Swarm intelligent approaches to auto-localization of nodes in static UWB networks

Monica, Stefania; Ferrari, Gianluigi

doi:10.1016/j.asoc.2014.07.025

Cited by 21 publications

(16 citation statements)

References 15 publications

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“…In this paper, we extend [12] by considering three dimensional node position estimation. In order to keep the derivation more tractable, we investigate Time Of Arrival (TOA) localization strategies.…”

Section: Introductionmentioning

confidence: 99%

“…Even if the TSML algorithm is particuarly interesting, as it can attain the Cramer-Rao lower bound [11], in [9] [10] the PSO algorithm is shown to outperform the TSML algorithm. In [12], the use of the PSO algorithm is investigated to estimate the positions, with the use of a few "beacons" and considering Time Difference Of Arrival (TDOA) approaches, of nodes laying on a plane. We remark that the use of the PSO algorithm for localization purposes is not novel [13].…”

Section: Introductionmentioning

confidence: 99%

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A Swarm Intelligence Approach to $$3$$D Distance-Based Indoor UWB Localization

Monica

Ferrari

2015

Applications of Evolutionary Computation

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Abstract. In this paper, we focus on the application of Ultra Wide Band (UWB) technology to the problem of locating static nodes in threedimensional indoor environments, assuming to know the positions of a few nodes, denoted as "beacons." The localization algorithms which are considered throughout the paper are based on the Time Of Arrival (TOA) of signals traveling between pairs of nodes. In particular, we propose to apply the Particle Swarm Optimization (PSO) algorithm to solve the localization problem and we compare its performance with that of the Two-Stage Maximum-Likelihood (TSML) algorithm. Simulation results show that the former allows achieving accurate position estimates even in scenarios where, because of ill-conditioning problems associated with the network topology, TSML fails.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Swarm Intelligence Approach to $$3$$D Distance-Based Indoor UWB Localization

Monica

Ferrari

2015

Applications of Evolutionary Computation

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“…In order to speed up the convergence, PPSO [37] sorts all particles such that

f (P_{i}) \leq f (P_{j})

, if

i \leq j

, and replaces the positions of particles

⌊ \frac{M}{2} ⌋ + 1

to M with positions close to

P_{1}

. The rule of replacement is:

\begin{matrix} P_{⌊ \frac{M}{2} ⌋ + k, j} = P_{1 j} + ρ_{k j} & j = 1, 2; & k = 1, 2, \dots, ⌈ \frac{M}{2} ⌉, \end{matrix}

where

ρ_{k j}

is a random number uniformly distributed in (−0.5,0.5).…”

Section: A Survey Of Pso-based Localization Algorithmsmentioning

confidence: 99%

Parameter Selection and Performance Comparison of Particle Swarm Optimization in Sensor Networks Localization

Cui

Shu

Song

et al. 2017

Sensors

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Localization is a key technology in wireless sensor networks. Faced with the challenges of the sensors’ memory, computational constraints, and limited energy, particle swarm optimization has been widely applied in the localization of wireless sensor networks, demonstrating better performance than other optimization methods. In particle swarm optimization-based localization algorithms, the variants and parameters should be chosen elaborately to achieve the best performance. However, there is a lack of guidance on how to choose these variants and parameters. Further, there is no comprehensive performance comparison among particle swarm optimization algorithms. The main contribution of this paper is three-fold. First, it surveys the popular particle swarm optimization variants and particle swarm optimization-based localization algorithms for wireless sensor networks. Secondly, it presents parameter selection of nine particle swarm optimization variants and six types of swarm topologies by extensive simulations. Thirdly, it comprehensively compares the performance of these algorithms. The results show that the particle swarm optimization with constriction coefficient using ring topology outperforms other variants and swarm topologies, and it performs better than the second-order cone programming algorithm.

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“…The PSO algorithm outlined previously is used to solve the localization problem formulated in (28) and, hence, to solve the localization problem described in (13). The same algorithm can be applied also when the system of equations (24), where averaged range estimatesr K i,j defined in (11) appear, is used.…”

Section: A Two-stage Maximum-likelihood Algorithmmentioning

confidence: 99%

“…In order to solve the minimization problem (28), thus finding estimates for the abscissa and the ordinate of the TN, we proposed to use the PSO algorithm [28]. The PSO algorithm was first introduced in [29] and it considers the set of potential solutions of an optimization problem as a swarm of S particles which move through a search space according to proper rules.…”

Section: A Two-stage Maximum-likelihood Algorithmmentioning

confidence: 99%

Experimental Results on Agent-Based Indoor Localization using WiFi Signaling

Monica¹,

Bergenti²

2018

ijacsa

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Abstract-This paper discusses experimental results on the possibility of accurately estimating the position of smart devices in known indoor environments using agent technology. Discussed localization approaches are based on WiFi signaling, which can be considered as an ubiquitous technology in the large majority of indoor environments. The use of WiFi signaling ensures that no specific infrastructures nor special on-board sensors are required to support localization. Localization is performed using range estimates from the fixed access points of the WiFi network, which are assumed to have known positions. The performance of two range-based localization algorithms are discussed. The first, called Two-Stage Maximum-Likelihood algorithm, is well-known in the literature, while the second is a recent optimization-based algorithm that uses particle swarm techniques. Results discussed in the last part of the paper show that a proper processing of WiFi-based range estimates allows obtaining accurate position estimates, especially if the optimization-based algorithm is used.

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Swarm intelligent approaches to auto-localization of nodes in static UWB networks

Cited by 21 publications

References 15 publications

A Swarm Intelligence Approach to $$3$$D Distance-Based Indoor UWB Localization

A Swarm Intelligence Approach to $$3$$D Distance-Based Indoor UWB Localization

Parameter Selection and Performance Comparison of Particle Swarm Optimization in Sensor Networks Localization

Experimental Results on Agent-Based Indoor Localization using WiFi Signaling

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