We present DynamicSLAM: an indoor localization technique that eliminates the need for the daunting calibration step. DynamicSLAM is a novel Simultaneous Localization And Mapping (SLAM) framework that iteratively acquires the feature map of the environment while simultaneously localizing users relative to this map. Specifically, we employ the phone inertial sensors to keep track of the user's path. To compensate for the error accumulation due to the low-cost inertial sensors, DynamicSLAM leverages unique points in the environment (anchors) as observations to reduce the estimated location error. DynamicSLAM introduces the novel concept of mobile human anchors that are based on the encounters with other users in the environment, significantly increasing the number and ubiquity of anchors and boosting localization accuracy. We present different encounter models and show how they are incorporated in a unified probabilistic framework to reduce the ambiguity in the user location. Furthermore, we present a theoretical proof for system convergence and the human anchors ability to reset the accumulated error. Evaluation of DynamicSLAM using different Android phones shows that it can provide a localization accuracy with a median of 1.1m. This accuracy outperforms the state-of-the-art techniques by 55 percent, highlighting DynamicSLAM promise for ubiquitous indoor localization.

Robust and accurate indoor localization has been the goal of several research efforts over the past decade. Due to the ubiquitous availability of WiFi indoors, many indoor localization systems have been proposed relying on WiFi fingerprinting. However, due to the inherent noise and instability of the wireless signals, the localization accuracy usually degrades and is not robust to dynamic changes in the environment.We present WiDeep, a deep learning-based indoor localization system that achieves a fine-grained and robust accuracy in the presence of noise. Specifically, WiDeep combines a stacked denoising autoencoders deep learning model and a probabilistic framework to handle the noise in the received WiFi signal and capture the complex relationship between the WiFi APs signals heard by the mobile phone and its location. WiDeep also introduces a number of modules to address practical challenges such as avoiding over-training and handling heterogeneous devices.We evaluate WiDeep in two testbeds of different sizes and densities of access points. The results show that it can achieve a mean localization accuracy of 2.64m and 1.21m for the larger and the smaller testbeds, respectively. This accuracy outperforms the state-of-the-art techniques in all test scenarios and is robust to heterogeneous devices.

Mapping and navigation applications are now considered popular services for mobile phones users. However, despite the fact that people spend most of their time indoors, these applications are still limited to indoor spaces due to the lack of large-scale indoor floorplan databases. In this paper, we present JustWalk: a crowd intelligence-based system for the automatic construction of buildings floorplans. JustWalk employs a participatory sensing approach using smartphones that are ubiquitously available with users who visit a building to automatically and transparently construct accurate motion traces. These accurate traces are generated by reducing the errors in the inertial motion traces by using the points of interest in the indoor environment (e.g., elevators and stairs, etc.) for error resetting. The collected traces are then processed by means of different mathematical and image processing techniques to detect the overall floorplan shape as well as higher level semantics such as detecting rooms and corridors shapes along with a variety of points of interest in the environment. In comparison to other approaches, our system depends only on the users walking patterns and motion traces. It does not require any explicit inputs or obtrusive actions (e.g., taking photos). Experimental evaluation of JustWalk using different android phones in three testbeds shows that it achieves high accuracy for the point of interest detection (0.6 percent false positive and 1 percent false negative rates). In addition, the proposed error resetting technique leads to more than 12 times enhancement in the median distance error compared to the state-of-the-art. Moreover, the detailed floorplan can be accurately estimated with a relatively small number of traces. This number is amortized over the number of users visiting the building. Finally, we show that JustWalk has a small energy footprint on cell-phones, and could be generalized to other buildings; highlighting its promise as a ubiquitous indoor mapping service.

We present CrowdMeter: a participatory system that leverages the sensed data collected from users' phones during their daily train commutes to gauge the real-time congestion level in railway stations. CrowdMeter tracks the passenger's position in the station as well as identifies her context (e.g., waiting for a train, buying a ticket) along her trajectory from the station's entrance to the train. Therefrom, CrowdMeter extracts novel features, based on the user's location and context, from the phone sensors. These features capture the passenger's behavior (e.g., the walking pattern) and the ambient environment characteristics (e.g., the ambient sound) that can indicate the surrounding congestion level along the passenger's route in a railway station. Finally, the system highlights each area of the station with a specific color (green, amber, red) that corresponds to one of a three congestion levels (low, medium, high).Evaluation of CrowdMeter through a field experiment in 10 different train stations in Japan shows that it can infer the congestion levels accurately, highlighting its promise as a ubiquitous travel-support service.

WiFi fingerprinting is one of the mainstream technologies for indoor localization. However, it requires an initial calibration phase during which the fingerprint database is built manually by site surveyors. This process is labour intensive, tedious, and needs to be repeated with any change in the environment. While a number of recent systems have been introduced to reduce the calibration effort through RF propagation models and/or crowdsourcing, these still have some limitations. Other approaches use the recently developed iBeacon technology as an alternative to WiFi for indoor localization. However, these beacon-based solutions are limited to a small subset of high-end phones.

Water accounts for about 60% of the human body, and when the body loses it (e.g., through urine, sweat, etc.) in higher rate than its intake rate (through drinking), dehydration symptoms occur. The dehydration causes many severe health problems like organ and cognitive impairment. Therefore, it is critical for the human to drink water in a sustained manner to avoid dehydration. To prevent humans from dehydration, continuous day-scale tracking of the water intake is needed. In this paper, we propose an unobtrusive method to recognize the drinking activity as well as estimate the water intake amount in milliliter scale by leveraging smartwatches. Our basic idea is to track the arm motion and discriminate the drinking activities from the similar hand-based motions like food intake, phone calls, etc. Thereafter, we estimate the water intake amount from the drinking duration.

Existing Location-based social networks (LBSNs), e.g., Foursquare, depend mainly on GPS or cellular-based localization to infer users' locations. However, GPS is unavailable indoors and cellular-based localization provides coarse-grained accuracy. This limits the accuracy of current LBSNs in indoor environments, where people spend 89 percent of their time. This in turn affects the user experience, in terms of the accuracy of the ranked list of venues, especially for the small screens of mobile devices, misses business opportunities, and leads to reduced venues coverage. In this paper, we present CheckInside: a system that can provide a fine-grained indoor location-based social network. CheckInside leverages the crowd-sensed data collected from users' mobile devices during the check-in operation and knowledge extracted from current LBSNs to associate a place with a logical name and a semantic fingerprint. This semantic fingerprint is used to obtain a more accurate list of nearby places as well as to automatically detect new places with similar signature. A novel algorithm for detecting fake check-ins and inferring a semantically-enriched floorplan is proposed as well as an algorithm for enhancing the system performance based on the user implicit feedback. Furthermore, CheckInside encompasses a coverage extender module to automatically predict names of new venues increasing the coverage of current LBSNs. Experimental evaluation of CheckInside in four malls over the course of six weeks with 20 participants shows that it can infer the actual user place within the top five venues 99 percent of the time. This is compared to 17 percent only in the case of current LBSNs. In addition, it increases the coverage of existing LBSNs by more than 37 percent.

We present TransitLabel, a crowd-sensing system for automatic enrichment of transit stations indoor floorplans with different semantics like ticket vending machines, entrance gates, drink vending machines, platforms, cars' waiting lines, restrooms, lockers, waiting (sitting) areas, among others. Our key observations show that certain passengers' activities (e.g., purchasing tickets, crossing entrance gates, etc) present identifiable signatures on one or more cell-phone sensors. TransitLabel leverages this fact to automatically and unobtrusively recognize different passengers' activities, which in turn are mined to infer their uniquely associated stations semantics. Furthermore, the locations of the discovered semantics are automatically estimated from the inaccurate passengers' positions when these semantics are identified. We evaluate TransitLabel through a field experiment in eight different train stations in Japan. Our results show that TransitLabel can detect the fine-grained stations semantics accurately with 7.7% false positive rate and 7.5% false negative rate on average. In addition, it can consistently detect the location of discovered semantics accurately, achieving an error within 2.5m on average for all semantics. Finally, we show that TransitLabel has a small energy footprint on cell-phones, could be generalized to other stations, and is robust to different phone placements; highlighting its promise as a ubiquitous indoor maps enriching service.

We present PassActiv, a mobile sensing system for the automatic activity recognition of railway passengers. Our key observations show that certain passengers' activities (e.g., purchasing tickets, etc) present identifiable signatures on one or more cell-phone sensors which can be leveraged to automatically recognize those activities. Evaluation of PassActiv through a field experiment in major train and subway stations in Japan shows that PassActiv can detect different activities accurately with at most 3% false positive rate and 4% false negative rate for all types of passengers' activities.

Availability of semantic-rich indoor floorplans; where places are labeled with their business names or categories; enables ubiquitous deployment of a wide range of indoor location-based services. In this paper, we present SemSense: a crowdsourcing-based system for automatic enrichment of indoor floorplans with semantic labels. SemSense exploits phone sensors data collected from users during their normal check-ins to location-based social networks (LBSNs) and combines them with data extracted from the LBSNs databases to associate a venue name with its location on an unlabeled floorplan. At the core of SemSense are different modules for handling incorrect location estimates, fake check-ins, as well as increasing the coverage of indoor venues by means of a novel category inference technique. Our experimental evaluation of SemSense using different Android phones in four malls in two cities shows that it can achieve a high semantic labeling accuracy of 87% using a relatively small number of check-ins at each venue in the presence of up to 50% erroneous check-ins. In addition, the proposed coverage extension technique leads to more than 27% enhancement in the places coverage ratio compared to the current LBSNs.

Existing location-based social networks (LBSNs), e.g. Foursquare, depend mainly on GPS or network-based localization to infer users' locations. However, GPS is unavailable indoors and network-based localization provides coarse-grained accuracy. This limits the accuracy of current LBSNs in indoor environments, where people spend 89% of their time. This in turn affects the user experience, in terms of the accuracy of the ranked list of venues, especially for the small-screens of mobile devices; misses business opportunities; and leads to reduced venues coverage.