36,547 research outputs found
The Fog Makes Sense: Enabling Social Sensing Services With Limited Internet Connectivity
Social sensing services use humans as sensor carriers, sensor operators and
sensors themselves in order to provide situation-awareness to applications.
This promises to provide a multitude of benefits to the users, for example in
the management of natural disasters or in community empowerment. However,
current social sensing services depend on Internet connectivity since the
services are deployed on central Cloud platforms. In many circumstances,
Internet connectivity is constrained, for instance when a natural disaster
causes Internet outages or when people do not have Internet access due to
economical reasons. In this paper, we propose the emerging Fog Computing
infrastructure to become a key-enabler of social sensing services in situations
of constrained Internet connectivity. To this end, we develop a generic
architecture and API of Fog-enabled social sensing services. We exemplify the
usage of the proposed social sensing architecture on a number of concrete use
cases from two different scenarios.Comment: Ruben Mayer, Harshit Gupta, Enrique Saurez, and Umakishore
Ramachandran. 2017. The Fog Makes Sense: Enabling Social Sensing Services
With Limited Internet Connectivity. In Proceedings of The 2nd International
Workshop on Social Sensing, Pittsburgh, PA, USA, April 21 2017
(SocialSens'17), 6 page
Continuous Spatial Query Processing:A Survey of Safe Region Based Techniques
In the past decade, positioning system-enabled devices such as smartphones have become most prevalent. This functionality brings the increasing popularity of
location-based services
in business as well as daily applications such as navigation, targeted advertising, and location-based social networking.
Continuous spatial queries
serve as a building block for location-based services. As an example, an Uber driver may want to be kept aware of the nearest customers or service stations. Continuous spatial queries require updates to the query result as the query or data objects are moving. This poses challenges to the query efficiency, which is crucial to the user experience of a service. A large number of approaches address this efficiency issue using the concept of
safe region
. A safe region is a region within which arbitrary movement of an object leaves the query result unchanged. Such a region helps reduce the frequency of query result update and hence improves query efficiency. As a result, safe region-based approaches have been popular for processing various types of continuous spatial queries. Safe regions have interesting theoretical properties and are worth in-depth analysis. We provide a comparative study of safe region-based approaches. We describe how safe regions are computed for different types of continuous spatial queries, showing how they improve query efficiency. We compare the different safe region-based approaches and discuss possible further improvements
Algorithms for Stable Matching and Clustering in a Grid
We study a discrete version of a geometric stable marriage problem originally
proposed in a continuous setting by Hoffman, Holroyd, and Peres, in which
points in the plane are stably matched to cluster centers, as prioritized by
their distances, so that each cluster center is apportioned a set of points of
equal area. We show that, for a discretization of the problem to an
grid of pixels with centers, the problem can be solved in time , and we experiment with two slower but more practical algorithms and
a hybrid method that switches from one of these algorithms to the other to gain
greater efficiency than either algorithm alone. We also show how to combine
geometric stable matchings with a -means clustering algorithm, so as to
provide a geometric political-districting algorithm that views distance in
economic terms, and we experiment with weighted versions of stable -means in
order to improve the connectivity of the resulting clusters.Comment: 23 pages, 12 figures. To appear (without the appendices) at the 18th
International Workshop on Combinatorial Image Analysis, June 19-21, 2017,
Plovdiv, Bulgari
Tangos: the agile numerical galaxy organization system
We present Tangos, a Python framework and web interface for database-driven
analysis of numerical structure formation simulations. To understand the role
that such a tool can play, consider constructing a history for the absolute
magnitude of each galaxy within a simulation. The magnitudes must first be
calculated for all halos at all timesteps and then linked using a merger tree;
folding the required information into a final analysis can entail significant
effort. Tangos is a generic solution to this information organization problem,
aiming to free users from the details of data management. At the querying
stage, our example of gathering properties over history is reduced to a few
clicks or a simple, single-line Python command. The framework is highly
extensible; in particular, users are expected to define their own properties
which tangos will write into the database. A variety of parallelization options
are available and the raw simulation data can be read using existing libraries
such as pynbody or yt. Finally, tangos-based databases and analysis pipelines
can easily be shared with collaborators or the broader community to ensure
reproducibility. User documentation is provided separately.Comment: Clarified various points and further improved code performance;
accepted for publication in ApJS. Tutorials (including video) at
http://tiny.cc/tango
Probabilistic Guarantees for Safe Deep Reinforcement Learning
Deep reinforcement learning has been successfully applied to many control
tasks, but the application of such agents in safety-critical scenarios has been
limited due to safety concerns. Rigorous testing of these controllers is
challenging, particularly when they operate in probabilistic environments due
to, for example, hardware faults or noisy sensors. We propose MOSAIC, an
algorithm for measuring the safety of deep reinforcement learning agents in
stochastic settings. Our approach is based on the iterative construction of a
formal abstraction of a controller's execution in an environment, and leverages
probabilistic model checking of Markov decision processes to produce
probabilistic guarantees on safe behaviour over a finite time horizon. It
produces bounds on the probability of safe operation of the controller for
different initial configurations and identifies regions where correct behaviour
can be guaranteed. We implement and evaluate our approach on agents trained for
several benchmark control problems
Continuous Obstructed Detour Queries
In this paper, we introduce Continuous Obstructed Detour (COD) Queries, a novel query type in spatial databases. COD queries continuously return the nearest point of interests (POIs) such as a restaurant, an ATM machine and a pharmacy with respect to the current location and the fixed destination of a moving pedestrian in presence of obstacles like a fence, a lake or a private building. The path towards a destination is typically not predetermined and the nearest POIs can change over time with the change of a pedestrian\u27s current location towards a fixed destination. The distance to a POI is measured as the summation of the obstructed distance from the pedestrian\u27s current location to the POI and the obstructed distance from the POI to the pedestrian\u27s destination. Evaluating the query for every change of a pedestrian\u27s location would incur extremely high processing overhead. We develop an efficient solution for COD queries and verify the effectiveness and efficiency of our solution in experiments
PRM-RL: Long-range Robotic Navigation Tasks by Combining Reinforcement Learning and Sampling-based Planning
We present PRM-RL, a hierarchical method for long-range navigation task
completion that combines sampling based path planning with reinforcement
learning (RL). The RL agents learn short-range, point-to-point navigation
policies that capture robot dynamics and task constraints without knowledge of
the large-scale topology. Next, the sampling-based planners provide roadmaps
which connect robot configurations that can be successfully navigated by the RL
agent. The same RL agents are used to control the robot under the direction of
the planning, enabling long-range navigation. We use the Probabilistic Roadmaps
(PRMs) for the sampling-based planner. The RL agents are constructed using
feature-based and deep neural net policies in continuous state and action
spaces. We evaluate PRM-RL, both in simulation and on-robot, on two navigation
tasks with non-trivial robot dynamics: end-to-end differential drive indoor
navigation in office environments, and aerial cargo delivery in urban
environments with load displacement constraints. Our results show improvement
in task completion over both RL agents on their own and traditional
sampling-based planners. In the indoor navigation task, PRM-RL successfully
completes up to 215 m long trajectories under noisy sensor conditions, and the
aerial cargo delivery completes flights over 1000 m without violating the task
constraints in an environment 63 million times larger than used in training.Comment: 9 pages, 7 figure
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