Place Lab: Device Positioning Using Radio Beacons in the Wild

Pedestrian Positioning Using an Enhanced Ensemble Transform Kalman Filter

Due to the unavailability of GPS indoors, various indoor pedestrian positioning approaches have been designed to estimate the position of the user leveraging sensory data measured from inertial measurement units (IMUs) and wireless signal receivers, such as pedestrian dead reckoning (PDR) and received signal strength (RSS) fingerprinting. This study is similar to the previous study in that it estimates the user position by fusing noisy positional information obtained from the PDR and RSS fingerprinting using the Bayes filter in the indoor pedestrian positioning system. However, this study differs from the previous study in that it uses an enhanced state estimation approach based on the ensemble transform Kalman filter (ETKF), called QETKF, as the Bayes filer for the indoor pedestrian positioning instead of the SKPF proposed in the previous study. The QETKF estimates the updated user position by fusing the predicted position by the PDR and the positional measurement estimated by the RSS fingerprinting scheme using the ensemble transformation, whereas the SKPF calculates the updated user position by fusing them using both the unscented transformation (UT) of UKF and the weighting method of PF. In the field of Earth science, the ETKF has been widely used to estimate the state of the atmospheric and ocean models. However, the ETKF algorithm does not consider the model error in the state prediction model; that is, it assumes a perfect model without any model errors. Hence, the error covariance estimated by the ETKF can be systematically underestimated, thereby yielding inaccurate state estimation results due to underweighted observations. The QETKF proposed in this paper is an efficient approach to implementing the ETKF applied to the indoor pedestrian localization system that should consider the model error. Unlike the ETKF, the QETKF can avoid the systematic underestimation of the error covariance by considering the model error in the state prediction model. The main goal of this study is to investigate the feasibility of the pedestrian position estimation for the QETKF in the indoor localization system that uses the PDR and RSS fingerprinting. Pedestrian positioning experiments performed using the indoor localization system implemented on the smartphone in a campus building show that the QETKF can offer more accurate positioning results than the ETKF and other ensemble-based Kalman filters (EBKFs). This indicates that the QETKF has great potential in performing better position estimation with more accurately estimated error covariances for the indoor pedestrian localization system.

Inferring transportation mode from smartphone sensors: Evaluating the potential of Wi-Fi and Bluetooth

Understanding which transportation modes people use is critical for smart cities and planners to better serve their citizens. We show that using information from pervasive Wi-Fi access points and Bluetooth devices can enhance GPS and geographic information to improve transportation detection on smartphones. Wi-Fi information also improves the identification of transportation mode and helps conserve battery since it is already collected by most mobile phones. Our approach uses a machine learning approach to determine the mode from pre-prepocessed data. This approach yields an overall accuracy of 89% and average F₁ score of 83% for inferring the three grouped modes of self-powered, car-based, and public transportation. When broken out by individual modes, Wi-Fi features improve detection accuracy of bus trips, train travel, and driving compared to GPS features alone and can substitute for GIS features without decreasing performance. Our results suggest that Wi-Fi and Bluetooth can be useful in urban transportation research, for example by improving mobile travel surveys and urban sensing applications.

Creating Personalized Recommendations in a Smart Community by Performing User Trajectory Analysis through Social Internet of Things Deployment

Despite advancements in the Internet of Things (IoT) and social networks, developing an intelligent service discovery and composition framework in the Social IoT (SIoT) domain remains a challenge. In the IoT, a large number of things are connected together according to the different objectives of their owners. Due to this extensive connection of heterogeneous objects, generating a suitable recommendation for users becomes very difficult. The complexity of this problem exponentially increases when additional issues, such as user preferences, autonomous settings, and a chaotic IoT environment, must be considered. For the aforementioned reasons, this paper presents an SIoT architecture with a personalized recommendation framework to enhance service discovery and composition. The novel contribution of this study is the development of a unique personalized recommender engine that is based on the knowledge–desire–intention model and is suitable for service discovery in a smart community. Our algorithm provides service recommendations with high satisfaction by analyzing data concerning users’ beliefs and surroundings. Moreover, the algorithm eliminates the prevalent cold start problem in the early stage of recommendation generation. Several experiments and benchmarking on different datasets are conducted to investigate the performance of the proposed personalized recommender engine. The experimental precision and recall results indicate that the proposed approach can achieve up to an approximately 28% higher F-score than conventional approaches. In general, the proposed hybrid approach outperforms other methods.

Pressure-Pair-Based Floor Localization System Using Barometric Sensors on Smartphones

As smartphone technology advances and its market penetration increases, indoor positioning for smartphone users is becoming an increasingly important issue. Floor localization is especially critical to indoor positioning techniques. Numerous research efforts have been proposed for improving the floor localization accuracy using information from barometers, accelerometers, Bluetooth Low Energy (BLE), and Wi-Fi signals. Despite these existing efforts, no approach has been able to determine what floor smartphone users are on with near 100% accuracy. To address this problem, we present a novel pressure-pair based method called FloorPair, which offers near 100% accurate floor localization. The rationale of FloorPair is to construct a relative pressure map using highly accurate relative pressure values from smartphones with two novel features: first, we marginalized the uncertainty from sensor drifts and unreliable absolute pressure values of barometers by paring the pressure values of two floors, and second, we maintained high accuracy over time by applying an iterative optimization method, making our method sustainable. We evaluated the validity of the FloorPair approach by conducting extensive field experiments in various types of buildings to show that FloorPair is an accurate and sustainable floor localization method.

Detecting Bipolar Depression From Geographic Location Data

Objective

This paper aims to identify periods of depression using geolocation movements recorded from mobile phones in a prospective community study of individuals with bipolar disorder (BD).

Methods

Anonymized geographic location recordings from 22 BD participants and 14 healthy controls (HC) were collected over 3 months. Participants reported their depressive symptomatology using a weekly questionnaire (QIDS-SR₁₆). Recorded location data were preprocessed by detecting and removing imprecise data points and features were extracted to assess the level and regularity of geographic movements of the participant. A subset of features were selected using a wrapper feature selection method and presented to 1) a linear regression model and a quadratic generalized linear model with a logistic link function for questionnaire score estimation; and 2) a quadratic discriminant analysis classifier for depression detection in BD participants based on their questionnaire responses.

Results

HC participants did not report depressive symptoms and their features showed similar distributions to nondepressed BD participants. Questionnaire score estimation using geolocation-derived features from BD participants demonstrated an optimal mean absolute error rate of 3.73, while depression detection demonstrated an optimal (median ± IQR) F₁ score of 0.857 ± 0.022 using five features (classification accuracy: 0.849 ± 0.016; sensitivity: 0.839 ± 0.014; specificity: 0.872 ± 0.047).

Conclusion

These results demonstrate a strong link between geographic movements and depression in bipolar disorder.

Significance

To our knowledge, this is the first community study of passively recorded objective markers of depression in bipolar disorder of this scale. The techniques could help individuals monitor their depression and enable healthcare providers to detect those in need of care or treatment.

Measuring large-scale social networks with high resolution

This paper describes the deployment of a large-scale study designed to measure human interactions across a variety of communication channels, with high temporal resolution and spanning multiple years-the Copenhagen Networks Study. Specifically, we collect data on face-to-face interactions, telecommunication, social networks, location, and background information (personality, demographics, health, politics) for a densely connected population of 1000 individuals, using state-of-the-art smartphones as social sensors. Here we provide an overview of the related work and describe the motivation and research agenda driving the study. Additionally, the paper details the data-types measured, and the technical infrastructure in terms of both backend and phone software, as well as an outline of the deployment procedures. We document the participant privacy procedures and their underlying principles. The paper is concluded with early results from data analysis, illustrating the importance of multi-channel high-resolution approach to data collection.

An OpenLS privacy-aware middleware supporting location-based applications

Purpose

– The paper aims to resolve three major issues in location-based applications (LBA) known as heterogeneity, user privacy, and context-awareness by proposing an elastic and open design platform named OpenLS privacy-aware middleware (OPM) for LBA.

Design/methodology/approach

– The paper analyzes relevant approaches ranging from both academia and mobile industry community and insists the importance of heterogeneity, user privacy, and context-awareness towards the development of LBA.

Findings

– The paper proposes the OPM by design. As a result, the OPM consists of two main component named application middleware and location middleware, which are cooperatively functioned to achieve the above goals. In addition, the paper has given the implementation of the OPM as well as its experiments. It is noted that two privacy-preserving techniques at two different levels are integrated into the OPM, including Memorizing algorithm at the application level and Bob-tree at the database level. Last but not least, the paper shows further discussion about other problems and improvements that might be needed for the OPM.

Research limitations/implications

– Each issue has its sub problems that cause more influences to the OPM. Besides, each of the issues requires more investigations in depth in order to have better solutions in detail. Therefore, more overall experiments should be conducted to assure the OPM's scalability and effectiveness.

Practical implications

– The paper hopefully promotes and speeds up the development of LBA when providing the OPM with suitable application programming interfaces and conforming the OpenLS standard.

Originality/value

– This paper shows its originality towards location-based service (LBS) providers to develop their applications and proposes the OPM as a unified solution dealing with heterogeneity, user privacy, and context-awareness in the world of LBS.

A Unified Approach to Uncertainty-Aware Ubiquitous Localisation of Mobile Users

Localisation is a standard feature in many mobile applications today, and there are numerous techniques for determining a user’s location both indoors and outdoors. The provided location information is often organised in a format tailored to a particular localisation system’s needs and restrictions, making the use of several systems in one application cumbersome. The presented approach models the details of localisation systems and uses this model to create a unified view on localisation in which special attention is paid to uncertainty coming from different localisation conditions and to its presentation to the user. The work discusses technical considerations, challenges and issues of the approach, and reports on a user study on the acceptance of a mobile application’s behaviour reflecting the approach. The results of the study show the suitability of the approach and reveal users’ preference toward automatic and informed changes they experienced while using the application.

Automatic training method applied to a WiFi+ultrasound POMDP navigation system

This paper presents an automatic training method based on the Baum–Welch algorithm (also known as EM algorithm) and a robust low-level controller. The method has been applied to the indoor autonomous navigation of a surveillance robot that utilizes a WiFi+Ultrasound Partially Observable Markov Decision Process (POMDP). This method uses a robust local navigation system to automatically provide some WiFi+Ultrasound maps. These maps could be employed within probabilistic global robot localization systems. These systems use a priori probabilistic map in order to estimate the global robot position. The method has been tested in a real environment using two commercial Pioneer 2AT robotic platforms in the premises of the Department of Electronics at the University of Alcalá. Some experimental results and conclusions are presented.

A survey of platforms for mobile networks research

The use of smartphones is growing at an unprecedented rate and is projected to soon pass laptops as consumers' mobile platform of choice. The proliferation of these devices has created new opportunities for mobile researchers; however, when faced with hundreds of devices across nearly a dozen development platforms, selecting the ideal platform is often met with unanswered questions. In this paper I consider desirable characteristics of mobile platforms necessary for mobile networks research. Based on these characteristics, I assess five smartphone platforms: Android (Linux), BlackBerry, iPhone (Mac OS X), Symbian, and Windows Mobile. This survey is current as of December 2008. A living version of this survey is available at: http://blizzard.cs.uwaterloo.ca/eaoliver/platforms/.

A practical evaluation of radio signal strength for ranging-based localization

Radio signal strength (RSS) is notorious for being a noisy signal that is difficult to use for ranging-based localization. In this study, we demonstrate that RSS can be used to localize a multi-hop sensor network, and we quantify the effects of various environmental factors on the resulting localization error. We achieve 4.1m error in a 49 node network deployed in a half-football field sized area, demonstrating that RSS localization can be a feasible alternative to solutions like GPS given the right conditions. However, we also show that this result is highly sensitive to subtle environmental factors such as the grass height, radio enclosure, and elevation of the nodes from the ground.

A case study in building layered DHT applications

Recent research has shown that one can use Distributed Hash Tables (DHTs) to build scalable, robust and efficient applications. One question that is often left unanswered is that of simplicity of implementation and deployment. In this paper, we explore a case study of building an application for which ease of deployment dominated the need for high performance. The application we focus on is Place Lab, an end-user positioning system. We evaluate whether it is feasible to use DHTs as an application-independent building block to implement a key component of Place Lab: its "mapping infrastructure." We present Prefix Hash Trees, a data structure used by Place Lab for geographic range queries that is built entire on top of a standard DHT. By strictly layering Place Lab's data structures on top of a generic DHT service, we were able to decouple the deployment and management of Place Lab from that of the underlying DHT. We identify the characteristics of Place Lab that made it amenable for deploying in this layered manner, and comment on its effect on performance.