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Performance of Linear Field Reconstruction Techniques With Noise and Uncertain Sensor Locations
"... Abstract—We consider a wireless sensor network, sampling a bandlimited field, described by a limited number of harmonics. Sensor nodes are irregularly deployed over the area of interest or subject to random displacement; in addition sensors measurements are affected by noise. Our goal is to obtain a ..."
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Cited by 11 (7 self)
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Abstract—We consider a wireless sensor network, sampling a bandlimited field, described by a limited number of harmonics. Sensor nodes are irregularly deployed over the area of interest or subject to random displacement; in addition sensors measurements are affected by noise. Our goal is to obtain a high quality reconstruction of the field, with the mean square error (MSE) of the estimate as performance metric. In particular, we analytically derive the performance of several reconstruction/estimation techniques based on linear filtering. For each technique, we obtain the MSE, as well as its asymptotic expression in the case where the number of fieldharmonics and the number of sensors grow to infinity, while their ratio is kept constant. Through numerical simulations, we show the validity of the asymptotic analysis, even for a small number of sensors. We provide some novel guidelines for the design of sensor networks when many parameters, such as field bandwidth, number of sensors, reconstruction quality, and sensor displacement characteristics, to be traded off. Index Terms—Irregular sampling, linear filtering, sensor networks. I.
Information retrieval and processing in sensor networks: deterministic scheduling vs. random access
 In Proc. o.t. Int. Symp. on Information Theory
, 2004
"... Abstract—We investigate the effect of mediumaccess control (MAC) used in information retrieval by a mobile access point (AP) on information processing in largescale sensor network, where sensors are unreliable and subject to outage. We focus on a 1D sensor network and assume that the location inf ..."
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Cited by 10 (4 self)
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Abstract—We investigate the effect of mediumaccess control (MAC) used in information retrieval by a mobile access point (AP) on information processing in largescale sensor network, where sensors are unreliable and subject to outage. We focus on a 1D sensor network and assume that the location information is available locally at each sensor and unavailable to the AP. For a fixed collection interval, two types of MAC schemes are considered: the deterministic scheduling, which collects data from predetermined sensors locations, and random access, which collects data from random locations. We compare the signal estimation performance of the two MACs, using the expected maximum distortion as the performance measure. For large sensor networks with fixed density, we show that there is a critical threshold on the sensor outage probability out: For out (1+ (1)), where is the throughput of the random access protocol, the deterministic scheduling provides better reconstruction performance. However, (1+ (1)) for out, the performance degradation from missing data samples due to sensor outage does not justify the effort of scheduling; simple random access outperforms the optimal scheduling. Index Terms—Estimation, information retrieval, random access, random field, sampling, scheduling, sensor network. I.
How much information can one get from a wireless ad hoc sensor network over a correlated random field?,” submitted to
 IEEE Trans. Inform. Theory
, 2008
"... Abstract—New largedeviations results that characterize the asymptotic information rates for general dimensional (D) stationary Gaussian fields are obtained. By applying the general results to sensor nodes on a twodimensional (2D) lattice, the asymptotic behavior of ad hoc sensor networks deploy ..."
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Cited by 9 (4 self)
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Abstract—New largedeviations results that characterize the asymptotic information rates for general dimensional (D) stationary Gaussian fields are obtained. By applying the general results to sensor nodes on a twodimensional (2D) lattice, the asymptotic behavior of ad hoc sensor networks deployed over correlated random fields for statistical inference is investigated. Under a 2D hidden Gauss–Markov random field model with symmetric firstorder conditional autoregression and the assumption of no innetwork data fusion, the behavior of the total obtainable information [nats] and energy efficiency [nats/J] defined as the ratio of total gathered information to the required energy is obtained as the coverage area, node density, and energy vary. When the sensor node density is fixed, the energy efficiency decreases to zero with rate and the pernode information under fixed pernode energy also diminishes to zero with rate as the number of network nodes increases by increasing the coverage area. As the sensor spacing increases, the pernode information converges to its limit with rate for a given diffusion rate . When the coverage area is fixed and the node density increases, the pernode information is inversely proportional to the node density. As the total energy consumed in the network increases, the total information obtainable from the network is given by for the fixed node density and fixed coverage case and by for the fixed pernode sensing energy and fixed density and increasing coverage case. Index Terms—Ad hoc sensor networks, asymptotic Kullback–Leibler information rate, asymptotic mutual information rate, conditional autoregressive model, Gauss–Markov random fields, large deviations principle, stationary Gaussian fields. I.
The Impact of QuasiEqually Spaced Sensor Topologies on Signal Reconstruction
, 2010
"... A wireless sensor network with randomly deployed nodes can be used to provide an irregular sampling of a physical field of interest. We assume that a sink node collects the data gathered by the sensors and uses a linear filter for the reconstruction of a bandlimited scalar field defined over a ddim ..."
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A wireless sensor network with randomly deployed nodes can be used to provide an irregular sampling of a physical field of interest. We assume that a sink node collects the data gathered by the sensors and uses a linear filter for the reconstruction of a bandlimited scalar field defined over a ddimensional domain. Sensors ’ locations are assumed to be known at the sink node, up to a certain position error. We then take the mean square error (MSE) of the reconstructed field as performance metric, and evaluate the effect of both uniform and quasiequally spaced sensor layouts on the quality of the reconstructed field. We define a parameter that provides a measure of the regularity of the sensors deployment, and, through asymptotic analysis, we derive the MSE in the case of different sensor spatial distributions. For two of them, an approximate closed form expression is obtained. We validate our analysis through numerical results, and we show that an excellent match exists between analysis and simulation even for a small number of sensors.
[ Qing Zhao, Ananthram Swami, and Lang Tong] The Interplay Between Signal Processing and Networking in Sensor Networks
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"... Power system real time security assessment is one of the fundamental modules of the electricity markets. Typically, when a contingency occurs, it is required that security assessment and enhancement module shall be ready for action within about 20 minutes ’ time to meet the real time requirement. Th ..."
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Power system real time security assessment is one of the fundamental modules of the electricity markets. Typically, when a contingency occurs, it is required that security assessment and enhancement module shall be ready for action within about 20 minutes ’ time to meet the real time requirement. The recent California block out again highlighted the importance of system security. This paper proposes an approach for power system security assessment and enhancement based on the information provided from the predefined system parameter space. The proposed scheme opens up an efficient way for real time security assessment and enhancement in a competitive electricity market.
Sensor Network over a Correlated Random Field?
, 903
"... New large deviations results that characterize the asymptotic information rates for general ddimensional (dD) stationary Gaussian fields are obtained. By applying the general results to sensor nodes on a twodimensional (2D) lattice, the asymptotic behavior of ad hoc sensor networks deployed over ..."
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New large deviations results that characterize the asymptotic information rates for general ddimensional (dD) stationary Gaussian fields are obtained. By applying the general results to sensor nodes on a twodimensional (2D) lattice, the asymptotic behavior of ad hoc sensor networks deployed over correlated random fields for statistical inference is investigated. Under a 2D hidden GaussMarkov random field model with symmetric first order conditional autoregression and the assumption of no innetwork data fusion, the behavior of the total obtainable information [nats] and energy efficiency [nats/J] defined as the ratio of total gathered information to the required energy is obtained as the coverage area, node density and energy vary. When the sensor node density is fixed, the energy efficiency decreases to zero with rate Θ ( area −1/2) and the pernode information under fixed pernode energy also diminishes to zero with rate O(N −1/3 t) as the number Nt of network nodes increases by increasing the coverage area. As the sensor spacing dn increases, the pernode information converges to its limit D with rate D − √ dne −αdn for a given diffusion rate α. When the coverage area is fixed and the node density increases, the pernode information is inversely proportional to the node density. As the total energy Et consumed in the network