163,335 research outputs found
Sequential Decision Algorithms for Measurement-Based Impromptu Deployment of a Wireless Relay Network along a Line
We are motivated by the need, in some applications, for impromptu or
as-you-go deployment of wireless sensor networks. A person walks along a line,
starting from a sink node (e.g., a base-station), and proceeds towards a source
node (e.g., a sensor) which is at an a priori unknown location. At equally
spaced locations, he makes link quality measurements to the previous relay, and
deploys relays at some of these locations, with the aim to connect the source
to the sink by a multihop wireless path. In this paper, we consider two
approaches for impromptu deployment: (i) the deployment agent can only move
forward (which we call a pure as-you-go approach), and (ii) the deployment
agent can make measurements over several consecutive steps before selecting a
placement location among them (which we call an explore-forward approach). We
consider a light traffic regime, and formulate the problem as a Markov decision
process, where the trade-off is among the power used by the nodes, the outage
probabilities in the links, and the number of relays placed per unit distance.
We obtain the structures of the optimal policies for the pure as-you-go
approach as well as for the explore-forward approach. We also consider natural
heuristic algorithms, for comparison. Numerical examples show that the
explore-forward approach significantly outperforms the pure as-you-go approach.
Next, we propose two learning algorithms for the explore-forward approach,
based on Stochastic Approximation, which asymptotically converge to the set of
optimal policies, without using any knowledge of the radio propagation model.
We demonstrate numerically that the learning algorithms can converge (as
deployment progresses) to the set of optimal policies reasonably fast and,
hence, can be practical, model-free algorithms for deployment over large
regions.Comment: 29 pages. arXiv admin note: text overlap with arXiv:1308.068
Deploy-As-You-Go Wireless Relay Placement: An Optimal Sequential Decision Approach using the Multi-Relay Channel Model
We use information theoretic achievable rate formulas for the multi-relay
channel to study the problem of as-you-go deployment of relay nodes. The
achievable rate formulas are for full-duplex radios at the relays and for
decode-and-forward relaying. Deployment is done along the straight line joining
a source node and a sink node at an unknown distance from the source. The
problem is for a deployment agent to walk from the source to the sink,
deploying relays as he walks, given that the distance to the sink is
exponentially distributed with known mean. As a precursor, we apply the
multi-relay channel achievable rate formula to obtain the optimal power
allocation to relays placed along a line, at fixed locations. This permits us
to obtain the optimal placement of a given number of nodes when the distance
between the source and sink is given. Numerical work suggests that, at low
attenuation, the relays are mostly clustered near the source in order to be
able to cooperate, whereas at high attenuation they are uniformly placed and
work as repeaters. We also prove that the effect of path-loss can be entirely
mitigated if a large enough number of relays are placed uniformly between the
source and the sink. The structure of the optimal power allocation for a given
placement of the nodes, then motivates us to formulate the problem of as-you-go
placement of relays along a line of exponentially distributed length, and with
the exponential path-loss model, so as to minimize a cost function that is
additive over hops. The hop cost trades off a capacity limiting term, motivated
from the optimal power allocation solution, against the cost of adding a relay
node. We formulate the problem as a total cost Markov decision process,
establish results for the value function, and provide insights into the
placement policy and the performance of the deployed network via numerical
exploration.Comment: 21 pages. arXiv admin note: substantial text overlap with
arXiv:1204.432
BriskStream: Scaling Data Stream Processing on Shared-Memory Multicore Architectures
We introduce BriskStream, an in-memory data stream processing system (DSPSs)
specifically designed for modern shared-memory multicore architectures.
BriskStream's key contribution is an execution plan optimization paradigm,
namely RLAS, which takes relative-location (i.e., NUMA distance) of each pair
of producer-consumer operators into consideration. We propose a branch and
bound based approach with three heuristics to resolve the resulting nontrivial
optimization problem. The experimental evaluations demonstrate that BriskStream
yields much higher throughput and better scalability than existing DSPSs on
multi-core architectures when processing different types of workloads.Comment: To appear in SIGMOD'1
Reuleaux: Robot Base Placement by Reachability Analysis
Before beginning any robot task, users must position the robot's base, a task
that now depends entirely on user intuition. While slight perturbation is
tolerable for robots with moveable bases, correcting the problem is imperative
for fixed-base robots if some essential task sections are out of reach. For
mobile manipulation robots, it is necessary to decide on a specific base
position before beginning manipulation tasks.
This paper presents Reuleaux, an open source library for robot reachability
analyses and base placement. It reduces the amount of extra repositioning and
removes the manual work of identifying potential base locations. Based on the
reachability map, base placement locations of a whole robot or only the arm can
be efficiently determined. This can be applied to both statically mounted
robots, where position of the robot and work piece ensure the maximum amount of
work performed, and to mobile robots, where the maximum amount of workable area
can be reached. Solutions are not limited only to vertically constrained
placement, since complicated robotics tasks require the base to be placed at
unique poses based on task demand.
All Reuleaux library methods were tested on different robots of different
specifications and evaluated for tasks in simulation and real world
environment. Evaluation results indicate that Reuleaux had significantly
improved performance than prior existing methods in terms of time-efficiency
and range of applicability.Comment: Submitted to International Conference of Robotic Computing 201
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