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

Abstract

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and high accuracy, however they have many drawbacks: creating radio maps is time consuming, the radio maps will become outdated with any environmental change, different mobile devices read the received signal strength (RSS) differently, and peoples’ presence in LOS between access points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.

Accurate Indoor-Positioning Model Based on People Effect and Ray-Tracing Propagation

“…This process was done by matching the initial RSSI vector measured by the user at a specific position with the RSSI vectors in the RM database [19]. The KNN algorithm was adopted as the fingerprint matching algorithm because of its acceptable accuracy and its relevance with the limitations of mobile devices [48].…”

“…The KNN algorithm requires two parameters: (1) "k", which is the number of the considered nearest neighbors, and (2) the distance function. The distance function was set to the Euclidean distance, while "k" was set to a value of 3 to get the best accuracy [48]. The output of this process is an initial location estimation, which is still less accurate because it has not incorporated the effects of people around the user.…”

Detecting and Correcting for Human Obstacles in BLE Trilateration Using Artificial Intelligence

“…All of these models ( [9][10][11][12][13][14][15]) are computationally efficient and easy to implement, though less able to tackle the sudden changes in the real environment. Unlike analytical or empirical models, Artificial Intelligence (AI) networks can learn from observed data [16][17][18] in real environments and identify patterns that might not be captured by rule-based thresholding techniques. AI is particularly useful when the correlation between the input and output values of a system is ambiguous or subject to noise [19].…”

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

“…For a big scale outdoor positioning GPS is the optimal choice but for indoor positioning many methods could be used such as WiFi, as in Alshami et al (2017) which used fingerprinting to estimate positions or WSN which this paper is implementing using the work flow shown in Fig. 1.…”

“…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.…”