3,678 research outputs found
User Attraction via Wireless Charging in Cellular Networks
A strong motivation of charging depleted battery can be an enabler for
network capacity increase. In this light we propose a spatial attraction
cellular network (SAN) consisting of macro cells overlaid with small cell base
stations that wirelessly charge user batteries. Such a network makes battery
depleting users move toward the vicinity of small cell base stations. With a
fine adjustment of charging power, this user spatial attraction (SA) improves
in spectral efficiency as well as load balancing. We jointly optimize both
enhancements thanks to SA, and derive the corresponding optimal charging power
in a closed form by using a stochastic geometric approach.Comment: to be presented in IEEE International Symposium on Modeling and
Optimization in Mobile, Ad Hoc and Wireless Networks (WiOpt) Workshop on
Green Networks (GREENNET) 2016, Arizona, USA (8 pages, 4 figures
Percolation properties of growing networks under an Achlioptas process
We study the percolation transition in growing networks under an Achlioptas
process (AP). At each time step, a node is added in the network and, with the
probability , a link is formed between two nodes chosen by an AP. We
find that there occurs the percolation transition with varying and the
critical point is determined from the power-law behavior
of order parameter and the crossing of the fourth-order cumulant at the
critical point, also confirmed by the movement of the peak positions of the
second largest cluster size to the . Using the finite-size scaling
analysis, we get and , which
implies and . The Fisher exponent
for the cluster size distribution is obtained and shown to
satisfy the hyperscaling relation.Comment: 4 pages, 5 figures, 1 table, journal submitte
Live Prefetching for Mobile Computation Offloading
The conventional designs of mobile computation offloading fetch user-specific
data to the cloud prior to computing, called offline prefetching. However, this
approach can potentially result in excessive fetching of large volumes of data
and cause heavy loads on radio-access networks. To solve this problem, the
novel technique of live prefetching is proposed in this paper that seamlessly
integrates the task-level computation prediction and prefetching within the
cloud-computing process of a large program with numerous tasks. The technique
avoids excessive fetching but retains the feature of leveraging prediction to
reduce the program runtime and mobile transmission energy. By modeling the
tasks in an offloaded program as a stochastic sequence, stochastic optimization
is applied to design fetching policies to minimize mobile energy consumption
under a deadline constraint. The policies enable real-time control of the
prefetched-data sizes of candidates for future tasks. For slow fading, the
optimal policy is derived and shown to have a threshold-based structure,
selecting candidate tasks for prefetching and controlling their prefetched data
based on their likelihoods. The result is extended to design close-to-optimal
prefetching policies to fast fading channels. Compared with fetching without
prediction, live prefetching is shown theoretically to always achieve reduction
on mobile energy consumption.Comment: To appear in IEEE Trans. on Wireless Communicatio
Generalized gravity model for human migration
The gravity model (GM) analogous to Newton's law of universal gravitation has
successfully described the flow between different spatial regions, such as
human migration, traffic flows, international economic trades, etc. This simple
but powerful approach relies only on the 'mass' factor represented by the scale
of the regions and the 'geometrical' factor represented by the geographical
distance. However, when the population has a subpopulation structure
distinguished by different attributes, the estimation of the flow solely from
the coarse-grained geographical factors in the GM causes the loss of
differential geographical information for each attribute. To exploit the full
information contained in the geographical information of subpopulation
structure, we generalize the GM for population flow by explicitly harnessing
the subpopulation properties characterized by both attributes and geography. As
a concrete example, we examine the marriage patterns between the bride and the
groom clans of Korea in the past. By exploiting more refined geographical and
clan information, our generalized GM properly describes the real data, a part
of which could not be explained by the conventional GM. Therefore, we would
like to emphasize the necessity of using our generalized version of the GM,
when the information on such nongeographical subpopulation structures is
available.Comment: 14 pages, 6 figures, 2 table
MuNES: Multifloor Navigation Including Elevators and Stairs
We propose a scheme called MuNES for single mapping and trajectory planning
including elevators and stairs. Optimized multifloor trajectories are important
for optimal interfloor movements of robots. However, given two or more options
of moving between floors, it is difficult to select the best trajectory because
there are no suitable indoor multifloor maps in the existing methods. To solve
this problem, MuNES creates a single multifloor map including elevators and
stairs by estimating altitude changes based on pressure data. In addition, the
proposed method performs floor-based loop detection for faster and more
accurate loop closure. The single multifloor map is then voxelized leaving only
the parts needed for trajectory planning. An optimal and realistic multifloor
trajectory is generated by exploring the voxels using an A* algorithm based on
the proposed cost function, which affects realistic factors. We tested this
algorithm using data acquired from around a campus and note that a single
accurate multifloor map could be created. Furthermore, optimal and realistic
multifloor trajectory could be found by selecting the means of motion between
floors between elevators and stairs according to factors such as the starting
point, ending point, and elevator waiting time. The code and data used in this
work are available at https://github.com/donghwijung/MuNES
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