A Survey of Localization in Wireless Sensor Network

Cheng, Long; Wu, Chengdong; Zhang, Yunzhou; Wu, Hao; Li, Mengxin; Maple, Carsten

doi:10.1155/2012/962523

Cited by 231 publications

(156 citation statements)

References 84 publications

(78 reference statements)

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“…WSN is a group of collaborative sensors with communication infrastructures for monitoring and recording of physical or environmental conditions, such as temperature, sound, pressure, humidity, light and wind, for onward data organisation and transmission to a central location or server through a WLAN (Mautz, 2012;Akyildiz, Su, Sankarasubramaniam, & Cayirci, 2002). A WSN consists of few or several sensor nodes that are small, lightweight and portable (Yick, Mukherjee, & Ghosal, 2008;L. Cheng et al, 2012).…”

Section: Wsn-based Positioning Systemmentioning

confidence: 99%

“…Wang, Bischoff, Laur, & Paul, 2009). The sensor node, as well as the WSN that is used mainly for monitoring and tracking purposes, has its application prominent in consumer and industrial areas, which include health, traffic, automation, smart home and positioning (Yick et al, 2008;L. Cheng et al, 2012;Potdar et al, 2009).…”

Section: Wsn-based Positioning Systemmentioning

confidence: 99%

“…WSN-based positioning systems can be basically implemented in two ways namely, range-based (distance-based) and range-free (connectivity-based) (L. Cheng et al, 2012;Zhong & He, 2009).…”

Section: Wsn-based Positioning Systemmentioning

confidence: 99%

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A State-of-the-Art Survey of Indoor Positioning and Navigation Systems and Technologies

Sakpere

Oshin

Mlitwa

2017

SACJ

149

129

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The research and use of positioning and navigation technologies outdoors has seen a steady and exponential growth. Based on this success, there have been attempts to implement these technologies indoors, leading to numerous studies. Most of the algorithms, techniques and technologies used have been implemented outdoors. However, how they fare indoors is different altogether. Thus, several technologies have been proposed and implemented to improve positioning and navigation indoors. Among them are Infrared (IR), Ultrasound, Audible Sound, Magnetic, Optical and Vision, Radio Frequency (RF), Visible Light, Pedestrian Dead Reckoning (PDR)/Inertial Navigation System (INS) and Hybrid. The RF technologies include Bluetooth, Ultra-wideband (UWB), Wireless Sensor Network (WSN), Wireless Local Area Network (WLAN), Radio-Frequency Identification (RFID) and Near Field Communication (NFC). In addition, positioning techniques applied in indoor positioning systems include the signal properties and positioning algorithms. The prevalent signal properties are Angle of Arrival (AOA), Time of Arrival (TOA), Time Difference of Arrival (TDOA) and Received Signal Strength Indication (RSSI), while the positioning algorithms are Triangulation, Trilateration, Proximity and Scene Analysis/ Fingerprinting. This paper presents a state-of-the-art survey of indoor positioning and navigation systems and technologies, and their use in various scenarios. It analyses distinct positioning technology metrics such as accuracy, complexity, cost, privacy, scalability and usability. This paper has profound implications for future studies of positioning and navigation.

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Section: Wsn-based Positioning Systemmentioning

confidence: 99%

Section: Wsn-based Positioning Systemmentioning

confidence: 99%

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A State-of-the-Art Survey of Indoor Positioning and Navigation Systems and Technologies

Sakpere

Oshin

Mlitwa

2017

SACJ

149

129

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“…Applying the multi-lateration algorithm [30], the location of the transmiter can be estimated using the location of the 8 receivers:…”

Section: Statistical Analysis Of the Localisation Errormentioning

confidence: 99%

An Adaptive Path Loss Channel Model for Wave Propagation in Multilayer Transmission Medium

Ramezani¹,

Blanes-Vidal²,

Nadimi³

2015

PIER

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Abstract-Advances in micro robots in non-invasive medicine have enabled physicians to perform diagnostic and therapeutic procedures with higher resolution and lower risk than before. However, navigation and precise localisation of such micro robots inside human body still remains a challenge. This is mostly due to the 1) lack of precise communication channel models, 2) inhomogeneity of the propagation medium and 3) non-geometric boundaries of the tissues morphometric parameters. In this study, we derive novel intra-body path loss channel models for wave propagation in wireless capsule endoscopy, i.e., propagation through the gastrointestinal tract and the abdominal wall. We formulate an adaptive attenuation parameter as a function of permittivity, conductivity and the thickness of various layers between the transmitter and the receiver. The standard deviation of modelling error of the path loss using our adaptive channel model is smaller than 50% of that of existing channel models. We further analyse the sensitivity of the path loss model to the variations of thickness of different abdominal wall layers. We finally show that the thickness of the fat layer has the greatest influence on the total attenuation parameter of the path loss model and therefore, we modify our adaptive model accordingly.

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“…The LBS can largely be classified into outdoor and indoor environments. While the outdoor environment has made considerable progress using the global positioning system (GPS), which can identify locations using various satellite signals, the indoor environment [26]. Although the method that measures the range using the time during the propagation of radio waves such as UWB and CSS has a relatively accurate the LQI is not provided in Wi-Fi, which has recently been used as the most general-purpose communication network.…”

Section: Introductionmentioning

confidence: 99%

Improved Adaptive Smoothing Filter for Indoor Localization Using RSSI

Kim¹,

Seong²,

Ha³

et al. 2015

Journal of the Korean Society of Marine Engineering

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Abstract:In the indoor location estimation system, which has recently been actively studied, the received signal strength indicator contains a high level of noise when measuring the signal strength in the range between two nodes consisting of a receiver and a transceiver. To minimize the noise level, this paper proposes an improved adaptive smoothing filter that provides different exponential weights to the current value and previous averaged one of the data that were obtained from the nodes, because the characteristic signal attenuation of the received signal strength indicator generally has a log distribution. The proposed method can effectively decrease the noise level by using a feedback filter that can provide different weights according to the noise level of the obtained data and thus increase the accuracy in the distance and location without an additional filter such as the link quality indicator, which can verify the communication quality state to decrease the range errors in the indoor location recognition using ZigBee based on IEEE 802.15.4. For verifying the performance of the proposed improved adaptive smoothing filter, actual experiments are conducted in three indoor locations of different spatial sections. From the experimental results, it is verified that the proposed technique is superior to other techniques in range measurement.

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A Survey of Localization in Wireless Sensor Network

Cited by 231 publications

References 84 publications

A State-of-the-Art Survey of Indoor Positioning and Navigation Systems and Technologies

A State-of-the-Art Survey of Indoor Positioning and Navigation Systems and Technologies

An Adaptive Path Loss Channel Model for Wave Propagation in Multilayer Transmission Medium

Improved Adaptive Smoothing Filter for Indoor Localization Using RSSI

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