12 research outputs found
Message and time efficient multi-broadcast schemes
We consider message and time efficient broadcasting and multi-broadcasting in
wireless ad-hoc networks, where a subset of nodes, each with a unique rumor,
wish to broadcast their rumors to all destinations while minimizing the total
number of transmissions and total time until all rumors arrive to their
destination. Under centralized settings, we introduce a novel approximation
algorithm that provides almost optimal results with respect to the number of
transmissions and total time, separately. Later on, we show how to efficiently
implement this algorithm under distributed settings, where the nodes have only
local information about their surroundings. In addition, we show multiple
approximation techniques based on the network collision detection capabilities
and explain how to calibrate the algorithms' parameters to produce optimal
results for time and messages.Comment: In Proceedings FOMC 2013, arXiv:1310.459
Achieving Dilution without Knowledge of Coordinates in the SINR Model
Considerable literature has been developed for various fundamental
distributed problems in the SINR (Signal-to-Interference-plus-Noise-Ratio)
model for radio transmission. A setting typically studied is when all nodes
transmit a signal of the same strength, and each device only has access to
knowledge about the total number of nodes in the network , the range from
which each node's label is taken , and the label of the device
itself. In addition, an assumption is made that each node also knows its
coordinates in the Euclidean plane. In this paper, we create a technique which
allows algorithm designers to remove that last assumption. The assumption about
the unavailability of the knowledge of the physical coordinates of the nodes
truly captures the `ad-hoc' nature of wireless networks.
Previous work in this area uses a flavor of a technique called dilution, in
which nodes transmit in a (predetermined) round-robin fashion, and are able to
reach all their neighbors. However, without knowing the physical coordinates,
it's not possible to know the coordinates of their containing (pivotal) grid
box and seemingly not possible to use dilution (to coordinate their
transmissions). We propose a new technique to achieve dilution without using
the knowledge of physical coordinates. This technique exploits the
understanding that the transmitting nodes lie in 2-D space, segmented by an
appropriate pivotal grid, without explicitly referring to the actual physical
coordinates of these nodes. Using this technique, it is possible for every weak
device to successfully transmit its message to all of its neighbors in
rounds, as long as the density of transmitting nodes in any
physical grid box is bounded by a known constant. This technique, we feel, is
an important generic tool for devising practical protocols when physical
coordinates of the nodes are not known.Comment: 10 page
Distributed Deterministic Broadcasting in Uniform-Power Ad Hoc Wireless Networks
Development of many futuristic technologies, such as MANET, VANET, iThings,
nano-devices, depend on efficient distributed communication protocols in
multi-hop ad hoc networks. A vast majority of research in this area focus on
design heuristic protocols, and analyze their performance by simulations on
networks generated randomly or obtained in practical measurements of some
(usually small-size) wireless networks. %some library. Moreover, they often
assume access to truly random sources, which is often not reasonable in case of
wireless devices. In this work we use a formal framework to study the problem
of broadcasting and its time complexity in any two dimensional Euclidean
wireless network with uniform transmission powers. For the analysis, we
consider two popular models of ad hoc networks based on the
Signal-to-Interference-and-Noise Ratio (SINR): one with opportunistic links,
and the other with randomly disturbed SINR. In the former model, we show that
one of our algorithms accomplishes broadcasting in rounds, where
is the number of nodes and is the diameter of the network. If nodes
know a priori the granularity of the network, i.e., the inverse of the
maximum transmission range over the minimum distance between any two stations,
a modification of this algorithm accomplishes broadcasting in
rounds.
Finally, we modify both algorithms to make them efficient in the latter model
with randomly disturbed SINR, with only logarithmic growth of performance.
Ours are the first provably efficient and well-scalable, under the two
models, distributed deterministic solutions for the broadcast task.Comment: arXiv admin note: substantial text overlap with arXiv:1207.673
Beyond Geometry : Towards Fully Realistic Wireless Models
Signal-strength models of wireless communications capture the gradual fading
of signals and the additivity of interference. As such, they are closer to
reality than other models. However, nearly all theoretic work in the SINR model
depends on the assumption of smooth geometric decay, one that is true in free
space but is far off in actual environments. The challenge is to model
realistic environments, including walls, obstacles, reflections and anisotropic
antennas, without making the models algorithmically impractical or analytically
intractable.
We present a simple solution that allows the modeling of arbitrary static
situations by moving from geometry to arbitrary decay spaces. The complexity of
a setting is captured by a metricity parameter Z that indicates how far the
decay space is from satisfying the triangular inequality. All results that hold
in the SINR model in general metrics carry over to decay spaces, with the
resulting time complexity and approximation depending on Z in the same way that
the original results depends on the path loss term alpha. For distributed
algorithms, that to date have appeared to necessarily depend on the planarity,
we indicate how they can be adapted to arbitrary decay spaces.
Finally, we explore the dependence on Z in the approximability of core
problems. In particular, we observe that the capacity maximization problem has
exponential upper and lower bounds in terms of Z in general decay spaces. In
Euclidean metrics and related growth-bounded decay spaces, the performance
depends on the exact metricity definition, with a polynomial upper bound in
terms of Z, but an exponential lower bound in terms of a variant parameter phi.
On the plane, the upper bound result actually yields the first approximation of
a capacity-type SINR problem that is subexponential in alpha
On the Impact of Geometry on Ad Hoc Communication in Wireless Networks
In this work we address the question how important is the knowledge of
geometric location and network density to the efficiency of (distributed)
wireless communication in ad hoc networks. We study fundamental communication
task of broadcast and develop well-scalable, randomized algorithms that do not
rely on GPS information, and which efficiency formulas do not depend on how
dense the geometric network is. We consider two settings: with and without
spontaneous wake-up of nodes. In the former setting, in which all nodes start
the protocol at the same time, our algorithm accomplishes broadcast in rounds under the SINR model, with high probability (whp), where
is the diameter of the communication graph and is the number of
stations. In the latter setting, in which only the source node containing the
original message is active in the beginning, we develop a slightly slower
algorithm working in rounds whp. Both algorithms are based on a
novel distributed coloring method, which is of independent interest and
potential applicability to other communication tasks under the SINR wireless
model
Deterministic Digital Clustering of Wireless Ad Hoc Networks
We consider deterministic distributed communication in wireless ad hoc
networks of identical weak devices under the SINR model without predefined
infrastructure. Most algorithmic results in this model rely on various
additional features or capabilities, e.g., randomization, access to geographic
coordinates, power control, carrier sensing with various precision of
measurements, and/or interference cancellation. We study a pure scenario, when
no such properties are available. As a general tool, we develop a deterministic
distributed clustering algorithm. Our solution relies on a new type of
combinatorial structures (selectors), which might be of independent interest.
Using the clustering, we develop a deterministic distributed local broadcast
algorithm accomplishing this task in rounds, where
is the density of the network. To the best of our knowledge, this is
the first solution in pure scenario which is only polylog away from the
universal lower bound , valid also for scenarios with
randomization and other features. Therefore, none of these features
substantially helps in performing the local broadcast task. Using clustering,
we also build a deterministic global broadcast algorithm that terminates within
rounds, where is the diameter of the
network. This result is complemented by a lower bound , where is the path-loss parameter of the
environment. This lower bound shows that randomization or knowledge of own
location substantially help (by a factor polynomial in ) in the global
broadcast. Therefore, unlike in the case of local broadcast, some additional
model features may help in global broadcast