5,721 research outputs found
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
Self-stabilizing TDMA Algorithms for Wireless Ad-hoc Networks without External Reference
Time division multiple access (TDMA) is a method for sharing communication
media. In wireless communications, TDMA algorithms often divide the radio time
into timeslots of uniform size, , and then combine them into frames of
uniform size, . We consider TDMA algorithms that allocate at least one
timeslot in every frame to every node. Given a maximal node degree, ,
and no access to external references for collision detection, time or position,
we consider the problem of collision-free self-stabilizing TDMA algorithms that
use constant frame size.
We demonstrate that this problem has no solution when the frame size is , where is the chromatic number for
distance- vertex coloring. As a complement to this lower bound, we focus on
proving the existence of collision-free self-stabilizing TDMA algorithms that
use constant frame size of . We consider basic settings (no hardware
support for collision detection and no prior clock synchronization), and the
collision of concurrent transmissions from transmitters that are at most two
hops apart. In the context of self-stabilizing systems that have no external
reference, we are the first to study this problem (to the best of our
knowledge), and use simulations to show convergence even with computation time
uncertainties
High-Performance Distributed ML at Scale through Parameter Server Consistency Models
As Machine Learning (ML) applications increase in data size and model
complexity, practitioners turn to distributed clusters to satisfy the increased
computational and memory demands. Unfortunately, effective use of clusters for
ML requires considerable expertise in writing distributed code, while
highly-abstracted frameworks like Hadoop have not, in practice, approached the
performance seen in specialized ML implementations. The recent Parameter Server
(PS) paradigm is a middle ground between these extremes, allowing easy
conversion of single-machine parallel ML applications into distributed ones,
while maintaining high throughput through relaxed "consistency models" that
allow inconsistent parameter reads. However, due to insufficient theoretical
study, it is not clear which of these consistency models can really ensure
correct ML algorithm output; at the same time, there remain many
theoretically-motivated but undiscovered opportunities to maximize
computational throughput. Motivated by this challenge, we study both the
theoretical guarantees and empirical behavior of iterative-convergent ML
algorithms in existing PS consistency models. We then use the gleaned insights
to improve a consistency model using an "eager" PS communication mechanism, and
implement it as a new PS system that enables ML algorithms to reach their
solution more quickly.Comment: 19 pages, 2 figure
Cooperative Synchronization in Wireless Networks
Synchronization is a key functionality in wireless network, enabling a wide
variety of services. We consider a Bayesian inference framework whereby network
nodes can achieve phase and skew synchronization in a fully distributed way. In
particular, under the assumption of Gaussian measurement noise, we derive two
message passing methods (belief propagation and mean field), analyze their
convergence behavior, and perform a qualitative and quantitative comparison
with a number of competing algorithms. We also show that both methods can be
applied in networks with and without master nodes. Our performance results are
complemented by, and compared with, the relevant Bayesian Cram\'er-Rao bounds
Self-Stabilizing TDMA Algorithms for Dynamic Wireless Ad-hoc Networks
In dynamic wireless ad-hoc networks (DynWANs), autonomous computing devices
set up a network for the communication needs of the moment. These networks
require the implementation of a medium access control (MAC) layer. We consider
MAC protocols for DynWANs that need to be autonomous and robust as well as have
high bandwidth utilization, high predictability degree of bandwidth allocation,
and low communication delay in the presence of frequent topological changes to
the communication network. Recent studies have shown that existing
implementations cannot guarantee the necessary satisfaction of these timing
requirements. We propose a self-stabilizing MAC algorithm for DynWANs that
guarantees a short convergence period, and by that, it can facilitate the
satisfaction of severe timing requirements, such as the above. Besides the
contribution in the algorithmic front of research, we expect that our proposal
can enable quicker adoption by practitioners and faster deployment of DynWANs
that are subject changes in the network topology
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