2,598 research outputs found
Fast Desynchronization For Decentralized Multichannel Medium Access Control
Distributed desynchronization algorithms are key to wireless sensor networks
as they allow for medium access control in a decentralized manner. In this
paper, we view desynchronization primitives as iterative methods that solve
optimization problems. In particular, by formalizing a well established
desynchronization algorithm as a gradient descent method, we establish novel
upper bounds on the number of iterations required to reach convergence.
Moreover, by using Nesterov's accelerated gradient method, we propose a novel
desynchronization primitive that provides for faster convergence to the steady
state. Importantly, we propose a novel algorithm that leads to decentralized
time-synchronous multichannel TDMA coordination by formulating this task as an
optimization problem. Our simulations and experiments on a densely-connected
IEEE 802.15.4-based wireless sensor network demonstrate that our scheme
provides for faster convergence to the steady state, robustness to hidden
nodes, higher network throughput and comparable power dissipation with respect
to the recently standardized IEEE 802.15.4e-2012 time-synchronized channel
hopping (TSCH) scheme.Comment: to appear in IEEE Transactions on Communication
Simplicial Homology for Future Cellular Networks
Simplicial homology is a tool that provides a mathematical way to compute the
connectivity and the coverage of a cellular network without any node location
information. In this article, we use simplicial homology in order to not only
compute the topology of a cellular network, but also to discover the clusters
of nodes still with no location information. We propose three algorithms for
the management of future cellular networks. The first one is a frequency
auto-planning algorithm for the self-configuration of future cellular networks.
It aims at minimizing the number of planned frequencies while maximizing the
usage of each one. Then, our energy conservation algorithm falls into the
self-optimization feature of future cellular networks. It optimizes the energy
consumption of the cellular network during off-peak hours while taking into
account both coverage and user traffic. Finally, we present and discuss the
performance of a disaster recovery algorithm using determinantal point
processes to patch coverage holes
Decentralized Maximum Likelihood Estimation for Sensor Networks Composed of Nonlinearly Coupled Dynamical Systems
In this paper we propose a decentralized sensor network scheme capable to
reach a globally optimum maximum likelihood (ML) estimate through
self-synchronization of nonlinearly coupled dynamical systems. Each node of the
network is composed of a sensor and a first-order dynamical system initialized
with the local measurements. Nearby nodes interact with each other exchanging
their state value and the final estimate is associated to the state derivative
of each dynamical system. We derive the conditions on the coupling mechanism
guaranteeing that, if the network observes one common phenomenon, each node
converges to the globally optimal ML estimate. We prove that the synchronized
state is globally asymptotically stable if the coupling strength exceeds a
given threshold. Acting on a single parameter, the coupling strength, we show
how, in the case of nonlinear coupling, the network behavior can switch from a
global consensus system to a spatial clustering system. Finally, we show the
effect of the network topology on the scalability properties of the network and
we validate our theoretical findings with simulation results.Comment: Journal paper accepted on IEEE Transactions on Signal Processin
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
A Survey on IT-Techniques for a Dynamic Emergency Management in Large Infrastructures
This deliverable is a survey on the IT techniques that are relevant to the three use cases of the project EMILI. It describes the state-of-the-art in four complementary IT areas: Data cleansing, supervisory control and data acquisition, wireless sensor networks and complex event processing. Even though the deliverable’s authors have tried to avoid a too technical language and have tried to explain every concept referred to, the deliverable might seem rather technical to readers so far little familiar with the techniques it describes
Distributed Decision Through Self-Synchronizing Sensor Networks in the Presence of Propagation Delays and Asymmetric Channels
In this paper we propose and analyze a distributed algorithm for achieving
globally optimal decisions, either estimation or detection, through a
self-synchronization mechanism among linearly coupled integrators initialized
with local measurements. We model the interaction among the nodes as a directed
graph with weights (possibly) dependent on the radio channels and we pose
special attention to the effect of the propagation delay occurring in the
exchange of data among sensors, as a function of the network geometry. We
derive necessary and sufficient conditions for the proposed system to reach a
consensus on globally optimal decision statistics. One of the major results
proved in this work is that a consensus is reached with exponential convergence
speed for any bounded delay condition if and only if the directed graph is
quasi-strongly connected. We provide a closed form expression for the global
consensus, showing that the effect of delays is, in general, the introduction
of a bias in the final decision. Finally, we exploit our closed form expression
to devise a double-step consensus mechanism able to provide an unbiased
estimate with minimum extra complexity, without the need to know or estimate
the channel parameters.Comment: To be published on IEEE Transactions on Signal Processin
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