Localization Systems for Wireless Sensor Networks

Boukerche, Azzedine; Oliveira, Horácio A. B. F.; Nakamura, Eduardo F.; Loureiro, Antônio A. F.

doi:10.1002/9780470396360.ch11

Cited by 151 publications

(202 citation statements)

References 31 publications

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“…Instrum. 87, 041502 (2016) for WSNs, compared with the low-cost but less accurate RSS method (Boukerche et al, 2007).…”

Section: B Wireless Sensor Networkmentioning

confidence: 99%

“…The design of the sensor network determines the applicability of localization algorithms and thus has been investigated for years (Sohrabi et al, 2000;Langendoen and Reijers, 2003;Römer and Mattern, 2004;AlKaraki and Kamal, 2004;and Wang, 2008). To improve source localization, different types of signals (e.g., acoustic sound, ultrasound, or radio) transmitted by sources may result in different estimation errors (Boukerche et al, 2007). Whitehouse (2002) tested an acoustic source tracking system and observed source location estimation errors of approximately 23 cm.…”

Section: B Wireless Sensor Networkmentioning

confidence: 99%

“…If TOA or TDOA approaches are used to estimate transmitter location, at least three sensors are required for two-dimensional (2D) position estimation and four are required for three-dimensional (3D) position estimation, according to the principles of trilateration. When more sensors are available, an over-determined system (ODS) may be solved by using the multilateration method (Boukerche et al, 2007). In most cases, a basic assumption is that there is line-of-sight (LOS) between a transmitter and receiver and the transmitter's signal follows a direct line-ofsight path to a receiver.…”

Section: Introductionmentioning

confidence: 99%

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Contributed Review: Source-localization algorithms and applications using time of arrival and time difference of arrival measurements

Deng

Rauchenstein

et al. 2016

Review of Scientific Instruments

127

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Locating the position of fixed or mobile sources (i.e., transmitters) based on measurements obtained from sensors (i.e., receivers) is an important research area that is attracting much interest. In this paper, we review several representative localization algorithms that use time of arrivals (TOAs) and time difference of arrivals (TDOAs) to achieve high signal source position estimation accuracy when a transmitter is in the line-of-sight of a receiver. Circular (TOA) and hyperbolic (TDOA) position estimation approaches both use nonlinear equations that relate the known locations of receivers and unknown locations of transmitters. Estimation of the location of transmitters using the standard nonlinear equations may not be very accurate because of receiver location errors, receiver measurement errors, and computational efficiency challenges that result in high computational burdens. Least squares and maximum likelihood based algorithms have become the most popular computational approaches to transmitter location estimation. In this paper, we summarize the computational characteristics and position estimation accuracies of various positioning algorithms. By improving methods for estimating the time-of-arrival of transmissions at receivers and transmitter location estimation algorithms, transmitter location estimation may be applied across a range of applications

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“…Instrum. 87, 041502 (2016) for WSNs, compared with the low-cost but less accurate RSS method (Boukerche et al, 2007).…”

Section: B Wireless Sensor Networkmentioning

confidence: 99%

Section: B Wireless Sensor Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Contributed Review: Source-localization algorithms and applications using time of arrival and time difference of arrival measurements

Deng

Rauchenstein

et al. 2016

Review of Scientific Instruments

127

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“…Surveys on handover literature can be found in [32] and [33], with a survey specifically on the used algorithms in [114][115][116]. The handover methods proposed by the literature can be organized into the four categories: hierarchical, fast, proxy, and predictive; all of which are focused on resolving one or more of the problems in the mobile IP handover.…”

Section: Chapter 2 Related Workmentioning

confidence: 99%

Predictive mobile IP handover for vehicular networks

Magnano

Xin

Boukerche

2015

2015 IEEE 40th Conference on Local Computer Networks (LCN)

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Vehicular networks are an emerging technology that offer potential for providing a variety of new services. However, extending vehicular networks to include IP connections is still problematic, due in part to the incompatibility of mobile IP handovers with the increased mobility of vehicles. The handover process, consisting of discovery, registration, and packet forwarding, has a large overhead and disrupts connectivity. With increased handover frequency and smaller access point dwell times in vehicular networks, the handover causes a large degradation in performance. This thesis proposes a predictive handover solution, us-

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“…In recent years, location estimation in wireless sensor networks (WSN) has raised a lot of interest from researchers [1], [2], [3]. In that sense, cooperative location algorithms has meant an improvement in terms of estimation accuracy.…”

Section: Introductionmentioning

confidence: 99%

Real-time path loss and node selection for cooperative localization in wireless sensor networks

Bel

Vicario

2010

2010 IEEE 21st International Symposium on Personal, Indoor and Mobile Radio Communications Workshops

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Abstract-RSS-based localization is considered a lowcomplexity algorithm with respect to other range techniques such as TOA or AOA. The accuracy of RSS methods depends on the suitability of the propagation models used for the actual propagation conditions. In indoor environments, in particular, it is very difficult to obtain a good propagation model. For that reason, we present a cooperative location algorithm that dynamically estimates the path loss exponent by using RSS measurements. Since the energy consumption is a key point in sensor networks, we propose a node selection in order to avoid the use of all the nodes in the network for positioning purposes. Hence, we derive a practical solution tailored to the strict requirements of sensor networks in terms of complexity, size and cost. We present results based on both computer simulations and experimentation with the Crossbow IRIS motes showing that the proposed scheme offers a good performance in terms of position accuracy.

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Localization Systems for Wireless Sensor Networks

Cited by 151 publications

References 31 publications

Contributed Review: Source-localization algorithms and applications using time of arrival and time difference of arrival measurements

Contributed Review: Source-localization algorithms and applications using time of arrival and time difference of arrival measurements

Predictive mobile IP handover for vehicular networks

Real-time path loss and node selection for cooperative localization in wireless sensor networks

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