SVM+KF Target Tracking Strategy Using the Signal Strength in Wireless Sensor Networks

A Review of Wi-Fi-Based Traffic Detection Technology in the Field of Intelligent Transportation Systems

With the increasing innovation and development of Wi-Fi technology, its penetration in the various fields of industry and academia is becoming more and more profound. As the core infrastructure of traffic data collection in the field of Intelligent Transportation Systems (ITS), Wi-Fi-based traffic detectors have great potential for use in traffic target positioning, perception, and pattern recognition due to their low cost and extensive infrastructure deployment. This paper conducts a comprehensive review of three major Wi-Fi-based traffic detection applications in the field of ITS: target positioning, traffic parameter extraction, and travel mode identification. Among these, target positioning is one of the most widespread applications of Wi-Fi technology, which is also the basis for two other research aspects. Moreover, Wi-Fi-based positioning can be divided into two categories: ranging-based positioning and range-free one; in the field of transportation, it can also be categorized into pedestrian positioning and vehicle positioning based on travel mode. To further demonstrate the effectiveness of Wi-Fi-based ITS applications in practice, this study compares the various Wi-Fi-involved models and algorithms around the world, as well as provides some ideas and inspiration along with this direction.

Augmented CWT Features for Deep Learning-Based Indoor Localization Using WiFi RSSI Data

Localization is one of the current challenges in indoor navigation research. The conventional global positioning system (GPS) is affected by weak signal strengths due to high levels of signal interference and fading in indoor environments. Therefore, new positioning solutions tailored for indoor environments need to be developed. In this paper, we propose a deep learning approach for indoor localization. However, the performance of a deep learning system depends on the quality of the feature representation. This paper introduces two novel feature set extractions based on the continuous wavelet transforms (CWT) of the received signal strength indicators’ (RSSI) data. The two novel CWT feature sets were augmented with additive white Gaussian noise. The first feature set is CWT image-based, and the second is composed of the CWT PSD numerical data that were dimensionally equalized using principal component analysis (PCA). These proposed image and numerical data feature sets were both evaluated using CNN and ANN models with the goal of identifying the room that the human subject was in and estimating the precise location of the human subject in that particular room. Extensive experiments were conducted to generate the proposed augmented CWT feature set and numerical CWT PSD feature set using two analyzing functions, namely, Morlet and Morse. For validation purposes, the performance of the two proposed feature sets were compared with each other and other existing feature set formulations. The accuracy, precision and recall results show that the proposed feature sets performed better than the conventional feature sets used to validate the study. Similarly, the mean localization error generated by the proposed feature set predictions was less than those of the conventional feature sets used in indoor localization. More particularly, the proposed augmented CWT-image feature set outperformed the augmented CWT-PSD numerical feature set. The results also show that the Morse-based feature sets trained with CNN produced the best indoor positioning results compared to all Morlet and ANN-based feature set formulations.

Sensors and Sensing Technologies for Indoor Positioning and Indoor Navigation

The last 10 years have seen enormous technical progress in the field of indoor positioning and indoor navigation; yet, in contrast with outdoor well-established GNSS solutions, no technology exists that is cheap and accurate enough for the general market. The potential applications of indoor localization are all-encompassing, from home to wide public areas, from IoT and personal devices to surveillance and crowd behavior applications, and from casual use to mission-critical systems. This special issue is focused on the recent developments within the sensors and sensing technologies for indoor positioning and indoor navigation networks domain. The papers included in this special issue provide useful insights to the implementation, modelling, and integration of novel technologies and applications, including location-based services, indoor maps and 3D building models, human motion monitoring, robotics and UAV, self-contained sensors, wearable and multi-sensor systems, privacy and security for indoor localization systems.