52,938 research outputs found
On Compact Routing for the Internet
While there exist compact routing schemes designed for grids, trees, and
Internet-like topologies that offer routing tables of sizes that scale
logarithmically with the network size, we demonstrate in this paper that in
view of recent results in compact routing research, such logarithmic scaling on
Internet-like topologies is fundamentally impossible in the presence of
topology dynamics or topology-independent (flat) addressing. We use analytic
arguments to show that the number of routing control messages per topology
change cannot scale better than linearly on Internet-like topologies. We also
employ simulations to confirm that logarithmic routing table size scaling gets
broken by topology-independent addressing, a cornerstone of popular
locator-identifier split proposals aiming at improving routing scaling in the
presence of network topology dynamics or host mobility. These pessimistic
findings lead us to the conclusion that a fundamental re-examination of
assumptions behind routing models and abstractions is needed in order to find a
routing architecture that would be able to scale ``indefinitely.''Comment: This is a significantly revised, journal version of cs/050802
Medians and Beyond: New Aggregation Techniques for Sensor Networks
Wireless sensor networks offer the potential to span and monitor large
geographical areas inexpensively. Sensors, however, have significant power
constraint (battery life), making communication very expensive. Another
important issue in the context of sensor-based information systems is that
individual sensor readings are inherently unreliable. In order to address these
two aspects, sensor database systems like TinyDB and Cougar enable in-network
data aggregation to reduce the communication cost and improve reliability. The
existing data aggregation techniques, however, are limited to relatively simple
types of queries such as SUM, COUNT, AVG, and MIN/MAX. In this paper we propose
a data aggregation scheme that significantly extends the class of queries that
can be answered using sensor networks. These queries include (approximate)
quantiles, such as the median, the most frequent data values, such as the
consensus value, a histogram of the data distribution, as well as range
queries. In our scheme, each sensor aggregates the data it has received from
other sensors into a fixed (user specified) size message. We provide strict
theoretical guarantees on the approximation quality of the queries in terms of
the message size. We evaluate the performance of our aggregation scheme by
simulation and demonstrate its accuracy, scalability and low resource
utilization for highly variable input data sets
Smoothed Analysis of Dynamic Networks
We generalize the technique of smoothed analysis to distributed algorithms in
dynamic network models. Whereas standard smoothed analysis studies the impact
of small random perturbations of input values on algorithm performance metrics,
dynamic graph smoothed analysis studies the impact of random perturbations of
the underlying changing network graph topologies. Similar to the original
application of smoothed analysis, our goal is to study whether known strong
lower bounds in dynamic network models are robust or fragile: do they withstand
small (random) perturbations, or do such deviations push the graphs far enough
from a precise pathological instance to enable much better performance? Fragile
lower bounds are likely not relevant for real-world deployment, while robust
lower bounds represent a true difficulty caused by dynamic behavior. We apply
this technique to three standard dynamic network problems with known strong
worst-case lower bounds: random walks, flooding, and aggregation. We prove that
these bounds provide a spectrum of robustness when subjected to
smoothing---some are extremely fragile (random walks), some are moderately
fragile / robust (flooding), and some are extremely robust (aggregation).Comment: 20 page
The Incremental Cooperative Design of Preventive Healthcare Networks
This document is the Accepted Manuscript version of the following article: Soheil Davari, 'The incremental cooperative design of preventive healthcare networks', Annals of Operations Research, first published online 27 June 2017. Under embargo. Embargo end date: 27 June 2018. The final publication is available at Springer via http://dx.doi.org/10.1007/s10479-017-2569-1.In the Preventive Healthcare Network Design Problem (PHNDP), one seeks to locate facilities in a way that the uptake of services is maximised given certain constraints such as congestion considerations. We introduce the incremental and cooperative version of the problem, IC-PHNDP for short, in which facilities are added incrementally to the network (one at a time), contributing to the service levels. We first develop a general non-linear model of this problem and then present a method to make it linear. As the problem is of a combinatorial nature, an efficient Variable Neighbourhood Search (VNS) algorithm is proposed to solve it. In order to gain insight into the problem, the computational studies were performed with randomly generated instances of different settings. Results clearly show that VNS performs well in solving IC-PHNDP with errors not more than 1.54%.Peer reviewe
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