3,438 research outputs found
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
Impact of Channel Errors on Decentralized Detection Performance of Wireless Sensor Networks: A Study of Binary Modulations, Rayleigh-Fading and Nonfading Channels, and Fusion-Combiners
We provide new results on the performance of wireless sensor networks in which a number of identical sensor nodes transmit their binary decisions, regarding a binary hypothesis, to a fusion center (FC) by means of a modulation scheme. Each link between a sensor and the fusion center is modeled independent and identically distributed (i.i.d.) either as slow Rayleigh-fading or as nonfading. The FC employs a counting rule (CR) or another combining scheme to make a final decision. Main results obtained are the following: 1) in slow fading, a) the correctness of using an average bit error rate of a link, averaged with respect to the fading distribution, for assessing the performance of a CR and b) with proper choice of threshold, ON/OFF keying (OOK), in addition to energy saving, exhibits asymptotic (large number of sensors) performance comparable to that of FSK; and 2) for a large number of sensors, a) for slow fading and a counting rule, given a minimum sensor-to-fusion link SNR, we determine a minimum sensor decision quality, in order to achieve zero asymptotic errors and b) for Rayleigh-fading and nonfading channels and PSK (FSK) modulation, using a large deviation theory, we derive asymptotic error exponents of counting rule, maximal ratio (square law), and equal gain combiners
Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey
Wireless sensor networks (WSNs) consist of autonomous and resource-limited
devices. The devices cooperate to monitor one or more physical phenomena within
an area of interest. WSNs operate as stochastic systems because of randomness
in the monitored environments. For long service time and low maintenance cost,
WSNs require adaptive and robust methods to address data exchange, topology
formulation, resource and power optimization, sensing coverage and object
detection, and security challenges. In these problems, sensor nodes are to make
optimized decisions from a set of accessible strategies to achieve design
goals. This survey reviews numerous applications of the Markov decision process
(MDP) framework, a powerful decision-making tool to develop adaptive algorithms
and protocols for WSNs. Furthermore, various solution methods are discussed and
compared to serve as a guide for using MDPs in WSNs
- …