Dynamic WLAN Fingerprinting RadioMap for Adapted Indoor Positioning Model

The Global Positioning System demonstrates the significance of Location Based Services but it cannot be used indoors due to the lack of line of sight between satellites and receivers. Indoor Positioning Systems are needed to provide indoor Location Based Services. Wireless LAN fingerprints are one of the best choices for Indoor Positioning Systems because of their low cost, and high accuracy, however they have many drawbacks: creating radio maps is time consuming, the radio maps will become outdated with any environmental change, different mobile devices read the received signal strength (RSS) differently, and peoples’ presence in LOS between access points and mobile device affects the RSS. This research proposes a new Adaptive Indoor Positioning System model (called DIPS) based on: a dynamic radio map generator, RSS certainty technique and peoples’ presence effect integration for dynamic and multi-floor environments. Dynamic in our context refers to the effects of people and device heterogeneity. DIPS can achieve 98% and 92% positioning accuracy for floor and room positioning, and it achieves 1.2 m for point positioning error. RSS certainty enhanced the positioning accuracy for floor and room for different mobile devices by 11% and 9%. Then by considering the peoples’ presence effect, the error is reduced by 0.2 m. In comparison with other works, DIPS achieves better positioning without extra devices.

show abstract

“…The model of the proposed system is illustrated in Figure 1 . It combines and extends our previous works described in [ 47 , 48 , 49 , 50 , 51 ].…”

Section: Methodssupporting

confidence: 61%

Adaptive Indoor Positioning Model Based on WLAN-Fingerprinting for Dynamic and Multi-Floor Environments

Alshami

Ahmad

Sahibuddin

et al. 2017

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…Figure 1. Work process in WLAN finger print [28] The matching process utilizes positioning algorithm that includes deterministic [9] and probabilistic methods [29]. One of the most popular methods is k Nearest Neighbour (kNN) [30,33].…”

Section: Methodsmentioning

confidence: 99%

Fingerprint indoor positioning based on user orientations and minimum computation time

2019

Self Cite

View full text Add to dashboard Cite

Indoor Positioning System (IPS) has an important role in the field of Internet of Thing. IPS works based on many existing radio frequency technologies. One of the most popular methods is WLAN Fingerprint because this technology has been installed widely inside buildings and it provides a high level of accuracy. The performance is affected by people who hold mobile devices (user) and also people around the users. This research aimed to minimize the computation time of kNN searching process. The results showed that when the value of k in kNN was greater, the computation time increased, especially when using Cityblock and Minkowski distance function. The smallest average computation time was 2.14 ms, when using Cityblock. Then the computational time for Euclidean and Chebychev were relatively stable, i.e. 2.2 ms and 2.23 ms, respectively.

show abstract

“…Even though RSSI is less accurate but it is the most applicable to be implemented in real environment due to the implementation difficulties that time-related techniques require such as time synchronization between network nodes (Henniges, 2012). This paper uses RSSI for positioning in WSN which is using Zigbee technology and there are other approaches that use WiFi technology for indoor positioning as in Alshami et al (2014Alshami et al ( , 2017.…”

Section: Introductionmentioning

confidence: 99%

Modified weighted centroid algorithm for indoor and outdoor positioning using wireless sensors network

Alnahari¹

2018

Int. j. adv. appl. sci.

View full text Add to dashboard Cite

Positioning, either outdoor or indoor, has been one of the most attractive fields for researchers to study and they apply different approaches in order to allocate a moving object. Some positioning approaches use Received Signal Strength (RSSI) in order to determine the location such as Fingerprinting, and Weighted Centroid Localization (WCL). On the other hand, some approaches use signal travelling time such as Time of Arrival (TOA) and Time Difference of Arrival (TDOA). However, the accuracy is still low due to the effect of RSSI to moving objects. This paper shows how to allocate a blind node in a wireless sensor network (WSN) using the proposed WCL approach and shows how the enhancement of the algorithm made better accuracy with mean square error, MSE, of 64.5 cm for indoor positioning and 123.0 cm for outdoor positioning. One of previous WCL approaches reached accuracy with an error of as low as 15.0 cm but in simulation, and others used different approaches with MSE of 80.0 cm and another as high as 2.6 m.

show abstract

Dynamic WLAN Fingerprinting RadioMap for Adapted Indoor Positioning Model

Cited by 6 publications

References 31 publications

Adaptive Indoor Positioning Model Based on WLAN-Fingerprinting for Dynamic and Multi-Floor Environments

Adaptive Indoor Positioning Model Based on WLAN-Fingerprinting for Dynamic and Multi-Floor Environments

Fingerprint indoor positioning based on user orientations and minimum computation time

Modified weighted centroid algorithm for indoor and outdoor positioning using wireless sensors network

Contact Info

Product

Resources

About