Harmonization of position providers

Bekkelien, A.; Deriaz, Michel

doi:10.1109/ipin.2012.6418855

Cited by 14 publications

(16 citation statements)

References 8 publications

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“…However, the studies of Feldmann, Kyamakya, Zapater, and Lue (2003), Bekkelien (2012), Rida et al (2015) and Kriz et al (2016) showed that position computation also occurs in the mobile device, thus improving user privacy. In this case, the Bluetooth sensors communicate with the server that stores the position information of the sensors, but the server does not compute position.…”

Section: Bluetooth-based Positioning Systemmentioning

confidence: 99%

“…In this case, the Bluetooth sensors communicate with the server that stores the position information of the sensors, but the server does not compute position. The application in the mobile device requests the position information from the Bluetooth sensors before computing the position of the device with the information (Rida et al, 2015;Kriz et al, 2016;Bekkelien, 2012). In addition, the sensors are placed within the environment in such a way as to make their operation optimal.…”

Section: Bluetooth-based Positioning Systemmentioning

confidence: 99%

“…However, device discovery is inherently a slow process in the Bluetooth-based positioning methods, thus affecting real-time positioning, accuracy and performance, thereby making Bluetooth undesirable for tracking and navigation purposes (Feldmann et al, 2003;Bekkelien, 2012). In addition, due to the amount of infrastructures required, the cost of the system rises, with the use of the server raising privacy concerns (Deak et al, 2012).…”

Section: Bluetooth-based Positioning Systemmentioning

confidence: 99%

See 2 more Smart Citations

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

Sakpere

Oshin

Mlitwa

2017

SACJ

150

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: Bluetooth-based Positioning Systemmentioning

confidence: 99%

Section: Bluetooth-based Positioning Systemmentioning

confidence: 99%

Section: Bluetooth-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

150

129

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“…The second category of collision detection approaches is based on global or relative positioning, i.e., the coordinates of UAVs and obstacles within the airspace. These positions are acquired either by global continuous positioning systems such as the Global Positioning System (GPS) and Beidou or by obtaining exact indoor location information with Wi-Fi [14] or beacons (Bluetooth Low Energy, BLE) [15]. Fiorini and Shiller presented a computationally efficient heuristic approach applicable to the online planning of UAVs [16].…”

Section: Introductionmentioning

confidence: 99%

Collision Detection for UAVs Based on GeoSOT-3D Grids

Zhai

Tong

Miao

et al. 2019

IJGI

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The increasing number of unmanned aerial vehicles (UAVs) has led to challenges related to solving the collision problem to ensure air traffic safety. The traditional approaches employed for collision detection suffer from two main drawbacks: first, the computational burden of a pairwise calculation increases exponentially with an increasing number of spatial entities; second, existing grid-based approaches are unsuitable for complicated scenarios with a large number of objects moving at high speeds. In the proposed model, we first identified UAVs and other spatial objects with GeoSOT-3D grids. Second, the nonrelational spatial database was initialized with a multitable strategy, and spatiotemporal data were inserted with the GeoSOT-3D grid codes as the primary key. Third, the collision detection procedure was transformed from a pairwise calculation to a multilevel query. Four simulation experiments were conducted to verify the feasibility and efficiency of the proposed collision detection model for UAVs in different environments. The results also indicated that 64 m GeoSOT-3D grids are the most suitable basic grid size, and the reduction in the time consumption compared with traditional methods reached approximately 50–80% in different scenarios.

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“…However, it has limitations in indoor environments [4]. Alternative solutions consist of using other types of signals, that are available in closed environments, like ultra wide band, WiFi, zigbee, bluetooth,etc [5], [6], [7], [8]. One of the advantages of WiFi signals over the others is that one can rely only on the Access Points (APs) present inside the building, with no additional hardware.…”

Section: Introductionmentioning

confidence: 99%

Zoning-based localization in indoor sensor networks using belief functions theory

Alshamaa

Mourad-Chehade

Honeiné

2016

2016 IEEE 17th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

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Localization is an essential issue in wireless sensor networks to process the information retrieved by sensor nodes. This paper presents an indoor zoning-based localization technique that works efficiently in real environments. The targeted area is composed of several zones, the objective being to find the zone where the mobile node is instantly located. The proposed approach collects first strengths of received WiFi signals from neighboring access points and builds a fingerprints database. It then uses belief functions theory to combine all measured data and define an evidence framework, to be used afterwards for estimating the most probable node's zone. Real experiments demonstrate the effectiveness of this approach and its competence compared to state-of-the-art methods.

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Harmonization of position providers

Cited by 14 publications

References 8 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

Collision Detection for UAVs Based on GeoSOT-3D Grids

Zoning-based localization in indoor sensor networks using belief functions theory

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