A Hierarchical Signal-Space Partitioning Technique for Indoor Positioning with WLAN to Support Location-Awareness in Mobile Map Services

2012 ◽  
Vol 69 (2) ◽  
pp. 689-719 ◽  
Author(s):  
Mohammad H. Vahidnia ◽  
Mohammad R. Malek ◽  
Nazila Mohammadi ◽  
Ali A. Alesheikh
Keyword(s):  
Indoor Positioning ◽  
Location Awareness ◽  
Space Partitioning ◽  
Signal Space ◽  
Partitioning Technique

Indoor Positioning in WLAN Environment Based on Support Vector Regression and Space Partitioning

2011 ◽  
Vol 204-210 ◽  
pp. 1599-1602 ◽  
Author(s):  
Zhi An Deng ◽  
Yu Bin Xu ◽  
Di Wu
Keyword(s):  
Support Vector Regression ◽  
Wireless Local Area Network ◽  
Local Area Network ◽  
Physical Space ◽  
Mapping Function ◽  
Local Area ◽  
Indoor Positioning ◽  
Support Vector ◽  
Area Network ◽  
Space Partitioning

Indoor positioning system in wireless local area network (WLAN) has been a subject of intensive research due to its cost effectiveness and reasonable positioning accuracy. A new WLAN indoor positioning algorithm based on support vector regression (SVR) and space partitioning is proposed. The whole positioning environment is partitioned into several subspaces by combining k-means clustering method and binary support vector classifiers (SVC). Then the mapping function between received signal strength (RSS) and the physical space is established by SVR machine for each subspace. Subspace with much smaller physical range means more compact input feature space and leads to the enhancement of generalization capability for each SVR machine. The proposed algorithm and other well-known positioning algorithms are carried and compared in a real WLAN environment. Experimental results show that the proposed algorithm achieves 14.6 percent (0.31m) improvement than the single SVR algorithm in the sense of mean positioning error.

Dynamic Collision Detection Using Space Partitioning

1990 ◽  
Author(s):  
M. A. Ganter ◽  
B. P. Isarankura
Keyword(s):  
Collision Detection ◽  
Computation Time ◽  
Local Region ◽  
Detection Time ◽  
Space Partitioning ◽  
Worst Case ◽  
Simulated Environment ◽  
Time Required ◽  
Six Degree Of Freedom ◽  
Partitioning Technique

Abstract A technique termed space partitioning is employed which dramatically reduces the computation time required to detect dynamic collision during computer simulation. The simulated environment is composed of two nonconvex polyhedra traversing two general six degree of freedom trajectories. This space partitioning technique reduces collision detection time by subdividing the space containing a given object into a set of linear partitions. Using these partitions, all testing can be confined to the local region of overlap between the two objects. Further, all entities contained in the partitions inside the region of overlap are ordered based on their respective minimums and maximums to further reduce testing. Experimental results indicate a worst-case collision detection time for two one thousand faced objects is approximately three seconds per trajectory step.

scholarly journals Indoor Location Tracking System Based on Android Application Using Bluetooth Low Energy Beacons for Ubiquitous Computing Environment

2020 ◽  
pp. 572-576
Author(s):  
Khamla NonAlinsavath ◽  
◽  
Lukito Edi Nugroho ◽  
Widyawan Widyawan ◽  
Kazuhiko Hamamoto
Keyword(s):  
Radio Frequency Identification ◽  
Tracking System ◽  
Signal Strength ◽  
Smartphone Application ◽  
Indoor Positioning ◽  
Low Energy ◽  
Positioning System ◽  
Location Awareness ◽  
Android Application ◽  
Bluetooth Low Energy

Indoor positioning and tracking systems have become enormous issue in location awareness computing due to its improvement of location detection and positioning identification. The locations are normally detected using position technologies such as Global Positioning System, radio frequency identification, Bluetooth Beacon, Wi-Fi fingerprinting, pedometer and so on. This research presents an indoor positioning system based on Bluetooth low energy 4.0 Beacons; we have implemented Bluetooth signal strength for tracking the specific location and detect the movement of user through Android application platform. Bluetooth low energy was addressed to be an experiment technique to set up into the real environment of interior the building. The signal strength of beacons is evaluated and measured the quality of accuracy as well as the improvement of provide raw data from Beacons to the system to get better performance of the direction map and precise distance from current location to desire’s positioning. A smartphone application detects the location-based Bluetooth signal strength accurately and can be achieved the destination by provided direction map and distance perfectly.

scholarly journals AN EXPERIMENTAL ANALYSIS OF SPATIAL INDEXING ALGORITHMS FOR REAL TIME SAFETY CRITICAL MAP APPLICATION

2021 ◽  
Vol V-4-2021 ◽  
pp. 41-48
Author(s):  
F. Çetin ◽  
M. O. Kulekci
Keyword(s):  
Data Structure ◽  
Real Time ◽  
Data Structures ◽  
Spatial Indexing ◽  
Space Partitioning ◽  
Worst Case ◽  
Real Time System ◽  
Safety Critical ◽  
Range Search ◽  
Partitioning Technique

Abstract. This paper presents a study that compares the three space partitioning and spatial indexing techniques, KD Tree, Quad KD Tree, and PR Tree. KD Tree is a data structure proposed by Bentley (Bentley and Friedman, 1979) that aims to cluster objects according to their spatial location. Quad KD Tree is a data structure proposed by Berezcky (Bereczky et al., 2014) that aims to partition objects using heuristic methods. Unlike Bereczky’s partitioning technique, a new partitioning technique is presented based on dividing objects according to space-driven, in the context of this study. PR Tree is a data structure proposed by Arge (Arge et al., 2008) that is an asymptotically optimal R-Tree variant, enables data-driven segmentation. This study mainly aimed to search and render big spatial data in real-time safety-critical avionics navigation map application. Such a real-time system needs to efficiently reach the required records inside a specific boundary. Performing range query during the runtime (such as finding the closest neighbors) is extremely important in performance. The most crucial purpose of these data structures is to reduce the number of comparisons to solve the range searching problem. With this study, the algorithms’ data structures are created and indexed, and worst-case analyses are made to cover the whole area to measure the range search performance. Also, these techniques’ performance is benchmarked according to elapsed time and memory usage. As a result of these experimental studies, Quad KD Tree outperformed in range search analysis over the other techniques, especially when the data set is massive and consists of different geometry types.

scholarly journals ThingsLocate: A ThingSpeak-Based Indoor Positioning Platform for Academic Research on Location-Aware Internet of Things

Technologies ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 50 ◽  
Author(s):  
Luca De De Nardis ◽  
Giuseppe Caso ◽  
Maria Gabriella Di Benedetto
Keyword(s):  
Internet Of Things ◽  
Academic Research ◽  
Cloud Service ◽  
Indoor Positioning ◽  
Location Awareness ◽  
Graduate Studies ◽  
Iot Devices ◽  
Location Channel ◽  
User Friendly ◽  
The Impact

Seamless location awareness is considered a cornerstone in the successful deployment of the Internet of Things (IoT). Support for IoT devices in indoor positioning platforms and, vice versa, availability of indoor positioning functions in IoT platforms, are however still in their early stages, posing a significant challenge in the study and research of the interaction of indoor positioning and IoT. This paper proposes a new indoor positioning platform, called ThingsLocate, that fills this gap by building upon the popular and flexible ThingSpeak cloud service for IoT, leveraging its data input and data processing capabilities and, most importantly, its native support for cloud execution of Matlab code. ThingsLocate provides a flexible, user-friendly WiFi fingerprinting indoor positioning service for IoT devices, based on Received Signal Strength Indicator (RSSI) information. The key components of ThingsLocate are introduced and described: RSSI channels used by IoT devices to provide WiFi RSSI data, an Analysis app estimating the position of the device, and a Location channel to publish such estimate. A proof-of-concept implementation of ThingsLocate is then introduced, and used to show the possibilities offered by the platform in the context of graduate studies and academic research on indoor positioning for IoT. Results of an experiment enabled by ThingsLocate with limited setup and no coding effort are presented, focusing on the impact of using different devices and different positioning algorithms on positioning accuracy.

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