A Wireless Sensor Network for RF-Based Indoor Localization

Kaseva, Ville; Kohvakka, Mikko; Kuorilehto, Mauri; Hännikäinen, Marko; Hämäläinen, Timo D.

doi:10.1155/2008/731835

Cited by 21 publications

(6 citation statements)

References 58 publications

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“…Our previous research (Kohvakka, Suhonen, Hannikainen & Hamalainen 2006) presents a transmission power based path loss metering method, using only small amount of varying power level beacon transmissions to remove the need for RSSI functionality. Our work presented in (Kaseva et al 2008) extends this method to node positioning.…”

Section: Rf-based Positioningmentioning

confidence: 93%

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Positioning in Synchronized Ultra Low-Power Wireless Sensor Networks

Kaseva¹,

Hämäläinen²,

Hännikäinen

2011

jgps

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Wireless Sensor Networks (WSNs) consist of densely deployed, independent, and collaborating low cost sensor nodes. The nodes are highly resource-constrained in terms of energy, processing, and data storage capacity. Thus, the protocols used in WSNs must be highly energy-efficient. WSN communication protocols achieving the lowest power consumption minimize radio usage by accurately synchronizing transmissions and receptions with their neighbors. In this paper, we show how network signaling frames of state-of-the-art synchronized communication protocols for low-power WSNs supporting mobile nodes can be used for positioning. We derive mathematical models for node power consumption analysis. Both centralized and distributed positioning architectures are modeled. The models provide a tool for estimating what kind of network lifetimes can be expected when average positioned node speed, the amount of anchor nodes required by the location estimation algorithm, and the location refresh rate required by the application are known. The presented analysis results are based on two kinds of node hardware: with and without Received Signal Strength Indicator (RSSI). The results show that the positioning parameters and used hardware have significant impact on node power consumption and network lifetime. In the presented results, the network lifetime ranges from over 10 years to 2 months with different positioning requirements and hardware.

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Section: Rf-based Positioningmentioning

confidence: 93%

“…WSNs form a potential technology for ubiquitous positioning due to their autonomous nature, low power consumption, and small size factor (Kaseva, Kohvakka, Kuorilehto, Hannikainen & Hamalainen 2008). The problem of positioning includes determining the physical coordinates or the area of a given node.…”

Section: Introductionmentioning

confidence: 99%

Positioning in Synchronized Ultra Low-Power Wireless Sensor Networks

Kaseva¹,

Hämäläinen²,

Hännikäinen

2011

jgps

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“…The special issue [20] brings together contributions from signal processing, communications and new algorithm design methodologies of cooperative localization systems. An application of wireless sensor networks for RF-based indoor localization is presented in [21]. In the special issue [22] the state of the art and emerging distributed signal processing techniques in wireless sensor networks, are presented, estimation being one of them.…”

Section: Related Workmentioning

confidence: 99%

Efficiency improvement in multi-sensor wireless network based estimation algorithms for distributed parameter systems with application at the heat transfer

Voloşencu

Curiac

2013

EURASIP J. Adv. Signal Process.

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This paper gives a technical solution to improve the efficiency in multi-sensor wireless network based estimation for distributed parameter systems. A complex structure based on some estimation algorithms, with regression and autoregression, implemented using linear estimators, neural estimators and ANFIS estimators, is developed for this purpose. The three kinds of estimators are working with precision on different parts of the phenomenon characteristic. A comparative study of three methods -linear and nonlinear based on neural networks and adaptive neuro-fuzzy inference system -to implement these algorithms is made. The intelligent wireless sensor networks are taken in consideration as an efficient tool for measurement, data acquisition and communication. They are seen as a "distributed sensor", placed in the desired positions in the measuring field. The algorithms are based on regression using values from adjacent and also on auto-regression using past values from the same sensor. A modelling and simulation for a case study is presented. The quality of estimation is validated using a quadratic criterion. A practical implementation is made using virtual instrumentation. Applications of this complex estimation system are in fault detection and diagnosis of distributed parameter systems and discovery of malicious nodes in wireless sensor networks.

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“…The strength of ‗cell-ID' techniques is their fairly simple implementation and modest HW requirements. Nevertheless, only a very coarsegrained location can be estimated, requiring a very dense grid of fixed nodes in order to reach small granularities [12].…”

Section: Related Workmentioning

confidence: 99%

A Bayesian Strategy to Enhance the Performance of Indoor Localization Systems

Iglesias

Bernardos

Casar

2011

Advances in Intelligent and Soft Computing

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Abstract. This work describes the probabilistic modelling of a Bayesian-based mechanism to improve location estimates of an already deployed location system by fusing its outputs with low-cost binary sensors. This mechanism takes advantage of the localization capabilities of different technologies usually present in smart environments deployments. The performance of the proposed algorithm over a real sensor deployment is evaluated using simulated and real experimental data.

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A Wireless Sensor Network for RF-Based Indoor Localization

Cited by 21 publications

References 58 publications

Positioning in Synchronized Ultra Low-Power Wireless Sensor Networks

Positioning in Synchronized Ultra Low-Power Wireless Sensor Networks

Efficiency improvement in multi-sensor wireless network based estimation algorithms for distributed parameter systems with application at the heat transfer

A Bayesian Strategy to Enhance the Performance of Indoor Localization Systems

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