the size and shape of the network and the number of agents moving
in the network.
We provide rigorous analysis for the \MRWP model (which takes paths with
minimum number of turns), a convenient model used previously to analyze mobile
agents, and find that with high probability the flood time is bounded by
, where agents move on an
grid. In addition to extensive simulations, we use a data set of
taxi trajectories to show that our method can successfully predict flood times
in both experimental settings and the real world.Comment: 10 pages, ACM SIGSPATIAL 2018, Seattle, U
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