9,158 research outputs found
Exploiting Interference for Efficient Distributed Computation in Cluster-based Wireless Sensor Networks
This invited paper presents some novel ideas on how to enhance the
performance of consensus algorithms in distributed wireless sensor networks,
when communication costs are considered. Of particular interest are consensus
algorithms that exploit the broadcast property of the wireless channel to boost
the performance in terms of convergence speeds. To this end, we propose a novel
clustering based consensus algorithm that exploits interference for
computation, while reducing the energy consumption in the network. The
resulting optimization problem is a semidefinite program, which can be solved
offline prior to system startup.Comment: Accepted for publication at IEEE Global Conference on Signal and
Information Processing (GlobalSIP 2013
Distributed Detection and Estimation in Wireless Sensor Networks
In this article we consider the problems of distributed detection and
estimation in wireless sensor networks. In the first part, we provide a general
framework aimed to show how an efficient design of a sensor network requires a
joint organization of in-network processing and communication. Then, we recall
the basic features of consensus algorithm, which is a basic tool to reach
globally optimal decisions through a distributed approach. The main part of the
paper starts addressing the distributed estimation problem. We show first an
entirely decentralized approach, where observations and estimations are
performed without the intervention of a fusion center. Then, we consider the
case where the estimation is performed at a fusion center, showing how to
allocate quantization bits and transmit powers in the links between the nodes
and the fusion center, in order to accommodate the requirement on the maximum
estimation variance, under a constraint on the global transmit power. We extend
the approach to the detection problem. Also in this case, we consider the
distributed approach, where every node can achieve a globally optimal decision,
and the case where the decision is taken at a central node. In the latter case,
we show how to allocate coding bits and transmit power in order to maximize the
detection probability, under constraints on the false alarm rate and the global
transmit power. Then, we generalize consensus algorithms illustrating a
distributed procedure that converges to the projection of the observation
vector onto a signal subspace. We then address the issue of energy consumption
in sensor networks, thus showing how to optimize the network topology in order
to minimize the energy necessary to achieve a global consensus. Finally, we
address the problem of matching the topology of the network to the graph
describing the statistical dependencies among the observed variables.Comment: 92 pages, 24 figures. To appear in E-Reference Signal Processing, R.
Chellapa and S. Theodoridis, Eds., Elsevier, 201
Task allocation in group of nodes in the IoT: A consensus approach
The realization of the Internet of Things (IoT) paradigm relies on the implementation of systems of cooperative intelligent objects with key interoperability capabilities. In order for objects to dynamically cooperate to IoT applications' execution, they need to make their resources available in a flexible way. However, available resources such as electrical energy, memory, processing, and object capability to perform a given task, are often limited. Therefore, resource allocation that ensures the fulfilment of network requirements is a critical challenge. In this paper, we propose a distributed optimization protocol based on consensus algorithm, to solve the problem of resource allocation and management in IoT heterogeneous networks. The proposed protocol is robust against links or nodes failures, so it's adaptive in dynamic scenarios where the network topology changes in runtime. We consider an IoT scenario where nodes involved in the same IoT task need to adjust their task frequency and buffer occupancy. We demonstrate that, using the proposed protocol, the network converges to a solution where resources are homogeneously allocated among nodes. Performance evaluation of experiments in simulation mode and in real scenarios show that the algorithm converges with a percentage error of about±5% with respect to the optimal allocation obtainable with a centralized approach
Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications
Wireless sensor networks monitor dynamic environments that change rapidly
over time. This dynamic behavior is either caused by external factors or
initiated by the system designers themselves. To adapt to such conditions,
sensor networks often adopt machine learning techniques to eliminate the need
for unnecessary redesign. Machine learning also inspires many practical
solutions that maximize resource utilization and prolong the lifespan of the
network. In this paper, we present an extensive literature review over the
period 2002-2013 of machine learning methods that were used to address common
issues in wireless sensor networks (WSNs). The advantages and disadvantages of
each proposed algorithm are evaluated against the corresponding problem. We
also provide a comparative guide to aid WSN designers in developing suitable
machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial
Likelihood Consensus and Its Application to Distributed Particle Filtering
We consider distributed state estimation in a wireless sensor network without
a fusion center. Each sensor performs a global estimation task---based on the
past and current measurements of all sensors---using only local processing and
local communications with its neighbors. In this estimation task, the joint
(all-sensors) likelihood function (JLF) plays a central role as it epitomizes
the measurements of all sensors. We propose a distributed method for computing,
at each sensor, an approximation of the JLF by means of consensus algorithms.
This "likelihood consensus" method is applicable if the local likelihood
functions of the various sensors (viewed as conditional probability density
functions of the local measurements) belong to the exponential family of
distributions. We then use the likelihood consensus method to implement a
distributed particle filter and a distributed Gaussian particle filter. Each
sensor runs a local particle filter, or a local Gaussian particle filter, that
computes a global state estimate. The weight update in each local (Gaussian)
particle filter employs the JLF, which is obtained through the likelihood
consensus scheme. For the distributed Gaussian particle filter, the number of
particles can be significantly reduced by means of an additional consensus
scheme. Simulation results are presented to assess the performance of the
proposed distributed particle filters for a multiple target tracking problem
Split Distributed Computing in Wireless Sensor Networks
We designed a novel method intended to improve the performance of distributed computing in wireless sensor networks. Our proposed method is designed to rapidly increase the speed of distributed computing and decrease the number of the messages required for a network to achieve the desired result. In our analysis, we chose Average consensus algorithm. In this case, the desired result is that every node achieves the average value calculated from all the initial values in the reduced number of iterations. Our method is based on the idea that a fragmentation of a network into small geographical structures which execute distributed calculations in parallel significantly affects the performance
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