Wireless indoor positioning systems have become very popular in recent years. These systems have been successfully used in many applications such as asset tracking and inventory management. This paper provides an overview of the existing wireless indoor positioning solutions and attempts to classify different techniques and systems. Three typical location estimation schemes of triangulation, scene analysis, and proximity are analyzed. We also discuss location fingerprinting in detail since it is used in most current system or solutions. We then examine a set of properties by which location systems are evaluated, and apply this evaluation method to survey a number of existing systems. Comprehensive performance comparisons including accuracy, precision, complexity, scalability, robustness, and cost are presented.

Survey of Wireless Indoor Positioning Techniques and Systems

Instrumenting the physical world through large networks of wireless sensor nodes, particularly for applications like environmental monitoring of water and soil, requires that these nodes be very small, lightweight, untethered, and unobtrusive. The problem of localization, that is, determining where a given node is physically located in a network, is a challenging one, and yet extremely crucial for many of these applications. Practical considerations such as the small size, form factor, cost and power constraints of nodes preclude the reliance on GPS of all nodes in these networks. We review localization techniques and evaluate the effectiveness of a very simple connectivity metric method for localization in outdoor environments that makes use of the inherent RF communications capabilities of these devices. A fixed number of reference points in the network with overlapping regions of coverage transmit periodic beacon signals. Nodes use a simple connectivity metric, which is more robust to environmental vagaries, to infer proximity to a given subset of these reference points. Nodes localize themselves to the centroid of their proximate reference points. The accuracy of localization is then dependent on the separation distance between two-adjacent reference points and the transmission range of these reference points. Initial experimental results show that the accuracy for 90 percent of our data points is within one-third of the separation distance. However, future work is needed to extend the technique to more cluttered environments.

/pdf/gps-less-low-cost-outdoor-localization-for-very-small-3cpquwbe1v.pdf

GPS-less low-cost outdoor localization for very small devices

Growing convergence among mobile computing devices and embedded technology sparks the development and deployment of "context-aware" applications, where location is the most essential context. In this paper we present LANDMARC, a location sensing prototype system that uses Radio Frequency Identification (RFID) technology for locating objects inside buildings. The major advantage of LANDMARC is that it improves the overall accuracy of locating objects by utilizing the concept of reference tags. Based on experimental analysis, we demonstrate that active RFID is a viable and cost-effective candidate for indoor location sensing. Although RFID is not designed for indoor location sensing, we point out three major features that should be added to make RFID technologies competitive in this new and growing market.

LANDMARC: indoor location sensing using active RFID

Usually, a proof of a theorem contains more knowledge than the mere fact that the theorem is true. For instance, to prove that a graph is Hamiltonian it suffices to exhibit a Hamiltonian tour in it; however, this seems to contain more knowledge than the single bit Hamiltonian/non-Hamiltonian.In this paper a computational complexity theory of the “knowledge” contained in a proof is developed. Zero-knowledge proofs are defined as those proofs that convey no additional knowledge other than the correctness of the proposition in question. Examples of zero-knowledge proof systems are given for the languages of quadratic residuosity and 'quadratic nonresiduosity. These are the first examples of zero-knowledge proofs for languages not known to be efficiently recognizable.

/pdf/the-knowledge-complexity-of-interactive-proof-systems-31apre0ecf.pdf

The knowledge complexity of interactive proof-systems

http://www.eecs.northwestern.edu/~peters/references/LocalizationTechniquesMaoAustralia.pdf

Wireless sensor network localization techniques

We address the problem of locating users inside buildings using a radio-frequency (RF) wireless LAN. A previous paper presented the basic design and a limited evaluation of a user-location system we have developed. In this paper, we analyze shortcomings of the basic system, and develop and evaluate solutions to address these shortcomings. Additionally, we describe several new enhancements, including a novel access point-based environmental profiling scheme, and a Viterbi-like algorithm for continuous user tracking and disambiguation of candidate user locations. Using extensive data collected from our deployment, we evaluate our system’s performance over multiple wireless LAN technologies and in different buildings on our campus. We also discuss significant practical issues that arise in implementing such a system. Our techniques are implemented purely in software and are easily deployable over a standard wireless LAN.

/pdf/enhancements-to-the-radar-user-location-and-tracking-system-atvw5e6fmg.pdf

Enhancements to the RADAR User Location and Tracking System

The proliferation of mobile computing devices and local-area wireless networks has fostered a growing interest in location-aware systems and services A key requirement for enabling such services is user location and tracking In this paper we address this problem in the context of a popular radio-frequency wireless network technology Our approach is based on recording and processing real-time signal strength information available at multiple base stations positioned to provide overlapping coverage in the area of interest We employ techniques that combine empirical measurements with signal propagation modeling We present concrete experimental results that demonstrate the feasibility of estimating user location with a high degree of accuracy

/pdf/user-location-and-tracking-in-an-in-building-radio-network-r5s78u4arc.pdf

User Location and Tracking in an In-Building Radio Network

We argue that heterogeneity is hindering technological innovation in the home---homes differ in terms of their devices and how those devices are connected and used. To abstract these differences, we propose to develop a home-wide operating system. A HomeOS can simplify application development and let users easily add functionality by installing new devices or applications. The development of such an OS is an inherently inter-disciplinary exercise. Not only must the abstractions meet the usual goals of being efficient and easy to program, but the underlying primitives must also match how users want to manage and secure their home. We describe the preliminary design of HomeOS and our experience with developing applications for it.

https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/hotnets2010-homeos.pdf

The home needs an operating system (and an app store)

We intend to develop a smartphone app that can tell whether its user is a driver or a passenger in an automobile. While the core problem can be solved relatively easily with special installations in new high-end vehicles (e.g., NFC), constraints of backward compatibility makes the problem far more challenging. We design a Driver Detection System (DDS) that relies entirely on smartphone sensors, and is thereby compatible with all automobiles. Our approach harnesses smartphone sensors to recognize micro-activities in humans, that in turn discriminate between the driver and the passenger. We demonstrate an early prototype of this system on Android NexusS and Apple iPhones. Reported results show greater than 85% accuracy across 6 users in 2 different cars.

/pdf/i-am-a-smartphone-and-i-know-my-user-is-driving-4jr48wr3zj.pdf

I am a smartphone and I know my user is driving

There are a lot of applications that run on modern mobile operating systems. Inevitably, some of these applications fail in the hands of users. Diagnosing a failure to identify the culprit, or merely reproducing that failure in the lab is difficult. To get insight into this problem, we interviewed developers of five mobile applications and analyzed hundreds of trouble tickets. We find that support for diagnosing unexpected application behavior is lacking across major mobile platforms. Even when developers implement heavy-weight logging during controlled trials, they do not discover many dependencies that are then stressed in the wild. They are also not well-equipped to understand how to monitor the large number of dependencies without impacting the phone's limited resources such as CPU and battery. Based on these findings, we argue for three fundamental changes to failure reporting on mobile phones. The first is spatial spreading, which exploits the large number of phones in the field by spreading the monitoring work across them. The second is statistical inference, which builds a conditional distribution model between application behavior and its dependencies in the presence of partial information. The third is adaptive sampling, which dynamically varies what each phone monitors, to adapt to both the varying population of phones and what is being learned about each failure. We propose a system called MobiBug that combines these three techniques to simplify the task of diagnosing mobile applications.

Victor Bahl

Papers

Enhancements to the RADAR User Location and Tracking System

User Location and Tracking in an In-Building Radio Network

The home needs an operating system (and an app store)

I am a smartphone and I know my user is driving

Diagnosing mobile applications in the wild