Supplementary open dataset for WiFi indoor localization based on received signal strength

Learning Indoor Localization using Radio Received Signal Strength

Minimum Euclidean Distance Algorithm for Indoor WiFi Received Signal Strength (RSS) Fingerprinting

While WiFi-based indoor localization is attractive, the need for a significant degree of pre-deployment effort is a key challenge. In this paper, indoor localization with no pre-deployment effort in an indoor space, such as an office building corridor, with WiFi coverage but no apriori knowledge of the placement of the access points(APs) is implemented for mobile devices. WiFi Received Signal S...

Comparative evaluation of Received Signal-Strength Index (RSSI) based indoor localization techniques for construction jobsites

This paper evaluates the accuracy of several RSSI-based localization techniques on a live jobsite and compares them to results obtained in an operating building. RSSI-based localization algorithms were tested due to their relative low cost and potential for accuracy. Four different localization algorithms (MinMax, Maximum Likelihood, Ring Overlapping Circle RSSI and k-Nearest Neighbor) were eva...

Received Signal Strength Indicator - Based Adaptive Localization Algorithm for Indoor Wireless Sensor Networks

Solutions for indoor localization have become more critical with recent advancement in context and location-aware technologies. When wireless sensor network (WSN) used in complex indoor environment, great propagation loss will be caused and it is very difficult to estimate adaptively the location of target nodes when environment changed. In this paper, an indoor adaptive localization algorithm ...

Transfer Learning for WiFi-based Indoor Localization

The WiFi-based indoor localization problem (WILP) aims to detect the location of a client device given the signals received from various access points. WILP is a complex and very important task for many AI and ubiquitous computing applications. A major approach to solving this task is through machine learning, where upto-date labeled training data are required in a large scale indoor environmen...