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
We combine here theoretical analysis (average case 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 (average case 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: We investigate the Vehicle Routing Problem with Time Windows (VRPTW) under an eco-friendly framework that demands the delivery of balanced and compact customer clusters. We present a new approach consisting of three major phases: (i) a first clustering of customers with compatible time windows; (ii) a second clustering of customers with close geographic proximity based on various methods (natural cuts, KaHIP, quad trees); (iii) a refinement phase that either splits a cluster into smaller ones, or merges clusters to form a bigger compact cluster. Our approach turns out to be beneficial when used in an on-line environment, where changes to the initial tour are requested (add a new customer to the tour or drop some customers). The new method serves as a warm starting point for re-evaluating and further optimizing the solution of VRPTW. Experiments with real data sets demonstrate that our approach compares favorably with standard approaches that start from a basic (cold) solution.
Abstract: Geographicrouting is becoming the protocol of choice for many sensor network applications. Some very efficient geographicrouting algorithms exist, however they require a preliminary planarization of the communication graph. Planarization induces overhead which makes this approach not optimal when lightweight protocols are required. On the other hand, georouting algorithms which do not rely on planarization have fairly low success rates and either fail to route messages around all but the simplest obstacles or have a high topology control overhead (e.g. contour detection algorithms). In this entry we describe the GRIC algorithm which was designed to overcome some of those limitations. The GRIC algorithm was proposed in [PN07a]. It is the first lightweight and efficient on demand (i.e. all-to-all) geographicrouting algorithm which does not require planarization, has almost 100% delivery rates (when no obstacles are added), and behaves well in the presence of large communication blocking obstacles.
Abstract: Geographicrouting scales well in sensor networks, mainly
due to its stateless nature. Still, most of the algorithms are
concerned with finding some path, while the optimality of
the path is difficult to achieve. In this paper we are presenting
a novel geographicrouting algorithm with obstacle
avoidance properties. It aims at finding the optimal path
from a source to a destination when some areas of the network
are unavailable for routing due to low local density or
obstacle presence. It locally and gradually with time (but,
as we show, quite fast) evaluates and updates the suitability
of the previously used paths and ignores non optimal paths
for further routing. By means of extensive simulations, we
are comparing its performance to existing state of the art
protocols, showing that it performs much better in terms of
path length thus minimizing latency, space, overall traffic
and energy consumption.
Abstract: Grids offer a transparent interface to geographically scattered computation, communication, storage and
other resources. In this chapter we propose and evaluate QoS-aware and fair scheduling algorithms for
Grid Networks, which are capable of optimally or near-optimally assigning tasks to resources, while taking
into consideration the task characteristics and QoS requirements. We categorize Grid tasks according to
whether or not they demand hard performance guarantees. Tasks with one or more hard requirements are
referred to as Guaranteed Service (GS) tasks, while tasks with no hard requirements are referred to as Best
Effort (BE) tasks. For GS tasks, we propose scheduling algorithms that provide deadline or computational
power guarantees, or offer fair degradation in the QoS such tasks receive in case of congestion. Regarding
BE tasks our objective is to allocate resources in a fair way, where fairness is interpreted in the max-min fair
share sense. Though, we mainly address scheduling problems on computation resources, we also look at
the joint scheduling of communication and computation resources and propose routing and scheduling
algorithms aiming at co-allocating both resource type so as to satisfy their respective QoS requirements.
Abstract: Geographicrouting is becoming the protocol of choice for
many sensor network applications. The current state of the art is unsatisfactory:
some algorithms are very efficient, however they require a
preliminary planarization of the communication graph. Planarization induces
overhead and is thus not realistic for some scenarios such as the
case of highly dynamic network topologies. On the other hand, georouting
algorithms which do not rely on planarization have fairly low success
rates and fail to route messages around all but the simplest obstacles.
To overcome these limitations, we propose the GRIC geographicrouting
algorithm. It has absolutely no topology maintenance overhead, almost
100% delivery rates (when no obstacles are added), bypasses large convex
obstacles, finds short paths to the destination, resists link failure
and is fairly simple to implement. The case of hard concave obstacles
is also studied; such obstacles are hard instances for which performance