Abstract

This study provides a new Crowdsourcing-based approach to identify the most crowded places in an indoor environment. The Crowdsourcing Indoor Localization system (CSI) has been one of the most used techniques in location-based applications. However, many applications suffer from the inability to locate the most crowded locations for various purposes such as advertising. These applications usually need to perform a survey before identifying target places, which require additional cost and time consuming. For example, Access Points (APs) installation can rely on an automated system to identify the best places where these APs should be placed without the need to use primitive ways to determine the best locations. In this work, we present a new approach for Wi-Fi designers and advertising companies to recognize the proper positions for placing APs and advertisement activities in indoor buildings. The recorded data of the accelerometer sensors are analyzed and processed to detect user’s steps and thereby predict the most crowded places in a building. Our experiments show promising results in terms of the most widely used metrics in the subject as the accuracy for detecting users’ steps reaches 95.8% and the accuracy for detecting the crowded places is 90.4%.

Highlights

  • The extensive utilization of Smartphones and wireless network have magnified the importance of Indoor Positioning Service (IPS)

  • The considerable manual cost, time-consuming, labor-intensive and vulnerable to environmental dynamics are the main drawbacks of fingerprintingbased methods; which can be solved by Crowdsourcing based approaches

  • Compared to the previous techniques, when we reassess our approach, we find that our method does not require any extra hardware or software. it is only based on the built-in sensors in the Smartphones

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Summary

INTRODUCTION

The extensive utilization of Smartphones and wireless network have magnified the importance of Indoor Positioning Service (IPS). In any indoor environment, there are uncrowded places; it would be helpful to have an approach that recognizes crowded areas for installing Wi-Fi access points to guarantee the best distribution of the network [9]. We proposed a new approach based on Smartphones and Crowdsourcing technique to automatically detect the best places where the access points must be placed inside the target building. Based on this base, our proposed approach collects www.ijacsa.thesai.org (IJACSA) International Journal of Advanced Computer Science and Applications, Vol 10, No 4, 2019 acceleration values of the building’s visitors, which depend on their movements in the target building.

RELATED WORK
Indoor Positioning System Techniques
Motions Estimation
Pre-processing Stage
Step Detection Stage
Crowded Algorithm Stage
EXPERIMENTS AND RESULTS
Crowdsourcing Results
Pre-processing Results
Step-Detection Results
Crowd Algorithm Results
ERROR ANALYSIS AND SYSTEM ACCURACY
Findings
CONCLUSION
Full Text
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