An Effective PSO-Based Node Localization Scheme for Wireless Sensor Networks

Chuang, Po‐Jen; Wu, Cheng-Pei

doi:10.1109/pdcat.2008.73

Cited by 61 publications

(40 citation statements)

References 10 publications

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“…The simulation results presented by Sayadnavard and others highlight that the ICA-based approach considerably outperforms the APS. Moreover, it calculates estimates characterized by a higher accuracy than the ones obtained by the PSO-based localization scheme using the RSSI ranging technique (Chuang and Wu, 2008), but with more computational time.…”

Section: Absolute Location Calculationmentioning

confidence: 99%

Localization in wireless sensor networks: Classification and evaluation of techniques

Niewiadomska-Szynkiewicz¹

2012

International Journal of Applied Mathematics and Computer Science

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Recent advances in technology have enabled the development of low cost, low power and multi functional wireless sensing devices. These devices are networked through setting up a Wireless Sensor Network (WSN). Sensors that form a WSN are expected to be remotely deployed in large numbers and to self-organize to perform distributed sensing and acting tasks. WSNs are growing rapidly in both size and complexity, and it is becoming increasingly difficult to develop and investigate such large and complex systems. In this paper we provide a brief introduction to WSN applications, i.e., properties, limitations and basic issues related to WSN design and development. We focus on an important aspect of the design: accurate localization of devices that form the network. The paper presents an overview of localization strategies and attempts to classify different techniques. A set of properties by which localization systems are evaluated are examined. We then describe a number of existing localization systems, and discuss the results of performance evaluation of some of them through simulation and experiments using a testbed implementation.

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Section: Absolute Location Calculationmentioning

confidence: 99%

Localization in wireless sensor networks: Classification and evaluation of techniques

Niewiadomska-Szynkiewicz¹

2012

International Journal of Applied Mathematics and Computer Science

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“…2, all 1 B lies to other nodes about its location, e.g., 1 B  , it would be difficult for 2 B to identify the false location information unless it is able to measure both signal strength and signal angle. If 2 B takes into account the evaluations on 1 B from the other beacon nodes, 2 B can easily detect that 1 B is not a trustworthy beacon node and can consequently reduce the trust value for 1 B . …”

Section: Behavior Evaluationmentioning

confidence: 99%

“…Suppose the number of trustworthy beacon nodes around an unknown sensor node is n with coordinates 1 6), resulting from subtracting the last equation from each of the first 1 n  equations.…”

Section: The Sensor Localization Schemementioning

confidence: 99%

“…Location information is required for providing many services such as network topology, geographical coverage of networks, routing and other location-based services. Some novel and generic sensor localization algorithms have been proposed among which range-based localization algorithms 1,2 and rangefree localization algorithms 3,4 are the most recognized methods. Meanwhile, computational intelligence has been applied to solving various challenging problems in WSNs in recent years including sensor localization [5][6][7][8] .…”

Section: Introductionmentioning

confidence: 99%

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Secure Sensor Localization in Wireless Sensor Networks based on Neural Network

Zhang¹,

He²,

Zhang³

2012

IJCIS

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Localization of sensor nodes in wireless sensor networks (WSNs) is very important since it associates spatial context with the data collected by sensor nodes and used in applications. Rapid development of wireless sensor technologies and wide applications of wireless sensor networks have also made security in sensor localization a primary concern as well as a great challenge. Without adequate security measures, the performance of sensor localization, e.g., the accuracy of localization results, cannot be ensured in hostile environments. In this paper, we propose a trust-based secure sensor localization scheme (TSLS) for WSNs following the theory of neural network (NN). The proposed TSLS scheme can ensure that unknown sensor nodes will get credible information to perform localization through the evaluation of beacon nodes in a WSN. The evaluation model is comprised of the evaluation of both the identity and the behavior of beacon nodes as well as a filtering mechanism to deal with the slander behaviors between beacon nodes. Simulation results show that the proposed TSLS scheme can improve the accuracy of sensor localization in hostile environments for both static and dynamic WSNs.

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“…Among these methods, PSO is adopted in this study because PSO is suitable for a wide-area search on continuous variables (three-dimensional coordinate) with lower complexity. As the conventional studies, the PSO localization with RSS signals has been proposed in [11,12]. However, the estimation error is relatively high because RSS signals are utilized.…”

Section: Introductionmentioning

confidence: 99%

Low-complexity indoor UWB localization scheme using particle swarm optimization

Okamoto

Horiba

Nakashima

et al. 2015

NOLTA

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Abstract:We propose a low-complexity indoor localization scheme using a hybirid of particle swarm optimization (PSO) and Newton-Raphson (NR) search. The signal of global positioning system (GPS) can be utilized outdoors only, and other schemes are needed for indoor localization. A triangulation-based location estimation using ultra-wide band (UWB) signals between more than three reference terminals and the target node is widely used for centimeter-order localization. In particular, a time of arrival (TOA)-based least square (LS) estimation is popular because the balanced performance in terms of calculation complexity and the accuracy is obtained. However, when the height of reference terminals and the target node is close, the three-dimensional LS-based estimation tends to fall into a local-minimum solution and it needs an accurate initial value of search to keep the estimation performance, resulting in the calculation complexity increase. Therefore, in this paper, we adopt a particle swarm optimization (PSO) method which effectively searches in wide-area space and propose an LS-based localization scheme using the combination of PSO and NR method achieving lower calculation complexity. The improved performances are shown with comparing to conventional search schemes by computer simulations.

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An Effective PSO-Based Node Localization Scheme for Wireless Sensor Networks

Cited by 61 publications

References 10 publications

Localization in wireless sensor networks: Classification and evaluation of techniques

Localization in wireless sensor networks: Classification and evaluation of techniques

Secure Sensor Localization in Wireless Sensor Networks based on Neural Network

Low-complexity indoor UWB localization scheme using particle swarm optimization

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