An Enhanced Ranging Scheme Using WiFi RSSI Measurements for Ubiquitous Location

Park, DoWoo; Park, Joon Goo

doi:10.1109/cnsi.2011.29

Cited by 17 publications

(13 citation statements)

References 5 publications

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“…For the first principle, according to reference [43], the signal strength error is greater when mobile devices are too far from the APs, resulting in increased positioning error. The positioning error near the inside border of a queue zone is the main factor that affects topological correctness.…”

Section: Theory and Methodological Frameworkmentioning

confidence: 99%

Queuing Time Prediction Using WiFi Positioning Data in an Indoor Scenario

Shu

Pei

et al. 2016

Sensors

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Queuing is common in urban public places. Automatically monitoring and predicting queuing time can not only help individuals to reduce their wait time and alleviate anxiety but also help managers to allocate resources more efficiently and enhance their ability to address emergencies. This paper proposes a novel method to estimate and predict queuing time in indoor environments based on WiFi positioning data. First, we use a series of parameters to identify the trajectories that can be used as representatives of queuing time. Next, we divide the day into equal time slices and estimate individuals’ average queuing time during specific time slices. Finally, we build a nonstandard autoregressive (NAR) model trained using the previous day’s WiFi estimation results and actual queuing time to predict the queuing time in the upcoming time slice. A case study comparing two other time series analysis models shows that the NAR model has better precision. Random topological errors caused by the drift phenomenon of WiFi positioning technology (locations determined by a WiFi positioning system may drift accidently) and systematic topological errors caused by the positioning system are the main factors that affect the estimation precision. Therefore, we optimize the deployment strategy during the positioning system deployment phase and propose a drift ratio parameter pertaining to the trajectory screening phase to alleviate the impact of topological errors and improve estimates. The WiFi positioning data from an eight-day case study conducted at the T3-C entrance of Beijing Capital International Airport show that the mean absolute estimation error is 147 s, which is approximately 26.92% of the actual queuing time. For predictions using the NAR model, the proportion is approximately 27.49%. The theoretical predictions and the empirical case study indicate that the NAR model is an effective method to estimate and predict queuing time in indoor public areas.

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Section: Theory and Methodological Frameworkmentioning

confidence: 99%

Queuing Time Prediction Using WiFi Positioning Data in an Indoor Scenario

Shu

Pei

et al. 2016

Sensors

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“…Location based fingerprint [7] Wi-Fi indoor positioning technology using Wi-Fi received signal strength and the position of the reference mapping relationship to estimate the location of the user, reception facilities nearer the distance signal source, the strength of the received signal strength, receiving equipment the farther the distance signal source, the received signal strength is weak, so it can be in different positions have different Wi-Fi RSSI information [8], the localization algorithm is divided into two stages: offline data acquisition, online orientation phase. In the offline data acquisition stage, the receiving signal strength samples of multiple aps are collected at the preset reference points, and then stored in the database together with the location information, which is the location fingerprint database.…”

Section: Implementation Of Positioning Algorithmmentioning

confidence: 99%

Localizable Mobile Payment Shopping Cart based on Location Fingerprint Algorithm

Zhao¹,

Li²,

Yang³

et al. 2019

Proceedings of the 3rd International Conference on Computer Engineering, Information Science &Amp; Application Technology (ICCI

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The intelligent shopping cart is designed with embedded system, and has small area positioning and navigation, mobile payment, area storage and anti-theft functions. The Android system obtains the Wi-Fi signal strength and MAC address of AP, and sends the RSSI data to the server. The server obtains the coordinates through the location fingerprint database positioning algorithm. Meanwhile, the server can realize the navigation through the global shortest path planning by Floyd algorithm to facilitate customers to find the goods as soon as possible. The shopping cart realizes payment liberalization by setting up a small electronic cash register system. The new shopping cart system is equipped with two weighing boards, which allow customers to weigh bulk goods directly. Zonal storage is intended to protect fragile and frozen goods and reduce losses. The intelligent shopping cart system makes shopping time -saving and labor -saving, more convenient and faster.

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“…Some related works [8,9] present that the RSSI approach to the ten times the logarithm of the ratio between the received power (P r ) at the receiving point and the reference power (P ref ). Based on propagation mode, the received power is inversely proportional to the square of distance (d).…”

Section: Rssi Based Localization Methodsmentioning

confidence: 99%