1,495 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
Research on Wireless Multi-hop Networks: Current State and Challenges
Wireless multi-hop networks, in various forms and under various names, are
being increasingly used in military and civilian applications. Studying
connectivity and capacity of these networks is an important problem. The
scaling behavior of connectivity and capacity when the network becomes
sufficiently large is of particular interest. In this position paper, we
briefly overview recent development and discuss research challenges and
opportunities in the area, with a focus on the network connectivity.Comment: invited position paper to International Conference on Computing,
Networking and Communications, Hawaii, USA, 201
Spatial networks with wireless applications
Many networks have nodes located in physical space, with links more common
between closely spaced pairs of nodes. For example, the nodes could be wireless
devices and links communication channels in a wireless mesh network. We
describe recent work involving such networks, considering effects due to the
geometry (convex,non-convex, and fractal), node distribution,
distance-dependent link probability, mobility, directivity and interference.Comment: Review article- an amended version with a new title from the origina
Connectivity of confined 3D Networks with Anisotropically Radiating Nodes
Nodes in ad hoc networks with randomly oriented directional antenna patterns
typically have fewer short links and more long links which can bridge together
otherwise isolated subnetworks. This network feature is known to improve
overall connectivity in 2D random networks operating at low channel path loss.
To this end, we advance recently established results to obtain analytic
expressions for the mean degree of 3D networks for simple but practical
anisotropic gain profiles, including those of patch, dipole and end-fire array
antennas. Our analysis reveals that for homogeneous systems (i.e. neglecting
boundary effects) directional radiation patterns are superior to the isotropic
case only when the path loss exponent is less than the spatial dimension.
Moreover, we establish that ad hoc networks utilizing directional transmit and
isotropic receive antennas (or vice versa) are always sub-optimally connected
regardless of the environment path loss. We extend our analysis to investigate
boundary effects in inhomogeneous systems, and study the geometrical reasons
why directional radiating nodes are at a disadvantage to isotropic ones.
Finally, we discuss multi-directional gain patterns consisting of many equally
spaced lobes which could be used to mitigate boundary effects and improve
overall network connectivity.Comment: 12 pages, 10 figure
Topology Control for Maintaining Network Connectivity and Maximizing Network Capacity Under the Physical Model
In this paper we study the issue of topology control under the physical Signal-to-Interference-Noise-Ratio (SINR) model, with the objective of maximizing network capacity. We show that existing graph-model-based topology control captures interference inadequately under the physical SINR model, and as a result, the interference in the topology thus induced is high and the network capacity attained is low. Towards bridging this gap, we propose a centralized approach, called Spatial Reuse Maximizer (MaxSR), that combines a power control algorithm T4P with a topology control algorithm P4T. T4P optimizes the assignment of transmit power given a fixed topology, where by optimality we mean that the transmit power is so assigned that it minimizes the average interference degree (defined as the number of interferencing nodes that may interfere with the on-going transmission on a link) in the topology. P4T, on the other hand, constructs, based on the power assignment made in T4P, a new topology by deriving a spanning tree that gives the minimal interference degree. By alternately invoking the two algorithms, the power assignment quickly converges to an operational point that maximizes the network capacity. We formally prove the convergence of MaxSR. We also show via simulation that the topology induced by MaxSR outperforms that derived from existing topology control algorithms by 50%-110% in terms of maximizing the network capacity
Fundamentals of Large Sensor Networks: Connectivity, Capacity, Clocks and Computation
Sensor networks potentially feature large numbers of nodes that can sense
their environment over time, communicate with each other over a wireless
network, and process information. They differ from data networks in that the
network as a whole may be designed for a specific application. We study the
theoretical foundations of such large scale sensor networks, addressing four
fundamental issues- connectivity, capacity, clocks and function computation.
To begin with, a sensor network must be connected so that information can
indeed be exchanged between nodes. The connectivity graph of an ad-hoc network
is modeled as a random graph and the critical range for asymptotic connectivity
is determined, as well as the critical number of neighbors that a node needs to
connect to. Next, given connectivity, we address the issue of how much data can
be transported over the sensor network. We present fundamental bounds on
capacity under several models, as well as architectural implications for how
wireless communication should be organized.
Temporal information is important both for the applications of sensor
networks as well as their operation.We present fundamental bounds on the
synchronizability of clocks in networks, and also present and analyze
algorithms for clock synchronization. Finally we turn to the issue of gathering
relevant information, that sensor networks are designed to do. One needs to
study optimal strategies for in-network aggregation of data, in order to
reliably compute a composite function of sensor measurements, as well as the
complexity of doing so. We address the issue of how such computation can be
performed efficiently in a sensor network and the algorithms for doing so, for
some classes of functions.Comment: 10 pages, 3 figures, Submitted to the Proceedings of the IEE
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