GPS and Wi-Fi/GSM fingerprinting were used to derive information regarding location. This method was tested by tracking the location of attendees of the 2011 Lord Mayors Show in London. introduced mathematical methods for inferring and visualizing real-time information based on the data collected through people’s mobile phones. Therefore, the succinct contributions made by us in our manuscript include: Although indoor localization has become easier, analytics of the results remains in the initial stages. Indoor localization helps these industries get an idea about customer preferences, and by tracking their movement, they can understand which part of their physical space the customer is more interested in.ĭespite the variety of applications, indoor localization has no fixed standards. There are many uses of indoor localization, for example, in fields such as augmented reality and navigation in shopping malls, airports, and parking lots, tourist locations, hotels, and amusement parks, among many others. Therefore, these probe requests contain time and space information of the device.
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These probe requests consist of the device’s MAC address, as well as the timestamp, latitude, and longitude of the known access point. To improve the user experience, Wi-Fi probe requests are repeatedly broadcast. Many Wi-Fi-enabled devices are available, and a whole lot of data is gathered from public Wi-Fi.
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Currently, many technologies such as smartphones, Wi-Fi, Bluetooth antennas and beacons placed at specific distances are used for indoor positioning. We use the network of many devices to locate people inside a building. The focus of indoor localization is to estimate the crowd’s position accurately without a breach of the privacy of any individual. For indoor positioning, we use indoor localization methods. Specifying the floor in a building is a difficult task and requires precision and tremendous amounts of effort. Outdoor localization focuses on latitude and longitude, but in indoor localization, we also need to consider the altitude, as indoor localization includes multistorey buildings. Existing technologies have successfully solved the problem of outdoor localization, but indoor localization is still a work in progress. GPS and satellite technologies are used for navigation purposes, but they are not precise regarding multistorey buildings, airports, and other indoor spaces. Finally, we performed pedestrian flow analysis by identifying the most common paths followed inside our place of interest. Subsequently, we mapped each probe request to the section of our place of interest where it was captured. We used historical data from a live store in Dubai to forecast the use of two different models, which we conclude by comparing. Second, we attempted to understand human behaviour. In contrast, the ARMA model may require more effort and deep statistical analysis but allows the user to tune it and reach a more personalized result.
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The Prophet model is an additive model that requires no manual effort and can easily detect and handle outliers or missing data. In addition, in this paper, we perform a comparison between the Prophet model and our implementation of the autoregressive moving average (ARMA) model. These packets are known as Wi-Fi probe requests and can encapsulate various types of spatiotemporal information from the device carrier. Smart devices recurrently broadcast automatic connectivity requests. This paper aims to improve analytics research, focusing on data collected through indoor localization methods. Indoor localization is used to locate objects and people within buildings where outdoor tracking tools and technologies cannot provide precise results.
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