Abstract: We introduce a new model of
ad-hoc mobile networks, which we call hierarchical,
that are comprised of dense subnetworks of mobile
users (corresponding to highly populated
geographical areas, such as cities),
interconnected across access ports
by sparse but frequently used connections
(such as highways).
For such networks, we present
an efficient routing protocol which extends
the idea (introduced in WAE00) of exploiting the co-ordinated
motion of a small part of an ad-hoc mobile
network (the ``support'') to achieve
very fast communication between any two mobile users of the network.
The basic idea of the new protocol presented here is, instead
of using a unique (large) support for the whole network,
to employ a hierarchy of (small) supports (one for each city)
and also take advantage of the regular traffic
of mobile users across the interconnection highways to communicate
between cities.
We combine here theoretical analysis (averagecase estimations based on random walk properties) and experimental implementations (carried out using the LEDA platform) to claim and validate results showing that such a hierarchical routing approach is,
for this class of ad-hoc mobile networks, significantly more efficient than a simple extension of the
basic ``support'' idea presented in WAE00.
Abstract: We introduce a new model of ad-hoc mobile networks,
which we call hierarchical, that are comprised of
dense subnetworks of mobile users (corresponding to highly
populated geographical areas), interconnected across access
ports by sparse but frequently used connections.
To implement communication in such a case, a possible
solution would be to install a very fast (yet limited) backbone
interconnecting such highly populated mobile user areas, while
employing a hierarchy of (small) supports (one for each lower level
site). This fast backbone provides a limited number of access
ports within these dense areas of mobile users.
We combine here theoretical analysis (averagecase estimations based on
random walk properties) to claim and validate
results showing that such a hierarchical routing approach is,
for this class of ad-hoc mobile networks, significantly
more efficient than a simple extension of the
basic ``support'' idea presented in [WAE00,DISC01].
Abstract: When one engineers distributed algorithms, some special characteristics
arise that are different from conventional (sequential or parallel)
computing paradigms. These characteristics include: the need for either a
scalable real network environment or a platform supporting a simulated
distributed environment; the need to incorporate asynchrony, where arbitrarya
synchrony is hard, if not impossible, to implement; and the generation
of “difficult” input instances which is a particular challenge. In this
work, we identifys ome of the methodological issues required to address
the above characteristics in distributed algorithm engineering and illustrate
certain approaches to tackle them via case studies. Our discussion
begins byad dressing the need of a simulation environment and how asynchronyis
incorporated when experimenting with distributed algorithms.
We then proceed bys uggesting two methods for generating difficult input
instances for distributed experiments, namelya game-theoretic one and another
based on simulations of adversarial arguments or lower bound proofs.
We give examples of the experimental analysis of a pursuit-evasion protocol
and of a shared memorypro blem in order to demonstrate these ideas.
We then address a particularlyi nteresting case of conducting experiments
with algorithms for mobile computing and tackle the important issue of
motion of processes in this context. We discuss the two-tier principle as
well as a concurrent random walks approach on an explicit representation
of motions in ad-hoc mobile networks, which allow at least for averagecaseanalysis and measurements and may give worst-case inputs in some
cases. Finally, we discuss a useful interplay between theory and practice
that arise in modeling attack propagation in networks.
Abstract: Smart Dust is a set of a vast number of ultra-small fully autonomous computing and communication devices, with very restricted energy and computing capabilities, that co-operate to quickly and efficiently accomplish a large sensing task. Smart Dust can be very useful in practice, i.e., in the local detection of a remote crucial event and the propagation of data reporting its realization. In this work we make an effort towards the research on smart dust from an algorithmic point of view. We first provide a simple but realistic model for smart dust and present an interesting problem, which is how to propagate efficiently information on an event detected locally. Then we present various smart dust protocols for local detection and propagation that are simple enough to be implemented on real smart dust systems, and perform, under some simplifying assumptions, a rigorous averagecaseanalysis of their efficiency and energy consumption (and their interplay). This analysis leads to concrete results showing that our protocols are very efficient and robust. We also validate the analytical results by extensive experiments.
Abstract: Smart Dust is a set of a ast number of ultra-small fully autonomous computing and communication devices, with very restricted energy and computing capabilities, that cooperate to quickly and efficiently accomplish a large sensing task. Smart Dust can be very useful in practice i.e. in the local detection of a remote crucial event and the propagation of data reporting its realization. In this work we make an effort towards the research on smart dust from a basic algorithmic point of view. We first provide a simple but realistic model for smart dust and present an interesting problem, which is how to propagate efficiently information on an event detected locally. Then we present smart dust protocols for local detection and propagation that are simple enough to be implemented on real smart dust systems, and perform, under some simplifying assumptions, a rigorous averagecaseanalysis of their efficiency and energy consumption (and their interplay). This analysis leads to concrete results showing that our protocols are very efficient.
Abstract: Smart Dust is a set of a vast number of ultra-small fully
autonomous computing and communication devices, with very restricted
energy and computing capabilities, that co-operate to quickly and efficiently
accomplish a large sensing task.
Smart Dust can be very useful in practice
i.e. in the local detection of a remote crucial event and
the propagation of data reporting its realization.
In this work we make an effort towards the research on smart dust
from a basic algorithmic point of view.
We first provide a simple but realistic model for smart dust
and present an interesting problem, which is how to propagate efficiently
information on an event detected locally.
Then we present smart dust protocols for local detection
and propagation that are simple enough to be implemented
on real smart dust systems, and perform, under some simplifying assumptions,
a rigorous averagecaseanalysis of their efficiency and energy consumption
(and their interplay).
This analysis leads to concrete results showing that our protocols
are very efficient.