A Survey of Crowd Sensing Opportunistic Signals for Indoor Localization <s> Introduction <s> The proliferation of mobile computing devices and local-area wireless networks has fostered a growing interest in location-aware systems and services. In this paper we present RADAR, a radio-frequency (RF)-based system for locating and tracking users inside buildings. RADAR operates by recording and processing signal strength information at multiple base stations positioned to provide overlapping coverage in the area of interest. It combines empirical measurements with signal propagation modeling to determine user location and thereby enable location-aware services and applications. We present experimental results that demonstrate the ability of RADAR to estimate user location with a high degree of accuracy. <s> BIB001 </s> A Survey of Crowd Sensing Opportunistic Signals for Indoor Localization <s> Introduction <s> To be widely adopted, location-aware computing must be as effortless, familiar and rewarding as web search tools like Google. We envisage the global scale Place Lab, consisting of an open software base and a community building activity as a way to bootstrap the broad adoption of location-aware computing. The initiative is a laboratory because it will also be a vehicle for research and instruction, especially in the formative stages. The authors draw on their experiences with campus and building-scale location systems to identify the technological and social barriers to a truly ubiquitous deployment. With a grasp of these "barriers to adoption," we present a usage scenario, the problems in realizing this scenario, and how these problems will be addressed. We conclude with a sketch of the multi-organization cooperative being formed to move this effort forward. <s> BIB002 </s> A Survey of Crowd Sensing Opportunistic Signals for Indoor Localization <s> Introduction <s> CAPTCHAs (Completely Automated Public Turing test to tell Computers and Humans Apart) are widespread security measures on the World Wide Web that prevent automated programs from abusing online services. They do so by asking humans to perform a task that computers cannot yet perform, such as deciphering distorted characters. Our research explored whether such human effort can be channeled into a useful purpose: helping to digitize old printed material by asking users to decipher scanned words from books that computerized optical character recognition failed to recognize. We showed that this method can transcribe text with a word accuracy exceeding 99%, matching the guarantee of professional human transcribers. Our apparatus is deployed in more than 40,000 Web sites and has transcribed over 440 million words. <s> BIB003 </s> A Survey of Crowd Sensing Opportunistic Signals for Indoor Localization <s> Introduction <s> This low-cost indoor navigation system runs on off-the-shelf camera phones. More than 2,000 users at four different large-scale events have already used it. The system uses built-in cameras to determine user location in real time by detecting unobtrusive fiduciary markers. The required infrastructure is limited to paper markers and static digital maps, and common devices are used, facilitating quick deployment in new environments. The authors have studied the application quantitatively in a controlled environment and qualitatively during deployment at four large international events. According to test users, marker-based navigation is easier to use than conventional mobile digital maps. Moreover, the users' location awareness in navigation tasks improved. Experiences drawn from questionnaires, usage log data, and user interviews further highlight the benefits of this approach. <s> BIB004 </s> A Survey of Crowd Sensing Opportunistic Signals for Indoor Localization <s> Introduction <s> We study the security challenges that arise in opportunistic people-centric sensing, a new sensing paradigm leveraging humans as part of the sensing infrastructure. Most prior sensor-network research has focused on collecting and processing environmental data using a static topology and an application-aware infrastructure, whereas opportunistic sensing involves collecting, storing, processing and fusing large volumes of data related to everyday human activities. This highly dynamic and mobile setting, where humans are the central focus, presents new challenges for information security, because data originates from sensors carried by people— not tiny sensors thrown in the forest or attached to animals. In this paper we aim to instigate discussion of this critical issue, because opportunistic people-centric sensing will never succeed without adequate provisions for security and privacy. To that end, we outline several important challenges and suggest general solutions that hold promise in this new sensing paradigm. <s> BIB005 </s> A Survey of Crowd Sensing Opportunistic Signals for Indoor Localization <s> Introduction <s> The paper presents an indoor navigation solution combining physical motion recognition with WLAN positioning on a smart phone. Orientationindependent features are extracted from vertical and horizontal components of acceleration. The simple features such as the mean of horizontal acceleration, the variance of acceleration magnitude, and the variance of horizontal acceleration are selected as the nodes in a decision tree. Six common motion modes during indoor navigation, e.g., static, standing with hand swinging, normal walking with holding the phone in hand, normal walking with hand swinging, fast walking, and U-turning are detected. A fingerprinting based WLAN positioning approach offers the headings and initial positions for pedestrian dead reckoning with about 10 seconds interval. The velocities for six motion modes are trained by five testers and applied in the dead reckoning during the WLAN positioning gap. Test results indicate that the motion mode is recognized correctly in 95% of test cases. The field test shows 4.6m horizontal mean error for motion recognition assisted indoor wireless positioning. <s> BIB006 </s> A Survey of Crowd Sensing Opportunistic Signals for Indoor Localization <s> Introduction <s> We introduce and motivate "crowd computing", which combines mobile devices and social interactions to achieve large-scale distributed computation. An opportunistic network of mobile devices offers substantial aggregate bandwidth and processing power. In this paper, we analyse encounter traces to place an upper bound on the amount of computation that is possible in such networks. We also investigate a practical task-farming algorithm that approaches this upper bound, and show that exploiting social structure can dramatically increase its performance. <s> BIB007 </s> A Survey of Crowd Sensing Opportunistic Signals for Indoor Localization <s> Introduction <s> An important question in behavioral epidemiology and public health is to understand how individual behavior is affected by illness and stress. Although changes in individual behavior are intertwined with contagion, epidemiologists today do not have sensing or modeling tools to quantitatively measure its effects in real-world conditions. In this paper, we propose a novel application of ubiquitous computing. We use mobile phone based co-location and communication sensing to measure characteristic behavior changes in symptomatic individuals, reflected in their total communication, interactions with respect to time of day (e.g., late night, early morning), diversity and entropy of face-to-face interactions and movement. Using these extracted mobile features, it is possible to predict the health status of an individual, without having actual health measurements from the subject. Finally, we estimate the temporal information flux and implied causality between physical symptoms, behavior and mental health. <s> BIB008 </s> A Survey of Crowd Sensing Opportunistic Signals for Indoor Localization <s> Introduction <s> Indoor pedestrian positioning sets severe challenges for a navigation system. To be applicable for pedestrian navigation the platform used has to be small in size and reasonably priced. Smartphones fulfill these requirements satisfyingly. GNSS signals are degraded indoors and in order to obtain accurate navigation aiding from other sensors is needed. Self-contained sensors provide valuable information about the motion of the pedestrian and when integrated with GNSS measurements a position solution is typically obtainable indoors. The accuracy is however decreased due to errors in the measurements of the self-contained sensors introduced by various environmental disturbances. When the effect of the disturbance is constrained using visualaiding the accuracy can be increased to an acceptable level. This paper introduces a visual-aided twodimensional indoor pedestrian navigation system integrating measurements from GNSS, Bluetooth, WLAN, self-contained sensors, and heading change information obtained from consecutive images. The integration is performed with an Extended Kalman filter. Reliability information of the heading change measurements calculated from images using vanishing points is provided to the filter and utilized in the integration. The visual-aiding algorithm is computationally lightweight taking into account the restricted resources of the smartphone. In the conducted experiment, the accuracy of the position solution is increased by 1.2 meters due to the visual-aiding. <s> BIB009 </s> A Survey of Crowd Sensing Opportunistic Signals for Indoor Localization <s> Introduction <s> The recent wave of sensor-rich, Internet-enabled, smart mobile devices such as the Apple iPhone has opened the door for a novel paradigm for monitoring the urban landscape known as participatory sensing. Using this paradigm, ordinary citizens can collect multi-modal data streams from the surrounding environment using their mobile devices and share the same using existing communication infrastructure (e.g., 3G service or WiFi access points). The data contributed from multiple participants can be combined to build a spatiotemporal view of the phenomenon of interest and also to extract important community statistics. Given the ubiquity of mobile phones and the high density of people in metropolitan areas, participatory sensing can achieve an unprecedented level of coverage in both space and time for observing events of interest in urban spaces. Several exciting participatory sensing applications have emerged in recent years. For example, GPS traces uploaded by drivers and passengers can be used to generate real time traffic statistics. Similarly, street-level audio samples collected by pedestrians can be aggregated to create a citywide noise map. In this advanced seminar, we will provide a comprehensive overview of this new and exciting paradigm and outline the major research challenges. <s> BIB010 </s> A Survey of Crowd Sensing Opportunistic Signals for Indoor Localization <s> Introduction <s> The rapid growth of human computation within research and industry has produced many novel ideas aimed at organizing web users to do great things. However, the growth is not adequately supported by a framework with which to understand each new system in the context of the old. We classify human computation systems to help identify parallels between different systems and reveal "holes" in the existing work as opportunities for new research. Since human computation is often confused with "crowdsourcing" and other terms, we explore the position of human computation with respect to these related topics. <s> BIB011 </s> A Survey of Crowd Sensing Opportunistic Signals for Indoor Localization <s> Introduction <s> Indoor positioning technologies have been widely studied with a number of solutions being proposed, yet substantial applications and services are still fairly primitive. Taking advantage of the emerging concept of the connected car, the popularity of smartphones and mobile Internet, and precise indoor locations, this study presents the development of a novel intelligent parking service called iParking. With the iParking service, multiple parties such as users, parking facilities and service providers are connected through Internet in a distributed architecture. The client software is a light-weight application running on a smartphone, and it works essentially based on a precise indoor positioning solution, which fuses Wireless Local Area Network (WLAN) signals and the measurements of the built-in sensors of the smartphones. The positioning accuracy, availability and reliability of the proposed positioning solution are adequate for facilitating the novel parking service. An iParking prototype has been developed and demonstrated in a real parking environment at a shopping mall. The demonstration showed how the iParking service could improve the parking experience and increase the efficiency of parking facilities. The iParking is a novel service in terms of cost- and energy-efficient solution. <s> BIB012 </s> A Survey of Crowd Sensing Opportunistic Signals for Indoor Localization <s> Introduction <s> The paper presents an indoor navigation solution by combining physical motion recognition with wireless positioning. Twenty-seven simple features are extracted from the built-in accelerometers and magnetometers in a smartphone. Eight common motion states used during indoor navigation are detected by a Least Square-Support Vector Machines (LS-SVM) classification algorithm, e.g., static, standing with hand swinging, normal walking while holding the phone in hand, normal walking with hand swinging, fast walking, U-turning, going up stairs, and going down stairs. The results indicate that the motion states are recognized with an accuracy of up to 95.53% for the test cases employed in this study. A motion recognition assisted wireless positioning approach is applied to determine the position of a mobile user. Field tests show a 1.22 m mean error in “Static Tests” and a 3.53 m in “Stop-Go Tests”. <s> BIB013 </s> A Survey of Crowd Sensing Opportunistic Signals for Indoor Localization <s> Introduction <s> The ubiquity of smartphones and their on-board sensing capabilities motivates crowd-sensing, a capability that harnesses the power of crowds to collect sensor data from a large number of mobile phone users. Unlike previous work on wireless sensing, crowd-sensing poses several novel requirements: support for humans-in-the-loop to trigger sensing actions or review results, the need for incentives, as well as privacy and security. Beyond existing crowd-sourcing systems, crowd-sensing exploits sensing and processing capabilities of mobile devices. In this paper, we design and implement Medusa, a novel programming framework for crowd-sensing that satisfies these requirements. Medusa provides high-level abstractions for specifying the steps required to complete a crowd-sensing task, and employs a distributed runtime system that coordinates the execution of these tasks between smartphones and a cluster on the cloud. We have implemented ten crowd-sensing tasks on a prototype of Medusa. We find that Medusa task descriptions are two orders of magnitude smaller than standalone systems required to implement those crowd-sensing tasks, and the runtime has low overhead and is robust to dynamics and resource attacks. <s> BIB014
(1) Mobile Indoor Localization. Location is an essential element of fast expanding modern information. People usually spend over 90% of their daily lives indoors where the mobile device, for example, smartphone, is like a shadow inseparably sticking to users, which greatly increases the interests of mobile indoor localization for academia and industry alike. Global Navigation Satellites Systems (GNSS) largely enrich the localization capability of mobile devices outdoors. However, GNSS signals are designed for outdoor applications originally, which lowers or disables satellitebased localization technologies indoors because of the weak signal or blocked signal in non-line-of-sight (NLOS) situations. To address localization in GNSS-degraded or denied area, manifold technologies are extensively researched. The sensor-rich smartphone offers the potential of continuous localization even though the localization infrastructures are not available. Typically, the measurements from smartphone built-in sensors, for example, accelerometer, magnetometer, and gyroscope, can be fused to estimate the smartphone carrier's motion dynamic, such as speed, heading, orientation, or motion states. An algorithm, namely, pedestrian dead reckoning (PDR), can utilize the above smartphone dynamic information to locate a pedestrian in GNSS challenging environments BIB006 BIB012 BIB013 . Phone camera is another potential sensor of smartphone-based localization BIB009 BIB004 . For instance, Zhou et al.  propose a visual SLAM (Simultaneous Localization and Mapping) algorithm to track user's location and simultaneously build a 3D map using the structure line of a building. Taking advantage of the widely integrated three-dimensional magnetometers in smartphones, IndoorAtlas provides the cutting edge magnetic fingerprint-based indoor localization [7] . Opportunistic radio frequency (RF) based signals, such as Wi-Fi BIB001  [10] , Bluetooth  , Near Field Communication (NFC), Radio Frequency Identification (RFID), and cellular networks, are pervasive providing the existing infrastructures. RF-based opportunistic signals have been widely applied for indoor localization for indoor environments BIB002 . (2) Crowd Sensing. In 2005, with outsources fast growing, crowdsourcing has emerged as a new collaborative paradigm, in which crowds of people can collaborate and accomplish a specific task. The most famous crowdsourcing project is the reCAPTCHA (Completely Automated Public Turing test to Tell Computers and Humans Apart), which is the most applied technology of preventing malicious codes from abusing web services BIB003 . The project achieves such goal by requesting the user input such as the distorted characters which can only be performed by human being so far. The most elegant design of the reCAPTCHA is to transform such human load into a useful application where the user inputs can be utilized to digitize old publications which are hardly recognized by optical character recognition (OCR). Normally, user's inputs are the distorted fragment scanned from those old publications. The reCAPTCHA toolkit had been deployed in more than 40,000 websites. With the involvements of crowd web users, the toolkit had digitalized over 440 million words by 2008. In results, the archives of New York Times over 150 years were unconsciously recognized and digitalized by web users within a few months. Skyhook, a worldwide Wi-Fi positioning provider, collects the access points and their locations contributed by participants via crowdsourcing  . The contribution will be used to update the Wi-Fi database after the quality control center verifies the crowdsourced information. Similar to crowdsourcing, the terms such as participatory sensing BIB010 , human computation BIB011 , opportunistic sensing BIB005 , crowd computing BIB007 , social sensing BIB008 , and crowd sensing BIB014 have a common idea which is to aggregate the collective intelligence of a crowd to achieve a goal with inexpensive cost. Unconscious participation is the profile which fundamentally distinguishes crowd sensing from other crowdsourcing-like solutions, which makes crowd sensing an ideal approach to generating fingerprints for indoor localization by amount of common mobile phone users instead of expert site surveyors.