With the wide deployment of Wi-Fi networks, Wi-Fi based indoor localization systems that are deployed without any special hardware have caught significant attention and have become a currently practical technology. At the same time, the Magnetic, Angular Rate, and Gravity (MARG) sensors installed in commercial mobile devices can achieve highly-accurate localization in short time. Based on this, we design a novel indoor localization system by using built-in MARG sensors and a Wi-Fi module. The innovative contributions of this paper include the enhanced Pedestrian Dead Reckoning (PDR) and Wi-Fi localization approaches, and an Extended Kalman Particle Filter (EKPF) based fusion algorithm. A new Wi-Fi/MARG indoor localization system, including an Android based mobile client, a Web page for remote control, and a location server, is developed for real-time indoor pedestrian localization. The extensive experimental results show that the proposed system is featured with better localization performance, with the average error 0.85 m, than the one achieved by using the Wi-Fi module or MARG sensors solely.

The aim of this paper is to present a new indoor localization approach by employing the Angle-of-arrival (AOA) and Received Signal Strength (RSS) measurements in Wi-Fi network. To achieve this goal, we first collect the Channel State Information (CSI) by using the commodity Wi-Fi devices with our designed three antennas to estimate the AOA of Wi-Fi signal. Second, we propose a direct path identification algorithm to obtain the direct signal path for the sake of reducing the interference of multipath effect on the AOA estimation. Third, we construct a new objective function to solve the localization problem by integrating the AOA and RSS information. Although the localization problem is non-convex, we use the Second-order Cone Programming (SOCP) relaxation approach to transform it into a convex problem. Finally, the effectiveness of our approach is verified based on the prototype implementation by using the commodity Wi-Fi devices. The experimental results show that our approach can achieve the median error 0.7 m in the actual indoor environment.

Owing to the rapid development of underwater applications, the research on underwater wireless sensor networks (UWSNs) is becoming more and more significant. Low bandwidth, high latency, limited energy and node float mobility are basic challenges for underwater sensor networks. Most of the theories for terrestrial sensor networks can not be applied to underwater sensor networks. In this paper, we focus on the localization and routing issues for dynamic UWSNs. Location based routing protocols can suit the dynamic situation of UWSNs well. However, most research assumes that the nodes know their locations, which may be not appropriate for some applications. This paper combines these two aspects together to design a localization based routing protocol for dynamic UWSNs. The simulation results show our method can effectively route data from the source nodes to the sink nodes in a dynamic environment.

Some pioneer WiFi-based passive human tracking systems have achieved submeter-level accuracy. However, their key limitation is that the location of WiFi devices must be known in advance. To this end, we propose WiSen, a novel system that estimates the location of a passive user in an indoor environment without any prior knowledge about the location of WiFi devices. Specifically, we first exploit a signal power model for human-induced reflection signal extraction and multidimensional parameter estimation. Due to measurement noise and low-resolution parameter estimates, we further design a confidence-aware-based path pruning (PP) method that combines the distribution of path parameters from successive windows to select reliable human-induced reflection paths. Before that, we also adopt a novel data augmentation (DA) method to increase the number of available path parameters for better learning parameter distribution. These path parameters after PP are then used to statistically estimate the relative location of the transmitter with respect to the receiver and finally localize the passive user. We implement WiSen on commodity WiFi devices and validate its performance in real indoor environments. The experimental results show that WiSen can realize the submeter-level accuracy for passive human tracking and device localization with only a single WiFi link.

In recent decades, the proliferation of smart phones, tablets, and wireless networks has fostered a growing interest in indoor passive positioning. The Wi-Fi-based passive positioning systems can provide Location-Based Services (LBSs) to the third party such as market and security departments. Most of the existing systems are based on the Receive Signal Strength Indication (RSSI) information, which are generally time-consuming and susceptible to environmental change. To overcome this problem, we propose the Wi-Fi compass for indoor passive positioning system (WIPP), an Angle of Arrival (AOA) based passive positioning system using the existing commodity Wi-Fi network. In this paper, we first propose a new algorithm for the joint estimation of AOA and Time of Arrival (TOA) measurements based on the fine-grained Channel State Information (CSI), which is collected by an off-the-shelf Wi-Fi device equipped with only three antennas. Second, we use the affinity propagation clustering algorithm to identify the direct signal path from the target to each Wi-Fi Access Point (AP). Finally, we deploy the WIPP in an actual indoor environment to conduct the performance comparison with the well-known radio-frequency (RF) based system for locating and tracking users inside buildings (RADAR), as well as the conventional passive positioning system using the AOA solely. The experimental results show that the WIPP is able to achieve the median positioning error 0.7 m, which is much lower than the ones by the RADAR system and the conventional system using the AOA solely.

With the popularity of indoor wireless network, device-free indoor localization has attracted more and more attention. Unlike device-based localization where the target is required to carry an active transmitter, their frequent signal scanning consumes a large amount of energy, which is inconvenient for devices with limited energy. In this work, we propose the MFPL, device-free localization (DFL) system based on WiFi distance measurement. First, we combine multi-subcarrier characteristic of Channel State Information (CSI) with classical Fresnel reflection model to get the linear relationship between the change of the length of reflection path and the subcarrier phase difference. Then we calculate the Fresnel phase difference between subcarrier pairs with different spacing from CSI amplitude time series. Finally, we get the change of the length of the reflection path caused by target moving to achieve distance measurement and localization. Using a combination of subcarriers with different spacing to achieve distance measurement effectively broadens the maximum unambiguous distance of the system. To solve the complex non-linear problem of the intersection of two elliptic equations, we introduce Newton's method to transform the non-linear problem into a linear one. The effectiveness of our approach is verified using commodity WiFi infrastructures. The experimental results show our method achieves a median error of 0.87 m in actual indoor environment.