An optimality analysis of sensor-target geometries for signal strength based localization

Bishop, Adrian N.; Jensfelt, Patric

doi:10.1109/issnip.2009.5416784

Cited by 71 publications

(73 citation statements)

References 19 publications

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“…Finally, we note that the number of quantization bits and thresholds are incorporated in the γ m parameter in Eqn. (11). In general, the more bits allocated combined with an intelligent choice of quantization intervals results in increase of the sensor's contribution to estimation error reduction.…”

Section: System Model and Problem Formulationmentioning

confidence: 99%

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An Auction-Bidding Protocol for Distributed Bit Allocation in RSSI-based Localization Networks

Ababneh¹

2016

ijacsa

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Abstract-Several factors (e.g., target energy, sensor density) affect estimation error at a point of interest in sensor networks. One of these factors is the number of allocated bits to sensors that cover the point of interest when quantization is employed. In this paper, we investigate bit allocation in such networks such that estimation error requirements at multiple points of interest are satisfied as best as possible. To solve this nonlinear integer programming problem, we propose an iterative distributed auctionbidding protocol. Starting with some initial bit distribution, a network is divided into a a number of clusters each with its own auction. Each cluster head (CH) acts as an auctioneer and divides sensors into buyers or sellers of bits (i.e., commodity). With limited messaging, CHs redistribute bits among sensors, each bit at a time such that the difference between achieved and required estimation errors within each cluster is reduced in each round. We propose two bit-pricing schemes used by sensors to decide on exchanging bits. Finally, simulation results show that our proposed 'distributed' protocol's error performance can be within 5%−10% of that of a 'centralized' genetic algorithm (GA) solution.

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Section: System Model and Problem Formulationmentioning

confidence: 99%

“…These include; sensor positions with respect to target, target-related parameters (e.g., energy profile), measurement model [10], [11]. Moreover, in practical networks with imposed bandwidth and energy limits, measurement quantization is usually employed.…”

Section: Introductionmentioning

confidence: 99%

An Auction-Bidding Protocol for Distributed Bit Allocation in RSSI-based Localization Networks

Ababneh¹

2016

ijacsa

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“…received powers) to distance estimates [24], and is commonly made in both theoretical studies (e.g. [25], [20], [26], [27]) and experimental studies (e.g. [14], [28]) on RSS-based sensor localization.…”

Section: Assumptionmentioning

confidence: 99%

“…Use the same notations b and T r(C RSS ) as in Section II and m 1 and σ 1 as defined by (20) and (21). Define a sequence of random variables…”

Section: Theoremmentioning

confidence: 99%

“…Provided that the measuring technique and the noise statistics of measurements are both known, the scalar metric can be regarded as a function of the sensor-anchor geometry. A valuable problem arises to be seeking to minimize the scalar metric, equivalent to identifying optimal sensor-anchor geometries for sensor localization, and has been widely studied [18], [19], [20], [21]. Moreover, since the scalar metric can be infinite, implying that a localization problem is badly conditioned (e.g.…”

Section: Introductionmentioning

confidence: 99%

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Performance limits in sensor localization

Huang¹,

Li²,

Anderson³

et al. 2013

Automatica

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In this paper, we study performance limits of sensor localization from a novel perspective. Specifically, we consider the Cramér-Rao Lower Bound (CRLB) in single-hop sensor localization using measurements from received signal strength (RSS), time of arrival (TOA) and bearing, respectively, but differently from the existing work, we statistically analyze the trace of the associated CRLB matrix (i.e. as a scalar metric for performance limits of sensor localization) by assuming anchor locations are random. By the Central Limit Theorems for U -statistics, we show that as the number of the anchors increases, this scalar metric is asymptotically normal in the RSS/bearing case, and converges to a random variable which is an affine transformation of a chi-square random variable of degree 2 in the TOA case. Moreover, we provide formulas quantitatively describing the relationship among the mean and standard deviation of the scalar metric, the number of the anchors, the parameters of communication channels, the noise statistics in measurements and the spatial distribution of the anchors. These formulas, though asymptotic in the number of the anchors, in many cases turn out to be remarkably accurate in predicting performance limits, even if the number is small. Simulations are carried out to confirm our results. Index TermsCramér-Rao lower bound, received signal strength (RSS), time of arrival (TOA), bearing, U -statistics.

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