Barometer-Assisted 3D Indoor WiFi Localization for Smart Devices-Map Selection and Performance Evaluation

Recently, indoor localization becomes a hot topic no matter in industry or academic field. Smart phones are good candidates for localization since they are carrying various sensors such as GPS, Wi-Fi, accelerometer, barometer and etc, which can be used to estimate the current location. But there are still many challenges for 3D indoor geolocation using smart phones, among which the map selection and 3D performance evaluation problems are the most common and crucial. In the indoor environment, the popular outdoor Google maps cannot be utilized since we need maps showing the layout of every individual floor. Also, layout of different floors differ from one another. Therefore, algorithms are required to detect whether we are inside or outside a building and determine on which floor we are located so that an appropriate map can be selected accordingly. For Wi-Fi based indoor localization, the performance of location estimation is closely related to the algorithms and deployment that we are using. It is difficult to find out a general approach that can be used to evaluate any localization system. On one hand, since the RF signal will suffer extra loss when traveling through the ceilings between floors, its propagation property will be different from the empirical ones and consequently we should design a new propagation model for 3D scenarios. On the other hand, properties of sensors are unique so that corresponding models are required before we analyze the localization scheme. In-depth investigation on the possible hybrid are also needed in case more than one sensor is operated in the localization system. In this thesis, we firstly designed two algorithms to use GPS signal for detecting whether the smart device is operating inside or outside a building, which is called outdoor-indoor transition detection. We also design another algorithm to use barometer data for determining on which floor are we located, which is considered as a multi-floor transition detection. With three scenarios designed inside the Akwater Kent Laboratory building (AK building) at Worcester Polytechnic Institute (WPI), we collected raw data from an Android phone with a version of 4.3 and conducted experimental analysis based on that. An efficient way to quantitatively evaluate the 3D localization systems is using Cramer-Rao Lower Bound (CRLB), which is considered as the lower bound of the estimated error for any localization system. The characteristics of Wi-Fi and barometer signals are explored and proper models are introduced as a foundation. Then we extended the 2D CRLB into a 3D format so that it can fit the our 3D scenarios. A barometer-assisted CRLB is introduced as an improvement for the existing Wi-Fi Receive Signal Strength (RSS)-only scheme and both of the two schemes are compared with the contours in every scenario and the statistical analysis

Using Smartphone Sensors for Localization in BAN

Nowadays, various sensors are embedded in smartphone, making it a great candidate for localization applications. In this chapter, we explored and listed the localization sensors in smartphone, their characteristics, platforms, coordinate system and how they can be used in BAN. These sensors can be roughly divided into three types: physical IMU sensors (accelerometer, gyroscope and magnetometer), virtual IMU (gravity, step counter and electronic compass) and the environmental sensors (barometer, proximity and other miscellaneous). By applying different mathematical methods, the location of the target or the users can be calculated and used for further use, such as navigation, healthcare or military purpose

Indoor Motion Detection Using Wi-Fi Channel State Information in Flat Floor Environments Versus in Staircase Environments

Recently, Wi-Fi channel state information (CSI) motion detection systems have been widely researched for applications in human health care and security in flat floor environments. However, these systems disregard the indoor context, which is often complex and consists of unique features, such as staircases. Motion detection on a staircase is also meaningful and important for various applications, such as fall detection and intruder detection. In this paper, we present the difference in CSI motion detection in flat floor and staircase environments through analysing the radio propagation model and experiments in real settings. For comparison in the two environments, an indoor CSI motion detection system is proposed with several novel methods including correlation-based fusion, moving variance segmentation (MVS), Doppler spread spectrum to improve the system performance, and a correlation check to reduce the implementation cost. Compared with existing systems, our system is validated to have a better performance in both flat floor and staircase environments, and further utilized to verify the superior CSI motion detection performance in staircase environments versus flat floor environments

Symmetric Multi-Scale Residual Network Ensemble with Weighted Evidence Fusion Strategy for Facial Expression Recognition

To extract facial features with different receptive fields and improve the decision fusion performance of network ensemble, a symmetric multi-scale residual network (SMResNet) ensemble with a weighted evidence fusion (WEF) strategy for facial expression recognition (FER) was proposed. Firstly, aiming at the defect of connecting different filter groups of Res2Net only from one direction in a hierarchical residual-like style, a symmetric multi-scale residual (SMR) block, which can symmetrically extract the features from two directions, was improved. Secondly, to highlight the role of different facial regions, a network ensemble was constructed based on three networks of SMResNet to extract the decision-level semantic of the whole face, eyes, and mouth regions, respectively. Meanwhile, the decision-level semantics of three regions were regarded as different pieces of evidence for decision-level fusion based on the Dempster-Shafer (D-S) evidence theory. Finally, to fuse the different regional expression evidence of the network ensemble, which has ambiguity and uncertainty, a WEF strategy was introduced to overcome conflicts within evidence based on the support degree adjustment. The experimental results showed that the facial expression recognition rates achieved 88.73%, 88.46%, and 88.52% on FERPlus, RAF-DB, and CAER-S datasets, respectively. Compared with other state-of-the-art methods on three datasets, the proposed network ensemble, which not only focuses the decision-level semantics of key regions, but also addresses to the whole face for the absence of regional semantics under occlusion and posture variations, improved the performance of facial expression recognition in the wild

RF Cloud for Cyberspace Intelligence

Wireless information networks have become a necessity of our day-to-day life. Over a billion Wi-Fi access points, hundreds of thousands of cell towers, and billions of IoT devices, using a variety of wireless technologies, create the infrastructure that enables this technology to access everyone, everywhere. The radio signal carrying the wireless information, propagates from antennas through the air and creates a radio frequency (RF) cloud carrying a huge amount of data that is commonly accessible by anyone. The big data of the RF cloud includes information about the transmitter type and addresses, embedded in the information packets; as well as features of the RF signal carrying the message, such as received signal strength (RSS), time of arrival (TOA), direction of arrival (DOA), channel impulse response (CIR), and channel state information (CSI). We can benefit from the big data contents of the messages as well as the temporal and spatial variations of their RF propagation characteristics to engineer intelligent cyberspace applications. This paper provides a holistic vision of emerging cyberspace applications and explains how they benefit from the RF cloud to operate. We begin by introducing the big data contents of the RF cloud. Then, we explain how innovative cyberspace applications are emerging that benefit from this big data. We classify these applications into three categories: wireless positioning systems, gesture and motion detection technologies, and authentication and security techniques. We explain how Wi-Fi, cell-tower, and IoT wireless positioning systems benefit from big data of the RF cloud. We discuss how researchers are studying applications of RF cloud features for motion, activity and gesture detection for human-computer interaction, and we show how authentication and security applications benefit from RF cloud characteristics