Wi-Fi Based Indoor Next Location Prediction Using Mixed State-Weighted Markov-Chain Model

Abstract

I. INTRODUCTIONNowadays, Wi-Fi enabled connectivity has become one of the standard functions for smart phones and tablets with the trend of increasing number of indoor Wi-Fi enabled venues. For example, shopping malls provide free Wi-Fi connection to attract more shoppers. These free Wi-Fi services can bring more traffic to increase the visit volume of the shopping malls. Wi-Fi is also a "must-have" for retail stores [1]. Retailers want to be the special preference of their customers, in order to do that, they desire to have a direct relationship with their customers and remember their preferences. For example, once the customers come to their shops, these customers can be identified via Wi-Fi signal which is captured by their mobiles or tablets, a popup will be displayed in their mobile devices to inform them about new products or promotions and provide recommendations based on their historical purchase record.In addition, in order to attract more customers into the shopping mall, these Wi-Fi services can also be used to help capturing indoor shoppers' location. Given a particular location, the smart phone can be represented by its Wireless LANs Address (MAC) uniquely and the received strength of the Wi-Fi signal indicator (RSSI) from different access points Manuscript received April 9, 2014; revised July 8, 2014 can be measured. By modeling the distribution of the RSSI in different locations, an Indoor Positioning System (IPS) as in [2], [3] can be built and trained with collected RSSI data to predict the device location from the signal strength. Although there are other options for indoor positioning, such as the indoor-messaging-system (IMES) in which positioning uses the indoor-GPS devices [4], however, because of the low-cost setup and the simple infrastructure needed, the Wi-Fi based IPS is more attractive to many retailers. Normally, the accuracy of these Wi-Fi Based IPS ranges from 3 to 10 meters [5]. If we keep recording the location of a MAC address for a period of time, these chronological location data can represent the movement or trajectory of a specified person.In this work, we aim to develop a model to predict the pedestrian's next location based on his historical movement which can be represented by his trajectory. There are many potential ways in which users can benefit from this application of the next location prediction. By being aware of the movement of the shoppers in advance, the retailers can quickly target shoppers by both interest and location, turning window-shopping into actual sales [6]. In order to maximize the effect of coupon promotion, delivering the coupons in specified regions and at a particular time, namely "user oriented coupon dispersion", can be carried out. Leveraging the application of next location prediction, organizers can rearrange the manpower with more efficiency and at a lower cost.This paper presents an improved model for the next location prediction and the remainder of this paper is organized as follows. First, we review relevant related works in Section II b...

A Spatial-Contextual Indoor Trajectory Prediction Approach via Hidden Markov Models

“…However, unlike the outdoor positioning system (GPS), indoor positioning systems such as Bluetooth, Infrared, RFID, or Wi-Fi have only been mature in recent years and started to emerge in commercial markets [9]. While much more researches focusing on trajectory prediction in the context of outdoor positioning systems have been published [10], there is still not sufficient related work on the same application in indoor spaces.…”

“…Existing works aiming at predicting indoor movements employ the method of pattern mining and variance of Markov Chain [1,10,17,19,25,26]; however, the unique feature of semantic information in indoor environment such as shop categories has not been studied for prediction due to the limitation of their data source. What is more, their methods are only tested on small data sets, which only contain a small number of users or trajectory points, and in [19], the datasets used were collected in a controlled environment: data was gathered from participants who are aware of the Additional information about the physical environment is provided by the owner of the mall including floor plans of the stores, the shop categories, and the location of Wi-Fi access point.…”

A Spatial-Temporal-Semantic Method for Location Prediction in Indoor Spaces

“…Existing work trying to predict indoor movements employed the technique of pattern mining and variance of Markov chain [1,2,27,[35][36][37], but the temporal information and semantic section information in the trajectory were not used. What is more, the aforementioned methods were only validated on small or synthetic datasets (7 POIs in Lam et al [37], only 72 hours' data in [1], only 48 hours' data in [38]).…”

“…During the past decades, a large number of studies have concentrated on predicting the further whereabouts of human beings [1][2][3][4]. However, with the prevalence of global positioning system (GPS), the majority of works are mainly focused on outdoor trajectory prediction, but researches show that people tend to spend over 87% of their lifetime in indoor settings, for instance, conference rooms, shopping centers, transition terminals, and private homes [5,6].…”