Highly accurate indoor localization of smartphones is critical to enable novel location based features for users and businesses. In this paper, we first conduct an empirical investigation of the suitability of WiFi localization for this purpose. We find that although reasonable accuracy can be achieved, significant errors (e.g., $6\sim8m$) always exist. The root cause is the existence of distinct locations with similar signatures, which is a fundamental limit of pure WiFi-based methods. Inspired by high densities of smartphones in public spaces, we propose a peer assisted localization approach to eliminate such large errors. It obtains accurate acoustic ranging estimates among peer phones, then maps their locations jointly against WiFi signature map subjecting to ranging constraints. We devise techniques for fast acoustic ranging among multiple phones and build a prototype. Experiments show that it can reduce the maximum and 80-percentile errors to as small as $2m$ and $1m$, in time no longer than the original WiFi scanning, with negligible impact on battery lifetime.

/pdf/push-the-limit-of-wifi-based-localization-for-smartphones-4u0037ehzc.pdf

Push the limit of WiFi based localization for smartphones

With the marvelous development of wireless techniques and ubiquitous deployment of wireless systems indoors, myriad indoor location-based services (ILBSs) have permeated into numerous aspects of modern life. The most fundamental functionality is to pinpoint the location of the target via wireless devices. According to how wireless devices interact with the target, wireless indoor localization schemes roughly fall into two categories: device based and device free. In device-based localization, a wireless device (e.g., a smartphone) is attached to the target and computes its location through cooperation with other deployed wireless devices. In device-free localization, the target carries no wireless devices, while the wireless infrastructure deployed in the environment determines the target’s location by analyzing its impact on wireless signals. This article is intended to offer a comprehensive state-of-the-art survey on wireless indoor localization from the device perspective. In this survey, we review the recent advances in both modes by elaborating on the underlying wireless modalities, basic localization principles, and data fusion techniques, with special emphasis on emerging trends in (1) leveraging smartphones to integrate wireless and sensor capabilities and extend to the social context for device-based localization, and (2) extracting specific wireless features to trigger novel human-centric device-free localization. We comprehensively compare each scheme in terms of accuracy, cost, scalability, and energy efficiency. Furthermore, we take a first look at intrinsic technical challenges in both categories and identify several open research issues associated with these new challenges.

https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=5886&context=sis_research

A Survey on Wireless Indoor Localization from the Device Perspective

A mouse has been one of the most successful user interfaces due to its intuitive use. As more devices are equipped with displays and offer rich options for users to choose from, a traditional mouse that requires a surface to operate is no longer sufficient. While different types of air mice are available in the market, they rely on accelerometers and gyroscopes, which significantly limit the accuracy and ease of use. In this paper, we develop a system that can accurately track hand movement to realize a mouse. A unique advantage of our scheme is that it achieves high tracking accuracy using the existing hardware already available in the mobile devices (e.g., smart phones and smart watches) and equipment to be controlled (e.g., smart TVs). More specifically, our approach sends inaudible sound pulses at a few selected frequencies, and uses the frequency shifts to estimate the speed and distance traveled. We then develop techniques to quickly calibrate the distance between speakers and narrow down the device's initial position using its movement trajectory. Based on the information, we continuously track the device's new position in real time. This is feasible because many devices, such as smart TVs, PCs, and laptops, already have multiple speakers. When only one speaker is available, we can leverage the frequency shift of sound along with the phase of received WiFi signal to enable tracking. Our evaluation and user study demonstrate that our system achieves high tracking accuracy (e.g., median error of around 1.4 cm) and ease of use.

Demo: Turning a Mobile Device into a Mouse in the Air

This article provides an overview of the recent literature on the use of internet-based testing to address important questions in perception research. Our goal is to provide a starting point for the perception researcher who is keen on assessing this tool for their own research goals. Internet-based testing has several advantages over in-lab research, including the ability to reach a relatively broad set of participants and to quickly and inexpensively collect large amounts of empirical data, via services such as Amazon’s Mechanical Turk or Prolific Academic. In many cases, the quality of online data appears to match that collected in lab research. Generally-speaking, online participants tend to be more representative of the population at large than those recruited for lab based research. There are, though, some important caveats, when it comes to collecting data online. It is obviously much more difficult to control the exact parameters of stimulus presentation (such as display characteristics) with online research. There are also some thorny ethical elements that need to be considered by experimenters. Strengths and weaknesses of the online approach, relative to others, are highlighted, and recommendations made for those researchers who might be thinking about conducting their own studies using this increasingly-popular approach to research in the psychological sciences.

Conducting perception research over the internet: a tutorial review.

In this article we present SmartRoad, a crowd-sourced road sensing system that detects and identifies traffic regulators, traffic lights, and stop signs, in particular. As an alternative to expensive road surveys, SmartRoad works on participatory sensing data collected from GPS sensors from in-vehicle smartphones. The resulting traffic regulator information can be used for many assisted-driving or navigation systems. In order to achieve accurate detection and identification under realistic and practical settings, SmartRoad automatically adapts to different application requirements by (i) intelligently choosing the most appropriate information representation and transmission schemes, and (ii) dynamically evolving its core detection and identification engines to effectively take advantage of any external ground truth information or manual label opportunity. We implemented SmartRoad on a vehicular smartphone test bed, and deployed it on 35 external volunteer users’ vehicles for two months. Experiment results show that SmartRoad can robustly, effectively, and efficiently carry out the detection and identification tasks.

/pdf/smartroad-smartphone-based-crowd-sensing-for-traffic-1olcqg69xs.pdf

SmartRoad: Smartphone-Based Crowd Sensing for Traffic Regulator Detection and Identification

High-speed, locational, phone-to-phone (HLPP) games and apps constitute a provocative class of mobile apps that are currently unsupported on commodity mobile devices. This work looks at a key problem for enabling HLPP: a specific variant of the localization problem in which two phones estimate each other's relative positions in 3D space without infrastructure support. Moreover, position estimates should reflect changes due to the phones' possible mobility.We present a solution for achieving high speed 3D continuous localization for phone-to-phone scenarios. Our basic approach uses acoustic cues based on time-of-arrival and power level. It assumes at least two microphones and one speaker per phone, which is common on new smartphones. Accelerometers and digital compasses assist in resolving ambiguous acoustic-only localization. Continuous localization is achieved with the aid of a loose time synchronization protocol and an extended Kalman filter. Experimental results across a range of motion paths show localization resolution to within 13.9 centimeters for 90% of estimates, and to within 4.9 centimeters for 50% of estimates when the phones are several meters apart.

On the feasibility of real-time phone-to-phone 3D localization

Ethernet is becoming a preferred technology to be extended to Metro Area Networks (MAN) due to its low cost, simplicity and ubiquity. However, current Ethernet lacks fast failure recovery mechanism. It reconstructs the spanning tree after the failure is detected, which commonly requires tens of seconds. Some researchers have proposed fast failure handling methods based on multiple spanning trees, but these approaches are either centralized or require periodic message broadcasting over the entire network. In this paper, we propose a local restoration mechanism for Metro Ethernet, which is distributed, fast, and does not need failure notification. Upon failure of a single link, upstream switch locally restores traffic to pre-configured backup spanning trees. We propose two approaches for backup tree selection (connection based and destination based), and formulate the working spanning tree assignment and backup spanning tree configuration problem as an integer linear programming problem. We also develop heuristics for each strategy to reduce computation complexity. The simulation results show that our algorithms have comparable performance close to the optimal solutions, and both strategies can efficiently utilize the network bandwidth to handle single link failure.

Local restoration with multiple spanning trees in metro ethernet

Techniques for generating a real-time 3D position of one device relative another device are described herein. In one embodiment, a first device may be equipped with one or more transmitters and receivers to collect location information based on direct localized signal transmission between the first device and a second device. The first device may also be equipped with an accelerometer and/or digital compasses to assist in resolving ambiguous locations when the first device and the second device are not at an alignment position. Each device may transmit part or all of its collected location information to another device to assist in obtaining the relative location. Alternatively, a separate server may receive the location information from the devices and calculate the relative location of each device based on the received location information.

Device-to-device relative localization

Ethernet is becoming a preferred technology to be extended to metropolitan area networks (MANs) due to its low cost, simplicity, and ubiquity. However, current Ethernet lacks a fast failure recovery mechanism as it reconstructs the spanning tree after the failure is detected, which commonly requires tens of seconds. Some fast failure-handling methods based on multiple spanning trees have been proposed in the literature, but these approaches are either centralized or require periodic message broadcasting over the entire network. In this paper, we propose a local restoration mechanism for metro Ethernet using multiple spanning trees, which is distributed and fast and does not need failure notification. Upon failure of a single link, the upstream switch locally restores traffic to preconfigured backup spanning trees. We propose two restoration approaches, connection-based and destination-based, to select backup trees. We formulate the tree preconfiguration problem that includes working spanning tree assignment and backup spanning tree configuration. We prove that the preconfiguration problem is NP-complete and develop an integer linear programming model. We also develop heuristic algorithms for each restoration approach to reduce the computation complexity. To evaluate the effectiveness of our heuristic algorithms, we carry out the simulation on grid and random networks. The simulation results show that our heuristic algorithms have comparable performance close to the optimal solutions, and both restoration approaches can efficiently utilize the network bandwidth to handle single link failures.

Local restoration with multiple spanning trees in metro ethernet networks

Ethernet is becoming a preferred technology to be deployed in metro domain due to its low cost, simplicity and ubiquity. However, spanning tree based Ethernet protocol does not meet the requirement for Metro Area Networks in terms of network resilience, despite the advancement of Ethernet standardization and commercialization. In this paper, we propose a fast spanning tree reconnection (FSTR) mechanism for Metro Ethernet networks to handle any single link failure, which has features of fast recovery, backup capacity guarantees and ease of implementation. Upon failure of a link on a spanning tree, a distributed failure recovery protocol is activated to reconnect the broken spanning tree using a reconnect-link not on the spanning tree. We present the details of the protocol, including failure notification and forwarding table reconfiguration procedures. The pre-configuration of the reconnect-links to reconnect each spanning tree is formulated as an integer linear programming (ILP) problem. The optimization results show that with lower implementation cost, fast spanning tree reconnection mechanism can achieve comparative or considerably better performance than other resilient mechanisms for Metro Ethernet networks.

Jian Qiu

Papers

On the feasibility of real-time phone-to-phone 3D localization

Local restoration with multiple spanning trees in metro ethernet

Device-to-device relative localization

Local restoration with multiple spanning trees in metro ethernet networks

Fast Spanning Tree Reconnection for Resilient Metro Ethernet Networks