The growing commercial interest in indoor location-based services (ILBS) has spurred recent development of many indoor positioning techniques. Due to the absence of global positioning system (GPS) signal, many other signals have been proposed for indoor usage. Among them, Wi-Fi (802.11) emerges as a promising one due to the pervasive deployment of wireless LANs (WLANs). In particular, Wi-Fi fingerprinting has been attracting much attention recently because it does not require line-of-sight measurement of access points (APs) and achieves high applicability in complex indoor environment. This survey overviews recent advances on two major areas of Wi-Fi fingerprint localization: advanced localization techniques and efficient system deployment. Regarding advanced techniques to localize users, we present how to make use of temporal or spatial signal patterns, user collaboration, and motion sensors. Regarding efficient system deployment, we discuss recent advances on reducing offline labor-intensive survey, adapting to fingerprint changes, calibrating heterogeneous devices for signal collection, and achieving energy efficiency for smartphones. We study and compare the approaches through our deployment experiences, and discuss some future directions.

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Wi-Fi Fingerprint-Based Indoor Positioning: Recent Advances and Comparisons

Statistical physics of vaccination

The recent outbreak of COVID-19 has taken the world by surprise, forcing lockdowns and straining public health care systems COVID-19 is known to be a highly infectious virus, and infected individuals do not initially exhibit symptoms, while some remain asymptomatic Thus, a non-negligible fraction of the population can, at any given time, be a hidden source of transmissions In response, many governments have shown great interest in smartphone contact tracing apps that help automate the difficult task of tracing all recent contacts of newly identified infected individuals However, tracing apps have generated much discussion around their key attributes, including system architecture, data management, privacy, security, proximity estimation, and attack vulnerability In this article, we provide the first comprehensive review of these much-discussed tracing app attributes We also present an overview of many proposed tracing app examples, some of which have been deployed countrywide, and discuss the concerns users have reported regarding their usage We close by outlining potential research directions for next-generation app design, which would facilitate improved tracing and security performance, as well as wide adoption by the population at large

A Survey of COVID-19 Contact Tracing Apps

This paper provides an overview of the most significant existing methods for indoor positioning on a contemporary smartphone. The approaches include Wi-Fi and Bluetooth based positioning, magnetic field fingerprinting, map aided navigation using building floor plans, and aiding from self-contained sensors. Wi-Fi and Bluetooth based positioning methods considered in this survey are fingerprint approaches that determine a user's position using a database of radio signal strength measurements that were collected earlier at known locations. Magnetic field fingerprinting can be used in an information fusion algorithm to improve positioning. The map-matching algorithms include application of wall constraints, topological indoor maps, and building geometry for heading correction. Finally, methods for step counting, step length and direction estimation, orientation tracking, motion classification, transit mode detection, and floor change detection in multi-storey buildings are discussed.

A Survey of Selected Indoor Positioning Methods for Smartphones

One of the most engaging challenges for mobile operators today is how to manage the exponential data traffic increase. Mobile data offloading stands out as a promising and low-cost solution to reduce the burden on the cellular network. To make this possible, we need a new hybrid network paradigm that leverages the existence of multiple alternative communication channels. This entails significant modifications in the way data are handled, affecting also the behavior of network protocols. In this paper, we present a comprehensive survey of data offloading techniques in cellular networks and extract the main requirements needed to integrate data offloading capabilities into today's mobile networks. We classify existing strategies into two main categories, according to their requirements in terms of content delivery guarantees: delayed and nondelayed offloading. We overview the technical aspects and discuss the state of the art in each category. Finally, we describe in detail the novel functionalities needed to implement mobile data offloading in the access network, as well as current and future research challenges in the field, with an eye toward the design of hybrid architectures.

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Data Offloading Techniques in Cellular Networks: A Survey

The availability of “always-on” communications has tremendous implications for how people interact socially. In particular, sociologists are interested in the question if such pervasive access increases or decreases face-to-face interactions. Unlike triangulation which seeks to precisely define position, the question of face-to-face interaction reduces to one of proximity, i.e., are the individuals within a certain distance? Moreover, the problem of proximity estimation is complicated by the fact that the measurement must be quite precise (1-1.5 m) and can cover a wide variety of environments. Existing approaches such as GPS and Wi-Fi triangulation are insufficient to meet the requirements of accuracy and flexibility. In contrast, Bluetooth, which is commonly available on most smartphones, provides a compelling alternative for proximity estimation. In this paper, we demonstrate through experimental studies the efficacy of Bluetooth for this exact purpose. We propose a proximity estimation model to determine the distance based on the RSSI values of Bluetooth and light sensor data in different environments. We present several real world scenarios and explore Bluetooth proximity estimation on Android with respect to accuracy and power consumption.

Face-to-Face Proximity Estimation Using Bluetooth On Smartphones

With an increasing number of organizations allowing personal smart phones onto their networks, considerable security risk is introduced. The security risk is exacerbated by the tremendous heterogeneity of the personal mobile devices and their respective installed pool of applications. Furthermore, by virtue of the devices not being owned by the organization, the ability to authoritatively enforce organizational security polices is challenging. As a result, a critical part of organizational security is the ability to drive user security behavior through either on-device mechanisms or security awareness programs. In this paper, we establish a baseline for user security behavior from a population of over one hundred fifty smart phone users. We then systematically evaluate the ability to drive behavioral change via messaging centered on morality, deterrence, and incentives. Our findings suggest that appeals to morality are most effective over time, whereas deterrence produces the most immediate reaction. Additionally, our findings show that while a significant portion of users are securing their devices without prior intervention, it is difficult to influence change in those who do not.

http://cups.cs.cmu.edu/soups/2013/proceedings/a10_VanBruggen.pdf

Modifying smartphone user locking behavior

Over the past few years, smartphones have emerged as one of the most popular mechanisms for accessing content across the Internet driving considerable research to improve wireless performance. A key foundation for such research efforts is the proper understanding of user behavior. However, the gathering of live smartphone data at scale is often difficult and expensive. The focus of this paper is to explore the lessons learned from a two year study of two hundred smart phone users at the University of Notre Dame. In this paper, we offer commentary with regards to the entire process of the study covering aspects including funding considerations, technical architecture design, lessons learned, and recommendations for future efforts gathering live user data.

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Lessons learned from the netsense smartphone study

Wireless network providers are under tremendous pressure to deliver unprecedented amounts of data to a variety of mobile devices. A powerful concept that has only gained limited traction in practice has been the concept of opportunistic networks whereby nodes opportunistically communicate with each other when in range to augment or overcome existing wireless systems. One of the key impediments towards the adoption of opportunistic communications has been the inability to demonstrate viability at scale, namely showing that sufficient opportunities exist and more importantly exist when needed to offer significant network performance gains. We demonstrate through a large-scale, longitudinal study of smartphone users that significant opportunities are indeed prevalent, are indeed stable, and end up being reasonably reciprocal both on short and long-term timescales. In this paper, we propose a framework dubbed PSR (Prevalence, Stability, Reciprocity) to capture key aspects that characterize the net potential for opportunistic networks which we feel merit significantly increased attention.

Exploring the potential in practice for opportunistic networks amongst smart mobile devices

With the advent of the smartphone, mobile data usage has exploded which in turn has created tremendous pressure on cellular data networks. A promising candidate to reduce the impact of cellular data growth is WiFi offloading. However, recent data from our study of two hundred student smartphone users casts doubts on the reductions that can be gained from WiFi offloading. Despite the users operating in a dense university WiFi environment, cellular consumption still dominated overall data usage. We believe the root cause of lesser WiFi utilization can be traced to the WiFi being optimized for laptop WiFi reception rather than the more constrained smartphone WiFi reception. Our work examines the relationship of WiFi versus 3G usage through a variety of aspects including active phone usage, application types, and traffic volume over an eight week period from the Spring of 2012.

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Shu Liu

Papers

Face-to-Face Proximity Estimation Using Bluetooth On Smartphones

Modifying smartphone user locking behavior

Lessons learned from the netsense smartphone study

Exploring the potential in practice for opportunistic networks amongst smart mobile devices

Casting doubts on the viability of WiFi offloading