73 research outputs found
Extremal Properties of Three Dimensional Sensor Networks with Applications
In this paper, we analyze various critical transmitting/sensing ranges for
connectivity and coverage in three-dimensional sensor networks. As in other
large-scale complex systems, many global parameters of sensor networks undergo
phase transitions: For a given property of the network, there is a critical
threshold, corresponding to the minimum amount of the communication effort or
power expenditure by individual nodes, above (resp. below) which the property
exists with high (resp. a low) probability. For sensor networks, properties of
interest include simple and multiple degrees of connectivity/coverage. First,
we investigate the network topology according to the region of deployment, the
number of deployed sensors and their transmitting/sensing ranges. More
specifically, we consider the following problems: Assume that nodes, each
capable of sensing events within a radius of , are randomly and uniformly
distributed in a 3-dimensional region of volume , how large
must the sensing range be to ensure a given degree of coverage of the region to
monitor? For a given transmission range, what is the minimum (resp. maximum)
degree of the network? What is then the typical hop-diameter of the underlying
network? Next, we show how these results affect algorithmic aspects of the
network by designing specific distributed protocols for sensor networks
Randomized Initialization of a Wireless Multihop Network
Address autoconfiguration is an important mechanism required to set the IP
address of a node automatically in a wireless network. The address
autoconfiguration, also known as initialization or naming, consists to give a
unique identifier ranging from 1 to for a set of indistinguishable
nodes. We consider a wireless network where nodes (processors) are randomly
thrown in a square , uniformly and independently. We assume that the network
is synchronous and two nodes are able to communicate if they are within
distance at most of of each other ( is the transmitting/receiving
range). The model of this paper concerns nodes without the collision detection
ability: if two or more neighbors of a processor transmit concurrently at
the same time, then would not receive either messages. We suppose also that
nodes know neither the topology of the network nor the number of nodes in the
network. Moreover, they start indistinguishable, anonymous and unnamed. Under
this extremal scenario, we design and analyze a fully distributed protocol to
achieve the initialization task for a wireless multihop network of nodes
uniformly scattered in a square . We show how the transmitting range of the
deployed stations can affect the typical characteristics such as the degrees
and the diameter of the network. By allowing the nodes to transmit at a range
r= \sqrt{\frac{(1+\ell) \ln{n} \SIZE}{\pi n}} (slightly greater than the one
required to have a connected network), we show how to design a randomized
protocol running in expected time in order to assign a
unique number ranging from 1 to to each of the participating nodes
Optimal Initialization and Gossiping Algorithms for Random Radio Networks
The initialization problem, also known as naming, consists to give a unique identifier ranging from to to a set of indistinguishable nodes in a given network. We consider a network where nodes (processors) are randomly deployed in a square (resp. cube) . We assume that the time is slotted and the network is synchronous, two nodes are able to communicate if they are within distance at most of of each other ( is the transmitting/receiving range). Moreover, if two or more neighbors of a processor transmit concurrently at the same time slot, then would not receive either messages. We suppose also that the anonymous nodes know neither the topology of the network nor the number of nodes in the network. Under this extremal scenario, we first show how the transmitting range of the deployed processors affects the typical characteristics of the network. Then, by allowing the nodes to transmit at various ranges we design sub-linear randomized initialization protocols~: In the two, resp. three, dimensional case, our randomized initialization algorithms run in , resp. , time slots. These latter protocols are based upon an optimal gossiping algorithm which is of independent interest
Forbidden Subgraphs in Connected Graphs
Given a set of connected non acyclic graphs, a
-free graph is one which does not contain any member of as copy.
Define the excess of a graph as the difference between its number of edges and
its number of vertices. Let {\gr{W}}_{k,\xi} be theexponential generating
function (EGF for brief) of connected -free graphs of excess equal to
(). For each fixed , a fundamental differential recurrence
satisfied by the EGFs {\gr{W}}_{k,\xi} is derived. We give methods on how to
solve this nonlinear recurrence for the first few values of by means of
graph surgery. We also show that for any finite collection of non-acyclic
graphs, the EGFs {\gr{W}}_{k,\xi} are always rational functions of the
generating function, , of Cayley's rooted (non-planar) labelled trees. From
this, we prove that almost all connected graphs with nodes and edges
are -free, whenever and by means of
Wright's inequalities and saddle point method. Limiting distributions are
derived for sparse connected -free components that are present when a
random graph on nodes has approximately edges. In particular,
the probability distribution that it consists of trees, unicyclic components,
, -cyclic components all -free is derived. Similar results are
also obtained for multigraphs, which are graphs where self-loops and
multiple-edges are allowed
ANOTHER PROOF OF WRIGHT'S INEQUALITIES
We present a short way of proving the inequalities obtained by Wright in [Journal of Graph Theory, 4: 393 - 407 (1980)] concerning the number of connected graphs with edges more than vertices
On the probability of planarity of a random graph near the critical point
Consider the uniform random graph with vertices and edges.
Erd\H{o}s and R\'enyi (1960) conjectured that the limit
\lim_{n \to \infty} \Pr\{G(n,\textstyle{n\over 2}) is planar}} exists
and is a constant strictly between 0 and 1. \L uczak, Pittel and Wierman (1994)
proved this conjecture and Janson, \L uczak, Knuth and Pittel (1993) gave lower
and upper bounds for this probability.
In this paper we determine the exact probability of a random graph being
planar near the critical point . For each , we find an exact
analytic expression for
In particular, we obtain .
We extend these results to classes of graphs closed under taking minors. As
an example, we show that the probability of being
series-parallel converges to 0.98003.
For the sake of completeness and exposition we reprove in a concise way
several basic properties we need of a random graph near the critical point.Comment: 10 pages, 1 figur
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